Spaceborne LiDAR provides a promising method for large-scale characterizing LAI. However, the quality of point cloud data from spaceborne LiDAR, especially ICESat-2, is susceptible to atmosphere and background noise, introducing considerable uncertainty in LAI retrieval. Thus, efficiently screening out the high-quality point cloud is a significant guarantee for high-quality LAI retrieval. In this study, we proposed a quality control (QC) method that employed the number of 10 m windows without ground points in the ICESat-2 100 m segment as the QC flag. This method divided segments into 11 QC flags from 0 to 10 and was applied to LAI retrieval across Chinese forests from 2019 to 2020. The field measurements at locations identical to ICESat-2 ground tracks were used to validate the ICESat-2 LAI at different QC flags. The results showed that the proposed method effectively improved point cloud quality recognition and LAI accuracy, with ICESat-2 LAI (QC < 3) reducing RMSE by 26.36% compared to all ICESat-2 LAIs. It also showed good agreement with MODIS and GLASS LAI and mitigated saturation issues in passive optical imagery. The ICESat-2 LAI with QC < 3 performed better in deciduous broadleaved, evergreen needle-leaved, deciduous needle-leaved, and mixed forests, but not in evergreen broadleaved forests. ICESat-2 LAI was particularly adept at capturing high LAI values, which had the highest proportion of LAI values over 6.0 compared to MODIS and GLASS LAI. The proposed method has the potential for large-scale and high-quality LAI retrieval using ICESat-2 data on a global scale.

Terrain elevation over forested areas is important for studies such as hydrological modeling and soil erosion. The spaceborne full-waveform LiDAR missions including Geoscience Laser Altimeter System (GLAS) and Global Ecosystem Dynamics Investigation (GEDI) provide freely available terrain elevation products indirectly and directly. However, the accuracies have seldom been evaluated in the same region. Here, we examined the terrain elevation accuracy and assessed the influence of terrain slope in forested areas using high-resolution airborne LiDAR data as a reference. The root mean square error (RMSE) of terrain elevation computed from all the data of GLAS and GEDI is 5.1 m and 8.4 m, respectively. Even though the footprint diameter of GEDI is much smaller than GLAS (25 m vs. 65 m), we still found a significant terrain effect with the increase of slope in GEDI. The RMSE of terrain elevation from GLAS is 3.4 m, 7.6 m, and 10.5 m when the slope ranges from 0° to 30° with an increment of 10°. The RMSE of terrain elevation from GEDI is 5.2 m, 8.8 m, 12.2 m, 14.1 m, and 16. 9 m when the slope ranges from 0° to 50° with an increment of 10°.

Land surface temperature plays a crucial role in ecosystem energy balance and material exchanges. Remote sensing is vital for investigating brightness temperature variations. The intricate canopy structure poses challenges, especially with strong directional anisotropy in brightness temperature, leading to assessment inaccuracies. The radiative transfer model provides valuable insights into how canopy structure influences directional brightness temperature (DBT). Traditional models, assuming a turbid medium or randomly distributed ideal geometry, exhibit notable errors. However, the PATH_RT model, incorporating path length distribution, shows commendable performance in the optical domain. To enhance applicability, we modify the PATH_RT model, successfully implementing path length distributions for simulating DBT in the thermal domain. Validation using abstract scenes, cross-validated with SAIL and FRT, and referencing DART, highlights significant improvement attributed to the efficacy of path length distribution.

In this paper, we develop the Slant Leaf Area Index based Method (SLAIM) to estimate the leaf area of individual trees from single-scan Terrestrial Laser Scanner (TLS) data by introducing the concept of Slant Leaf Area Index (SLAI). SLAI quantifies the amount of leaves along the view direction and can be retrieved at given view zeniths using gap probability. Subsequently, leaf area can be accumulated by SLAI across the whole crown. The innovative SLAIM offers several advantages. Firstly, it operates with single-scan point clouds, which are more accessible than multiple-scan data. Secondly, it effectively corrects the clumping effect resulting from non-uniform leaf distribution. Both simulated and field-measured TLS point clouds of trees are used to test the method. The results show that the error of SLAIM is less than 10% in most cases.

Normalized difference vegetation index (NDVI) is one of the most widely used vegetation indices (VIs) to retrieve vegetation parameters such as fractional vegetation cover (FVC) and leaf area index (LAI). Due to the bidirectional reflectance distribution function (BRDF) effect on the surface, NDVI is greatly affected by the Sun-view angle, leading to considerable uncertainty in the parameters derived from NDVI. The angle effect can be corrected using the BRDF model. Nevertheless, the majority of satellites are unable to collect sufficient multiangle data in near real time to retrieve the parameters of the BRDF model. In this study, we proposed a correction model for the Sun-view angle effect of NDVI (SVAC) that only needed single view-angle observation for implementation. The SVAC model was developed based on a published cosine correction model (CCM) that corrected NDVI’s Sun angle effect. The simulated data and MODerate-resolution Imaging Spectroradiometer (MODIS) product data were used in validation. The results showed that the SVAC model performed well for all simulated scenes, where the NDVI’s uncertainty originating from the Sun-view angle was reduced by over 70% and 30% for homogeneous and nonhomogeneous vegetation, respectively. The validation with real data at the VAlidation of Land European Remote Sensing Instrumentations (VALERI) sites and the ImagineS sites demonstrated a percentage decrease of root mean square error (RMSE) of approximately 30%. The SVAC model can effectively reduce the NDVI’s sensitivity to Sun-view angles with high applicability and simplicity and is expected to facilitate the acquisition of vegetation parameters using angular-independent NDVI.

Leaf area parameters are crucial in ecosystem studies. As ecophysiological models advance toward finer detail, accurately estimating LA at various scales becomes essential, particularly for diverse units like urban individual trees. Several algorithms based on terrestrial laser scanning (TLS) data have been developed to obtain the LA of individual trees. However, their use at the stand level needs further research. In this study, the comparative shortest-path algorithm (CSP) is introduced for the automatic individual tree segmentation, thereby facilitating the application of the path length distribution model (PATH) for leaf area estimation at the stand level. Using high-density TLS data, we presented a bottom-up estimation of stand leaf area index (LAI) from 50 individual tree measurements and validated the results at different scales. At the tree scale, the LA derived from TLS and allometric model were highly correlated, with an R-value of 0.83. At the stand scale, the proposed method provides consistent results with the allometric and TRAC instrument measurements, performing better than vertical upward photography. Generally, 23 shared stations under the forest are enough to accurately obtain the LA of 50 trees and the LAI in an urban forest stand. Sensitivity analysis shows that the method is not sensitive to TLS scan resolution and parameters used in tree crown envelope reconstruction. The proposed bottom-up approach provides a new way of estimating the LAI at stand level using TLS and has the advantage of providing multi-level leaf area information and avoiding the scale effect.

Individual trees are fundamental to urban ecosystems as they play an important role in energy transfer, pollutant removal, and habitat formation. Leaf area (LA) is an important factor to quantify the effect of individual trees on urban ecosystems. Terrestrial laser scanners (TLSs) are widely recognized as the most accurate devices for tree structural measurements. However, they face challenges in estimating LA from LA index (LAI) for individual trees primarily due to arbitrary and confusing horizontal projection areas. Occlusion and clumping effects further hinder the objective and accurate LA measurements of individual trees. Therefore, we developed the slant leaf area index-based method (SLAIM) to estimate the LA of individual trees from single-scan TLS data by introducing the concept of slant leaf area index (SLAI). SLAI quantifies the amount of leaves along the view direction, and it can be retrieved at given view zeniths using gap probability. Subsequently, LA can be accumulated by SLAI across the whole crown. Tests with simulated and field-measured TLS point clouds demonstrate SLAIM’s accuracy, with the relative errors (REs) in LA below 10% in most cases. Stratified LA validation reveals an$R^{2}$exceeding 0.77 across all realistic crowns, along with a root-mean-square error (RMSE) under 2 m2. SLAIM’s advantages include compatibility with single-scan point clouds, effective correction of clumping effects, and consideration of variations in leaf projection coefficients at different zeniths. SLAIM proves more efficient and practical for actual LA measurements, showcasing its potential for advanced urban ecosystem research.

The evaluation and validation of climate data records (CDRs) derived from remote sensing play crucial roles in their generation and applications. However, many existing evaluation schemes rely on simplistic, spatiotemporally invariant metrics to assess the products’ overall quality, which leads to the long-term neglect of intra-product inconsistencies stemming from observation conditions, algorithmic differences, and sensor degradation. Leaf area index (LAI) is a crucial variable for land surface and climate modeling, and the intra-product inconsistency will increase the uncertainty in related studies. In order to improve the evaluation scheme of LAI products and ensure their reliability, we propose a new perspective for evaluating global LAI time series. In this study, we utilize the Moderate Resolution Imaging Spectroradiometer (MODIS) C6.1 LAI product as an example to infer its internal consistency through cross-comparisons among different sensors and spatiotemporal correlations between two adjacent years of the product. We found that compared to the main algorithm, the backup algorithm of the MODIS LAI product tends to underestimate the retrieval results. This inconsistency is particularly pronounced in tropical regions but relatively minor in most other areas. Additionally, these inconsistencies can lead to unusual fluctuations in the LAI time series, impacting the magnitude and direction of short-term vegetation monitoring. However, the influence on long-term trend analyses is negligible. Therefore, special attention should be given to the intra-product consistency in certain studies. In conclusion, the evaluation perspective proposed in this study is of great significance for improving the LAI evaluation scheme and ensuring the use and improvement of remote sensing products.

Changes in the sun zenith angle (SZA) alter the normalized difference vegetation index (NDVI) and introduce uncertainties into the estimation of vegetation biochemical and biophysical parameters. For the NDVI obtained from narrow swath width sensors, there is not a unified and easy-to-use approach to correct the sun-angle effect. In this study, the cosine correction model (CCM) was proposed to reduce the sun-angle effect on NDVI based on the path length (PL) of light calculated from the SZA without the need for multi-angle observations. The PL was found to be closely correlated to the simple ratio vegetation index (SR) and can mitigate the impact on the NDVI caused by SZA variations. The CCM performed well when correcting the sun-angle effect on NDVI for different types of data. After correction for the simulated data (e.g., the reference SZA of 10°), the coefficient of variation (CV) of the NDVI concerning SZA variations from 10° to 60° was reduced by 5.42%, and the root-mean-square error (RMSE) was reduced by 0.049. For the field-measured data, the CV of the NDVI under various SZAs was reduced by up to 5.55% after correction, and the maximum difference between the uncorrected and corrected NDVI was 0.099. The RMSE of corrected nadir NDVI from MODIS satellite data was reduced by 34.2% on average. The CCM, as an easily-implemented method, can attenuate the sun-angle effect on NDVI without relying on the BRDF products and hence has the potential to improve the accuracy of remote sensing monitoring of vegetation dynamics.

Clumping effect denotes the nonrandomness of foliage. It deviates from the random distribution assumption of Beer’s law which is usually applied to leaf area index (LAI) retrieval from large-footprint full-waveform light detection and ranging (LiDAR). Some studies correct for large gaps-induced between-crown clumping, yet ignore the within-crown clumping. The error of LAI caused by these clumping effects and the influence of the forest structure parameters on them have not been quantitatively studied. This study quantified the between-crown, within-crown, and total clumping indices through a theoretical derivation, clarifying the mechanism of clumping; we used airborne LiDAR point clouds data in 11 290 footprints (diameter = 25 m) to estimate these indices in real forests. We found that: 1) the underestimation of LAI caused by directly applying Beer’s law could be up to 93%, and it decreases with fractional crown coverage but increases with crown length and leaf area density; 2) the method of correcting between-crown clumping improves LAI retrieval for cylindrical canopies effectively; however, 3) considerable underestimation (up to 58%) exists if we neglect the within-crown clumping for other canopies, which has not been realized before; and 4) both the between-crown and the within-crown clumping can be the dominant contributor, and the within-crown clumping was greater than the between-crown clumping in 47% of the studied footprints. In the two physically based LAI retrieval methods, Beer’s law has been commonly used due to its simplicity. Pathways to improve future LAI retrieval would be instrument improvement to capture the between-crown gaps and method study to correct the within-crown clumping further.

To improve our capacity to map long-term vegetation dynamics in heterogeneous landscapes, this study proposed a new prior knowledge-based spatiotemporal enhancement method, namely, PK-STEM, to fuse MODIS and Landsat FPAR products following the remote sensing trend surface framework. PK-STEM uses historical Landsat FPAR images as prior knowledge and fuses them with new satellite-derived FPAR data. PK-STEM can work in three modes: 1) using only MODIS data; 2) using only Landsat data; and 3) using both MODIS and Landsat data. This study retrieved FPAR from Landsat images using a scaling-based method and tested the performance of PK-STEM in a regional application. For the entire year of 2012, we compared the performance of PK-STEM in different modes and with that of two typical spatiotemporal fusion methods, the enhanced spatial and temporal adaptive reflectance model (ESTARFM) and unmixing-based linear mixing growth model (LMGM). Then, a long time series FPAR data set at 30-m resolution and eight-day intervals was generated for 13 years (2000–2012). Our results show that PK-STEM in mode III is the most robust and accurate (root mean squared error (RMSE) = 0.062; mean$R = 0.851$) among the three modes and more accurate than ESTARFM (mean RMSE = 0.065; mean$R = 0.776$) and LMGM (mean RMSE = 0.074; mean$R = 0.734$). For the 12 years (2000–2011), PK-STEM also achieves high accuracies with mean RMSE = 0.066 and$R = 0.938$. PK-STEM is very flexible with a continual update mechanism and is efficient for long time series applications.

Remote sensing estimation based on the dimidiate pixel model (DPM) using vegetation indices (VIs) is a common approach for mapping fractional vegetation cover (FVC). The major drawback of DPM is that it does not consider real endmember conditions and multiple scattering between soil and vegetation. An analysis of FVC uncertainties caused by these model deficiencies is still lacking. Here, we first calculated the FVC theoretical uncertainty caused by reflectance uncertainties based on the law of prapagation of uncertainty (LPU). Then, we tested the performance of DPM using six VIs over 3-D forest scenes. We simulated both Aqua-MODIS and Landsat-OLI surface reflectance (SR) at their corresponding spatial resolutions and spectral response functions (SRFs) using a well-validated 3-D radiative transfer (RT) model which helps to separate the model and input uncertainties. We found that ratio vegetation index (RVI)- and enhanced vegetation index (EVI)-based models were most affected by sensors, followed by the normalized difference vegetation index (NDVI)-, enhanced vegetation index 2 (EVI2)-, renormalized difference vegetation index (RDVI)-, and difference vegetation index (DVI)-based models. Without considering SR uncertainties, the DVI-based model performed best (FVC absolute difference < 0.1); however, the commonly used NDVI model reached a maximum difference of 0.35. At the same time, input uncertainty increased the uncertainty of FVC retrieval. We noticed that the increase of solar zenith angle (SZA) resulted in a clear increase of retrieved FVC under the uniform distribution, which can be explained by the increased shadow proportion. Besides, model accuracy was dominated by the purity of soil (vegetation) endmember in low (high) vegetation cover area. This study provides a reference for the selection of the optimal VI for FVC retrieval based on the DPM.

Bidirectional reflectance distribution function (BRDF) models are used to correct surface bidirectional effects and estimate land surface albedo. Many operational BRDF/albedo algorithms adopt a Roujean linear kernel-driven BRDF (RLKB) model because of its simple form and good performance in fitting multidirectional surface reflectance values. However, this model does not explicitly consider topographic effects, resulting in errors when applied over rugged terrain. To address this issue, we proposed a hybrid algorithm suitable for both flat and rugged terrain, called topographical kernel-driven (Topo-KD). First, we constructed a linear kernel-driven BRDF model considering terrain (LKB_T) which describes the topographic effects with a mountain radiative transfer (MRT) model. Then, the Topo-KD algorithm adaptively selects the most suitable model (RLKB or LKB_T) according to the terrain conditions and fitting residuals. The performances of Topo-KD and RLKB using the RossThick–LiSparseReciprocal (RTLSR) kernel are compared using simulated data sets and moderate-resolution imaging spectroradiometer (MODIS) observations. The results show that the BRDF of the pixel is affected by topography. But the RTLSR model does not specifically account for it, resulting in larger biases over rugged terrain than the Topo-KD algorithm in both the red and near-infrared (NIR) bands. The experiment using MODIS data sets demonstrates that the Topo-KD algorithm reduces fitting residuals in the red and NIR bands by 21.5% and 27.4% compared with the RTLSR model. These results indicate that the Topo-KD algorithm can be a better choice for retrieving land surface parameters and describing the radiative transfer process in mountainous areas.

Leaf inclination angle distribution plays an important role in indirect leaf area measurement methods. Path length distribution method (PATH) is an indirect leaf area measurement method based on Beer's Law, which has been applied to isolated trees with Terrestrial Laser Scanning (TLS). However, it can only set the leaf projection G to a constant value. In this paper, the PATH method considering leaf inclination models is introduced, which allows the G function to be a variable corresponding to leaf inclination. On the basis of this method, this paper explores the effect of different leaf inclination angle distribution assumptions on the inversion of the leaf area of isolated trees based on TLS. The results show that compared with the measured leaf inclination, the relative errors of the inversion results based on the six typical leaf inclination assumptions are between −14.3 % to +41.2%, which indicates that leaf inclination has a significant effect on the inversion of the leaf area. Further, experiments in this paper show that the mean value of the G function is a relatively accurate representation of it.

Occlusion effect, an inherent problem of terrestrial laser scanning (TLS) measurements, limits the potential of TLS data in tree attribute estimation. Multiple scans seek to mitigate this effect to provide enhanced scan completeness. However, the numbers and locations of the scans (i.e., the scan design) are usually determined via a subjective assessment of the tree density, spatial patterns of trees, and attributes to be derived. These could cause suboptimal scan completeness and limit tree attribute estimation. This study proposed an iterative-mode scan design to minimize the occlusion effect. First, we introduced a PoTo index based on visibility analysis to evaluate how many trees can be scanned from a location and to select effective candidates for the optimal TLS location. Second, we introduced a cumulative degree of ring closure (CDRC) to quantify the scan completeness for each candidate and determine the optimal TLS location. The TLS data sets of virtual forests with field-measured and synthetic plot parameter settings were simulated according to iterative- and regular-mode designs by using a Heidelberg light detection and ranging (LiDAR) Operations Simulator (HELIOS). The results demonstrated that an iterative-mode design can improve the scan completeness of trees compared to the regular-mode design. The tree attribute (diameter at breast height (DBH), tree height, stem curve, and crown volume) estimates of the iterative-mode design were less erroneous than those of the regular-mode design (e.g., the root-mean-square error (RMSE) could decrease the stem curve estimation by 38% and the crown volume estimation by 15%). This study suggests that the iterative-mode design can obtain an improved quality of the TLS data, especially for dense stands.

The 3-D information collected from sample plots is significant for forest inventories. Terrestrial laser scanning (TLS) has been demonstrated to be an effective device in data acquisition of forest plots. Although TLS is able to achieve precise measurements, multiple scans are usually necessary to collect more detailed data, which generally requires more time in scan preparation and field data acquisition. In contrast, mobile laser scanning (MLS) is being increasingly utilized in mapping due to its mobility. However, the geometrical peculiarity of forests introduces challenges. In this article, a test backpack-based MLS system, i.e., backpack laser scanning (BLS), is designed for forest plot mapping without a global navigation satellite system/inertial measurement unit (GNSS-IMU) system. To achieve accurate matching, this article proposes to combine the line and point features for calculating transformation, in which the line feature is derived from trunk skeletons. Then, a scan-to-map matching strategy is proposed for correcting positional drift. Finally, this article evaluates the effectiveness and the mapping accuracy of the proposed method in forest sample plots. The experimental results indicate that the proposed method achieves accurate forest plot mapping using the BLS; meanwhile, compared to the existing methods, the proposed method utilizes the geometric attributes of the trees and reaches a lower mapping error, in which the mean errors and the root square mean errors for the horizontal/vertical direction in plots are less than 3 cm.

Estimation of downward shortwave radiation (DSR) is of great importance in global energy budget and climatic modeling. Although various algorithms have been proposed, effective validation methods are absent for rugged terrains due to the lack of rigorous methodology and reliable field measurements. We propose a two-step validation method for rugged terrains based on computer simulations. The first step is to perform point-to-point validation at local scale. Time-series measurements were applied to evaluate a three-dimensional (3-D) radiative transfer model. The second step is to validate the DSR at pixel-scale. A semiempirical model was built up to interpolate and upscale the DSR. Key terrain parameters were weighted by empirical coefficients retrieved from ground-based observations. The optimum number and locations of ground stations were designed by the 3-D radiative transfer model and Monte Carlo method. Four ground stations were selected to upscale the ground-based observations. Additional three ground stations were set up to validate the interpolated results. The upscaled DSR was finally applied to validate the satellite products provided by MODIS and Himawari-8. The results showed that the modeled and observed DSR exhibited good consistency at point scale with correlation coefficients exceeding 0.995. The average error was around 20 W/m2for the interpolated DSR and 10 W/m2for the upscaled DSR in theory. The accuracies of the satellite products were acceptable at most times, with correlation coefficients exceeding 0.94. From an operational point of view, our method has an advantage of using small amount of ground stations to upscale DSR with relatively high accuracy over rugged terrains.

Accurate estimation of the fine-resolution fraction of absorbed photosynthetically active radiation (FPAR) across broad spatial extents and long time periods requires efficient and applicable methods. The existing methods can hardly provide a balance between accuracy, simplicity, and transferability through space and time. Within the remote-sensing trend-surface conceptual framework, this article proposes a scaling-based method to efficiently retrieve FPAR from fine-resolution satellite data using coarse-resolution FPAR products as a reference. The method was particularly developed and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) FPAR product and Landsat imagery. First, necessary prior knowledge related to FPAR retrieval and scaling theories was used to explicitly linearize the complex relationship between MODIS FPAR and Landsat surface reflectance. Second, the explicit linear model for FPAR estimation was trained through one-pair image learning for each date to estimate FPAR from Landsat imagery in real time. Both homogeneous and heterogeneous cases were considered. The method was validated at ten selected worldwide sites from the Validation of Land European Remote Sensing Instruments (VALERI) program and derived an overall root mean squared error (RMSE) of 0.133. A long time series of FPAR data set at the 30-m resolution was generated at the regional scale (approximately 2000 km2) for 13 years (2000–2012). The results were accurate (RMSE = 0.072) and MODIS-consistent, which were significantly better than those of the normalized difference vegetation index (NDVI) downscaling-based and regression tree methods. The scaling-based method provides accurate, MODIS-consistent and spatially consistent FPAR estimates in real time, is highly transferrable through space and time, and allows for future extension of FPAR estimates to the era of the Landsat series satellites.

Soil-Adjusted Vegetation Index (SAVI) is found to be undesirable to estimate Leaf Area Index (LAI) with heterogeneous canopy structure in low vegetation cover. In this article, three new vegetation indices (VIs), such as Normalized Hotspot-Signature Vegetation Index 2 (NHVI2), Hotspot-Signature Soil-Adjusted Vegetation Index (HSVI), and Hotspot-Signature 2-Band Enhanced Vegetation Index (HEVI2), are proposed for a better quantitative estimation of LAI and soil-noise resistance than with SAVI. To obtain these new indices, the angular index called Normalized Difference between Hotspot and Darkspot (NDHD) is introduced which represents the distribution of foliage in vegetation canopy. The validity of new VIs is statistically verified using simulated data and field measurements. The Discrete Anisotropic Radiative Transfer (DART) model is used to simulate both the homogeneous and heterogeneous canopy for analyzing vegetation isolines behaviors, soil-noise resistance, and LAI estimation. In situ measurements of LAI and bidirectional reflectance factor from the Boreal Ecosystem-Atmosphere Study (BOREAS) are also used to test the robustness of the new VIs for the estimation of LAI. By considering the distribution of the foliage, the accuracy of LAI estimation of SAVI for heterogeneous canopy improved almost 16% using exponential regression analysis. With the improvement of multiangular remote-sensing and Bidirectional Reflectance Distribution Function (BRDF) models in the future, hotspot-signature VIs have the potential to provide a more accurate LAI estimation for heterogeneous canopy in strong soil-noise interference area.

Terrain affects surface solar radiation (SSR) of mountainous area greatly. However, reliable observational data and methods are absent in mountainous areas. The stations located in these areas, typically built on flat places equipped with horizontal radiometers, are hard to capture the topographic effects. We proposed a tilted SSR observation group for mountainous areas based on ground stations. In 2015 and 2016, the method was tested in Chengde, China. Five ground stations were built on hilltop, valley, and three slopes to measure SSR. The radiometers on slopes and hilltop were set up parallel to the ground surfaces, while the radiometer in the valley was set horizontally to compare with the tilted observation method. A topographic radiation model along with a 12.5m digital elevation model (DEM) data was used to simulate the downward SSR and compare with observations. The result showed good consistency with the observations on slopes with R2values as high as 0.99, but relatively big deviations were found at the hilltop and valley stations, caused by the slope calculation errors and unsuitable observational method. The results demonstrate the fact of that the topographic radiation model should be validated using proposed method with high accuracy DEM.

3D radiative transfer modeling in forest canopies is of great importance to upscale leaf level observations to canopy level, which, however, is particularly difficult in heterogeneous areas due to the complexity of forests. A common solution is to use physically based radiative transfer models. In this paper, we parameterized the LESS (LargE-Scale remote sensing data and image Simulation framework) model through a voxel-based reconstruction of airborne LiDAR data. For that, an airborne spectral image was simulated and compared with actual ASIA hyperspectral image. The results show a good agreement with R-squared being 0.5 and 0.56 for near infrared and red band, respectively. This demonstrates that the proposed voxel-based parameterization approach can successfully capture the fine-scale structures of forest canopies, and it can provide reliable data source for 3D radiative transfer models.

Urban areas have profound environmental impacts, while the existed products of urban areas have some issues, such as low spatial resolution and confused definition of urban. In this study, we developed a method based on image pattern recognition is developed to classify urban and rural from the artificial surfaces class in GlobaLand30. The global urban areas with 30m resolution in years 2000 and 2010 are extracted. The results are compared with the data from the China City Statistical Yearbooks (CCSY) and the US Census Bureau (USCB) in year 2010. The correlation coefficient between our urban areas and CCSY reached at 0.877. The user accuracy between our urban areas and USCB can reach at 91.82%. The major difference is from the green land and water in the urban areas and the urban fringe with more green lands, where are ignored by our data.

Land-surface bidirectional reflectance distribution function (BRDF) models are used for the description of surface bidirectional effects and the estimation of surface albedo. The semi-empirical linear kernel-driven BRDF model is one of them which has been adopted by the moderate resolution imaging spectroradiometer (MODIS) operational BRDF/Albedo algorithm, due to its briefness and well-fitting ability. However, this model does not consider the topography factors, and will lead to errors over rugged terrains. However, researches seldom analyze the models' uncertainties caused by rugged terrains quantitatively, as it is difficult to directly validate models over mountain areas at coarse resolution. This letter proposes a forward topographic BRDF simulation method by combining a canopy radiative transfer model (SAILH) and a mountain radiative transfer (MRT) model to investigate the uncertainty and sensitivity of the kernel-driven model over mountain areas theoretically. Results show that the topographic effects can cause over 20% uncertainties on both red and NIR bands. Topography leads to the asymmetry of BRDF distributions on azimuth, which cannot be captured by kernel-driven model at 1km scale. Both DEM types and observation situations influence the retrieval accuracy significantly. Therefore, this work is meaningful to study the optimal inversion scale and observation requirements depending on the topography.

Accurate and efficient measurement of leaf angle distribution (LAD) is important for characterizing canopy structures and understanding solar radiation regimes within the plant canopy. The main challenge for obtaining LAD is measuring the orientations of individual leaves rapidly and accurately in complex field conditions. In this letter, we propose an efficient and low-cost approach to estimate both leaf zenith and azimuth angles from smartphone photographs by using a structure from motion (SfM) point cloud and pyramid convolutional neural network (PCNN)-based leaf detection. This SfM-PCNN method first detects individual leaves from 2-D photographs by delineating leaf boundaries, while minimizing the influences of interior leaf textures. The segmented image with leaf annotations is then used to partition the 3-D SfM point cloud into leaf clusters, each of which is fit by a plane to calculate the leaf orientation. The method was validated with manual measurements for five plant species with different leaf sizes, leaf shapes, and leaf textures. The accuracy is satisfactory for a leaf-to-leaf comparison over a Euonymus japonicus Thunb. with R-squared values of 0.84 (RMSE = 6.27°) and 0.97 (RMSE = 12.61°) for zenith and azimuth angle estimations, respectively. The method allows researchers to efficiently acquire LADs of different plants with low cost yet high accuracy.

Rugged terrain, as a high percent of the Earth's terrestrial surface, can cause the directionality of the surface thermal radiation, and affect the retrieved land surface temperature (LST) and longwave radiation (SLR) from satellite measurements due to the limited instantaneous field of view and observation angles. New directional brightness temperature (DBT) and equivalent brightness temperature (EBT) models were established considering terrain effects. The biases between them were also analyzed based on a simulated scene using the Advanced Spacebome Thermal Emission and Reflection Radiometer (ASTER) LST, emissivity and topographic data. The results show that BTs at the valley and peak points are clearly anisotropic, while this directionality at the cropland point is not obvious. The DBT shows hotspot effects which is closely related to the solar position. The range of DBTs can reach up to about 9 K in the valley point and the standard deviation of this difference in all view directions is 1.05 K. Thus, it can be concluded that it is hard to meet the requirement of retrieval accuracy of LST or SLR over rugged terrain if ignoring the three-dimensional structure of mountainous region and its angular thermal radiation.

As the main process parameter of water and energy exchange, evapotranspiration (ET) is defined as the water being converted from liquid to gaseous and from land surface to atmosphere. Potential evapotranspiration (ETO) is defined as the evapotranspiration when water supply is sufficient of the land surface and reflect the ability of the surface to supply moisture. In this study, we explored the relationship between annual averaged ET (ET/ETO) and annual averaged 36.5 GHz emission, and provided a new train of thought of how to use passive microwave data to estimate annual averaged evapotranspiration. We found a non-linear relationship with a R2 of 0.52 between annual averaged 36.5 GHz emission and observed annual evapotranspiration at 28 flux tower sites of Asia and North America. We estimated ET and ETO of China and found a linear relationship with a R2 of 0.51 between the annual averaged (ET/ET0)1/2and the annual averaged 36.5 GHz emission at 9 flux tower sites of China.

The airborne laser scanner (ALS) provides great potential for mapping the leaf area index (LAI) at the landscape scale using grid cell statistics, while its application is restricted by the lack of clumping information, which has been an unsolved issue highlighted for a long time. ALS generally provides an effective LAI because its footprint is too large to capture small gaps to apply traditional ground-based clumping correction methods. Here, we present a grid cell method based on path length distribution model to calculate the clumping-corrected LAI using ALS data without the requirement of additional field measurements. We separated the within- and between-crown areas to consider between-crown clumping, and used the path length distribution as estimated by local canopy height distribution to consider 3-D foliage profile and within-crown clumping. The path length distribution model takes advantage of the 3-D information rather than the gap size distribution, thus avoiding the limitation of large ALS footprint. With the 0.4-m-footprint ALS data, the results are generally promising and a multilevel clumping analysis is consistent with landscape flown. The ALS LAIs of different resolutions are consistent, with a difference of less than 5% from 5- to 250-m resolutions. Due to its consistency and simple configuration, the method provides an opportunity to map the clumping-corrected LAI operationally and strengthens the ability of airborne lidar to monitor vegetation change and validate the satellite product. This grid cell method based on path length distribution is worth further testing and application using more recent laser technology.

Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation have been successfully generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) data since early 2000. As the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard, the Suomi National Polar-orbiting Partnership (SNPP) has inherited the scientific role of MODIS, and the development of a continuous, consistent, and well-characterized VIIRS LAI/FPAR data set is critical to continue the MODIS time series. In this paper, we build the radiative transfer-based VIIRS-specific lookup tables by achieving minimal difference with the MODIS data set and maximal spatial coverage of retrievals from the main algorithm. The theory of spectral invariants provides the configurable physical parameters, i.e., single scattering albedos (SSAs) that are optimized for VIIRS-specific characteristics. The effort finds a set of smaller red-band SSA and larger near-infrared-band SSA for VIIRS compared with the MODIS heritage. The VIIRS LAI/FPAR is evaluated through comparisons with one year of MODIS product in terms of both spatial and temporal patterns. Further validation efforts are still necessary to ensure the product quality. Current results, however, imbue confidence in the VIIRS data set and suggest that the efforts described here meet the goal of achieving the operationally consistent multisensor LAI/FPAR data sets. Moreover, the strategies of parametric adjustment and LAI/FPAR evaluation applied to SNPP-VIIRS can also be employed to the subsequent Joint Polar Satellite System VIIRS or other instruments.

Estimation of daily downward shortwave radiation (DSR) is of great importance in global energy budget and climatic modeling. The combination of satellite-based instantaneous measurements and temporal extrapolation models is the most feasible way to capture daily radiation variations at large scales. However, previous studies did not pay enough attention to topographic effects and simple temporal extrapolation methods were applied directly to rugged terrains which cover a large amount of the land surface. This paper, divided into two parts, aims at analyzing the topographic uncertainties of existing models and proposing a better method based on a mountain radiative transfer (MRT) model to calculate daily DSR. As the first part, this paper analyze the spatiotemporal variations of DSR influenced by topographic effects and checks the applicability of three temporal extrapolation methods on cloud-free days. Considering that clouds also have a strong influence on solar radiation, cloud-free days are chosen for targeted analysis of topographic effects on DSR. Three indices, the coefficient of variation, entropy-based dispersion coefficient (CH), and sill of semivariogram, are put forward to give a quantitative description of spatial heterogeneity. Our results show that the topography can dramatically strengthen the spatial heterogeneity of DSR. The index, CH, has an advantage for quantifying spatial heterogeneity as it offers a tradeoff between accuracy and efficiency. Spatial heterogeneity distorts the daily variation of DSR. Application of extrapolation methods in rugged terrains leads to overestimation of daily average DSR up to 60 W/m2 and a maximum 200 W/m2 error of instantaneous DSR on cloud-free days. This paper makes a quantitative analysis of topographic effects under different spatiotemporal conditions, which lays the foundation for developing a new extrapolation method.

Mixed pixels have a significant impact on the accurate estimation of Fractional Vegetation Cover (FVC) using digital photos acquired by Unmanned Aerial Vehicle (UAV). A single threshold is inadequate for the separation of vegetation and background when images contain numerous mixed pixels. We propose a spectral unmixing method to measure FVC with UAV-acquired digital images. In this method, the spectral mean values of vegetation and background are obtained as a priori spectral knowledge from the photos taken at a very low flight altitude around 5 meters above ground level (AGL). Two thresholds with high confidence level derived from the a priori knowledge are determined to select pure vegetation and background pixels from the photos taken at high flight altitudes ranging from dozens to hundreds of meters AGL. For the mixed pixels, endmember spectra are undertook by mean values of those two pure components. Images with different aggregation levels were generated from a 10 meters AGL image. A comparison with four commonly used methods indicated that our method could robustly characterize the FVC in a good agreement with the ground truth, and the accuracy of FVC estimates over corn crops was around 0.01 in terms of root mean square error (RMSE) value. All aggregated images produced stable FVC estimates and the corresponding standard deviation (STD) was around 0.01 with relative average deviation (RAD) being less than 0.15.

Linking the terrestrial water cycles, carbon cycles and energy exchange, evapotranspiration (ET), which combines the surface evaporation and plant transpiration, is a key land surface parameter in water and heat balance of land, lake or river surface, and is central to earth system science. In this study, based on the MERRA reanalysis dataset and MODIS NDVI and LAI product, a support vector machine was used to estimate the land surface ET at sites and global scales. The results showed that, the support vector machine model probably could explain 60%–80% of the land surface ET change at 242 global FLUXnet sites when ten indicators while 56%–79% when five indicators were used to drive the model. For different vegetable cover sites, compared with EC observations, the results of evergreen broadleaf forest was worse than others.

Directional anisotropy limits the application of land surface temperature (LST) and a simplified parametric model to effectively estimate directional brightness temperature (DBT) in the thermal infrared is critical. This study used a widely validated four-stream scattering by arbitrarily inclined leaves (4SAIL) model as a benchmark to evaluate the performance of the kernel bidirectional reflectance distribution function (BRDF) model and the three-kernel-model. Results showed that the two kernel-driven models can fit the DBT well and the maximum root mean square error (RMSE) is 0.13°C. The kernel BRDF model has a wider application scope including canopies of uniform, spherical, plagiophile and planophile LIDF with low LAI and hotspot. When LIDF is planophile and plagiophile, two models can reach the best fitting effect and the worst effect is the canopy with erectrophile LIDF. Under a specified LIDF, the relationship between fitting accuracy and LAI is negative while hotspot parameter is positive.

Land surface temperature is an important driving force in the exchange of water, heat, and even CO2at the surface-atmosphere interface in the desert regions. The rapid and continuous measurements of land surface temperature are meaningful to the ecological and environmental researches. A physically based single-frequency and double-polarization algorithm for retrieving land surface temperature is developed in this study. The 18.7 GHz vertically polarized emissivities are firstly estimated from the Polarization Ratio (PR, defined as the ratio of the horizontal to vertical brightness temperature at the same frequency) at 18.7 GHz. And then the estimated emissivities can be directly used to retrieve land surface temperature without considering the atmospheric effect. A preliminary validation is done in the Taklimakan desert. The retrieved land surface temperatures are compared to the infrared land surface temperature products for all the year of 2007 with a Root Mean Square Error (RMSE) of 3.05 K.

Based on our previous studies, a simple fusion algorithm is proposed to estimate surface shortwave fluxes with polar-orbiting and geostationary satellite data. A shortwave flux component of one geostationary moment can be retrieved by only five inputs which include the known flux of one polar-orbiting moment, solar zenith angles and cloud fractions of the two moments. The preliminary validations are performed in terms of both the simulated and realistic datasets. The R2for each component is higher than 0.90 in the simulated case. The accuracy is relatively lower for the more complicated realistic situations. All of the validation results show that the simple and practical fusion algorithm has the potential to estimate surface shortwave fluxes with acceptable accuracy. With the combination of polar-orbiting (MODIS) and geostationary (Fengyun-2C) satellite data, surface shortwave fluxes with the temporal resolution of one hour were retrieved over the Tibetan Plateau.

Evapotranspiration (ET) is the combination process of the surface evaporation and plant transpiration which occur simultaneously, and it links the terrestrial water cycles, carbon cycles and energy exchange. In this study, based on the observations from 242 global FLUXnet sites, with daily average temperature, relative humidity, wind speed, incident solar radiation, NDVI and observed ET as input data, we used a support vector machine to estimate the land surface daily ET at nine different vegetation type sites. The results show that, for all vegetation type sites, when the predicted ET was validated with the eddy covariance measurements, the support vector machine algorithm underestimates the ET and probably could explain 71%-86% of the land surface ET change.

Reconstructing a realistic and large-scale 3D forest scene has potential applications in visual representations and scientific research. Forest scene with explicitly described branches and leaves can provide a more accurate interpretation of interactions between light and canopy. In this study, a large-scale 3D forest scene reconstruction and simulation method is proposed. A series of individual trees with high level of details are generated using parameters derived from allometric equation, which populates plot leaf area index (LAI) into each individual tree. Based on the airborne laser scanning (ALS) data, the height, crown diameter and position of each individual tree are extracted by watershed segmentation algorithm. Finally, an emulation system based on ray-tracing is developed. It provides the capability to simulate RGB and multi-spectral images. These simulated datasets with “ground truth” can be used as benchmark for a variety of applications in remote sensing, forest investigation and computer graphic.

In this study, a novel approach is proposed to simultaneously detect clouds and cloud shadows for remotely sensed images. Unlike the existing methods that based on spectral tests, it is based on the simulated band radiance, so that it can be applied to any remotely sensed images. The results showed that it very effective compared to existing algorithms.

Spatial scale effects, defined as the phenomenon that the estimates at multiple resolution are inconsistent, have aroused wide concerns in remote sensing studies. But very few studies have paid attention to the effects of scale on phenological studies. This paper investigated the scale effects in estimating phenological transitional dates from remote sensing data. A prior-knowledge vegetation index (VI) time series at 30 m resolution was composed, based on which the time series at 240 m, 480 m and 960 m were derived. The green-up onset and dormancy onset dates were then estimated from the VI time series using a double-logistic plant growth model. The derived estimates at multiple resolutions were compared and the effects of spatial scales were verified. Landscape heterogeneities were found to be related to spatial scale effects. The changes in the estimated green-up onset dates and dormancy onset dates exhibited different patterns with the coarsening of spatial resolution.

An algorithm has been developed for retrieving instantaneous microwave land surface emissivity using brightness temperature and precipitable water vapor data. Unlike previous algorithms, the new technique does not need infrared land surface temperature as the input data, and overcomes the limitation of previous algorithms under cloudy conditions. Compared with the values from physical retrieval algorithm, the result demonstrates that this new algorithm has a Root Mean Square Error of 0.038 and a bias of 0.012. Although the accuracy is worse than 1%, this new algorithm presents the potential to obtain the instantaneous microwave land surface emissivity under both cloud-free and cloudy conditions, which can be applied in some weather prediction models.

Scale effect, which is caused by a combination of model nonlinearity and surface heterogeneity, has been of interest to the remote sensing community for decades. However, there is no current analysis of scale effect in the ground-based indirect measurement of leaf area index (LAI), where model nonlinearity and surface heterogeneity also exist. This paper examines the scale effect on the indirect measurement of LAI. We built multiscale data sets based on realistic scenes and field measurements. We then implemented five representative methods of indirect LAI measurement at scales (segment lengths) that range from meters to hundreds of meters. The results show varying degrees of deviation and fluctuation that exist in all five methods when the segment length is shorter than 20 m. The retrieved LAI from either Beer's law or the gap-size distribution method shows a decreasing trend with increasing segment lengths. The length at which the LAI values begin to stabilize is about a full period of row in row crops and 100 m in broadleaf or coniferous forests. The impacts of segment length on the finite-length averaging method, the combination of gap-size distribution and finite-length methods, and the path-length distribution method are relatively small. These three methods stabilize at the segment scale longer than 20 m in all scenes. We also find that computing the average LAI of all of the short segment lengths, which is commonly done, is not as good as merging these short segments into a longer one and computing the LAI value of the merged one.

Clouds are the strongest modulator of the solar radiation absorbed by the earth-atmosphere system. In this study, a regional cloud fraction (RCF) is involved to modify the classic one-dimensional radiative transfer model, in order to study the effect of inner-pixel broken clouds on the radiation field at large scale. A global sensitivity analysis (GSA) is performed to quantitatively understand the effect of 11 parameters on each surface shortwave radiation component. The GSA results show that three most influential parameters for the modified model are RCF, land surface albedo and solar zenith angle. Three less important parameters are ground altitude, visibility and cloud extinction coefficient which is the case only for the situation of optically thin clouds. The other five factors can be considered as not important. These findings will enhance our knowledge on how to accurately model the surface shortwave radiation fluxes at large scale for partial cloudy conditions.

Because of the complex influences of cirrus clouds on the estimation of Land Surface Temperature (LST), the traditional LST retrieval algorithms can only be used for clear-sky conditions and there is no LST when the pixel is identified as clouds by cloud mask algorithm. To retrieve LST under cirrus clouds, a three-channel algorithm what is dependent on cirrus optical depth (COD) and effective radius was proposed. The simulated data showed that the daytime LST could be retrieved using the three-channel algorithm with a root mean square error of less than 3.0 K when COD (at 12 μm) was less than 0.7 and viewing zenith angle was less than 60°. Compared with the results of the traditional clear-sky two-channel LST retrieval algorithm, where the maximum RMSE was 17.8 K, the algorithm proposed in this study could significantly improve the accuracy of the daytime LST retrieved using satellite thermal-infrared data.

Non-randomness within canopies and woody component are two factors limiting the accuracy of indirect leaf area index (LAI) measurement. Here we combine the path length distribution model and Multispectral Canopy Imager (MCI) together for the first time to improve the accuracy. The results show that non-randomness within canopies underestimates 17.1%-28.2% LAI, while woody component overestimates 14.6%-27.8% LAI in four forest sites. Although these two factors were sometimes offset, the degree of non-randomness within canopies and the proportion of woody component vary in different forests. More attention should be paid to the impact of the non-randomness within canopies and the woody component, especially in coniferous forest dominated by tree trunks and branches.

Topographic correction has become very important in areas with rugged terrain. Many studies have suggested that a digital elevation model (DEM) with an inadequate spatial resolution undesirably removes topographic effects. In this letter, a scientific experiment was performed to explore the sensitivity of the topographic correction to the DEM spatial scale based on remote sensing images simulated with a 5-m resolution DEM. Topographic corrections with different DEM resolutions that ranged from 5 to 500 m were performed for simulated images with resolutions of 30-500 m to estimate surface spectral reflectance. Five representative topographic feature points were selected for the analysis. The results demonstrate that the sensitivity to the DEM spatial scale primarily originates from the spatial heterogeneity of the terrain and from the spatial resolution of the image that is topographically corrected. More complex terrain is associated with topographic corrections that are more dependent on the spatial resolution of the DEM. In general, for 30-m resolution remote sensing images, the DEM spatial resolution must be at least 10 m, whereas for 90- to 500-m resolution remote sensing images, a 30-m DEM can achieve the required topographic correction accuracy.

Land surface temperature (LST) is a critical parameter for numerical weather forecasting, drought monitoring, water resources management and global climate change studies. Because of the supercooled temperature, the cirrus cloud can significantly reduce the LST retrieved from thermal infrared data. This paper focused on analyzing and reducing the influence of thin cirrus cloud on the accuracy of LST retrieved using the generalized split-window (GSW) algorithm. A correction method was proposed with the LST retrieval error expressed as linear functions of cirrus optical depth (COD). The slopes of the linear functions were further written as the combination of the difference and mean of two used channels emissivities and cirrus cloud top height (CTH). The results showed that the LST retrieval accuracy could be significantly improved with root mean square error (RMSE) of LST changing from 14.4 K before LST error correction to 1.8 K after LST error correction for COD equivalent to 0.3.

Longwave radiation is a key component of total energy that drives surface energy balance at the interface between the surface and atmosphere. To date, a number of algorithms have been developed toward accurately estimating surface longwave radiation from remotely sensed data. While most of these existing algorithms can only derive longwave radiation under clear-sky conditions due to the limited penetration of optical remote sensing thus leading to spatial incontinuity in derived radiation map. Wherein the land surface temperature (LST) play a key role in longwave radiation estimation, especially for surface emitted (upwelling) and net longwave flux. If LSTs under cloudy area can be recovered, the derivation of surface longwave ration under cloudy conditions would be straightforward. To this end, in this paper, a fusing strategy is proposed to combine the LST measurements from MODIS and AMSR-E. The results show that the proposed fusing strategy for combining microwave and optical space-based measurements in recovering surface LST under cloudy conditions is very effective. By fusion, the spatial coverage of valid LSTs over the globe is highly improved.

Terrestrial LiDAR systems have received lots of attention on three-dimensional (3D) structure reconstruction for trees, especially on the branches skeleton generation. On this basis, a method is proposed to add leaves structures based on point density by dividing small cube in the canopy to reduce the influence of uneven distribution of point cloud, combining gap fraction model to retrieve leaf area of a tree using terrestrial LiDAR data. It is successfully applied to reconstruct 3D trees using points data simulated by ray tracing algorithm as well as field measured points data. The relative error of leaf area between reconstructed and real structure is less than 0.9%. Meanwhile, the most relative error of directional gap fraction is also less than 4.1%. The experimental results prove that the method has gotten a satisfied consistency on visual sense and quantitative evaluation between the 3D structure reconstructed and real structure.

Shortwave (SW) radiative flux (usually within 0.3∼3μm) is the dominant energy source of our planet, which drives the climate as well as the matter and energy cycle of the Earth system. It is an indispensable component of surface total energy balance. Considering the importance of SW radiation, during the past decades, more and more studies have conducted for estimating surface SW radiation using satellite-based data, such as MODIS, CERES, GOES etc. Although great effort has been made, most researches neglect the topographic effect and mainly focus on the retrieval of SW radiation over ideal horizontal surfaces for both instantaneous and time-integrated radiation. For this point, we propose a topographic SW radiation model based on the existing studies. Based on this, the SW radiative flux components are derived from MODIS data by fully accounting for the surface topographic effect. The results show that the errors induced in the retrieved daily SW radiation can reach up to 400W/m2at 1km scale. For instantaneous radiation, the uncertainties of derived SW radiation can reach up to 300W/m2even at 5km scale due to topographic effect. The findings of this paper prove the importance of topographic modeling of surface radiation over rugged terrain.

This paper aimed at the case of nonisothermal pixels and proposed a daytime temperature-independent spectral indices (TISI) method to retrieve directional emissivity and effective temperature from daytime multiangular observed images in both middle and thermal infrared (MIR and TIR) channels by combining the kernel-driven bidirectional reflectance distribution function (BRDF) model and the TISI method. Four groups of angular observations and two groups of MIR and TIR channels with narrow and broad bandwidths were used to investigate the influence of angular observations and bandwidth on the retrieval accuracy. Model sensitivity analysis indicated that the new method can generally obtain directional emissivity and temperature with an error less than 0.015 and 1.5 K if the noise included in the measured directional brightness temperature (DBT) and atmospheric data was no more than 1.0 K and 10%, respectively. The analysis also indicated that 1) large-angle intervals among the angular observations and a larger viewing zenith angle, with respect to nadir direction, can improve the retrieval accuracy because those angle conditions can result in significant difference for components' fractions and DBT under different viewing directions; 2) narrow channels can produce better results than broad channels. The new method was finally applied to a multiangular MIR and TIR data set acquired by an airborne system, and a modified kernel-driven BRDF model was used for angular normalization to the surface temperature for the first time. The difference of the retrieved emissivity and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) emissivity was found to be approximately 0.012 in the study area.

Fractional Vegetation Cover (FVC) is a very important vegetation structural parameter. The coarse-resolution remote sensing images can be obtained easily and widely used. If the accuracy of FVC derived from coarse-resolution data is promoted, it will bring great convenience for application field. From this point of view, this paper aims to use the high-resolution data to validate the precision of FVC, which is calculated by the coarse-resolution data. We chose the research region in Heihe basin, China as the experiment area. The validation data is generated by fitting the field measured data and Advanced Spaceborne Thermal Emission and Reflection (ASTER) vegetation index - higher resolution remote sensed data. Finally, this paper compares the results and takes analysis.

Terrestrial LiDAR Scanning(TLS) technology can quickly acquire three-dimensional information of forest canopy with high precision. As a new technique of data collection, it has been gradually applied to characterize structural attributes such as plant area densities. This paper presented a ray-tracing method to simulate laser intersection with a single tree and retrieves the plant area index based on gap-fraction model. The simulation model, based on ray-tracing method, was highly dependent on the sensor configuration and the spatial characteristics of the tree examined. Plant area index was retrieved by the gap-fraction model using the simulated point clouds. Given the significant cost and complexity of LiDAR data acquisition, it was necessary to identify the operational parameters to maximize the benefit. Therefore, the factors that might affect the simulation and inversion procedures are discussed extensively. Results showed that the simulation model was capable of predicting what survey configuration would be optimal and facilitating inversion algorithm development.

The Fraction of Absorbed Photosynthetically Active Radiation (FPAR) of crop canopies directly measured in the field is affected by many factors, i.e. the structure of canopies, the solar incident angles, and the weather. Therefore, it is difficult to apply the measured data to validate the FPAR products of remote sensing directly. In this paper, FPARs of maize canopies are measured from May to July, 2012, in Zhangye City, Gansu province, which is located at Heihe River Basin, Northwest China. The relationships between FPAR and the structures of canopies (i.e. leaf area index-LAI, fraction vegetation cover-FVC and row directions) are analyzed based on the experimental data. The impacts of the solar incident angles and the ratios of skylight on FPAR of maize canopies are analyzed based on the data simulated by RGM (Radiosity-Graphics combined Model). It is found that (1) FPAR change little with the ratios of skylight; (2) It is acceptable that FPAR of maize canopies are measured and calculated by a simple algorithms based on the transmittance of canopies; (3) FPAR of maize canopies with different row directions change with the solar incident angles of one daytime, which shows obvious diurnal variation character; (4) FPAR is the exponential relationship with LAI, and the linear relationship with FVC.

The ground-measured emissivity is always affected by many kinds of noises, which lead the retrieval accuracy to be out of expectation. This paper investigates the influence of three major noises (formula simplification, surface temperature measurement, and temperature emissivity separation algorithm) on the spectral emissivity by using simulation data based on radiative transfer model and field measured data from portable 102F infrared spectrometer. The findings of this paper can provide some suggestions for the further emissivity measurement.

This paper describes two new methods that were used to generate 26 years (1985–2010) of broadband emissivity (BBE) products with spatiotemporal continuity at the global scale from satellite data recorded by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR). On the basis of emissivity libraries, the study began with establishing relationships for converting channel emissivities of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODIS to BBEs for the 8–13.5-$\mu\hbox{m}$spectral window and then developed two new algorithms from simultaneous ASTER emissivity products to estimate BBEs over vegetated surfaces using the MODIS and AVHRR data. The MODIS-data-based algorithm (MDBA) uses linear equations with MODIS normalized difference vegetation index (NDVI) and seven channels' albedo; the AVHRR-data-based algorithm uses nonlinear equations with AVHRR red and near-infrared reflectances. The proposed algorithms were first validated with ASTER emissivity products. Results indicated that the root-mean-square errors of both the proposed algorithms were less than 0.015 and their biases were less than 0.003. Comparison with MODIS emissivity products from the day/night algorithm showed that the estimated BBEs using the MDBA were generally smaller than the MODIS products. Cross-comparisons were also made between the proposed algorithms and the NDVI threshold method. Finally, strategies for mapping global BBE products from the MODIS and AVHRR data are presented, and some examples are discussed. The global BBE products are planned to be released throughout the network in the near future.

Accurate spectral calibration of the in-flight sensors is crucial for processing and exploration of remotely sensed data. This paper developed a strategy to make spectral recalibration (i.e., spectral response function, central wavelength, and bandwidth) for in-flight broadband sensor using a device-responsivity-decomposition model with a priori knowledge and an optimization algorithm. Sensitivity analysis indicates that an accurate result requires the targets to be observed under a dry and clear atmospheric condition (column water vapor2and visibility > 23 km) and no more than 5% error is included in the measured data. The new strategy was used to retrieve the spectral parameters along with radiometric calibration coefficients for a multichannel camera onboard an unmanned aerial vehicle from simultaneously remotely sensed and ground measured data sets over 19 (15 color-scaled and four gray-scaled) man-made surface targets, and the retrieved results were validated with a similar data set over another four man-made targets. It demonstrated that the camera's spectral parameters were accurately retrieved and an error less than 3.5 W/m2/μm/sr was brought to the channel radiance.

In this study, by processing the 78 months of GRACE Satellite data, validates the performance of GRACE solutions in the detection of water mass variations and compared with tidal data derived water circulation cycle of the Great Lakes. The results show that the GRACE monthly gravity field model can reflect the changes in the Great Lakes area, including the annual and seasonal changes. Combination of satellite altimetry data to further understand the water cycle of Caspian Sea, that is between groundwater and lake water infiltration cycle.

As one of the computer simulation models, RGM (Radiosity-Graphics combined Model) can take the processes of reflectance, transmittance and multiple scattering among and between canopies into account. It is appropriate for simulating the directional reflectance from some small canopies and the simulating results are validated well, but the model is difficult to simulate the reflectance of complex scenes, such as mountainous region because the algorithm of RGM is complicated and time-consuming. In order to research the characteristics of the radiation and the reflectance from complex mountainous region, RGM is extended to simulate the directional reflectance from all kinds of topography in this article. To conquer the two disadvantages in RGM, the model is modified in two steps. Firstly, complex mountainous scenes are simplified to only keep DEM. Secondly the algorithm of Radiosity is modified to couple other BRDF (Bidirectional Reflectance Distribution Function) models of vegetation, such as NADIM model, to consider directional radiation of the surfaces of DEM (Digital Elevation Model). A DEM scene produced by a normal distribution random number and a real DEM scene from Tibet Plateau are used to test the modified RGM and the results are analyzed simply.

This paper presents a portable Multi-Angle Observation System (MAOS) to quickly collect bi-directional reflectance factor (BRF) and directional thermal radiance of land surface along with the spectroradiometer and thermal radiometer. The new system is able to make more than 13 zenith measurements in six minutes at an arbitrary azimuth direction, with the angle-controlling accuracy better than 2°. More observations are sampled in the hot-spot direction. All operations of the MAOS and data-processing are automatically controlled by the computer. Field campaign of winter wheat canopy shows that the MAOS had captured the angular variations of the BRF.

Digital photography is now the most widely used method to obtain the Fractional Vegetation Cover (FVC) in field measurements. Its accuracy is affected by shooting conditions and classification methods of digital images. In this paper, we chose summer maize as the study plant, used computer simulation method to control the shooting conditions strictly and generate simulated scene. Then a physically based ray-tracing (PBRT) algorithm was used to render the scene to obtain simulated images under different shooting conditions. Supervised classification and CIE L*a*b* color space threshold method were used to extract FVC values from the simulated images. Comparing the extracted FVC values with the scene's true FVC value, we evaluated the FVC accuracy of different shooting conditions and classification methods. The results can act as a guidance of digital photography to obtain the FVC.

Airborne LiDAR data and optical imagery are two datasets used for 3D building reconstruction. The researchers have developed a variety of modeling method using these two kinds of data. In this paper, we firstly reconstructed the buildings in the test site using the above three kinds of data sources. And then, we compared the results quantitatively. We adopted the primitive-based building reconstruction method to reconstruct the buildings using the two types of data.

Gap fraction is a very important parameter to the indirect estimation of the true Leaf Area Index. In this paper, we combined the multispectral digital imageries (RGB color imagery and Near-Infrared imagery), which were obtained from a new device called Multispectral Canopy Imager (MCI), to estimate gap fraction. A new method incorporated with CIE L*a*b* color space has also been proposed to segment the multispectral digital imagery. The preliminary results of the estimated gap fraction have been showed in the conclusions section and been proved to be very well.

Net Surface Radiative Flux is the key parameter for global change studies. In this study, two models designed to directly estimate net surface radiative fluxes over horizontal surfaces are developed based on artificial neural network (ANN).These models not only avoid the error propagation involved in the existing algorithms, but also provide the necessary data for estimating fluxes over rugged terrain. The validation results show that the maximum root mean square error (RMSE) of the ANN models is less than 45W/m2and 25 W/m2for net shortwave and longwave fluxes, respectively. By coupling the outputs of ANN models, the shortwave and longwave topographic radiative models are subsequently proposed to derive the net surface fluxes over rugged terrain. The results indicate that great errors can be detected if the topographic effect is ignored over rugged area, especially for net shortwave radiative fluxes.

Land surface temperature (LST) at both high spatial and high temporal resolution is required for routine monitoring of surface energy fluxes. Disaggregating LST to the NDVI-pixel resolution is possible because of significant inverse relationship between LST and vegetation indices. A modified algorithm (SWISF) has been proposed for thermal imagery sharpening, in which multiple least-squares regression relationships between LST and vegetation indices were acquired for bins of pixels with different soil wetness index values. Applying both SWISF and Distrad which is originally proposed by Kustas et al. to simulated thermal maps at 360 m resolution and sharpening down to 90 m shows that the new algorithm slightly outperform the old one. Moreover, DisTrad does not have the ability to consider the fact that two pairs of pixels with the same NDVI difference may have distinct LST difference under different soil moisture conditions, while SWISF algorithm could consider it to some extent.

Fractional vegetation cover (FVC) is widely relevant for land surface process. In this paper, an algorithm is addressed on FVC retrieval, with the combination of MODIS and Huan Jing satellite (HJ), which is a newly launched constellation by China. In the developed model, we considered angular effect and utilized spatial and temporal information to a great extent. MODIS and HJ surface reflectance products provide data supply for the algorithm and play cooperative roles. A vegetation growth model was introduced to constrain the uncertainty of HJ data in a temporal scale. The uncertainty of using this algorithm was assessed by error propagation theory and field experiments. Retrieved FVC became more reasonable after consideration of the correlation among time series observations and the introduction of more observational data. A priori information is necessary to constrain the inversion process.

Continuous LAI values are very important in crop growth monitoring, however, all of the remotely sensed LAI products are limited by the temporal and spatial resolution. High spatial resolution is good for crop monitoring but with very poor temporal sampling. The popular MODIS 8 day LAI product is still not sufficient for crop monitoring. An empirical crop growth model was developed based on two years' ground truth. It was then coupled with SAILH model to retrieve the continuous LAI day by day. A rolling inversion strategy was proposed further to minimize the random noise in the observations. The models coupled inversion was tested by simulation inversion. Results show significant improvements of the new inversion method.

Accurate radiometric and spectral calibrations of hyperspectral remote sensing instruments are essential for optimum data processing and exploitation. Two improved methods for the refinement of the spectral calibration of air- and spaceborne imaging spectrometers are presented in this paper. Both spectral channel position and width can be retrieved by modeling the atmospheric absorption features around 760, 940, 1140, and 2060 nm without making use of external atmospheric or surface parameters. A sensitivity analysis based on synthetic data demonstrated that, for each of the two methods, the root-mean-square errors to be expected were less than 0.18 nm for the retrieval of channel wavelength center and less than 0.8 nm for channel full-width at half-maximum. The application of the proposed methods to a real Hyperion data set showed quite-similar cross-track variations in the spectral calibration for the two methods, although relatively large differences in magnitude were found near the 940- and 1140-nm H2O absorption features. The significant improvement of the reflectance spectra derived after the refinement of the instrument spectral calibration confirms the good performance of the proposed methods.

HJ-1B satellite is one of the small satellites in the constellation for disaster prediction and monitoring which will be launched in 2008. The infrared sensor, which is one of the payloads of HJ-1B satellite, contains the MIR and TIR channels. The improved capabilities of HJ-1B data offer an opportunity for the computation of subpixel fire temperature and fire area. The simulated HJ-1B MIR and TIR channel images are used in this paper for the algorithm test. Fires with various sizes and temperatures are simulated in a wide range of terrestrial biomes and climates conditions by MODTRAN 4. A bispectral method developed by L. Giglio and J. D. Kendall is adopted to retrieve the temperature and area of a subpixel fire within an otherwise homogeneous pixel. It is evident that HJ-1B satellite data are more sensitive to the smaller and the cooler fires than that of MODIS or AVHRR Data. For the HJ-1B data, if the fire area is about 450m2and fire temperature is about 1000K, it can also offer a capability of retrieving the fire temperature and area in a relatively high accuracy. It has been demonstrated that the accuracy will increase with the growing fire area or temperature. By sensitivity analysis it has been found that the uncertainties of the retrieved fire temperature and area using HJ-1B data are about 10.0% and 30% at the given simulation condition.

Among the key parameters acquired by remote sensing inversion, vegetation fraction cover is one of the crucial variables. Component temperatures inversion and leaf area index (LAI) inversion all have close relations with the vegetation fraction cover. The objective of this study is to develop a method to estimate the vegetation cover fraction from satellite observation. Traditional methods of inferring vegetation fraction cover from satellite remote sensing include spectral mixture analysis (SMA) and scaled normalized difference vegetation index (NDVI). Those methods often rely on a series of steps in the processing chain, including atmospheric correction, surface angular correction and so on. Generally, those procedures are very computationally demanding. In addition, the errors associated with each procedure may be accumulated and significantly affect to the accuracy of the final products. In this study, a new retrieval methodology is proposed to calculate vegetation fraction cover over mixed pixels directly from the AATSR spectral reflectance data at top-of-atmosphere (TOA). The method consists of extensive radiative transfer simulations under a wide variety of solar illumination and sensor view conditions, atmospheric profiles, aerosol types and conditions and vegetation canopy leaf angle distributions. The derivation of vegetation fraction cover from TOA observations requires several steps of processing.Important steps include, (1) Preparing for the model input variables: foliage and soil spectral data on red, green and near-infrared bands which measured from two kinds of vegetation and three kinds of soil in the field experiment; (2) Generating a database based on a canopy radiative transfer model, Scattering by Arbitrarily Inclined Leaves (SAIL), and a hybrid linear model with the spectral data combined with vegetation geometric construction data and observation geometric data; (3)Atmospheric correction that converts surface ensemble reflectance to TOA ensemble reflectance based on a radiative transfer model, Second Simulation of the Satellite Signal in the Solar (6S); (4) Mapping the relationships between spectral directional ensemble reflectance and vegetation fraction cover through a nonlinear regress method. The correction coefficients of the surface vegetation fraction cover computed with AATSR are provided.vegetation fraction cover retrieval from TOA data of AATSR does not exceed by 6% at nadir view and 9.7% at forward view, respectively. The performances of input parameters on estimates of vegetation fraction cover are given compared with the "true" surface vegetation fraction cover. The aim of estimating vegetation fraction cover is to prepare for inversing component temperatures using AATSR data, in which process vegetation fraction cover is an important parameter.

This paper gives a brief description of an Airborne Multi-angle Power Line Inspection System (AMPLIS). AMPLIS is composed by 3 CCD cameras, a Position and Orientation System (POS), a stabilized platform, the data collection and control subsystem. It can be equipped on a helicopter and fly along the lines at a speed of about lOOkm/h at a relative height of 100 m over the power lines. AMPLIS is capable of detecting the distance between the power lines and the ground surface with an accuracy of less than 0.5 m. It can automatically find the dangerous objects beneath the lines which can greatly decrease the man power and cost in power line inspection. It has been successfully tested with good performance in Wuhan, China, 2005.

We develop a photogrammetric power line inspection system. Its main objective is to monitor the relative distance between high voltage power line and around objects, and alert if the warning threshold is exceeded. Our photogrammetric power line inspection system generates DSM of the power line passage, which comprises ground surface and ground objects, for example trees and houses, etc. In order to reveal the dangerous regions, where ground objects are too close to the power line, 3D power line information should be extracted at the same time. In order to improve the automation level of extraction, reduce labour costs and human errors, an automatic pole tower and spacer numbering method is proposed. The pole tower automatic numbering is in accordance with GPS position data of the image having pole tower projection, finds the pole tower whose measurement position is closest to it, and the found pole tower's code number is set to pole tower projection. Then a block can be defined by a start pole tower and an end pole tower. The spacer automatic numbering process is limited within a block, and it can be achieved by using epipolar constraint after an aerial triangulation bundle adjustment. The flight experiment result shows the proposed method can give correct code number to pole towers and spacers, and the proper 3D power line information can be obtained by space intersection using found homologous projections.

There has been little investigation for the automatic extraction of power lines from aerial images due to the low resolution of aerial images in the past decades. With increasing aerial photogrammetric technology and sensor technology, it is possible for photogrammetrists to monitor the status of power lines. This letter analyzes the property of imaged power lines and presents an algorithm to automatically extract the power line from aerial images acquired by an aerial digital camera onboard a helicopter. This algorithm first uses a Radon transform to extract line segments of the power line, then uses the grouping method to link each segment, and finally applies the Kalman filter technology to connect the segments into an entire line. We compared our algorithm with the line mask detector method and the ratio line detector, and evaluated their performances. The experimental results demonstrated that our algorithm can successfully extract the power lines from aerial images regardless of background complexity. This presented method has successfully been applied in China National 863 project for power line surveillance, 3-D reconstruction, and modeling.

The inversion of physical models in remote sensing is difficult due to its ill-posed essence. Though scientists have been realizing that the inversion result is much concerned with sensitivities of parameters, how to define the sensitivity of a parameter in inversion is still under discussion. In this paper, an "S Index" is proposed to derive S Ratio, a ratio of one input parameter's S Index to the sum of all other S Indices, as a useful sensitivity criterion. Moreover, we analyzed S index and S Ratio based on the information transfer theory. It is shown that S Ratio is related with information distribution ratio in inversion. The value of S Ratio may vary with different ground covers, soil types, moisture, geometries and bands. We took SAIL model as an example to illustrate the use of S Ratio under several typical scenes. Multi-angular datasets were generated for these scenes and further been used to retrieve 7 parameters of the model. The results suggest that the inversion accuracy is strongly correlated to S Ratio. Another two sensitivity indices are also demonstrated as a comparison. As a result, we could use it to estimate the sensitivity of parameters in a certain inversion step and which type of datasets is better for inversion under various cases. Such a priori information could be important before data selection

In this study, a thermal model-based algorithm has been developed. This linearized algorithm can invert land surface component temperatures in an ATSR pixel. The proportion of each component in an ATSR pixel is gotten using matched ASTER data. Then, assumed that the radiation of an ATSR pixel is the sum of the radiation from several components, we get a simple linear thermal model. Atmospheric effects on ATSR thermal data are removed using a split-window algorithm. Before inversion, the sensitivity of parameters is analyzed using Uncertainty and Sensitivity Matrix (USM). During the inversion process, a Multi-stage Sample-direction Dependent Target-decisions (MSDT) strategy is taken, that is, the most sensitive and uncertain parameters are inverted first by fixing some less sensitive parameters at their prior values. Compared with the general direct inversion method, MSDT strategy can get a more robust result. With other prior knowledge, this algorithm can invert soil and vegetation temperature.

Various remote sensing sensor observe the Earth's surface from coarse spatial resolution to fine spatial resolution. We may get different results from remote sensing images captured at different resolution due to scale effects. On the other hand, vegetation cover is an important parameter in many environmental models. It often affects the model results greatly. So, it is very important to understand the scaling problem of vegetation in remote sensing. This article presents a method to describe the vegetation cover distributions at different scales based on Bayesian Inference techniques. The histograms of vegetation cover show changing shapes with various spatial resolutions, they are very similar to Beta distributions with different parameters. On the other hand, geography spatial distribution probability can be expressed with binominal distribution or negative binominal distribution. Then, given a binominal or negative binominal distribution as likelihood, Beta distribution as a priori, we can get posterior distribution using conjugate prior theory. Such a posterior distribution can be used to predict the histograms of vegetation cover at different scales. Parameters used by this method can be calculated using mean and variance of vegetation cover at various scales. MODIS, Amtis and TM images are used to validate the method. The result shows that if vegetation is scattered, the binominal distribution may be used as the likelihood, on the contrary, a negative binominal distribution is much better. Because determining the spatial distribution is difficult, we combine the two distributions by adding the weight in this paper, and get better result.

Traditional vegetation indices are usually constructed by using red and near-infrared band reflectance data under single solar incidence-observation geometry. However, because of the anisotropy of the Earth surface's reflectance, vegetation indices acquired from different solar-incidence observation geometries exhibit lots of variances. Meanwhile, most of those indices only utilize vegetation's spectral information, and anisotropic reflectance of vegetation is considered as a disturbing factor rather than a source of vegetation's structural information. In this paper, kernel-based vegetation index (KVI) is constructed based on the semi-empirical kernel-based BRDF model parameters. Validation results of ground measured bidirectional reflection data of different vegetation types show: kernel-based vegetation index has better linear relationship with corresponding vegetation's leaf area index (LAI) than widely used normalized difference vegetation index does. The results of upscaling KVI from local scale to global large scale suggest the effect of scale needs to be considered when using it to different scale data sets. This study suggests that KVI provides a new method of better using multi-spectrum and multi-angle reflectance data, and has certain potential for multi-angular remote sensing applications

The Airborne Multi-angle TIR/VNIR Imaging System (AMTIS) samples the surface at a number of view angles and offers the potential of retrieval of atmospheric aerosol properties, land surface bidirectional reflectance etc. This paper presents the retrieval algorithm of aerosol optical depth and single scattering albedo from visual and near-infrared bands of AMTIS based on a simplified path radiance model. Atmospheric parameters such as molecular scattering and absorption are calculated using MODTRAN4. Under the assumption that the aerosol optical depth above the sensor is not affected by the surface, the aerosol optical depth above the sensor (4.2 km) is also calculated using MODTRAN4. The path radiance is divided into two components: the singly scattered radiance and multiple-scattering radiance. The AMTIS images acquired on April 11, 2001 in the Shunyi experiment are used in the retrieval. The algorithm performs best over dark surfaces, such as water. The retrieved aerosol optical depth is close to the result from the synchronous Sun photometer data with an error about 0.05/spl sim/0.1. Retrieved single scattering albedo is very close to that of the continental aerosol model of 6S.

atmospheric profile is available. Under the assumption that the surface emissivity is isotropic, two atmospheric parameters are needed to remove the atmospheric effect: the water vapor content (W) and the effective mean atmosphere temperature (Ta). Ta can be estimated from the muti-angular observations of the pixel with “minimum standard deviation” of brightness temperatures. After Ta is known, W can be retrieved from the pixels with multiangular observations under the assumption of isotropic emissivity and horizontal uniform atmosphere. Then the atmospheric effect can be removed after the two atmospheric parameters are known. Through the sensitivity analysis of the algorithm to emissivity and transmittance, it can be found that W is very sensitive to the error of emissivity. However, it can also be estimated using the pixel with known surface temperature and emissivity measured synchronously.

Albedo is a key parameter in monitoring the energy exchanges between the solar radiations and the land surfaces. The MODIS team generates the albedo products every 16 days. The products need to be validated by ground truths under different environmental conditions. In this study, we developed a 3-step validation procedure. The Ambrals (Algorithm for Modeling Bidirectional Reflectance Anisotropies of the Land Surface) model inversion was used to retrieve the albedo from the measured BRDF data over the winter wheat fields at the point/plot scale. And then, as our second step, the albedo values from the Airborne Multiangular Thermal-infrared Imaging System (AMTIS) over the same target area were estimated and validated using the ground point measurements. Finally, the retrieved albedo from airborne data were aggregated and compared with the MODIS albedo products. Our validation procedure has demonstrated a practical method to validate that albedo from spacebrone remotely sensed data (e.g., MODIS). The validation results show that the MODIS albedo products are reasonably good. Albedo is a key parameter in monitoring the energy exchanges of land surfaces. The hemispherical albedo is traditionally observed by albedometer at local meteorological stations, where the observing targets are usually grassland in a specific environment. Because some applications require albedo over a large area, retrieving regional and global albedo products from remote sensing observations can be more productive. The MODIS albedo products are from the multi-angular remote sensing (MARS) observations of every 16-days accumulation. The production needs to be validated by ground truths. One of the main problems in the validation is that the field-measured albedo is different in scale from the albedo retrieval using remote sensing data. The albedometer field measurement is over a small area, less than 1m 2 , while the spatial resolution of the MODIS albedo product is about 1 km. Another problem is associated with the different wavebands between the albedometer and the MODIS sensors. As a possible solution, we created a 3-steps validation procedure. As the first step, we used the BRDF data measured in the field to retrieve the albedo by Ambrals model inversion. The observing target was winter wheat. The retrieved albedo is comparable with that one measured by albedometer since both measurements are in the same observing scale. The effect of the wavebands difference was also corrected at this step. In the second step, we retrieved the albedo from the airborne MARS observation data of the same target. The spatial resolution is 1.36m at nadir. The retrieved albedo from airborne AMTIS BRDF data can be validated by using our field measurement. Finally, the retrieved albedo from airborne data was compared with the MODIS albedo product. Scaling-up needs to be considered in the comparison. In this work, the field measurements and airborne data came from the large satellite-airborne-ground synchronous experiment in the April of 2001. The experimental region is in the Shunyi county, 50km northeast of the Beijing City, China.

We propose an iterative multistage inversion (IMI) algorithm to retrieve the land surface component temperatures for nonisothermal vegetation canopy. Our algorithm is based on a thermal emission model that can simulate the directional effects from the nonisothermal surfaces. Our IMI algorithm just inverts the most uncertain and most sensitive parameters at each step using the most sensitive observation samples, and then adjusts the initial values based on the retrieval results. This inversion process is repeated until convergence condition is satisfied. Compared with the inversion method that try to invert all of the parameters at the same time, the IMI algorithm tends to give more accurate mean values for the parameters and is more stable when the noise level is relative low.

Leaf area index (LAI) is an important parameter for describing vegetation canopy structure in the terrestrial ecosystem. LAI is closely related to plant transpiration, sunlight intercept, photosynthesis and Net Primary Productivity. Multiangular remote sensing is capable of providing more three-dimension information of vegetation, and it is powerful in solving the problem of the same object with different spectrum or vice versa. As a result, multiangular remote sensing and Bidirectional Reflectance Distribution Function (BRDF) model based inversion may be more suitable for Leaf Area index (LAI) retrieval over row crop canopies. However, it's still difficult to get LAI without enough a priori knowledge due to the underdetermined problems in inversion. We use the multispectral information to get the a priori estimation of LAI, and then perform BRDF model inversion. Different from the general one channel based BRDF model inversion methods, our new methods use the muiltiangular and multispectral data sets together to increase the available information in inversion, i.e., it is a synthetic method. From the inversion results we found that the new synthetic method is more effective in LAI inversion.

Leaf area index (LAI) is an important biophysical parameter, and remote sensing provides the possibility for the LAI retrieval over large area. Model based inversion is one of the main LAI retrieval methods, and the multi-angle data are the important data sets. However, the general model-fitting algorithm is time consuming in LAI inversion. In this paper, we proposed a kernel-driven model and neural network based LAI inversion algorithm to speed the process. The data obtained by the new Airborne Multi-angle Thermal/Visible Imaging System (AMTIS) is synchronous and has higher resolution. Compared with the low-resolution multi-angle data such as MISR and MODIS, it has a resolution as high as 1.36 m. Using the kernel-driven model, the BRF was reconstructed from the AMTIS data. On the other hand, a 3-dimension radiative transfer model and the measured parameters were used to model the BRF. Then LAI was inversed based on the neural network. Synchronous ground-based measurements of LAI for wheat were taken in Shunyi to validate our method. Some conclusions from the study: (1) LAI can be retrieved successfully using the high-resolution multi-angle data based on neural network; (2) based on the neural network and the kernel-driven model, the inversion rate can be improved; (3) by adjusting the soil moisture classification, the inversion precision can be improved.

Multi-angular remote sensing became a hot topic after the non-Lambert characteristic of the Earth's surface had been accepted popularly. A large amount of multi-angular remote sensing data has been obtained with the launch of multi-angle remote sensing sensors. Therefore, modeling of the bi-directional reflectance distribution function (BRDF) for the Earth objects is one of the main subjects at present. A large satellite-airborne-ground synchronous remote sensing experiment was carried out during March 29 to May 10, 2001 at Shunyi, China. The main observation target in this experiment is focused on winter wheat. To describe the BRDF of winter wheat in its early growing stages, we propose a geometric-optical model that is suitable for sparse vegetation, and then try to retrieve the structure parameters based on this model using the field measurements. The purport of the model and its inversion is to inspect the ravages of drought on the wheat just as it is turning green. The winter wheat in our measurement field is sparse and disperses without clear row structures in its turning-green stage. Typical row structure based models and uniform structure based models are not suitable. Our model is developed based on the Li-Strahler geometrical-optical model proposed in 1985. Each cluster of wheat is treated as a hemi-ellipsoid in this model. All of the leaves in the cluster are assumed to cover the hemi-ellipsoid randomly. Leaf area index and leaf angle distribution are two important parameters that are related to the surface area of the hemi-ellipsoid and the leaf distribution on this surface respectively. Leaf angle distribution is also related to the shape of the hemi-ellipsoid. Due to the large uncertainty of the number of hemi-ellipsoids in a unit area, we retrieve this parameter based on our model using the most sensitive samples first, and then treat it as a priori knowledge in the later inversion. The next stage is studying how to use multi-angle remote sensing data to invert vegetation structure parameters.

The sources of the inverse error of remote sensing physical models are analyzed and divided into two groups. From the point of view of controlling these errors, a synthetic inverse scenario is put forward. A case study using simulated data shows that this scenario is better than ordinary methods in robustness and global convergency.

In this paper, GA is used to invert remote sensing models to identify its applicability in remote sensing. For convenience the linear spectral mixing model and the GOMS model are used as the representation of linear and nonlinear models respectively. The inverse results by GA are compared with two common deterministic search algorithms. Our results show that for linear models, there is only difference in terms of efficiency between algorithms, but for nonlinear models, GA is much better than its counterpart in terms of accuracy, and percentage of successful solutions.

The main problem in the validation is that the field-measured albedo is different in scale from the albedo estimated using remote sensing data. The albedo-meter based field measurement is over a small area. On the contrary, the spatial resolution of the MODIS albedo production is about 1 km. Another problem is the different wavebands or albedo-meter and MODIS sensor. As a solution, we suggest to use the field multiangular measurements data that is captured in a small field of view (FOV) to retrieve the albedo by Ambrals model inversion. As a result, the retrieved albedo is comparable with that measured by albedo-meter since they have the same scale. At the same time, the effect of wavebands difference is also corrected. In the second step, we extend this method to the airborne MARS observations of the same target. Scaling-up can be considered based on the two inverted albedos. The retrieved albedo from airborne data can be validated using field measurement too. Finally, the airborne retrieved albedo can be used to validate the MODIS albedo production. In this framework, the field measured data sets come from a large satellite-airborne-ground synchronous experiment that was taken in Shunyi, which is in the north of Beijing, in April of 2001.

We take the kernel-driven model as an example, focus on the information content definition and calculation of multi-angular remote sensing (MARS) data. We study four methods to measure the information content of MARS data: Fisher statistic, information entropy, determinant and sum of the diagonal elements of the information matrix, how to use the Fisher statistic theory and information entropy to measure the information content of MARS data, to calculate the information content of the dataset on the three unknowns for different subsets of the data. The analyses show that information entropy is a good tool for measuring information content of MARS data.

We propose a thermal bidirectional gap model to describe the thermal directional emission from row crop canopies. An important concept of overlap index is used in this model to express the correlation between the gaps in the Sun and view directions. Detailed directional thermal emissions, row structure, LAI, component temperatures were measured in the experiment taken in Shunyi China, 2001. These data are used to validate our model. As an illustration, we compared our bidirectional gap model with the model that doesn't consider gaps (Kimes model) and the model only consider gaps in view direction. It is found that our model gives out the closest results to the field measurements.

A priori knowledge can come from field measurements of bidirectional reflectance factors for various surface cover types. How to express and use this kind of knowledge is very important currently. 73 sets of field observations are used to explore the possible expression of a priori knowledge in linear kernel-driven BRDF models in this paper.

We present an algorithm to retrieve land surface temperatures from wideband thermal infrared measurements using the model of Li et al. (1999). Forward simulation and inversion demonstrates the method to be stable in the presence of observation noise. Results from inversions performed using field measurements show that errors are generally less than the uncertainty in the observations.