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.

Chlorophyll fluorescence is closely related to the process of vegetation photosynthesis. However, the radiative transfer of chlorophyll fluorescence in complex canopies distorts the coupling relationship between the remotely sensed fluorescence signals and photosynthesis. The current 3D fluorescence radiative transfer models struggle to strike a balance between the canopy complexity and the computational efficiency, thus limiting their applications in interpreting fluorescence at various scales. This article proposes a 3D canopy fluorescence radiative transfer method using multiple representations of canopies, allowing for simulations at scales ranging from individual tree level to satellite pixel level. This method is applied to the LargE-Scale remote sensing data and image Simulation framework (LESS) model, maintaining the simplicity and efficiency of the LESS model. The simulated results show good consistency with both state-of-art models and field measurement.

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.

The frequency and intensity of droughts have increased rapidly up to now and will become more severe in the future. To better characterize the impacts of drought on vegetation productivity over large scales by remote-sensing-driven models, we should understand the responses of vegetation to historical drought events. In this study, the responses of vegetation gross primary productivity (GPP) to the droughts in 2004 and 2015 over tropical Asia were analyzed along with hydrometeorological data and compared with data-driven models. We found that the GPP anomalies in data-driven models are negative in both drought events. However, the simulation of light-use efficiency (LUE) models revealed the GPP anomalies are negative in 2004, while positive in 2015. We discussed its possible causation. A key factor for LUE models is how they represent the effect of water stresses on GPP, where soil moisture (SM) is an indicator that largely differed from other variables, e.g. saturated vapor pressure deficit and land surface water index in characterizing water stresses. The anomalies of SM are obviously different in 2004 and 2015, thus, the representation of SM stress could a crucial factor for improving the LUE models to better simulated GPP in characterizing responses of vegetation GPP to droughts over large scales.

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.

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.

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.

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.

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.

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.

Accurate spatiotemporal information about crop progress during the growing season is critical for crop yield estimation. Crop progress monitoring at field scale requires high resolution remote sensing data in both time and space. Remote sensing data from a single sensor cannot satisfy the requirement at present. Data fusion approach has been developed to fuse remote sensing imagery from Landsat and MODIS instruments. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is one of the most popular spatial and temporal data fusion algorithms and has been applied in many applications. The data fusion accuracy was evaluated for many sites. Previous studies found that the accuracy of data fusion results depended on the pair images used. In this study, several Landsat-8 reflectance images (path28/row31) in 2015 were selected as pair images to evaluate the data fusion accuracy. Results were assessed based on the observed Landsat data that have not been used as pair images due to partial cloud coverage or image gaps. Several statistic metrics, including average absolute difference, root mean square error, correlation coefficient, and the spectral angle mapper, were calculated to assess the data fusion results. The initial results show that the predictability of each images pair at different dates is different. Closer dates have better prediction accuracy as expected. Interestingly, the different crop type (corn and soybeans) shows different data fusion accuracies even using same image pair. This study suggests that data fusion results could be further improved if an appropriate image pair is selected. Accurate dense time-series data at Landsat resolution will enhance our ability in crop condition monitoring and crop yield estimation at field scale.

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.

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.

The diffuse and specular components of leaf reflection are both important to determine the leaf optical properties as well as to describe the leaf bidirectional reflectance distribution function (BRDF). However, the specular component is usually ignored in practice in numerous canopy reflectance models that describe the interaction between solar light and vegetated scene components. To evaluate the impact of leaf-specular reflection on canopy bidirectional reflectance factor (BRF) characteristics, we introduce a leaf BRDF model into the radiosity-graphics combined model (RGM; a 3-D scene model) to calculate canopy BRFs with nondiffuse leaves. The modified RGM is validated by comparing simulated BRFs against in situ measured BRFs over real maize canopies. The results show that ignorance of leaf-specular reflection can result in up to 50% of relative error in the blue band (435.8 nm). A series of maize canopies with different leaf angle distributions (LADs) is reconstructed to investigate the effect of five major biophysical/geometrical parameters such as leaf area index, LAD, leaf surface property, view direction, and solar zenith angle on leaf-specular reflection contributions to the canopy BRF. It is demonstrated that increasing the incident solar zenith angle and decreasing the mean leaf angle impact the angular distribution of the canopy BRF more significantly than other factors. The cumulative hemispherical relative and absolute errors of canopy BRF caused by the leaf-specular reflection are often too large to be ignored, even for canopies with rough surface leaves. Moreover, the relative error of BRF in visible waveband shows that, in general, leaf-specular reflection has a large impact than that in near-infrared waveband. However, such impact can be sufficiently accounted for by even just consideration of the first-order leaf-specular reflection in canopy reflectance calculation, leading to a substantial improvement in simulation accuracy for most vegetation canopies.

Crop growth condition information is critical for crop management and yield estimation. In order to monitor crop conditions from space, high spatial and temporal resolution remote sensing data are required. Data fusion approach provides a way to generate such data set from multiple remote sensing data sources. In this paper, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was used to generate daily Landsat-like surface reflectance over central Iowa from 2001 to 2014. The fused Landsat-MODIS results were compared to the actual Landsat observations. Constrains and limitations of data fusion approaches were discussed. Data fusion results will be applied to map crop condition at field scales. Crop condition results will be compared to the Crop Progress reports from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS).

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.

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.

Currently available land-surface parameter products are generated using parameter-specific algorithms from various satellite data and contain several inconsistencies. This paper developed a new data assimilation framework for consistent estimation of multiple land-surface parameters from time-series MODerate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data. If the reflectance data showed snow-free areas, an ensemble Kalman filter (EnKF) technique was used to estimate leaf area index (LAI) for a two-layer canopy reflectance model (ACRM) by combining predictions from a phenology model and the MODIS surface reflectance data. The estimated LAI values were then input into the ACRM to calculate the surface albedo and the fraction of absorbed photosynthetically active radiation (FAPAR). For snow-covered areas, the surface albedo was calculated as the underlying vegetation canopy albedo plus the weighted distance between the underlying vegetation canopy albedo and the albedo over deep snow. The LAI/FAPAR and surface albedo values estimated using this framework were compared with MODIS collection 5 eight-day 1-km LAI/FAPAR products (MOD15A2) and 500-m surface albedo product (MCD43A3), and GEOV1 LAI/FAPAR products at 1/112° spatial resolution and a ten-day frequency, respectively, and validated by ground measurement data from several sites with different vegetation types. The results demonstrate that this new data assimilation framework can estimate temporally complete land-surface parameter profiles from MODIS time-series reflectance data even if some of the reflectance data are contaminated by residual cloud or are missing and that the retrieved LAI, FAPAR, and surface albedo values are physically consistent. The root mean square errors of the retrieved LAI, FAPAR, and surface albedo against ground measurements are 0.5791, 0.0453, and 0.0190, respectively.

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.

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.

It is a complex ecological structure with grassland, forest and agriculture in arid and semi-arid regions in Northwest China. It is important for understanding and evaluating ecological efficiency to study net primary productivity of vegetation in Northwest China. In this paper, Gansu Province was selected in Northwest China to study vegetation NPP based on adjusted boreal ecosystem productivity simulator (A-BEPS) model in arid and semi-arid regions. Net primary productivity of vegetation was simulated in Gansu Province from the year of 2002 to 2010 with moderate resolution remote sensing imageries and meteorological data. And then, spatial-temporal distribution patterns of average NPP were analyzed in Gansu Province from the year of 2002 to 2010. The results show that it is high in the south and low in the north for spatial distribution, and obviously seasonal characteristics are got in Gansu Province. Finally, the trends of averaged NPP for three vegetation types and relative meteorological factors were analyzed for the past nine years. The results of a little decreasing NPP and precipitation are got for Gansu province.

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.

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.

Drought is the first place in all the natural disasters in the world. It is especially serious in North China Plain. In this paper, different soil water content control levels at winter wheat growth stages are performed on Gucheng Ecological Meteorological Integrated Observation Experiment Station of CAMS, China. Some canopy parameters, including growth conditions, dry weight, physiological parameters and hyperspectral reflectance, are measured from erecting stage to milk stage for winter wheat in 2009. The relationship between canopy parameters and soil relative moisture, canopy water content and water indices of winter wheat are established. The results show that some parameters, such as SPAD and dry weight of leaves, decrease with the increasing of soil relative moisture, while other parameters, including dry weight of caudexes, above ground dry weight, height, photosynthesis rate, intercellular CO2concentration, stomatal conductance and transpiration rate, increase corresponding to the soil relative moisture. Obvious linear relationship between stomatal conductance and transpiration rate is established with 45 samples, which R reaches to 0.6152. Finally, the fitting equations between canopy water content and water indices are regressed with b5, b6 and b7 of MODIS bands. The equations are best with b7 and worst with b5. So the fitting equations with b7 can be used to inverse the canopy water content of winter wheat using MODIS or other remote sensing images with similar bands range to MODIS in Hebei Province.

Qinghai-Tibetan Plateau plays an important role in estimating net primary productivity of grassland ecosystem for global carbon cycling research. In this paper, boreal ecosystem productivity simulator (BEPS) model was modified according to the characteristics of grassland canopy. A hypothesis of horizontal homogeneity and vertical layer was put forward for grassland canopy and BEPS was modified to GEPS (grassland ecosystem productivity simulator) to simulate the NPP of grassland ecosystem. With MODIS products (MOD15A2 and MOD12Q1) and routine meteorological data, net primary productivity of grassland ecosystem was simulated in Qinghai-Tibetan Plateau in 2006 based on GEPS. The result shows that NPP of grassland ecosystem in Qinghai-Tibetan Plateau is between 20 and 500 gC/m2·a, which is close to the other studies. The spatial distribution of NPP of grassland ecosystem in Qinghai-Tibetan Plateau has the trend of decreasing from east to west. Finally the seasonal change of NPP was investigated based on monthly NPP, which has good coherence with the seasonal changes of temperature. This study suggests that the process model - GEPS - is suitable to simulate NPP of grassland ecosystem in Qinghai-Tibetan Plateau.

PAR and FPAR are two important variables in agricultural field. Some researches show that many factors, such as LAI (leaf area index), LAD (leaf ange distribution) and the heterogeneity of vegetation will affect the distribution of PAR and FPAR. In order to understanding the exchange process of material and energy, Radiosity-Graphics combined Model (RGM) (Qin et al., 2000) is used to simulate the distribution of PAR and FPAR in canopy and some effect factors, such as the structure of canopy and sun zenith angle, can be analyze carefully. PAR and FPAR of a typical winter wheat canopy is simulated and the results are validated with the measured data. They agreed well. Next work is to simulate and analyze several factors of the distribution of PAR and FPAR, including sun incident angle, LAD, LAI, special for the heterogeneous canopies such as that crop with width and narrow ridges which can direct cropping patterns and remote sensing inversion.

Moderate Resolution Imaging Spectroradiometer (MODIS) data is suitable for water mapping, easy to get and having high temporal and wide spatial coverage. This study describes a comprehensive method to produce routinely maps of water cover in Tibetan Plateau with MODIS Surface Reflectance products (MOD09A1) at 463.51 m resolution. Multi-index end-member selecting algorithm was applied to identify end-members including water, forest, grasslands, barren and cloud. Based on the character of land cover spatial distribution, a typical-and-near end-member selecting method and a fully constrained linear unmixing method were adopted to unmix the mixed pixels. The accuracy of the water maps and performance of the algorithm were assessed using 6 pairs of synchronic MODIS/ETM+ images. The method is well-suited to mountainous environment

The accurate prediction of crop yield is of great help for grain policy making as the importance of food in human life. By assuming a homogeneous and vertical laminar structure and introducing a multilayer-two-big-leaf model, we developed a radiative transfer equation for winter wheat canopy and a model named RS-P-YEC (Remote Sensing — Photosynthesis — Yield Estimation for Crop) for winter wheat yield estimation. In this model, we converted the net primary productivity to winter wheat yield using harvest index. In this study, we estimated yield of winter wheat in North China Plain using the RS-P-YEC model. The simulated yield agrees well with observations from agro-meteorological stations and the R2reaches to 0.817. This study demonstrates that RS-P-YEC model is useful in the yield estimation of winter wheat in North China Plain with widely available remotely sensed images.

The distributions of polarized reflectance from several leaves surfaces are measured by the multi-direction instrument, including corn tender leaf, corn mature leaf, and lilac leaf. The degrees of polarization corresponding to different incident zenith angle and view zenith angle are calculated. Some results can be concluded by comparing the degree of polarization: the degree of polarization will increase with incident zenith angle and view zenith angle. These indicate that non-Lambertian of leaf surface will be distinct with the increasing of incident zenith angle.

The speed and accuracy of a hierarchical SVM (H-SVM) depend on its tree structure. To achieve high performance, more separable classes should be separated at the upper nodes of a decision tree. Because SVM separates classes at feature space determined by the kernel function, separability in feature space should be considered. In this paper, a separability measure in feature space based on support vector data description is proposed. Based on this measure, we present two kinds of H-SVM, binary tree SVM and k-tree SVM, the decision trees of which are constructed with two bottom-up agglomerative clustering algorithms respectively. Results of experimentation with remotely sensed data validate the effectiveness of the two proposed H-SVM.

As traditional classification methods use spectrum of objects only, they cannot distinct the same objects with different spectrum, and sometimes they will misclass the different objects into one class with the similar spectrum. In order to classify the different objects correctly, Mixed Decision Tree (MDT) method and Minimum Distance Texture Feature Vector (MDTFV) are presented in this paper. As a case study, TM image in Beijing City, including some parts of northern suburban in China, is selected. Considering the particularity of big city, lots of mixed pixels exist, we recognize not only pure pixels but also mixed pixels as a class for the result. Eight classes, including forest, grass, farm, water, building, useless, building and vegetation, useless and vegetation, are gotten in the research region. At last all the classes are overlaid into an image to get the classification map. The classification accuracy is up to 97.25% and Kappa coefficient reaches 0.9612, which is improved greatly than that of using spectral method only.

With the development of remote sensing and the technology of computer, computer simulation models are paid more and more attention to research Bidirectional Reflectance Distribution Function (BRDF) of the Earth surface, which can describe vegetations with much more detailed structures and simulate the interaction between light and vegetations on the Earth more reality. As known to all, according to the principles of the BRDF models, physical models of vegetation in the field of remote sensing can be divided into three categories: Geometry Optical models (GO), Radiance Transfer models (RT) and computer simulation models. To understand the process and mechanism of the interaction between light and vegetations better, some computer simulation models are provided, for example, DIANA which is based on the method of Radiosity, RAYTRAN, based on the method of ray tracing and Monte Carlo and so on. In this paper, a model based on Radiosity method is used to simulate the bidirectional reflectance factor (BRF) of summer corn field. It includes three parts: modeling the 3D scene; calculating the radiostiy of the components in the scene; and then accounting BRF of the scene. In the paper, at first 3D structures of corns are reconstructed by an extended L-system based on the measurement in the site of Luancheng Hebei Province, China in 2000. Then the Radiosity- Graphic combined model is applied to simulate the light of the scene of corn, and BRF of the scene of corn will be computed and compared with the measured BRF in the station of Luancheng. Simulated and measured results are fitted very well. Then the hemispherical BRF of the scene are calculated. At last, after analyzing the process of simulation and the results, several advices to advance the Radiosity model are put forward.

HYPERspectral Tethered Spectral Radiometer Buoy (Hyper-TSRB, Satlantic Inc.) has 123 channels from 400 nm to 800 nm to measure downwelling irradiance (E/sub d/) and upwelling radiance (L/sub u/). The supplied software for Hyper-TSRB data processing (AKA, PROSOFT) is based on Case I and/or empirical algorithms. A new semi-analytical method is proposed for Level 3 data processing. Basically, for open oceans case I waters, no large differences are found between the new method and PROSOFT (/spl sim/10%); while for coastal case II waters, the new method is much better than PROSOFT in computed remote sensing reflectance (/spl sim/10%-350%). The effect of phase function to the new method is also analyzed, it is shown that the new method can work stably for a wide phase function ranges.

This research focuses on the study of crop scene visualization based on crop simulation and knowledge engineering techniques. Corn data are measured in the field of Luancheng, Mebei Province and Shunyi, Beijing City. In the study of 3D simulation of crop realistic structure, the object-oriented knowledge Database of crop realistic structure is in further development. The data structure of realistic scene used here can organize the whole visualization process well. With the appearance of the realistic structure model of the quantity remote sensing, the field of crop visualization study is more and more important. During the process of studying visualization, the object-oriented 3D visualization data model arises, which is used as the formation of the crop realistic structure. 3D visualization data model can be applied to the description of crop structure successfully by object-oriented design and confirming the geometrical and logical relation. In this paper, we have the description and computer simulation of the scene of vegetation as a main clue. We are working on the data organization by design principle of Objected-Oriented DBMS. We use the corn scene data from the experiments in Shunyi, Beijing City, 2001 as an example to build the remote sensing data model. Then, we produce the 3D model using L-system, and present 3D scene of summer corn on the computer to show the visualization

Multitemporal analysis has been expected an effective tool for crop identification and monitoring with the easy access of regularly recorded remote sensing data. To make this approach feasible, an important study is to build crop models based on the observed measurements and simulate the crop spectra in the key growing stages. While a few vegetation indices have been developed, such as Normalized Difference Vegetation Index (NDVI) and the slope of red edge, little attention has been given to the problem of uncertainty contained in the data due to wavelength excursion, sun angle variation, and the background spectral influence, etc. The propagation and accumulation of the uncertainty in the recorded data and developed crop models need to be taken into account for reliably crop spectra modeling. How data's uncertainty affects spectra simulated in crop models driven by remote sensing data and decision-making is a critical issue. In this paper, several uncertainty sources are discussed. The contrast between the uncertainties of simulation spectral using Kimes model and Gap model is given at the same time. The result indicates that sun zenith, growth stage of winter wheat and observed angle have a closely relation with the spectral uncertainty. The experimental data sets were collected by the hyperspectral instrument SE590. The wavelength range is from 400 nm to 1100 nm and spectral resolution is about 3 nm. The two models were tested using the data recorded in ShunYi, Beijing, China (40/spl deg/00N'-40/spl deg/18'N, 116/spl deg/28'E-116/spl deg/58'E) in 2001.

A new LAI retrieval method is developed. The algorithm borrows ideas from the principles and methods of ground LAI measurements, and adopts a new frame which differs from traditional remote sensing LAI inversion methods. The ground data acquired from two field experiments are used to validate the algorithm. In order to resolve the scale exchange problem between high resolution ground observation and low resolution remote sensing data, two high resolution remote sensing images almost having the same resolutions with ground measurements are used as transitions.

With the developing of satellite technology, more and more sensors, based on various resolutions and functions, are launched to the sky to perform their missions. Enormous data are sent back to the Earth stations. In deriving surface parameters using these remotely sensed data, the transportability of algorithms from one resolution to another often cause the scale effect because of the surface heterogeneity on the Earth which can induce the change of reflectance. The problem, that the change of reflectance data affected by discontinuity as part of surface heterogeneity impacts the retrieval of vegetation leaf area index (LAI), is addressed in This work. Two cases, inducing the scaling issue in deriving surface parameters of interest, are considered here. One is the discontinuity between contrasting cover types within a mixed scene, the other is the nonlinear relationship of NDVI and LAI. Therefore, it is necessary to apply the correction based on NDVI-LAI relationships to modify scaling problem. In the processing, considering the field of wheat, firstly a series of 3D scenes with wheat and soil mixed are made based on the field measurement; secondly, computer simulation model $the method of radiosity, which can calculate the balance of light energy in the simulated scenes, is used to model BRF (bi-directional reflectance factor) of these scenes. If the NDVI-LAI relationship from homogeneous scenes can be taken as standard, the relationship from heterogeneous scenes will be modified according to contextural parameter. Some conclusions are drawn from the investigation: (1) different distributional contextures of vegetation even with the same LAI affect the reflectance heavily; (2) we compare the reflectance simulated by the method of radiosity with the mean reflectance calculated using the area-weighted linear relationship of reflectance from components, and find that the accuracy of the mean reflectance can be accepted so that the linear equation to calculate the reflectance of mixed pixels is reasonable to relate images with high and low resolution; (3) using contextural parameter for quantifying the scale effect can get promising results.

Aiming at shortages of current vegetation indices, we put forward Three-band Gradient Difference Vegetation In- dex(TGDVI), and established algorithms to inverse crown cover fraction and Leaf Area Index(LAI) from it. Theoretical analysis and model simulation show that TGDVI has high saturation point and the ability to remove the influence of background, and explicit functional relation with crown cover fraction and LAI can be established. We also theoretically analyzed why NDVI has low saturation point and indicate that relationship between Sim- ple Ratio Vegetation Index(SR) and LAI closes to piecewise linear instead of linear.

In the field of remote sensing, it is important to understand interaction between light and vegetation. The interrelation of them has been addressed in many works, and many different radiant models of vegetation have been proposed, such as: geometrical optical models, turbid medium models, hybrid models and computer simulation models. With developing of quantitative remote sensing research, computer simulation models, for example, Monte Carlo simulation model and Radiosity show their importance in analyzing the experimental data. In order to continue calculating the reflectivity from the vegetation by using a computer simulation model, it is essential to build the 3D structure of the vegetation. Therefore, many 3D structure data and optical parameters about the real winter wheat were measured firstly, i.e. height of stem, positions and sizes of the leaves, distributions on the field of wheat. Because these data are numerous and discrete, it is very difficult to simulate the virtual scene with them directly. To cope with it, we arranged all data and parameters in several layers based on the object oriented technique. Moreover, in order to simplify and deduce the structural variables that will be applied to build the 3D visual winter wheat model, we analyzed experimental data statistically in the process of realistic structural model. Several geometric and logical relations about structural variables were developed subsequently, and some variables varying with season were summarized to get the simple regulation with the purpose of simulating growing process of the winter wheat. The extended Lindenmayer system (L-system) method is then used to simulate the virtual scene of winter wheat by giving a few structural variables simplified before. Once the simulation is correct, scattering and reflectance from the 3D structural scene can be calculated using the Monte Carlo simulation model or Radiosity and so on. Our results show that (a) our lighting simulation system efficiently provides the required information at the desired level of accuracy, and (b) the plant growth model is extremely well calibrated against real plants. Furthermore, the method and the relations developed in this paper can be used in other subjects, such as computer graphics.