The leaf area index (LAI) is a key structural parameter of vegetation canopies. Accordingly, several moderate-resolution global LAI products have been produced and widely used in the field of remote sensing. However, the accuracy of the current moderate-resolution global LAI products cannot satisfy the requirements recommended by the LAI application communities, especially in heterogeneous areas composed of mixed land cover types. In this study, we propose a mixed-pixel correction (MPC) method to improve the accuracy of LAI retrievals over heterogeneous areas by considering the influence of heterogeneity caused by the mixture of different biome types with the help of high-resolution land cover maps. The DART-simulated LAI, the aggregated Landsat LAI, and the site-based high-resolution LAI reference maps are used to evaluate the performance of the MPC method. The results indicate that the MPC method can reduce the influences of spatial heterogeneity and biome misclassification to obtain the LAI with much better accuracy than the Moderate Resolution Imaging Spectroradiometer (MODIS) main algorithm, given that the high-resolution land cover map is accurate. The root mean square error (RMSE) (bias) decreases from 0.749 (0.486) to 0.414 (0.087), while the R2 increases from 0.084 to 0.524, and the proportion of pixels that fulfill the uncertainty requirement of the GCOS increases from 38.2% to 84.6% for the results of site-based high-resolution LAI reference maps. Spatially explicit information about vegetation fractional cover can further reduce uncertainties induced by variations in canopy density for the results of DART simulated data. The proposed method shows potential for improving global moderate-resolution LAI products.

High spatiotemporal resolution time series of leaf area index (LAI) are essential for monitoring crop dynamics and validating coarse-resolution LAI products. The optical satellite sensors at decametric-resolution have historically suffered from a long revisit cycle and cloud contamination issues that hampered the acquisition of frequent and high-quality observations. The 16-m/4-day resolution of the new generation Gaofen-1 (GF-1) and Gaofen-6 (GF-6) satellites provide an unprecedented opportunity to address these limitations. Here we developed an effective strategy to generate daily 16-m LAI maps combing GF-1/6 data and ground LAINet measurements. All high-quality GF-1/6 observations were utilized first to derive smoothed time series of vegetation indices (VIs). Second, a random forest regression (RF-r) model was trained to link the VIs with corresponding field LAI measurements. The trained RF-r was finally employed to generate the LAI maps. Results demonstrated the reliability of the reconstructed daily VIs (relative error2of 0.05, 0.59 and 0.75, respectively. The LAI time series well captured the spatiotemporal variation of crop growth. Furthermore, the continuous GF-1/6 LAI maps outperformed Sentinel-2 LAI estimates both in terms of temporal frequency and accuracy. Our study indicates the potential of GF-1/6 to generate continuous decametric-resolution LAI maps for fine-scale agricultural monitoring.

Vegetation index (VI) derived from remotely sensed images is a proxy of terrestrial vegetation information and widely used in land monitoring and global change studies. Recently, the prediction of vegetation properties has been an interest in related communities. With the accumulation of satellite records over the past few decades, the spatial–temporal prediction of VI becomes feasible. In this letter, we developed deep recurrent neural networks (RNNs) with long short-term memory (LSTM) and gated recurrent units (GRUs) to predict the short-term VI based on historical observations. The pixel-based fully connected networks GRU and LSTM (FCGRU and FCLSTM) and patch-based convolutional networks (ConvGRU and ConvLSTM) are established and compared with the traditional multilayer perceptron (MLP) model. Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 normalized difference VI (NDVI) data sets were used in the experiments. The prediction performance is evaluated globally in different regions, different vegetation types, and different growing seasons. Results demonstrate that the RNN models can predict VI with high accuracy (average root mean square error (RMSE) around 0.03), which is superior to the MLP model. In general, the pixel-based RNN models performed better than the patch-based models especially in regions with a larger proportion of outliers. And the prediction accuracy is stable over different vegetation types and growing seasons.

High-accuracy estimates of fractional vegetation cover (FVC) are vital for regional-scale vegetation growth monitoring. In this context, a question worth exploring is whether FVC estimation methods developed at 300-m to 1-km spatial resolution are suitable for finer spatial resolution satellite images. This study compared the performances of three types of algorithms [i.e., the pixel dichotomy model (PDM) based on either the normalized difference vegetation index (NDVI) or an index of near-infrared reflectance of vegetation (NIRv), the gap probability theory (GPT), and linear spectral mixture analysis (LSMA)] based on FVC ground measurements and fine resolution reference maps from the Validation of Land European Remote sensing Instrument (VALERI) project and the ImagineS field campaigns. For all vegetation types, the FVC estimates from the GPT method showed the best consistency with ground measurements of FVC [root mean square error (RMSE) = 0.17 and bias (BIAS) = 0.05]. For forest types, the PDM method based on NDVI also showed satisfactory results with ground measurements (RMSE = 0.17 and BIAS = 0.11). For sparse grasses, the PDM method based on NIRv showed better agreement with ground measurements (RMSE = 0.15 and BIAS = 0.01). This study provides a reference for selecting the method of FVC estimation with fine spatial resolution images.

Spatiotemporally continuous long-term leaf area index (LAI) products are urgently needed to monitor environmental changes. The current filter- or curve-fitting-based time series reconstructive algorithms fail to reconstruct the LAI time series with many continuous missing values or missing values in key phenological periods, which are common issues in high-spatial-resolution LAI time series. In this article, a meteorological data-driven backpropagation neural network (MBPNN) was proposed to reconstruct discontinuous LAI profiles with a two-step process using vegetation phenological information. As the basis of the strong dependence of vegetation growth on meteorological conditions, a reasonable growth trajectory of reconstructed LAI can be guaranteed by the algorithm even though if many observed values are missing. Validations for reconstructed LAI were conducted both spatially and temporally based on reference maps and field-measured long-term observations. The results showed that the LAI predicted by the MBPNN had a similar accuracy (RMSE = 0.4076) as the Landsat LAI inversions (RMSE = 0.4083) and a similar reconstructed trajectory as the field-measured LAI series even though over 100 days of continuous data were missing (RMSE = 0.1620). A comparison with the Harmonic ANalysis of Time Series (HANTS) algorithm showed that the accuracy of MBPNN was more stable regardless of the size/position of the missing data, and the proposed method performed much better when the data were continuously missing for 50 days or more.

Topographic and angular corrections on Sentinel-2 imagery are crucial for the generation of consistent surface reflectance. We propose a novel topographic-angular integrated normalization approach based on the combination of the path length correction (PLC) and C-factor approaches. The PLC-C normalization approach is a semiphysical method with limited use of auxiliary data: only a digital elevation model and a fixed set of kernel coefficients, ensuring its transferability for operational implementation. For the validation, we used two Sentinel-2A images over a mountainous area observed in backward (BS) and forward scattering (FS) directions from laterally adjacent orbit swaths. PLC-C significantly reduced both the topographic and directional anisotropy effects: the overlapping ratio between BS and FS observations was increased from 84.1% to 92.8% for the near-infrared band, and from 81.0% to 93.1% for the red band; the coefficient of variation of the reflectances across different aspects, which was used as a criterion of topographic effects, was reduced from 9.8%/12.2% to 3.6%/5.7% in BS/FS direction for the near-infrared band, and from 8.1%/9.7% to 4.5%/4.2% for the red band. PLC-C will contribute to the generation of analysis ready data from Sentinel-2 top of canopy reflectance.

The Leaf Area Index (LAI) is a key structure parameter of plant canopy and is a basic input variable in various terrestrial ecological models. Previous studies indicate that the spatial heterogeneity and the mixture of different land cover types in the moderate resolution pixels will cause large errors in the retrieval of moderate resolution LAI product. Therefore, the influence of spatial heterogeneity should be corrected to retrieve a more reasonable LAI. In this study, we propose a method to improve the accuracy of moderate resolution LAI retrievals based on the mixed-pixel clumping index. The data simulated by the LESS model are used to validate the proposed method. Results show that the LAI estimated by the MODIS operational algorithm become smaller with the increase in spatial heterogeneity of pixel. The proposed method can correct the influence of mixed land cover types and spatial heterogeneity to obtain a more reasonable LAI, and thus shows the potential in generating the global moderate resolution LAI product with improved accuracy.

Normalized difference vegetation index (NDVI) is a key parameter in land use/cover change and terrestrial modelling studies. With the accumulation of satellite records in the past few decades, the spatial-temporal prediction of vegetation index becomes feasible. In this paper, we established a convolutional GRU network (ConvGRU) to predict the short-term vegetation index considering the spatial and temporal patterns in the NDVI records. The predictive performance of the proposed method is evaluated for several vegetation types in different regions globally. The results demonstrate that the proposed model has sufficient ability in predicting satellite recorded NDVI. The validation on the MODIS NDVI datasets achieved an average RMSE around 0.06. And, the model performs better in regions of Australia while worse in North Europe.

Leaf chlorophyll content (Chlleaf) is a crucial parameter in carbon cycle modeling and agricultural monitor. Taking advantage of remotely sensed red-edge vegetation index (VI) is an easy approach to estimate Chlleafat a large spatial scale. However, the spectral signals of Chlleafand other canopy/foliar/background factors (e.g. leaf area, leaf angle, soil moisture, etc.) are always coupled together, leading to the relatively low accuracy in direct Chlleafestimation. A new chlorophyll sensitive index (CSI) based on the red-edge band is proposed to estimate Chlleaf, with minimal canopy structural influences. Validation results using in-situ measurements show CSI performed better to estimate Chlleafof winter wheat and soybean at canopy scale: RMSE=8.24μg/cm2for CSI; RMSE=10.04 μg/cm2for the best existing index, MTCI. CSI also has the potential ability to estimate Chlleafacross diverse structural species types with high accuracy. Therefore, CSI provides an effective and convenient way to estimate Chlleafover large areas using satellite data.

High-quality Leaf Area Index (LAI) time-series is important for many ecological applications. Unfortunately, troubles of observations missing and low spatial-temporal resolution greatly restrict their further applications. Due to the increasing of algorithm uncertainty, the current time-series optimized (TSO) algorithms perform poorly when LAI observations are lost continuously or unavailable on the key phenology nodes. It is an effective way of improving performance of TSO by introducing prior knowledge which replenishes time-series detail information. Meteorological data is completely competent since its great potential of describing vegetation growing rules. In this paper, focusing on data missing trouble, we develop a new LAI time-series reconstruction algorithm, called MNNR (Meteorology and Neural Network based Reconstruction), by introducing external meteorological data and other prior information into a neural network model. The results demonstrate that the proposed MNNR algorithm is well capable of spatial-temporal LAI reconstruction and performs excellently when observations are lost continuously.

The in situ measurement of the leaf area index (LAI) from gap fraction is often affected by terrain slope. Path length correction (PLC) is commonly used to mitigate the topographic effect on the LAI measurements. However, the terrain-induced uncertainty and the accuracy improvement of the PLC for LAI measurements have not been systematically analyzed, hindering the establishment of an appropriate protocol for LAI measurements over mountainous regions. In this article, the above knowledge gap was filled using a computer simulation framework, which enables the estimated LAI before and after PLC to be benchmarked against the known and precise model truth. The simulation was achieved by using CANOPIX software and a dedicatedly designed ray-tracing method for continuous and discrete canopies, respectively. Simulations show that the slope distorts the angular pattern of the gap fraction, i.e., increasing the gap fraction in the down-slope direction and reducing it in the up-slope direction. The horizontally equivalent hemispheric gap fraction from the PLC can reconstruct the azimuthally symmetric angular pattern of the real horizontal surface. The azimuthally averaged gap fraction for sloping terrain can both be underestimated or overestimated depending on the LAI and can be successfully corrected through PLC. The topography-induced uncertainty in LAI measurements is found to be ~14.3% and >20% for continuous and discrete canopies, respectively. This uncertainty can be, respectively, reduced to ~1.8% and <; 7.3% after PLC, meeting the up-to-date uncertainty threshold of 15% established by the Global Climate Observing System (GCOS). Closer analysis shows that the topographic effect is influenced by fractional crown cover, and the largest uncertainty which corresponds to extensively clumping canopy can reach nearly up to 50%. The accuracy of the estimated LAI after PLC safely meets the GCOS uncertainty threshold even for this extreme case. This study demonstrates the necessity of a topographic correction for LAI measurements and the applicability of PLC for reconstructing the horizontally equivalent gap fraction and improving the LAI measurements over sloping terrains. The results of this article throw light on the design of a protocol for LAI measurements over mountainous regions.

The availability of global high-resolution land cover maps provides promising a priori knowledge for characterizing subpixel heterogeneity and improving predictions of directional reflectance of coarse-resolution pixels. Due to mutual shadowing and sheltering effects between the adjacent forest and cropland patches, the spectral nonlinear mixing of patchy ecotones is significant, especially when the sun illuminates the ecotone from the forest side with high solar zenith angle. The spectral linear mixture (SLM) approach leads to overestimation of the bidirectional reflectance factor (BRF) in the red band in the principal plane (PP), with a maximum absolute error (MAE) of 0.0063 and a maximum relative error (MRE) of 52.5%, and to underestimation in the near-infrared band in PP with an MAE of 0.0940 and an MRE of 14.5%. In a scenario with randomly distributed boundary orientations, the overestimation of SLM increases with the degree of fragmentation and the view zenith angle. We propose a Radiative Transfer model for patchy ECotones (RTEC). which improves R2from 0.61 to 0.94 in the red band of Landsat-8 directional reflectance at the validation site. The RTEC model provides an efficient and analytical approach for directional reflectance predictions over heterogeneous patchy landscapes at coarse resolution and will be used for biophysical parameter retrievals [e.g., the leaf area index (LAI)] in future applications.

Understory vegetation has largely affected the accuracy of forest leaf area index (LAI) estimation. This study analyzed the components of overstorey and understory based on the forest reflectance and transmittance (FRT) model for six forest types in China: i.e. a planted fir forest, a mixed fir and pine forest, deciduous broad-leaved forest, evergreen broad-leaved forest, broad-leaved pinus koraiensis forest, and seasonal tropical rainforest. The contributions of understory were varied for different forest types within a year. The contributions of understory for the tropical rainforest and the coniferous and broad-leaved mixed forest were low due to the dense canopy with higher LAI of overstorey. The effect of understory was unneglectable for the whole wavelengths.

Topography significantly complicates the radiative transfer process and further to influence the parameter inversion by remote sensing approach. Neglecting the topographic effects may lead to large uncertainties when estimating LAI (Leaf area index) over rugged terrain. In this study, the topographic effects are quantitatively investigated and analyzed based on the DART (discrete anisotropic radiative transfer) simulations and ANN (artificial neural network) -based LAI inversion approach. And the influence factors on LAI inversion is analyzed. The results reveal that the topography can account for more than 50% uncertainties of LAI and may result in not invertible cases. the topographic effects on LAI cannot be neglected in the inversion process.

Modeling plays an important role to link the land surface physical/chemical properties with remotely sensed data. In the past decade of years, Multi-scale Remote Sensing Models have been developed (Chen, J. M. et. al. 1997, Huang, H. et. al. 2013). In Recent years, remote sensing communities tend to consider the complex terrain scenario more reasonable recently, such as mixed pixel, topography issues and so on. This paper present the newest progresses on how to and quantitatively measure the heterogeneity of the land surface. Spatial heterogeneity exists in the land surface at every scale, and it is one of the key factors that introduces inherent uncertainty into simulations of land surface radiative processes and parameter retrieval based on remotely sensed data. However, because of the lack of understanding of the heterogeneous characteristics of global mixed pixels, few studies have focused on modeling and inversion algorithms in heterogeneous areas. This paper presents a parameterization scheme to quantitatively describe pixel heterogeneity based on end member and boundary information and high -resolution land cover products (Li, X et. at 2011), which are used to characterize and quantify global land surface heterogeneity. Then, the recent BRDF modelling progresses for two typical mixed pixels are introduced.

Global leaf area index (LAI) generally produced based on the satellite sensors with 1 km spatial resolution, such as the advanced very high resolution radiometer (AVHRR), moderate resolution imaging spectroradiometer (MODIS) and VEGETATION. At present, there isn't a LAI product estimated from the Chinese Feng Yun No.3 (FY-3) images. This study aims to generate a 10-day composite LAI product from FY-3A with a medium resolution spectral imaging (MERSI) at global scale in 2011. Making use of the land cover type as priori knowledge, the LAI for pure vegetation types was inversed from a lookup-table (LUT) based on an stochastic three-dimensional radiative transfer model (3D RTM). For the mixed water and vegetation types, LAI was inversed based on an improved linear decomposition method. The accuracy of LAI inversion from FY-3A MERSI was assessed by LAI field measurements from the Chinese ecosystem research network (CERN) in 2011.

Soil semiempirical dielectric models (SEMs) are powerful, and they are generally considered a useful hybrid of both empirical and physical models. In this paper, the Wang-Schmugge dielectric model is improved to more accurately estimate the relative complex dielectric constants (CDCs) of moist soils. Instead of the Debye relaxation spectrum of liquid water located outside of the soil (i.e., free out-of-soil water) adopted in the Wang-Schmugge model, the Debye relaxation formula related to the free-water component inside the soil [i.e., free soil water (FSW)], which is correlated with the soil texture, is employed in the improved SEM. In addition, the effective conductivity loss term related to both soil texture and soil moisture is introduced to explain the ionic conductivity losses of FSW. Since the soil moisture influence is reduced at high frequencies, the effective conductivity loss term related to only the soil texture is also analyzed for 14-18 GHz. As in the Wang-Schmugge model, the relative CDC of bound soil water varies with the soil volumetric moisture content when the soil moisture is lower than the maximum bound water fraction in the new model, which takes a different approach than the Mironov mineralogy-based SEM. The proposed model obtains better fitting results than the three most widely employed SEMs. The improved model exhibits a significantly improved accuracy with a higher correlation coefficient (R2), a closer 1:1 relationship, and a lower root-mean-square error, including in the L-band, and especially in the imaginary part of the L-band.

While EVI and LAI are the most widely used vegetation parameters which can be used for remote sensing phenology extraction, this paper aims at assessing the differences of phenology information extracted from EVI and LAI time series and exploring either EVI or LAI time series performs well for all vegetation types over a large scale. To achieve this, GLASS-LAI phenology product(GLP) was generated by the same algorithm with MODIS-EVI phenology product(MLCD) over China from 2001 to 2012. The two phenology products were compared in different climate regions and vegetation types over a large scale and evaluated by ground observations. Results show that the missing rate of GLP(11.90%) is less than that of MLCD(22.84%). The difference between GPL and MLCD varies in different climate regions and vegetation types. GLP performs better than MLCD in croplands and forests, while MLCD performs better than GLP in grasslands.

Figure 2 shows the observed gap fraction and gap size distribution along with the sampling line. According to the algorithms of LAIcc, LAIlxand LAIpl, the results are obtained using the observed gap fraction and gap size distribution. respectively. Although the difference between Ωee and Ωlx is only smaller than 0.1. the difference between LAIccand LAIlxcan reach up to 0.36. Path length algorithm docs not use Ω as middle result and calculated LAI directly. However. LAIplclose to LAIe and the difference between the true LAI and LAIplis reach up to 1.

The state of the land surface and the water cycle over the South and East Asia can be determined by space observation. New or significantly improved algorithms have been developed and evaluated against ground measurements. Variables retrieved include land surface properties, i.e. NDVI, LAI, FPAR, albedo, soil moisture, glacier and lake levels. Based on these biophysical parameters derived from microwave and optical remote sensing observations, a hybrid remotely sensed evapotranspiration (ET) estimation model named ETMonitor was developed and applied to estimate the daily actual ET of the Southeast Asia at a spatial resolution of 1 km. The changes in glaciers and lakes on the Tibetan Plateau, and the drainage links between glaciers and lakes are determined in this climate-sensitive region.

Leaf area index (LAI) is a critical parameter in many terrestrial ecosystem models. Continuous LAI measurements from global sites are an important dataset for the validation of remotely sensed LAI products. However, the spatial scale mismatch between the site measurement and the product pixel grid hinders the utilization of multi-temporal ground LAI measurements. In this study, a pragmatic method is presented for spatial upscaling of ground LAI measurements to the product pixel grid. The method is divided into three parts: retrieving high-resolution LAI maps, spatial representativeness grading and spatial upscaling. The proposed method was applied to the Järvselja site in the VALERI project. Results show that this method can reduce the scale mismatch error between the site measurement and the product pixel grid well. Moreover, this method has the potential to be applied to global site LAI measurements, which consequently can improve the reliability of LAI product validation.

Landscape heterogeneity is a common natural phenomenon but is seldom considered in current radiative transfer models for predicting the surface reflectance. This paper developed an analytical Radiative Transfer model for heterogeneous Agro-Forestry scenes (RTAF). The scattering contribution of the non-boundary regions can be estimated from the SAILH model as homogeneous canopies, whereas that of the boundary regions is calculated based on the bidirectional gap probability by considering the interactions and mutual shadowing effects among different patches. The multi-angular airborne observations and Discrete Anisotropic Radiative Transfer (DART) model simulations were used to validate and evaluate the RTAF model over an agro-forestry scene in Heihe River Basin, China. The results suggest the RTAF model can accurately simulate the hemispherica-directional reflectance factors (HDRFs) of the heterogeneous scenes in the red and near-infrared (NIR) bands. The boundary effect can significantly influence the angular distribution of the HDRFs and consequently enlarge the HDRF variations between the backward and forward directions. Compared with the widely used dominant cover type (DCT) and spectral linear mixture (SLM) models, the RTAF model reduced the maximum relative error from 25.7% (SLM) and 23.0% (DCT) to 9.8% in the red band, and from 19.6% (DCT) and 13.7% (SLM) to 8.7% in the NIR band. The RTAF model provides a promising way to improve the retrieval of biophysical parameters (e.g. leaf area index) from remote sensing data over heterogeneous agro-forestry scenes.

The environment of Greater Mekong Sub-region (GMS) was highly payed attention to its economic development. Remote sensing technology was a useful tool for global and regional environment monitor. The fractional vegetation cover (FVC) with 30m spatial resolution for GMS was extracted from the HJ-1/CCD data in this study. The vegetation covers were highly for the entire GMS, and the spatial differences were influenced by vegetation types. Besides, FVC product with high temporal resolution (5 days) and 1km spatial resolution were used to analyze the vegetation damages by typhoon Haiyan from 2edto 10thNov., 2013 based on a change detection method. The damage extents of forest by typhoon were much seriously than cropland and grassland for GMS memberships, especially for Vietnam and China (Guangxi). The vegetation damages varied from -50% to 10% in the 300 km suffer areas on the typhoon Haiyan pathway.

Current bidirectional reflectance distribution function (BRDF) inversions using ordinary least squares (OLS) criterion can be easily contaminated by observations with residual cloud and undetected high aerosols, which leads to abrupt fluctuations in the normalized difference vegetation index (NDVI) time series. The OLS criterion assumes the noise has Gaussian distribution, which is often violated due to positive noise biases caused by clouds and high aerosols. A changing-weight iterative BRDF/NDVI inversion algorithm (CWI) based on a posteriori variance estimation of observation errors is presented to explicitly consider the asymmetrically distributed noise and observations with unequal accuracy in the BRDF retrieval. CWI employs a posteriori variance estimation and an NDVI-based indicator to iteratively adjust the weight of each observation according to its noise level. The validation results suggest CWI performs better than the Li-Gao and OLS approaches. The rmse was reduced from 0.074 to 0.028, and the relative error decreased from 13.4% to 3.8% at the U.S. Department of Agriculture Beltsville Agricultural Research Center site. Similarly, at the Harvard Forest site, the rmse was reduced from 0.086 to 0.031, and the relative error decreased from 9.5% to 2.7%. The average noise and relative noise of the CWI NDVI time series over ten EOS Land Validation Core Sites from 2003-2009 was smaller (0.028, 3.7%) than those of MOD13A2 (0.041, 5.2%), MYD13A2 (0.039, 4.9%) and MCD43B4 (0.030, 4.4%). The results demonstrate the robustness of the CWI approach in suppressing the influence of contaminated observations in BRDF retrievals by producing results that are less affected by undetected clouds and high aerosols.

Landscape heterogeneity is a common natural phenomenon but is seldom considered in current radiative transfer (RT) models for predicting the surface reflectance. This paper developed an analytical RT model for heterogeneous Agro-Forestry scenarios (RTAF) by dividing the scenario into nonboundary regions (NRs) and boundary regions (BRs). The scattering contribution of the NRs can be estimated from the scattering-by-arbitrarily-inclined-leaves-with-the-hot-spot-effect model as homogeneous canopies, whereas that of the BRs is calculated based on the bidirectional gap probability by considering the interactions and mutual shadowing effects among different patches. The multiangular airborne observations and discrete-anisotropic-RT model simulations were used to validate and evaluate the RTAF model over an agro-forestry scenario in the Heihe River Basin, China. The results suggest that the RTAF model can accurately simulate the hemispherical-directional reflectance factors (HDRFs) of the heterogeneous scenarios in the red and near-infrared (NIR) bands. The boundary effect can significantly influence the angular distribution of the HDRFs and consequently enlarge the HDRF variations between the backward and forward directions. Compared with the widely used dominant cover type (DCT) and spectral linear mixture (SLM) models, the RTAF model reduced the maximum relative error from 25.7% (SLM) and 23.0% (DCT) to 9.8% in the red band and from 19.6% (DCT) and 13.7% (SLM) to 8.7% in the NIR band. The RTAF model provides a promising way to improve the retrieval of biophysical parameters (e.g., leaf area index) from remote sensing data over heterogeneous agro-forestry scenarios.

We compare five slope correction methods developed by Walter et al., Montes et al., Schleppi et al., España et al., and Gonsamo et al. (referred to as WAL, MON, SCH, ESP, and GON, respectively) using artificial fisheye pictures simulated by graphics software and a lookup table (LUT) retrieval method. The LUT is built by simulating the directional gap fraction as a function of leaf area index (LAI) and average leaf inclination angle (ALIA) using the Poisson law. LAI and ALIA estimates correspond to the case of the LUT that provides the lowest root-mean-square error between the observed gap fractions after slope correction and the simulated ones. Three LAI values (1.5, 3.5, and 5.5), four ALIA values (26.8°, 45°, 57.5°, and 63.2°), and three slope angles (0°, 20°, and 50°) constituted 36 samples of random scenes. ESP is recommended because its results are accurate and independent on the leaf angle distribution (LAD), while GON only performs well for spherical LAD. The three other methods present less good performances with underestimation or overestimation of LAI and/or ALIA depending on the LAD, and the recommended order for them is MON, SCH, and WAL.

Spatial heterogeneity of land surface induces scaling bias in leaf area index (LAI) products. In optical remote sensing of vegetation, spatial heterogeneity arises both by textural and contextual effects. A case study made in the middle reach of the Heihe River Basin shows that the scaling bias in LAI retrieval is large up to 26% if the spatial heterogeneity within low-resolution pixels is ignored. To reduce the influence of spatial heterogeneity on LA! products, a correcting method combining both textural and contextual information is adopted, and the scaling bias may decrease to less than 2% in producing resolution-invariant LAI products.

Normalized difference vegetation index is a key parameter to describe physical and biological processes of plants. Vegetation compositing technology offers a new method to obtain NDVI products with special consistency and continuity. MODIS BRDF compositing scheme reduces angular, sun-target-sensor variations with use of a BRDF model, but the Walthall BRDF model inversion required at least five good quality observations. This limits the temporal resolution of NDVI product and increases the change uncertainty in the composite period, especially when the vegetation grow fast. Therefore, the multi-sensor composite strategy was developed to improve the temporal resolution to 4 days. We use a similar MODIS NDVI compositing as the initial algorithm to analyze the multi-sensor datasets, and A Multi-sensor NDVI composing algorithm was developed. Cross validation with MODIS NDVI products also show a satisfactory agreement.

Leaf area index is a key parameter to describe physical and biological processes of plants. Remote sensing technology offers a new method to obtain LAI at regional scales, but it generally records plants information in horizontal. Therefore, the canopy reflectance is easier to reach saturation when plants growth flourished. This paper firstly defines the issues of canopy reflectance saturation, and then analyzes the reflectance contribution of each layers based on multilayer SAIL and FRT model for continuous and discontinuous vegetation, respectively. Besides, this paper analyzes the factors influencing canopy LAI saturation. Results show that the lower part of plants has fewer contributions to canopy reflectance. The leaf angle distribution and view zenith angle are two mainly factors influencing canopy LAI saturation.

Land surface phenology dynamics reflect the response of terrestrial ecosystems to inter- and intra-annual dynamics of the climate. However, there are very few regional-to-global phenology products and existing phenology products still show some deficiencies in practical application. Meanwhile, none of existing methods for monitoring vegetation phenology has consistent performance for all vegetation types. Based on the existed research work, this paper developed a mixed model to monitor vegetation phenology in China. Different methods are used in this model according to different situation. This model was employed in China in 2007 assessed using field observed phenology and MLCD data. The root mean square error (RMSE) for different vegetation types are 4.0-33.5, the mean absolute error are -20.6-15.3 and the correlation are 0.404-0.887. By comparison with MLCD data, the success rate and the accuracy of the method have been highly improved.

Leaf area index (LAI) is an important ecological and environmental parameter. Currently, most LAI inversion methods are based on single model. For continuous vegetation canopy, PROSAIL model used to retrieve vegetation biophysical properties based on lookup table or neural network methods. As for discontinuous vegetation canopy, vegetation canopy reflectance was simulated from Geometric-Optical model, and the relationship between LAI and vegetation indexes was established using simulated reflectance. This paper introduces a method combining the PROSAIL and four-scale model simulated the continuous and discontinuous vegetation canopy reflectance respectively, and then establishes lookup tables among multispectral reflectance, vegetation indices and LAI. The new method using land cover map has been employed to inverse HJ-1B CCD2 LAI in zhangye region of the middle reaches of the Heihe river basin. Results show that the new inversion results have much more deviation with observed dataset, but have good agreement with MODIS LAI in crops.

Clumping index is an important vegetation structure parameter to describe the foliage clumping in canopy quantitatively. It is defined as the ratio of the effective leaf area index to the true leaf area index. In previous studies, it is generally considerate that cluster of canopy and below canopy scale in pure pixel. However, the in-pixel spatial heterogeneity need be taken into account estimating clumping index in mixed pixel, which is different from clumping index of pure pixel. A new method to calculate clumping index of mixed pixel based on fine spatial resolution image is proposed in this paper. The sensitivity analysis has been processed and its results show that the pixel spatial heterogeneity and view zenith angles cannot be ignored for calculating the mixed-pixel clumping index. The method is capable of correcting the scale difference caused by the heterogeneity of the vegetation cover inside the mixed pixel the view zenith angle. The formula presented can estimate clumping index of the mixed pixel more accurately, which is significant for LAI inversion of coarse spatial resolution and the precision accuracy application of carbon cycle model.

The high spatial and high temporal satellite data is necessary in the operational agricultural applications of remote sensing. But till now the advantages of high spatial and high temporal resolution still can not be realized in single sensor. The PSP method (Patch Spectral Purification Method) is capable of retrieving field patch average information from high temporal but moderate spatial resolution satellite data, which meets the requirement of high spatial and high temporal resolution information in the real monitoring applications. In this paper a PSP-based methodology is proposed to retrieve the high spatial and high temporal resolution information for the growth and drought monitoring using multiple satellite data. An application demonstration was made in Xinjiang, China to monitor the cotton growth and drought with MODIS and Landsat/TM data. And the processing software-AgRsis (Agricultural Remote Sensing Inversion System) was realized to generate the daily crop parameters standard maps(e.g. NDVI, TVDI) for the crop growth and drought monitoring.

Crop growth monitoring is one of major directions of remote sensing applications. A widely used way is to draw out the NDVI curve of the interesting region in crop growth seasons, then determine whether crop is good or not according to the characters of the NDVI curve and some empirical knowledge. Whether the pixel used to draw NDVI curve exactly describes the target crop species strictly affects the accuracy of the result. The objective of this research is to design an algorithm to find out pure pixel of target crop species from hyperspectral image. The algorithm is based on the spectral library, and it will obtain the sample spectra from the library. If the library returns zero sample canopy spectra, the algorithm will automatically simulate the sample spectra. Then it will aggregate narrow bands into broad bands to match the sensor bands, and at last compare the pixel spectra with the sample spectra. In this research, we use Hyperion, OMIS and MODIS data of different spatial and spectral resolution and use different methods to calculate distance

The polynomial expression is a new and powerful model to interpret the light scattering process inside leaf/soil system and decipher the nonlinear relationship between component spectra and canopy reflectance. In our previous work, we have outlined the forward model and analyzed its feature. For models with large number of parameters, their inversion is a challenging problem. This paper presents the algorithm and strategy that makes the complex multivariant inversion problem efficient and stable

The water quality of Taihu Lake is declining due to eutrophication, and the chlorophyll-laden water becomes an obvious sign. As to reflectance spectra of water vary with concentrations of organic and inorganic sediments, in this paper field reflectance spectra have been applied for monitoring the water quality of Taihu Lake, China. As the key-monitoring index, the chlorophyll-a contents were evaluated by linear spectral unmixing using water and chlorophyll-a endmember spectra of known content the results were compared to laboratory analyses of in situ, water samples.

The evapotranspiration of forestland surface was estimated using MODIS data. The study area is located in Jiangxi province, south China, dominated by tall indeciduous conifers. The parameters of land surface energy balance, i.e., leaf area index (LAI), land surface temperature (LST), surface albedo etc. were inversed from VIS/NIR and TIR band data of MODIS sensor. Some of the assistant data, such as meteorological factors and canopy structure parameters were obtained in-situ at Qianyanzhou ecological experiment station, which is one of the sites in the ecology observation system of Chinese Academy of Sciences. The radiation components and net radiation of surface were estimated with these parameters and ancillary data. Soil heat flux was estimated as a fraction of net radiation from the area coverage of the canopy. Surface sensible heat flux was decided according to surface temperature gradient and aerodynamic resistance. The excess-resistance for scalar flux transfer was also considered. Eventually, the latent heat flux (instantaneous evaporation rate) was obtained as the residual term of the surface energy balance equation. Daily evapotranspiration was then derived from the one-time-of-day estimation.

A novel method was proposed to simulate heat fluxes above partially vegetated surfaces. The interaction of the heat fluxes between the two components (soil and foliage) in canopy was modeled using the concept of "micro-advection", which refers to small-scale movement of air in a restricted area. To adjust the heat balance of the local plant-atmosphere system is the main function of the micro-advection in this model, i.e., part of the heat emanated from soil surface is transported to the foliage surface and then consumed by the transpiration effect of the leaves in this process. The magnitude of this part of heat transport between components can be estimated from the temperature gradient between them as well as the diffusion coefficients of them. Therefore, the overall effect of the micro-advection is to diminish the total sensible heat flux and increase the level of total latent heat flux. This model was validated using the data of row crop, and the result showed good agreement with field turbulent measurements.