Recently, solar-induced chlorophyll fluorescence (SIF) from satellites has shown potential for evaluating vegetation status and stress responses. Fluorescence quantum yield (ΦF) is essentially linked to vegetation stress. However, the complex physiological and structural responses of SIF and ΦFto drought need further study. This study normalized SIF as SIFnto account for angular variations and fluctuations in photosynthetically active radiation (PAR), aiming for more accurate drought monitoring. SIFnanomalies were compared to historical baselines (2019–2021 averages) of vegetation indices (VIs), raw SIF, and ΦFduring a 2019 drought in the North China Plain (NCP). The results show SIFnprovides an effective method for drought monitoring, showing the earliest decline compared to raw SIF, VIs, and ΦF. In the first two weeks of drought, SIFndecreased by 8.2%, 7.0%, 12.5%, and 8.2% across the four NCP subdivisions. SIFnoutperformed other indicators, proving sensitive to early drought detection. SIFnwas also examined for tracking drought alleviation by rainfall. The uncertainty under different viewing geometries was quantified. SIFnanomalies showed a strong correlation with rainfall anomalies (R: 0.45 ~ 0.52) and meteorological factors like PAR (R: 0.80 ~ 0.84) and relative humidity (R:0.52 ~ 0.54). The correlation of near-infrared reflectance (NIRv) and ΦFanomalies with SIF was weak during drought onset (R: 0.16 ~ 0.32) but strong at the end (R: 0.83 ~ 0.87). These suggest both canopy structure (mainly characterized by NIRv) and vegetation chlorophyll (ΦF) are impacted by drought and influence SIF at different stages.

The accurate simulation of Bidirectional Reflectance Distribution Function (BRDF) across varied Crop Residue Cover (CRC) scenarios is pivotal for crop residue monitoring and management. Addressing the limitations of prior research in simulating BRDF for cropland with CRC, we have developed the novel Crop Residue-covered Bidirectional Reflectance (CRBR) model. This model couples Geometric Optical (GO) and Radiative Transfer (RT) model, which involves adding a clumping index and Crop Residue Tilt Angle (CRTA) distribution function through terrestrial laser scanning to parameterize the spatial distribution of covered crop residue. Validation of the CRBR model was conducted using corn residue cover data from Lishu County, Jilin Province, China, collected in April 2023. The results demonstrated strong alignment between the simulated and measured multi-angle bands reflectance (R² = 0.90, RMSE = 0.03, MAPE = 8.91%). Under various CRC scenarios, the CRBR model consistently outperformed linear mixed models (R² ≥ 0.99, RMSE ≤ 0.02, MAPE ≤ 4.14% vs R² ≥ 0.97, RMSE ≤ 0.05, MAPE ≤ 23.69%). Sensitivity analysis revealed the impact of key model parameters on reflectance simulation. Furthermore, we also examined the adaptability of our model under different moisture conditions and CRC scenarios, confirming its robustness and flexibility. The CRBR model not only helps our understanding of radiative transfer in crop residue-soil scenarios but also offers a promising approach for efficient and precise CRC estimation on a regional scale. Such advancements in the CRBR model hold significant implications for conservation tillage monitoring, biomass energy reserve estimation, and cropland carbon storage capacity assessment.

Maize is a key global food crop, with China being a major producer vital for global maize supply and food security. Accurately analysing the relationships between grain yields of maize and influencing factors is crucial for enhancing crop production, evaluating arable land quality, and optimizing planting structure. However, when modelling those relationships, the coefficients of each influencing factor vary spatially and have different spatial scales, suggesting that the importance of the influencing factors is multiscale. Traditional global and local modelling methods such as Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models cannot accurately explain those multiscale spatial relationships. The Multiscale GWR (MGWR) model, an extension of GWR, addresses these limitations by allowing each explanatory variable to have a unique spatial scale. By aligning the neighbourhood structure of each variable with its corresponding spatial scale, MGWR improves the accuracy of local regression coefficient estimations, providing a more refined analysis of spatial heterogeneity. In this paper, the multiscale importance of influencing factors on maize grain yield of the Chinese mainland was apportioned via MGWR model. Our findings verified that the relationships between maize grain yield and influencing factors differ at multiple spatial scales. MGWR model can comprehensively apportion the importance of influencing factors at multiple scale, while global and local modelling methods provide biased estimations, with OLS method leaving large residues and GWR model attributing part contribution to spatially varying intercept terms. With the MGWR model, organic fertilizer and terrain aspect are globally important and their relationships with yields keep stationary; the relationships between yields and soil pH value, GDP, DEM and slope vary on a medium-scale, presenting obvious regional differences; cultivation convenience and hydrothermal conditions affect yields at small scales. The comprehensive apportionment of multiscale relationships is an important guideline for the scientific management of agriculture and arable land resources.

Accurately predicting spatially continuous daily air temperature (Ta) is critical for agriculture, environmental management, and ecology. While meteorological stations provide precise Ta data, their spatial coverage is limited. Remotely sensed land surface temperature (LST), often fused with meteorological data, offers broader spatial coverage but struggles due to complex relationships between Ta and LST, influenced by factors like topography and human activities. Traditional supervised learning methods often fail to capture the spatial autocorrelation and heterogeneity inherent in the relationships, indicating the need for a more robust approach that integrates geographic knowledge. This study proposes the spatially varying coefficients random forest (SVCRF) model, to integrate exploratory spatial data analysis (ESDAs) into random forest (RF) to capture spatially nonstationary relationships. It first stratifies the study area based on bivariate Local Indicators of Spatial Association and geographical detector, then builds several spatial RFs with specific spatial positions and extent. In each spatial RF, the distance from observation/prediction sites to its position is added as a key predictor variable to model the local spatial variations of the relationships within the spatial extent. Applied to daily Ta mapping at 1 km resolution across China using data from 5425 meteorological stations, the SVCRF model demonstrated superior accuracy, achieving root-mean-squared error (RMSE) of$1.315~^{\circ }$C and mean absolute error (MAE) of$1.014~^{\circ }$C. Compared to RF, regression kriging (RK), and geographically weighted regression (GWR), it reduced MAE by$0.351~^{\circ }$C,$0.786~^{\circ }$C, and$0.831~^{\circ }$C, respectively. The model also offers high interpretability, with uncertainty estimates aligning with actual errors and spatially resolved variable importance highlighting spatial patterns.

Solar-induced chlorophyll fluorescence (SIF) is an effective indicator to track the gross primary productivity (GPP). However, there is still a lack of a clear understanding for the contribution of physiological and structural factors to the SIF-GPP relationship at the canopy scale. To quantify the influence of different SIF components, particularly the photon escape ratio (${f}_{\text {esc}}$) and fluorescence yield (${\Phi }_{F}$), on the SIF-GPP relationship, this study evaluated the performance of various vegetation indices (VIs), SIF components, light use efficiency (LUE), and GPP over two typical biomes (crops and rainforest), using a range of satellite remote sensing products. In August of each year from 2018 to 2020, both SIF and GPP over United States (U.S.) Corn Belt are higher than those over the Amazon rainforest, attributed to the consistent pattern of higher$f_{\text {esc}}$and LUE over crops than over rainforest, as well as${\Phi }_{F}$. Furthermore, the structural signals represented by${f}_{\text {esc}}$($R =0.41$–0.64) can better capture the LUE variations than$\boldsymbol {\Phi }_{F}$($R =0.10$–0.30) for each biome. This study highlights that$f_{\text {esc}}$, determined by canopy structure, has great potential to capture LUE and GPP changes within and across biomes.

Solar-induced chlorophyll fluorescence (SIF) shows potential in exploring plant responses to environmental changes caused by extreme climatic factors. However, how to accurately assess climate stresses (especially the low-temperature stress) suffered on crops at the regional scale in a systematic approach has not been extensively explored. In this study, we developed a climate vegetation stress index (CVSI) to assess and quantify the impacts of climate stress on crops at large scales by combining TROPOspheric Monitoring Instrument (TROPOMI) SIF and land surface temperature (LST) data through an easy-to-operate approach. This index was employed to identify low-temperature stress conditions in Henan Province’s winter wheat in 2018. Results indicate that, influenced by climate characteristics, crops in the northern part of Henan Province experienced more severe low-temperature stress than those in the southern part. The daily average SIF values experienced reductions of 0.74, 0.45, 0.61, and 0.86 mW$\cdot ~\text{m}^{-2}~\cdot $sr$^{-1}~\cdot $nm−1 during the four cooling episodes within the two phenological periods, respectively. As low-temperature stress intensified, winter wheat growth was hindered, reducing grain yield. Indeed, the CVSI provides an accurate depiction of crop stress levels and patterns. In areas with high-CVSI values, yield losses are particularly severe. In addition, the significant positive correlation between the CVSI and net primary productivity (NPP), along with the similar spatial intensity pattern, shows the effectiveness of CVSI in monitoring low-temperature stress. CVSI provides a new approach to understand the impacts of climate change on overwintering crops and offers a practical reference for climate stress effects monitoring at the regional scale.

The near-infrared reflectance of vegetation (NIRv) has been increasingly used as a proxy of gross primary production (GPP) across various temporal scales, ecosystems, and climate conditions. However, topography significantly distorts NIRv and GPP estimations over mountainous areas. We evaluated the topographic effects on NIRv and applied a path length correction (PLC) for improving its performance over mountainous areas. The proposed topographically corrected NIRv (referred to TCNIRv) was evaluated by multiple Landsat-8 operational land imager (OLI) images with concurrent${ in}~{ situ}$GPP measurements over the Lägeren mountainous forest area. TCNIRv reduced topographic effects in the original NIRv and it was comparable to the normalized difference vegetation index (NDVI) and the green normalized difference vegetation index (GNDVI), which are often deemed to be independent of topographic effects. In addition, TCNIRv better agreed with GPP than the other vegetation indices (VIs): coefficient of determination$R^{2} $= 0.90 and root mean square error RMSE = 1.40$\text{g}\cdot $Cm$^{-2} \cdot \text{d}$−1for TCNIRv compared to$R^{2} $= 0.71 and RMSE = 2.47$\text{g}\cdot $Cm−2$\cdot \text{d}$−1for NIRv. The evaluation shows that TCNIRv is a reliable proxy of GPP, and because of its simplicity and physical soundness, it will facilitate vegetation monitoring over complex topography mountainous areas.

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

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 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.

Topographic correction is a prerequisite for generating radiometrically consistent Landsat 8 OLI vegetation reflectances in support of temporally continuous and spatially mosaicked applications. Path length correction (PLC) is a physically solid topographic correction method that avoids the involvement of any empirical parameter and is therefore suitable for reproducing the inherent reflectance of vegetation. This article compared two different implementation pathways of PLC, i.e., the explicit method (EM) and the implicit method (IM), which are based on the numerical inverse and analytical approximation of the PLC model, respectively. The results show that both EM and IM can obviously reduce the topographic effects on Landsat 8 OLI vegetation reflectances. EM performed slightly better than IM in eliminating the correlation between the topographic characteristics and the vegetation reflectances: the coefficient of determination between the green/red/near-infrared (Nir) band reflectance and the local illumination was reduced from 0.257/0.148/0.467 for the uncorrected (UNCORR) case to 0.016/0.004/0.012 and 0.027/0.014/0.094 for the EM and IM corrected results, respectively. The coefficient of variation of the three band reflectances across different aspects was reduced from 16.5%/18.5%/18.7% for the UNCORR case to 3.2%/1.8%/0.9% and 5.3%/7.1%/7.3% for the EM and IM corrected results, respectively. In addition, the intraclass reflectance variability was also reduced after both the EM and IM corrections. Nevertheless, due to the ill-posed nature of the numerical inverse process, EM cannot fully reproduce the inherent vegetation reflectances, and the reflectances after topographic correction overestimated the inherent vegetation values. In contrast, the IM can achieve an appropriate tradeoff between topographic effect elimination and vegetation inherent reflectance preservation. In addition, IM is computationally very efficient compared to EM: using an ordinary laptop, IM can finish the topographic correction for a Landsat OLI image within several seconds, while this would take more than 20 h for EM. This article highlights the potential of using IM for generating radiometrically consistent Landsat 8 OLI vegetation reflectances.

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.

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

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.

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

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

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.

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.

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.