The combination of Sentinel-2 multispectral instrument (MSI) and Landsat 8 operational land imager (OLI) creates a virtual constellation of decametric sensors with high revisiting frequency. However, the differences in the spectral characteristics of the two sensors cause inconsistencies in downstream applications. This study proposed a multiband constraint spectral harmonization method called HARMU. In comparison to existing methods, HARMU uses all the spectral bands in the source sensor to predict the reflectance of the targeting sensor and so fully exploits spectral linkage among different bands. HARMU was specifically implemented by Gaussian process regression (GPR), with training data collected from the spatiotemporally representative BEnchmark Land Multisite ANalysis and Intercomparison of Products 2.1 (BELMANIP2.1) sites. We reproduced the top of the canopy reflectance at both common bands of OLI and MSI and also reflectance at red-edge (RE) bands that are only equipped on MSI. The results indicated that HARMU performed satisfactorily with$R^{2}$larger than 0.91 and Rel-Bias less than 0.19 for all bands over BELMANIP2.1 sites. HARMU offered similar performances as the widely used Harmonized Landsat and Sentinel-2 (HLS) products: average$R^{2}$slightly improved from 0.86 for HLS to 0.88 for HARMU for the common bands as evaluated over ground-based observations for validation (GBOV) sites, and additionally, it well reconstructs the missing RE band in HLS-based OLI ($R^{2} \gt 0.81$and Rel-Bias <0.15). HARMU will substantially contribute to generating spatiotemporally continuous time series of decametric data from the MSI-OLI virtual constellation and monitoring vegetation dynamics in large-scale and long-time sequences.

Satellite products have served as the foundation for subsequent analysis, modeling, and decision-making. However, the errors or inconsistencies of satellite products may bias or even mislead the conclusions and decisions based on them. Using ground-based observation data to directly correct the errors in satellite products provides a more relaxed and direct method for constraining the errors of satellite products. However, it is challenged by the sparsity of ground station distribution and the spatial scale mismatch between ground observations and satellite pixels. To address this issue, this study pioneers an integrated and comprehensive methodological framework for pixel-by-pixel error correction based on sparsein situsite observation data across the globe. This methodological framework comprises several core components: the error correction models over the regions within situsites based on the pixel scale ground "truth", the spatial extension model to allocate optimal error correction model for regions withoutin situsites, and finally the pixel-by-pixel error correction of satellite products. MCD43A3 v061 was taken as an example to illustrate the methodology as well as its effectiveness. The RMSE of error-corrected MCD43A3 based on the optimal correction model was reduced from 0.05 to approximately 0.02. To conclude, the results and comparative analysis shown in this study suggested that the proposed framework for pixel-by-pixel error correction of satellite products based on ground observations from sparse networks has the potential to further improve the quality of satellite products across the globe.

Global variations in vegetation productivity are intricately linked to climatic fluctuations, yet the specific impacts of climate change on these constraints remain uncertain. We employed the extreme gradient-boosting model within the Shapley framework of additive explanations to delineate temperature, water, and solar radiation constraints on productivity and traced their temporal evolution from 1980 to 2022. The results revealed a distinctive spatiotemporal pattern of the climatic limitations: temperature emerged as the primary constraint early during the growing season in most temperate, boreal, and polar regions, and temperature and radiation jointly limited productivity in these regions during the intermediate and late growing season; water predominantly constrained productivity mainly in arid areas, whereas equatorial rainforests were primarily limited by radiation. Furthermore, our analysis indicated a moderated temperature constraint but an intensified water constraint on global productivity.

Snow-covered forests are widely distributed in middle- and high-latitude regions of the Northern Hemisphere and have significant impacts on global climate change and albedo feedback. However, knowledge of the radiative transfer mechanism in snow–canopy–atmosphere systems is insufficient. Existing bidirectional reflectance models often oversimplify, assuming that snow only persists on the floor, and neglect the interactions between the atmosphere and snow-covered forests. In our previous snow-covered forest bidirectional reflectance (SFBR) model, we considered ground snow, the concentration of soot pollution, needle leaf characteristics, and discontinuous canopy distributions. Furthermore, this study proposed an improved snow-covered forest bidirectional reflectance (SFBR2) model by considering canopy-intercepted snow (CIS) and atmospheric effects. Specifically, the SFBR2 model was constructed by a series of analytical models for CIS, ground snow (asymptotic radiative transfer (ART) snow model), soil [brightness shape moisture (BSM)], needle leaf [leaf incorporating biochemistry exhibiting reflectance and transmittance yields (LIBERTY)], canopy [two-layer version of four-stream scattering by arbitrarily inclined leaves (4SAIL2)], and atmosphere [simplified method for atmospheric correction (SMAC)], in which the CIS is parameterized by snow optical properties and two-stream theory, and the interaction between the atmosphere and snow-covered forests is optimized by a four-stream theory. It makes the model able to simulate the reflectance at the top of the canopy/atmosphere (TOC/TOA). Validations against 3-D large-scale remote sensing data and image simulation framework over heterogeneous 3-D scenes (LESS) model, unmanned aerial vehicle (UAV) observations, and MODIS data indicated a good consistency with SFBR2 model in the reflectance simulation. The established model has the ability to simulate the bidirectional reflectance of different snow-covered forest scenes whether CIS exists or not. Potential applications include satellite signal simulation, radiation mechanism analysis, and parameter inversion in snow-covered forests.

High-frequency leaf area index (LAI) dataset is essential for vegetation dynamic monitoring and crop yield estimation. However, due to the negative impacts of land surface heterogeneity, current hectometric-resolution LAI products cannot satisfy the uncertainty requirement of LAI dataset in practice. Here, we proposed a method named Utilization of Sub-Pixel Information (USPI) that leverages fine-scale remote sensing data to improve the accuracy of hectometric-resolution LAI retrieval. Specifically, based on machine learning (ML) models trained by representative samples, we retrieved the USPI LAI from MODIS reflectance by incorporating the sub-pixel information from Sentinel-2 LAI estimates. The USPI LAI was comprehensively evaluated using 30-m LAI reference maps in three aspects: the performance of different ML models, the comparison with MODIS LAI products, and the potential correction of USPI LAI for clumping effect. Results showed that Gaussian Process Regression (GPR) model outperformed other ML models for deriving LAI estimates. Furthermore, USPI LAI exhibited better performance than MODIS LAI product, with bias, root mean square error (RMSE), and R2of -0.308, 0.593, and 0.826, respectively, especially for pixels contaminated by atmospheric conditions. Nevertheless, the underestimation of USPI LAI should be noted because the effective LAI provided by Sentinel-2 was involved in the GPR training process. Thus, it is necessary to introduce the accurate clumping index dataset for further improvement of USPI LAI retrievals. Our study indicates that incorporating the sub-pixel information from decametric-resolution data can effectively reduce the uncertainty of hectometric-resolution LAI retrieval, which is promising for generating the high-accuracy LAI time series dataset.

Leaf area index (LAI) is a pivotal biophysical parameter for characterizing canopy structure and monitoring vegetation growth. Although the look-up table (LUT) method has been widely employed for LAI retrieval, the optimization of key retrieval processes remains to be explored. Here, we proposed a generic optimization strategy for LUT-based inversion based on Landsat -8 imagery and global ground LAI measurements. Specifically, based on the LUT generated by the PROSAIL model, LAI inversion was optimized by introducing several functions, including band selection, artificial noise addition, cost function (CF) substitution, and multiple solutions. Furthermore, the optimized LUT-based inversion method was compared to the Simplified Level 2 Product Prototype Processor (SL2P) method and the ground-measurement-derived (GMD) regression method to comprehensively evaluate its performance over various vegetation types. Results showed that the combination of Red, near-infrared (NIR), and shortwave infrared-1 (SWIR1) bands was well suited to capture LAI dynamics. In terms of accuracy and efficiency, the best performance was achieved by the optimal band combination and retrieval parameter settings (i.e., root-mean-square error (RMSE) as CF, noise level of 20%, and multiple solutions of 5%), with the RMSE and${R} ^{2}$of 0.817 and 0.740, respectively. In addition, the optimized LUT-based inversion was superior to SL2P method in accuracy and to GMD regression method in efficiency. Overall, the optimized LUT-based inversion strategy can be applied for estimating decametric-resolution LAI with high accuracy over different regions and observation dates at a global scale, exhibiting high adaptability and generalization capability, especially for crops, and requiring no ground LAI measurements.

Remotely sensed (RS) vegetation indices (VIs) are increasingly being employed as a direct proxy for gross primary productivity (GPP). When estimating mountain vegetation GPP from VI, efforts often focus on the RS-related topographic effect (i.e., distort VIs), while the micrometeorology-related topographic effect is so far ignored. Here, a topographically adjusted VI (TAVI) scheme was developed based on removing the RS-related effect by path length correction (PLC) first and integrating the micrometeorology-related effect associated with the topography-induced redistributions of radiation and water subsequently. The proposed TAVI scheme was applied to three VIs, namely, normalized difference VI (NDVI), enhanced VI (EVI), and near-infrared reflectance of vegetation (NIRv), at 14 eddy covariance (EC) sites. The determination coefficient (${R}^{2}$) and root-mean-square-error (RMSE) between VI-estimated and EC GPP were used for evaluation. Results showed that both EVI and NIRv outperformed NDVI in GPP estimation before correction, with${R}^{2}$increased by 0.14–0.15 and RMSE decreased by 0.42–0.44 gC$\cdot \text{m}^{-2}\cdot $day−1. After correcting the RS-related topographic effect, EVI and NIRv achieved an obvious improvement (${R}^{2}$= 0.71 and RMSE = 2.00 gC$\cdot \text{m}^{-2}\cdot $day−1), while NDVI showed little sensitivity to topography. Subsequently, EVI and NIRv showed a notable improvement (${R}^{2}$= ~0.77 and RMSE = ~1.82 gC$\cdot \text{m}^{-2}\cdot $day−1) after integrating the micrometeorology-related topographic effect, and the performance of NDVI was also improved (${R}^{2}$= 0.73 and RMSE = 1.94 gC$\cdot \text{m}^{-2}\cdot $day−1). This study suggests that integrating the micrometeorology-related topographic effect on vegetation photosynthesis into topographically corrected VIs (TCVIs) is an effective way to improve mountain vegetation GPP estimation.

Leaf area index (LAI) is an important biophysical variable widely used in ecosystem models. Recent advances in remote sensing technology have led to the development of different high-resolution LAI products but their consistency and accuracy remains unknown. This study compared high-resolution LAI remote sensing products derived from three different types of instruments, including NEON Airborne Observatory Platform Imaging Spectrometer (NIS), Sentinel-2 (S2), and Global Ecosystem Dynamics Investigation (GEDI) lidar products. These products were also compared with in-situ data derived from Digital Hemispheric Photos (DHPs) over 19 NEON forest sites in the US. Results at plot level (i.e. ~20m) suggested there were only moderate agreements between different remote sensing products (r2ranging from 0.23 to 0.35) and multiple factors could affect comparison results such as geolocation error and slope. Their agreements were much better at site level (r2ranging from 0.59 to 0.76). In comparison to DHPs estimate at site level, GEDI achieved a better agreement (r2=0.53) than that of NIS and S2 (r2= 0.33 and 0.30 respectively). These results suggest, despite large uncertainties at plot level, there is a great potential of improving LAI estimates at regional landscape levels by fusing different remote sensing products.

Topography distorts the angular distribution of the canopy gap fraction (GF). Path length (PL) correction is a simple and effective method to harmonize this distortion and improve canopy reflectance modelling andin situleaf area index measurements for vegetation, including both continuous (e.g., grass and crop) and discrete (e.g., forests) canopies, over sloping terrains. The rigorously theoretical derivation of PL correction for continuous canopies has been implemented. However, for discrete canopies, the PL show a serious heterogeneity, making it nearly impossible to be calculated. In this regard, there is still a need to develop theoretical derivation to evaluate and improve PL correction for forests over sloping terrains. In this study, (1) PL correction is proven to be equivalent to the correction of the canopy GF over sloping terrains, and our strategy concerns the canopy GF as a proxy of PL. (2) PL correction is first proven to be completely valid for forests with the Poisson trees distribution; yet it may produce uncertainty in certain directions for forests with tree distribution deviating from the Poisson model, especially for forests with regular tree distribution. (3) An improved model based on a Nilson and Peterson’s GF model for correcting PL for forests is given in this study. The results show that error produced by the PL correction for some forests can be effectively decreased by the improved model. The variation of directional tree distribution parameter cB(θ) with slope is the main cause of error produced by PL correction for forests. The study is of importance for better understanding and more accurate application of PL theory in topographic corrections andin situleaf area index measurements for forest canopies over sloping terrains.

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.

Leaf area index (LAI) is an important indicator for monitoring vegetation growth and estimating crop yields. The empirical-based model using vegetation indices (VIs) is an effective method for LAI estimation at the regional scale. However, due to the complexity of canopy radiation interaction processes, the leaf chlorophyll content (Cab) and saturation effects on canopy reflectance restrict the accuracy of VI-based LAI retrieval. To address these limitations, we propose a novel chlorophyll-insensitive vegetation index (CIVI) using red, red-edge and near-infrared bands to improve regional LAI mapping. The CIVI was developed based on the sensitivity analysis of red-edge band reflectance to LAI andCabusing the simulation dataset from the PROSAIL model. Then, the performance of CIVI was carefully evaluated from two aspects: the sensitivity of VI to LAI and other parameters, and the accuracy of LAI estimates using different VIs over homogeneous (cropland and grassland) and non-homogeneous (forest) biome canopies. The results suggested that CIVI can capture LAI variations well while remaining insensitive toCabvariations. Additionally, the sensitivity of CIVI to other vegetation biochemical and biophysical parameters did not increase significantly compared to that of other VIs. Furthermore, CIVI exhibited the best performance of LAI retrievals over both homogeneous (R2=0.938, RMSE=0.447 and rRMSE=21.3%) and non-homogenous (R2=0.635, RMSE=0.693 and rRMSE=14.0%) canopies among all selected VIs, especially for the high LAI. Our results indicated that the developed CIVI incorporating red-edge bands with a suitable formula can effectively reduce theCaband saturation effects, which is promising for improving VI-based LAI estimation.

Accurate estimation of photosynthetic phenology is of great importance for understanding carbon cycles. Most vegetation indices (VIs) calculated from remotely sensed reflectances represent the canopy structure and have high uncertainty in detecting the photosynthetic phenology. We compared the start/end of the photosynthetically active season (SOS/EOS) extracted from the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the near-infrared reflectance of vegetation (NIRv) and the product of NIRv and solar incident radiation (NIRvP) over northern deciduous broadleaf forests, and we used the metrics generated from solar-induced chlorophyll fluorescence (SIF), a proxy for photosynthesis, as reference. We found that the growing season extracted from the structural VIs was generally longer than the duration of photosynthetic activity retrieved from SIF: SOS derived from NDVI < NIRvP < EVI ≈ NIRv ≈ SIF and EOS from NDVI > NIRv ≈ EVI > NIRvP ≈ SIF. We investigated the mechanism underlying these phenological discrepancies using the paradigm of light-use efficiency. Our results show that the divergent performances of VIs were related to main factors limiting photosynthesis, which vary across different growth stages. The fraction of absorbed photosynthetically active radiation absorbed by chlorophyll (FAPARchl) that is well characterized by both EVI and NIRv, was the dominant factor of spring photosynthetic phenology, whilst NIRvP that is a proxy of the total amount of photosynthetically active radiation absorbed by chlorophyll (APARchl) was the dominant factor in autumn when radiation determines photosynthetic phenology. As such, we suggest that these factors be accounted for when selecting VIs for the extraction of photosynthetic phenology, i.e., EVI and NIRv are more suitable for accurate retrieval of SOS, and NIRvP is more suitable for accurate retrieval of EOS.

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.

Spatiotemporally continuous monitoring of aboveground biomass (AGB), an important indicator of grassland productivity, is crucial for achieving sustainable grassland development. Most existing grassland AGB estimation methods are empirical, and their temporally and spatially specific nature hinders operational application at large scales. Grass is herbaceous, so its AGB can be represented as the product of leaf area index (LAI) and dry matter content ($C_{m}$), both are the inputs of PROSAIL model. We, therefore, proposed a novel physical-based method through PROSAIL model inversion. Results showed that the estimated AGB presented good consistency with field-measured one, with$R^{2}= 0.87$and RMSE = 14.29 g/m2. We then implemented our method on the Google Earth Engine platform and generated daily and monthly AGB products covering the Tibetan Plateau (TP) and spanning from 2000 to 2021. These products characterized the spatiotemporally continuous dynamics of AGB on the TP. For example, it captured the decrease in dry matter caused by grazing during grassland dormancy, which is impossible for other existing AGB retrieval methods. Our method provides a promising tool to generate spatiotemporally continuous grassland AGB, which would inform the decision making for the conservation and restoration of grassland.

The complex terrain in mountainous areas distorts solar illumination, which brings a strong topographic effect on optical remote-sensing observations. Although many efforts have been done to correct this effect via normalizing solar illumination induced differences, there are still high uncertainty, especially for poor illuminated surfaces. In this study, a downwelling shortwave radiation (DSR)-based correction (DSRC) method was proposed. The topographic effects were accounted by normalizing DSR differences at different topographic conditions, and a stratified correction strategy was applied by separating the image into different groups according to normalized difference vegetation index (NDVI) to consider the spectral differences of different land-cover types. The DSRC method was applied to nine Landsat 8 scenes with high-resolution DSR data acquired by downscaling the Meteosat Second Generation (MSG) DSR product. The performance analysis indicates that the correlation coefficient between the corrected surface reflectance and illumination conditions notably decreased. Compared with SCS + C, empirical rotation, Statistical-Empirical, and Modified Minnaert methods, the DSRC method well retains inherent spectral pattern and provides good advantages in normalizing the aspect differences of surface reflectance. Furthermore, the comparison of NDVI values before and after correction indicated that DSRC preserved the original values and successfully corrected the overestimated NDVI values of poor illuminated surfaces. The corrected NDVI time series provide more reasonable cycle of the phenology of vegetated surfaces than the original series. In summary, the DSRC method has a strong potential for reducing topographic effects that currently limit the applications of remotely sensed data 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.

Downward shortwave radiation (DSR) plays a very important role in land surface radiation budget and land-surface processes modeling. Although there are several radiation products developed based on satellite observations, the coarse spatial resolution greatly limits their applications in regional or local scales. To get high-resolution and accuracy-reliable DSR data, a practical downscaling method for clear-sky condition was proposed by using the scale-invariant relationship of the radiative transfer process to decompose the global radiation into direct and diffuse components at horizontal level and conducting topographic correction finally. Based on this method, the time series of Meteosat Second Generation (MSG) DSR product covering part of Navarre province in the northern Spain was disaggregated into 30-m level with the use of the ALOS World 3D-30m digital elevation model (DEM) data. The downscaled results not only presented high spatial heterogeneity with respect to the changes in surface topography but also showed reasonable values at different times over different days in one year. The in situ validation indicated that the hourly downscaled DSR had quite high correlation with the surface measurements at each day with the coefficient of determination above 0.97 and the root-mean-squared error lower than 35 W/m2. Overall, the evaluation allows concluding on the proposed method that can be a good way to get reliable and high-resolution DSR data from coarse-resolution DSR product under clear-sky condition.

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.

Fine spatial details of vegetation growth are usually lost in leaf area index (LAI) products obtained from coarse spatial resolution satellite sensors. This may bring uncertainties in ecosystem process models, which usually require LAI products with fine spatiotemporal resolutions. Successful downscaling of LAI dynamics to fine spatial resolution is very important for meeting the demands of these models. Hence, a multiscale multisensor approach using the ensemble Kalman smoother (EnKS) technique is proposed in this paper. The LAI dynamics at a coarser spatial resolution are incorporated as prior information into the remotely sensed observations for time series LAI estimation at a finer spatial resolution. Downscaled LAI dynamics are evaluated based on spatial distribution and temporal trajectory. The results indicate the assimilated LAI to be in good agreement with the reference values at the different spatial scales. For example, the coefficient of determination (R2) between the reference values and fine-resolution LAI results retrieved by the proposed approach is 0.71 with a root-mean-square-error (RMSE) value of 0.65 on Julian day 185 at the Agro site. The method has proved to be effective for downscaling LAI dynamics, which improves the spatiotemporal patterns of fine-resolution LAI retrievals with respect to earlier methods.

The gross primary productivity (GPP) is an essential parameter of terrestrial carbon cycle, and simulation of GPP through terrestrial ecosystem process model usually needs a specification of model parameter. However, estimating model parameters in situ field or laboratory is a laborious and tedious work, causing a general lack of data. In this study, a reliable variational data assimilation scheme integrating Boreal Ecosystem Productivity Simulator (BEPS) with eddy covariance fluxes, was proposed to account for the seasonal variations of model parameters and improve temporally continuous GPP estimation. Results suggested that the proposed variational assimilation scheme in our study could effectively track the seasonal variations of model parameters. With optimal temporally continuous values of parameters, BEPS model had a better performance and potential ability for the GPP estimation.

We proposed a framework to generate high spatio-temporal resolution leaf area index (LAI) and uncertainty maps based on the integration of LAINet observation system, Consistent Adjustment of the Climatology to Actual Observations (CACAO) method and Gaussian process regression (GPR). LAINet, which is a wireless sensor network based automatic LAI observation instrument, was used to provide temporally continuous field measurements; CACAO, a data blending method, was used to blend the high and low spatial resolution remote sensing observations to obtain high spatio-temporal resolution remote sensing observations synchronous with the field measurements. GPR, a machine learning regression algorithm, was used to upscale the spatially discrete field measurements to spatially explicit LAI maps, and get the concomitant uncertainty maps. The performance of the proposed method was evaluated over a crop site, where seven LAI maps and their accompanying uncertainty maps all with 30 m and 8 days resolutions were generated. Results show that the framework can provide accurate LAI retrievals. In addition, the concomitant uncertainty maps provide insight into the reliability of the LAI retrievals. This paper contributes to precision agriculture and validation activities for coarse resolution LAI products.

GF-4 is the first Chinese high resolution geostationary orbit satellite. It has great application potential in many earth-related studies. Given the low geometric accuracy of GF-4 images in mountain area, in this paper, a new operational and practical automatic orthorectification approach was proposed to improve the orthorectification accuracy of GF-4 images. The new approach adopted a two-level area-based algorithm to automatically search tie points between GF-4 and the base Landsat images. Then the images was further orthorectifyed using the improved rational polynomial coefficients model optimized by tie points. Results demonstrated that the new orthorectification approach could significantly improve the orthorectification accuracy. It is also suitable for orthorectification of GF-4 images with different clouds coverage.

Ecosystems is survived in the suitable environment which provide appropriately abiotic resources for organism and IUCN have applied abiotic degradation as Criterion C to assess the risk of ecosystems. However, the origin and collapse status of the criterion is vague for assessors, and the results are inconsistent as the response of ecosystems to environment are different. Therefore, the ecological amplitude of ecosystem to environment stress was introduced in the ecosystems risk assessment with the relationship between criterion and ecological amplitude. To put this concept into practice, remote sensing was applied to acquire the optimum and tolerance of each ecosystem. The natural forest in xishuangbanna was assessed by this proposed method to identify the stress of temperature. The result show that the status of natural forest in the past is least concern (LC), and vulnerable (VU) in the feature. With temperature in the future significantly increasing, natural forest may be suffered with heat stress. The proposed method describe the risk derived from degradation of environment in mechanism greatly improved the consistency and feasibility of Criterion C in the ecosystems risk assessment.

We developed a 1-D model (the PLC-P model) for modeling canopy reflectance over sloping terrain. The effects of sloping terrain on single-order and diffuse scattering are accounted for by path length correction (PLC) and the P theory, respectively. Currently, we have developed the prototype of PLC-P model, in which only the sloping effects on the single-order are accounted for through path length correction. This first version of PLC-P model is called PLC model. The PLC model was validated via Monte Carlo simulations. The comparison with the Monte Carlo simulation revealed that the PLC model can capture the pattern of slope-induced reflectance distortion with high accuracy. The PLC-P model can provide a promising tool to improve the simulation of canopy reflectance and the retrieval of biophysical variables over mountainous regions.

Sloping terrain induces distortion of canopy reflectance (CR), and the retrieval of biophysical variables from remote sensing data needs to account for topographic effects. We developed a 1-D model (the path length correction (PLC)based model) for simulating CR over sloping terrain. The effects of sloping terrain on single-order and diffuse scatterings are accounted for by PLC and modification of the fraction of incoming diffuse irradiance, respectively. The PLC model was validated via both Monte Carlo and remote sensing image simulations. The comparison with the Monte Carlo simulation revealed that the PLC model can capture the pattern of slopeinduced reflectance distortion with high accuracy (red band: R2= 0.88; root-mean-square error (RMSE) = 0.0045; relative RMSE (RRMSE) = 15%; near infrared response (NIR) band: R2= 0.79; RMSE = 0.041; RRMSE = 16%). The comparison of the PLC-simulated results with remote sensing observations acquired by the Landsat8-OLI sensor revealed an accuracy similar to that with the Monte Carlo simulation (red band: R2= 0.83; RMSE = 0.0053; RRMSE = 13%; NIR band: R2= 0.77; RMSE = 0.023; RRMSE = 8%). To further validate the PLC model, we used it to implement topographic normalization; the results showed a large reduction in topographic effects after normalization, which implied that the PLC model captures reflectance variations caused by terrain. The PLC model provides a promising tool to improve the simulation of CR and the retrieval of biophysical variables over mountainous regions.

Surface soil moisture (SSM) is an important parameter at the land-atmosphere interface. In past decades, passive microwave remote sensing offers a good opportunity for obtaining SSM on a global scale, and many downscaling methods have been proposed using the triangle-based empirical soil moisture relationship models to overcome the limitation of coarse spatial resolution of its SSM products for regional applications. This paper aimed to examine and compare the effectiveness of five typical triangle-based empirical soil moisture relationship models for estimating SSM with Landsat-5 data and in situ measurements from the Maqu network on the northeastern part of the Tibetan Plateau for nine cloud-free days. The results showed that the model that treats the SSM as a second-order polynomial with land surface temperature, vegetation indices (VIs), and surface albedo as inputs exhibited the best performance compared with the results of other models. The VI comparison indicated that the use of the normalized difference VI or the fractional vegetation cover in this model outperformed other VIs, with the root-mean-square deviation of approximately 0.055 m3/m3and the coefficient of determination ($\text{R}^{2}$ ) above 0.78 at the nine-day average level. In addition, a significant spatial scale effect of the model was also found through analyzing the model fitting results at different window sizes. The study provides important insight into the best empirical relationship models for capturing soil moisture dynamics. These models can support the passive microwave soil moisture data spatial downscaling and validation applications in future studies.

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.