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Topography significantly affects remotely sensed reflectance data and subsequently impacts the retrieval of the leaf area index (LAI) from surface reflectance data over rugged terrains. However, most LAI inversion algorithms ignore the influence of terrain. This paper quantitatively analyzes the topographic effects on LAI values retrieved from remote sensing data at various spatial scales (30, 90, 270, 540, 1080, and 5400 m) over rugged terrains. The PRO4SAILT (PROSPECT + 4SAILT) model and the Proy algorithm were used to simulate multiscale surface reflectance for different LAI values over rugged terrains. Based on Gaussian process regression (GPR), an LAI inversion algorithm that ignores terrain effects was first developed. The simulated multiscale reflectance data were subsequently input into the inversion algorithm to retrieve LAI values. Finally, the retrieved LAI values were compared with the corresponding reference LAI values. The results demonstrate that the finer the spatial resolution is, the more significant the topographic effects on the retrieved LAI values are. When the reference LAI is five, as the spatial resolution increases from 5400 m to 30 m, the mean percentage error (MPE) of the retrieved LAI increases from 10.46% to 13.72%, and the root mean square error (RMSE) increases from 0.5376 to 1.005. Regardless of the spatial resolution, the error in the retrieved LAI values increases with an increasing terrain slope. When the reference LAI is five and the spatial resolution is 30 m, the MPE at a slope of 15°–30° is close to 5%, and the RMSE is close to 0.3. The MPE at a slope of 30°–45° is close to 20%, and the RMSE is close to one. In addition, the accuracy of the retrieved LAI values is closely related to the sky view factor (SVF). In general, the larger the SVF is, the smaller the error in the retrieved LAI values. In addition, the conversion relationships between the retrieved LAI values using the algorithm that ignores terrain effects and the true LAI values are provided in this study. [ABSTRACT FROM AUTHOR]

Several global leaf area index (LAI) products were generated using neural networks, but the training dataset for the neural networks was sensor specific, and the construction of the training dataset was time consuming. In this paper, an unsupervised domain adaptation-based method was proposed to estimate LAI from the Visible Infrared Imaging Radiometer Suite (VIIRS) surface reflectance dataset based on a training dataset constructed from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance dataset. A transfer component analysis (TCA) algorithm was first utilized to map the MODIS and VIIRS surface reflectance into the same subspace to reduce the distribution discrepancies between the MODIS and VIIRS surface reflectance. Then, the embedded data obtained from MODIS surface reflectance dataset, along with the LAI values produced by fusing the MODIS and the Carbon cYcle and Change in Land Observational Products from an Ensemble of Satellites (CYCLOPES) products, were employed to train general regression neural networks (GRNNs). Finally, for retrieving the LAI values, the embedded data acquired from the VIIRS surface reflectance dataset was input into the trained GRNNs. For multiple field sites with different biome types, we used this developed method to retrieve LAI values based on the VIIRS surface reflectance dataset. The results indicate that, based on the training dataset built from MODIS surface reflectance dataset, the domain adaptation-based retrieval method can effectively estimate LAI values from VIIRS surface reflectance dataset. By comparison with the VIIRS and MODIS LAI products, the retrieved LAI values with TCA are more consistent with the reference LAI values acquired from high-resolution remote sensing images. The coefficient of determination (R2) and root mean square error (RMSE) of the retrieved LAI values with TCA at all selected sites are 0.88 and 0.68, respectively. Furthermore, the accuracy of the retrieved LAI values with TCA is higher than the retrieved LAI values without TCA with the R2 0.81 and the RMSE 0.79. [ABSTRACT FROM AUTHOR]

There are several global leaf area index (LAI) products currently available. The spatial resolution of these products is 500 m and above, which is unsuitable for many applications requiring higher spatial resolution. In the past several years, we developed a method to estimate the LAI from time series satellite remote sensing data using general regression neural networks. The method has been used to generate global LAI products at 500 m and 1000 m from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data, and a global LAI product at 0.05° from Advanced Very High Resolution Radiometer (AVHRR) surface reflectance data. In this study, the method was extended to generate a global LAI product at 250 m (one of the MUltiscale Satellite remotE Sensing (MUSES) product suite) from MODIS surface reflectance data in the red and near-infrared (NIR) bands. As far as we know, it is the first global LAI product at 250 m spatial resolution and is the highest spatial resolution global LAI product available. The spatial and temporal consistency of the MUSES LAI product was evaluated by comparing it with the MODIS LAI product, and the MUSES LAI product was validated by high-resolution reference maps at the Validation of Land European Remote Sensing Instruments (VALERI) and Implementing Multi-Scale Agricultural Indicators Exploiting Sentinels (IMAGINES) sites representative of different biomes. The root mean square error (RMSE) of the MUSES LAI product versus the LAI values derived from the high-resolution reference maps over the VALERI and IMAGINES sites was 0.9984, and the bias of the MUSES LAI product was −0.2005. [ABSTRACT FROM AUTHOR]

The Global Land Surface Satellite (GLASS) leaf area index (LAI) product is one of the most widely used global LAI products in the scientific community. The latest (version 5) GLASS LAI product has been generated from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data. The purpose of this paper is to evaluate the quality of the version 5 GLASS LAI product. The GLASS LAI product was compared with the latest MODIS LAI product (MCD15A2 H, Collection 6) and the second version of Geoland2 (GEOV2) LAI product to evaluate their temporal and spatial discrepancies. A direct validation was conducted to compare these LAI products to the LAI values derived from the high-resolution reference maps from the Validation of Land European Remote Sensing Instruments (VALERI) and Implementing Multi-Scale Agricultural Indicators Exploiting Sentinels (IMAGINES) sites. The results show that the GLASS and GEOV2 LAI products have great spatial integrity. However, the MODIS LAI product contains many missing pixels in tropical areas. These LAI products follow fairly consistent seasonal characteristics. The spatial discrepancies of these LAI products mainly exist in forest areas, especially evergreen broadleaf forests where the GLASS LAI values are generally lower than the GEOV2 LAI values by approximately 1.0 LAI units and lower than the MODIS LAI values by 0.5 to 1.0 LAI units. The spatial distribution of these LAI products has slight discrepancies in savannahs, broadleaf crops, grasses/cereal crops and shrubs. The GLASS and GEOV2 LAI products capture a complete and reasonable temporal profile, contrasting with the MODIS LAI product, which shows dramatic fluctuations, particularly during the growing seasons. These LAI products show similar temporal trajectories and interannual variations for all biome types except evergreen broadleaf forests. The direct validation shows that the accuracy of the GLASS LAI product is better than the accuracy of the MODIS and GEOV2 LAI products. The coefficient of determination (R2) of the GLASS, MODIS and GEOV2 LAI products versus the LAI values derived from the high-resolution reference maps are 0.68, 0.47 and 0.55, respectively, and the root mean square error (RMSE) of these products are 0.86, 1.22 and 1.21, respectively. [ABSTRACT FROM AUTHOR]

Current remote sensing products are mainly generated from polar-orbiting satellite data using parameter-specific algorithms. These products lack physical consistency and cannot accurately characterize intra-day variations of parameters, such as the fraction of absorbed photosynthetically active radiation (FAPAR) and surface albedo. In this study, a multi-parameter consistent retrieval method is proposed to simultaneously retrieve aerosol optical depth (AOD), leaf area index (LAI), photosynthetically active radiation (PAR), FAPAR, surface albedo, and incident shortwave radiation (ISR) from top of atmosphere (TOA) reflectance data acquired by the Advanced Baseline Imager (ABI) aboard the Geostationary Operational Environmental Satellite-R series (GOES-R). The retrieved parameter values were evaluated through comparisons with corresponding Moderate Resolution Imaging Spectroradiometer (MODIS), the second version of the Geoland2 (GEOV2), and GOES-R products and ground measurements over five surface radiation budget network (SURFRAD) sites with different vegetation types. The results demonstrate that the retrieved AOD, PAR, ISR, and surface albedo values have consistent intra-day variations with the ground measurements, and the retrieved parameter values achieve good performance against the ground measurements for all the five SURFRAD sites. The root mean square errors of the retrieved AOD, shortwave albedo, ISR, and PAR values against the ground measurements are 0.071, 0.032, 50.943 W m–2, and 27.975 W m–2, respectively. [ABSTRACT FROM AUTHOR]

The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical input parameter in many climate and ecological models. Long time series of global FAPAR products are required for many applications, such as vegetation productivity, carbon budget calculations, and global change studies. Three long time series of global FAPAR products have been existing since the 1980s: Global LAnd Surface Satellite (GLASS) Advanced Very High Resolution Radiometer (AVHRR), National Centers for Environmental Information (NCEI) AVHRR, and third-generation Global Inventory Monitoring and Modeling System (GIMMS3g). Currently, no intercomparison studies exist that have evaluated these FAPAR products to understand their differences for effective applications. In this paper, these three long time series of global FAPAR products are first intercompared to evaluate their spatial and temporal consistencies, and then compared with FAPAR values derived from high-resolution reference maps of VAlidation of Land European Remote sensing Instruments sites. Our results demonstrate that the GLASS AVHRR FAPAR product is spatially complete, whereas the NCEI AVHRR and GIMMS3g FAPAR products contain many missing pixels, especially in rainforest regions and in middle- and high-latitude zones of the Northern Hemisphere. The GLASS AVHRR, NCEI AVHRR, and GIMMS3g FAPAR products are generally consistent in their spatial patterns. However, a relatively large discrepancy among these FAPAR products is observed in tropical forest regions and around 55°N–65°N. In latitudes between 15°N and 25°N, the mean GIMMS3g FAPAR values are clearly larger than the mean GLASS AVHRR and NCEI AVHRR FAPAR values during July–October each year. The GLASS AVHRR FAPAR product provides smooth FAPAR temporal profiles, whereas the NCEI AVHRR and GIMMS3g FAPAR products showed fluctuating trajectories, especially during the growing seasons. All three FAPAR products show high agreement coefficients (ACs) in vegetation regions with obvious seasonal variations and low ACs in tropical forest regions and sparsely vegetated areas. A comparison of these FAPAR products with the FAPAR values derived from high-resolution reference maps demonstrates that the GLASS AVHRR FAPAR product has the best performance [root mean square deviation (RMSD) = 0.0819 and bias = 0.0043], followed by the NCEI AVHRR FAPAR product (RMSD = 0.1061 and bias = 0.0371), and then finally, the GIMMS3g FAPAR product (RMSD = 0.1152 and bias = 0.0248). [ABSTRACT FROM AUTHOR]

An accurate and spatially continuous estimation of terrestrial latent heat flux (LE) is fundamental and crucial for the rational utilization of water resources in the Haihe River Basin (HRB). However, the sparsity of flux observation sites hinders the accurate characterization of spatiotemporal LE patterns over the HRB. In this study, we estimated the daily LE across the HRB using the gradient boosting regression tree (GBRT) from global land surface satellite NDVI data, reanalysis data and eddy covariance data. Compared with the random forests (RF) and extra tree regressor (ETR) methods, the GBRT obtains the best results, with R2 = 0.86 and root mean square error (RMSE = 18.1 W/m2. Then, we applied the GBRT algorithm to map the average annual terrestrial LE of the HRB from 2016 to 2018 with a spatial resolution of 0.05°. When compared with the Global Land Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS) LE products, the difference between the terrestrial LE estimated by the GBRT algorithm and the GLASS and MODIS products was less than 20 W/m2 in most areas; thus, the GBRT algorithm was reliable and reasonable for estimating the long-term LE estimation over the HRB. [ABSTRACT FROM AUTHOR]

Many plant species have dorsiventral leaves that have significant differences in optical properties from one side to the other. Several studies have revealed that ignoring this asymmetry induces significant errors in plant canopy reflectance, and current leaf models simulating leaf dorsiventrality are limited to the 0.4–2.5 μ m wavelength range. This article, partly based on two recently collected datasets in the 2.5–14 μ m wavelength range, demonstrates that ignoring leaf dorsiventrality induces significant errors in brightness temperature and effective emissivity at the canopy scale. The PROLIB model, which inherits from the PROSPECT-VISIR and LIBERTY models, is the first radiative transfer model to simulate the reflectance and transmittance of both leaf sides from 0.4 to 5.7 μ m. The palisade and spongy mesophylls are represented as plate and sphere layers, respectively, to account for the structural asymmetry of leaf cells. The sieve effect that explains the differences in transmittance between the adaxial and abaxial sides of the leaf is successfully incorporated into PROLIB. Evaluation of the model on several leaf datasets shows that: (1) It reproduces well the adaxial and abaxial optical properties of the leaves, with a root mean square error (RMSE) of 0.0109 for reflectance and transmittance. (2) It can be inversed to retrieve leaf traits, with RMSE values for leaf chlorophyll, carotenoid, anthocyanin, water, and dry matter content of 5.519 μ g/cm2, 2.344 μ g/cm2, 4.219 μ g/cm2, 0.0022 g/cm2, and 0.0017 g/cm2, respectively (corresponding normalized RMSE values of 22.0%, 34.0%, 49.4%, 19.6%, and 24.7%). However, better and more complete leaf datasets are needed for leaf dorsiventrality analysis and model calibration. • Adaxial and abaxial leaf surfaces have different spectral properties in 2.5–14 μ m. • A leaf model called PROLIB is developed to simulate leaf dorsiventrality. • PROLIB takes into account the sieve effect. • PROLIB reconstructs leaf spectra well from multiple datasets, with an RMSE of 0.0109. [ABSTRACT FROM AUTHOR]

Local topography significantly affects remotely sensed reflectance data and subsequently impacts leaf area index (LAI) retrieval over mountainous areas. Therefore, mountain vegetation LAI mapping from satellite observations at multiple scale levels is often obstructed by topographic distortion. To analyze the effects of topography on multiresolution LAI retrievals, consistent LAI estimations were first generated across six spatial scales (i.e., 960 m, 480 m, 240 m, 120 m, 60 m and 30 m) from MODIS and Landsat OLI reflectance data using the ensemble multiscale filter (EnMsF) approach over rugged surfaces. Subsequently, the topographic influence on LAI was evaluated based on spatial patterns and retrieval accuracies at multiple scale levels by comparing the EnMsF-based multiscale LAI results obtained before and after terrain correction of a Landsat image. The results demonstrated that the multiresolution LAI values retrieved from topographically corrected surface reflectance data outperformed those without topographic correction, regardless of various slopes or aspects at the 30 m scale or the differences in spatial resolutions. The accuracies were determined for the retrieved LAI values before (coefficient of determination (R2) = 0.32 and root mean square error (RMSE) = 1.03) and after (R2 = 0.60 and RMSE = 0.82) topographic correction when compared to the field measurements over slopes facing toward the sun at the 30 m resolution. The R2 and RMSE values were 0.57 and 0.54, respectively, for the LAI estimations with terrain corrections in the shady aspects. Moreover, the topographic effect on the LAI estimations depended on our spatial scales. The finer the spatial resolution is, the more significant the topographic effects on the multiscale LAI retrieval are, and vice versa. Thus, the results of this study open an encouraging path to deepen the understanding of terrain effects on multiscale LAI estimations, and further improve the LAI retrieval algorithm across different spatial scales over mountainous areas. [ABSTRACT FROM AUTHOR]

Terrestrial evapotranspiration ( ET ) for each plant functional type ( PFT ) is a key variable for linking the energy, water and carbon cycles of the atmosphere, hydrosphere and biosphere. Process-based algorithms have been widely used to estimate global terrestrial ET , yet each ET individual algorithm has exhibited large uncertainties. In this study, the support vector machine ( SVM ) method was introduced to improve global terrestrial ET estimation by integrating three process-based ET algorithms: MOD16 , PT - JPL and SEMI - PM . At 200 FLUXNET flux tower sites, we evaluated the performance of the SVM method and others, including the Bayesian model averaging ( BMA ) method and the general regression neural networks ( GRNNs ) method together with three process-based ET algorithms. We found that the SVM method was superior to all other methods we evaluated. The validation results showed that compared with the individual algorithms, the SVM method driven by tower-specific (Modern Era Retrospective Analysis for Research and Applications, MERRA ) meteorological data reduced the root mean square error ( RMSE ) by approximately 0.20 (0.15) mm/day for most forest sites and 0.30 (0.20) mm/day for most crop and grass sites and improved the squared correlation coefficient ( R 2 ) by approximately 0.10 (0.08) (95% confidence) for most flux tower sites. The water balance of basins and the global terrestrial ET calculation analysis also demonstrated that the regional and global estimates of the SVM -merged ET were reliable. The SVM method provides a powerful tool for improving global ET estimation to characterize the long-term spatiotemporal variations of the global terrestrial water budget. [ABSTRACT FROM AUTHOR]