Global LAnd Surface Satellite Products Research Articles

Climate impact greater on vegetation NPP but human enhance benefits after the Grain for Green Program in Loess Plateau

Net primary productivity (NPP) has been the subject of numerous studies, but the qualitative and comparative study on NPP that considers human activities, climate change, and their interactions is still insufficient. The Grain for Green Program (GTGP) was established in the Loess Plateau since 1999, could be used as a reference study. The temporal and spatial patterns, change trend, and driving factors of vegetation NPP were analysed in Loess Plateau using data derived from GLASS NPP (the Global Land Surface Satellite Product), meteorology, atmospheric quality, hydrology and land use by correlation and improved residual trend analysis from 2000 to 2020. After GTGP implementation, the climate and human contributions to vegetation NPP were 62 % and 38 %. NPP increased at a rate of 6.3 g C m−2 a−1 from 2000 to 2020, which was higher in 2010–2020 (6.9 g C m−2 a−1) than it was in 2000–2010 (5.7 g C m−2 a−1). The same trend was followed by the contribution of climate change to NPP between 2010 and 2020 (70.6 %) and 2000–2010 (65 %). The GTGP core area was mostly where the vegetation NPP rising zone was located. From the original bare land in the desert area, 4442 km2 of vegetation grew and increased the NPP (68.7 g C m−2). Under the warm–humid climatic trend, the climate factors contributed to NPP more in grasslands (40 %) compared to woodlands (29 %) and precipitation (33 %) was more sensitive than temperature (12.7 %). Consequently, sediment transport, PM2.5 and dusty weather decreased significantly. In conclusion, although climate change has a greater impact on vegetation, human activities have enhanced the impact faster in the first decade, which benefit and profit from the environment finally.

Significant discrepancies of land surface daily net radiation among ten remotely sensed and reanalysis products

ABSTRACT Land surface all-wave net radiation (Rn) is crucial in determining Earth's climate by contributing to the surface radiation budget. This study evaluated seven satellite and three reanalysis long-term land surface Rn products under different spatial scales, spatial and temporal variations, and different conditions. The results showed that during 2000-2018, Global Land Surface Satellite Product (GLASS)-Moderate Resolution Imaging Spectroradiometer (MODIS) performed the best (RMSE=25.54 Wm-2, bias=−1.26 Wm-2), followed by ERA5 (the fifth-generation of European Centre for Medium-Range Weather Forecast Reanalysis) (RMSE=32.17 Wm-2, bias=−4.88 Wm-2) and GLASS-AVHRR (Advanced Very-High-Resolution Radiometer) (RMSE=33.10 Wm-2, bias=4.03 Wm-2). During 1983-2018, GLASS-AVHRR and ERA5 ranked top and performed similarly, with RMSE values of 31.70 and 33.08 Wm-2 and biases of −4.56 and 3.48 Wm-2, respectively. The averaged multi-annual mean Rn over the global land surface of satellite products was higher than that of reanalysis products by about 10∼30 Wm-2. These products differed remarkably in long-term trends variations, particularly pre-2000, but no significant trends were observed. Discrepancies were more frequent in satellite data, while reanalysis products showed smoother variations. Large discrepancies were found in regions with high latitudes, reflectance, and elevation which could be attributed to input radiative components, meteorological variables (e.g., cloud properties, aerosol optical thickness), and applicability of the algorithms used. While further research is needed for detailed insights.

The Hi-GLASS all-wave daily net radiation product: Algorithm and product validation

The surface net radiation (Rn) represents the balance of the radiative budget on the land surface and drives many physical and biological processes. An accurate and long-term product for global daily coverage of Rn at a high spatial resolution is needed for a variety of applications at regional and local scales. This study proposes two algorithms, called the downward shortwave radiation (DSR)-based algorithm and the top-of-atmosphere (TOA)-based algorithm, to estimate Rn by using Landsat data. The DSR-based algorithm consists of three conditional models, and was developed based on the analysis of the relationship between Rn and shortwave radiation as well as ancillary information from ground measurements and various datasets. The TOA-based algorithm was developed by linking Rn to TOA observations from Landsat sensors and ancillary information. The two algorithms were developed by using the random forest method. The results of their validation against ground measurements showed that the DSR-based algorithm outperformed the TOA-based algorithm in terms of accuracy, with a determination coefficient (R2) of 0.93, root-mean-squared error (RMSE) of 17.58 Wm−2, and bias of −4.27 Wm−2. It was stable under various conditions. We then applied the DSR-based algorithm to generate a product of the global daily Rn, called the High-resolution (Hi)- Global LAnd Surface Satellite (GLASS), from 2013 to 2018 at a spatial resolution of 30 m under a clear sky based on remotely sensed products, including the DSR from GLASS, the normalized difference vegetation index (NDVI) obtained from Landsat, surface broadband albedo from Hi-GLASS, and meteorological factors based on reanalysis data from MERRA2. Following its validation using in-situ observations from 2013 to 2018, the overall accuracy of the daily Rn acquired by Hi-GLASS under clear sky was found to be satisfactory, with a value of R2 of 0.90 and an RMSE of 25.03 Wm−2. Moreover, compared with the daily Rn obtained from the GLASS product at a spatial resolution of 5 km, that obtained by Hi-GLASS can better characterize the surface by providing more details and capturing the variations in the measurements, especially large and small values. However, due to limitations of the available datasets and the algorithm, the data on Rn for most regions lacked information on cloudy skies and areas at high latitudes. This information thus cannot be provided by Hi-GLASS yet. Moreover, the influence of the topography on values of Rn has not been thoroughly considered. Nonetheless, values of Rn under clear sky obtained from Hi-GLASS offer promise for use in a wide range of areas, and efforts are underway to improve this product.

Ecological Response to Climate Change Across China From Combined Soil Temperature and Moisture Changes

AbstractA coupled soil temperature (ST) and moisture (SM) balance reflects a synthetic climate regime, having huge ecological impacts. This paper used ST and SM data from the European Center for Medium‐Range Weather Forecasts climate reanalysis‐Land and the Coupled Model Intercomparison Project Phase 6 and leaf area index (LAI) data from the Global Land Surface Satellite Product Suite. The focus was on understanding joint ST‐SM changes and the resulting ecological response across China. The results show that during 2000–2020, 24.5% of the land area in China experienced a warming‐drying trend resulting in a 9.7% LAI decrease, while 6.4% of the area experienced a warming‐wetting trend leading to an 8.6% LAI increase. During 2015–2100, 30.6% of the land area in China will be warmer and drier, while 55.2% of the area will be warmer but wetter across three shared socioeconomic pathways (SSP126, 245, and 585). Superimposed on the long‐term trends, there are also significant spatiotemporal variabilities in ST and SM on annual to decadal timescales. The LAI also showed substantial short‐term fluctuations in both typical regions and ecosystems despite consistent long‐term increases. Our findings suggest that ecosystems could be impaired on annual to decadal scales by adverse soil conditions in the twenty‐first century, but in terms of long‐term trends, ecosystems may be resilient partly because of the compensating effects of global warming and regional hydrological changes. Impact studies should thus focus more on annual to decadal soil‐ecosystem anomalous events.

Applying a Wavelet Transform Technique to Optimize General Fitting Models for SM Analysis: A Case Study in Downscaling over the Qinghai–Tibet Plateau

Soil moisture (SM) is an important land-surface parameter. Although microwave remote sensing is recognized as one of the most appropriate methods for retrieving SM, such retrievals often cannot meet the requirements of specific applications because of their coarse spatial resolution and spatiotemporal data gaps. A range of general models (GMs) for SM analysis topics (e.g., gap-filling, forecasting, and downscaling) have been introduced to address these shortcomings. This work presents a novel strategy (i.e., optimized wavelet-coupled fitting method (OWCM)) to enhance the fitting accuracy of GMs by introducing a wavelet transform (WT) technique. Four separate GMs are selected, i.e., elastic network regression, area-to-area regression kriging, random forest regression, and neural network regression. The fitting procedures are then tested within a downscaling analysis implemented between aggregated Global Land Surface Satellite products (i.e., LAI, FVC, albedo), Thermal and Reanalysis Integrating Medium-resolution Spatial-seamless LST, and Random Forest Soil Moisture (RFSM) datasets in both the WT space and the regular space. Then, eight fine-resolution SM datasets mapped from the trained GMs and OWCMs are analyzed using direct comparisons with in situ SM measurements and indirect intercomparisons between the aggregated OWCM-/GM-derived SM and RFSM. The results demonstrate that OWCM-derived SM products are generally closer to the in situ SM observations, and better capture in situ SM dynamics during the unfrozen season, compared to the corresponding GM-derived SM product, which shows fewer time changes and more stable trends. Moreover, OWCM-derived SM products represent a significant improvement over corresponding GM-derived SM products in terms of their ability to spatially and temporally match RFSM. Although spatial heterogeneity still substantially impacts the fitting accuracies of both GM and OWCM SM products, the improvements of OWCMs over GMs are significant. This improvement can likely be attributed to the fitting procedure of OWCMs implemented in the WT space, which better captures high- and low-frequency image features than in the regular space.

Estimating 250-m Land Surface and Atmospheric Variables From MERSI Top-of-Atmosphere Reflectance

The medium-resolution spectral imager (MERSI) onboard China’s new generation of polar-orbiting meteorological satellite series FengYun-3 (FY-3) is providing global observations of the Earth’s environment. One of the important characteristics of the MERSI sensor is that it has four bands at 250-m spatial resolution. However, very few high-level land surface and atmospheric products have been generated from MERSI data. This article presents an effective optimization method to estimate the continuous temporal distribution of multiple land surface and atmospheric variables at a 250-m spatial resolution from time series FY-3B MERSI top-of-atmosphere (TOA) observations. The MODIS cloud detection method was applied to the MERSI TOA reflectance to determine the clear observations. Then, a physics-based BRDF correction was adopt to reduce the topographic effects using the slope angle and aspect angle of the slope. Finally, a coupled land surface-atmosphere radiative transfer (RT) model and the shuffled complex evolution (SCE) optimization algorithm were used to estimate a suit of variables, including the leaf area index (LAI), the aerosol optical depth (AOD), the cloud optical thickness (COT), the cloud effective particle radius (CER), the land surface reflectance, the shortwave and visible albedo, the incident shortwave radiation (ISR), the incident photosynthetically active radiation (PAR), the fraction of absorbed PAR (FAPAR), the surface broadband emissivity (BBE), and the TOA shortwave albedo. The method was applied to the data from the 18 SURFRAD, the America FluxNet (AmeriFlux), and the Images and Beijing Normal University net (BNUnet) sites. All the estimated variables were also compared with Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Land Surface Satellite (GLASS) products. The estimated LAI, PAR, FAPAR, shortwave albedo, and ISR were also validated using ground measurements from the selected sites. The results show that our method can simultaneously estimate multiple temporally continuous variables in both clear-and cloudy-sky conditions, with an accuracy comparable to those of MODIS and GLASS products. The estimated LAI, PAR, FAPAR, shortwave albedo, and ISR are consistent with the field measurements, with coefficients of determination ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> ) of 0.692, 0.783, 0.788, 0.559, and 0.833, and root mean square errors (RMSEs) of 0.427, 56.681 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , 0.092, 0.084, and 115.305 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. The proposed algorithm can effectively estimate the instantaneous atmospheric and land surface variables directly from satellite observations without cumbersome atmospheric corrections. Moreover, it can potentially be applied to multiple satellite observations.

Evaluation of Six Satellite-Based Terrestrial Latent Heat Flux Products in the Vegetation Dominated Haihe River Basin of North China

In this study, six satellite-based terrestrial latent heat flux (LE) products were evaluated in the vegetation dominated Haihe River basin of North China. These LE products include Global Land Surface Satellite (GLASS) LE product, FLUXCOM LE product, Penman-Monteith-Leuning V2 (PML_V2) LE product, Global Land Evaporation Amsterdam Model datasets (GLEAM) LE product, Breathing Earth System Simulator (BESS) LE product, and Moderate Resolution Imaging Spectroradiometer (MODIS) (MOD16) LE product. Eddy covariance (EC) data collected from six flux tower sites and water balance method derived evapotranspiration (WBET) were used to evaluate these LE products at site and basin scales. The results indicated that all six LE products were able to capture the seasonal cycle of LE in comparison to EC observations. At site scale, GLASS LE product showed the highest coefficients of determination (R2) (0.58, p &lt; 0.01) and lowest root mean square error (RMSE) (28.2 W/m2), followed by FLUXCOM and PML products. At basin scale, the LE estimates from GLASS product provided comparable performance (R2 = 0.79, RMSE = 18.8 mm) against WBET, compared with other LE products. Additionally, there was similar spatiotemporal variability of estimated LE from the six LE products. This study provides a vital basis for choosing LE datasets to assess regional water budget.

Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops

We proposed a direct approach to validate hectometric and kilometric resolution leaf area index (LAI) products that involved the scaling up of field-measured LAI via the validation and recalibration of the decametric Sentinel-2 LAI product. We applied it over a test study area of maize crops in northern China using continuous field measurements of LAINet along the year 2019. Sentinel-2 LAI showed an overall accuracy of 0.67 in terms of Root Mean Square Error (RMSE) and it was used, after recalibration, as a benchmark to validate six coarse resolution LAI products: MODIS, Copernicus Global Land Service 1 km Version 2 (called GEOV2) and 300 m (GEOV3), Satellite Application Facility EUMETSAT Polar System (SAF EPS) 1.1 km, Global LAnd Surface Satellite (GLASS) 500 m and Copernicus Climate Change Service (C3S) 1 km V2. GEOV2, GEOV3 and MODIS showed a good agreement with reference LAI in terms of magnitude (RMSE ≤ 0.29) and phenology. SAF EPS (RMSE = 0.68) and C3S V2 (RMSE = 0.41), on the opposite, systematically underestimated high LAI values and showed systematic differences for phenological metrics: a delay of 6 days (d), 20 d and 24 d for the start, peak and the end of growing season, respectively, for SAF EPS and an advance of −4 d, −6 d and −6 d for C3S.

GBRT-Based Estimation of Terrestrial Latent Heat Flux in the Haihe River Basin from Satellite and Reanalysis Datasets

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.

Evaluation of the version 5.0 global land surface satellite (GLASS) leaf area index product derived from MODIS data

ABSTRACT 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.

The Global Land Surface Satellite (GLASS) Product Suite

Abstract:The Global Land Surface Satellite (GLASS) product suite currently contains 12 products, including leaf area index, fraction of absorbed photosynthetically active radiation, fraction of green vegetation coverage, gross primary production, broadband albedo, broadband longwave emissivity, downward shortwave radiation and photosynthetically active radiation, land surface temperature, downward and upwelling thermal radiation, all-wave net radiation, and evapotranspiration. These products are generated from the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer satellite data. Their unique features include long-term temporal coverage (many from 1981 to the present), high spatial resolutions of the surface radiation products (1 km and 0.05°), spatial continuities without missing pixels, and high quality and accuracy based on extensive validation using in situ measurements and intercomparisons with other existing satellite products. Moreover, the GLASS products are based on robust algorithms that have been published in peer-reviewed literature. Herein, we provide an overview of the algorithm development, product characteristics, and some preliminary applications of these products. We also describe the next steps, such as improving the existing GLASS products, generating more climate data records (CDRs), broadening product dissemination, and fostering their wider utilization. The GLASS products are freely available to the public.

Discrepancies in the Simulated Global Terrestrial Latent Heat Flux from GLASS and MERRA-2 Surface Net Radiation Products

Surface all-wave net radiation (Rn) is a crucial variable driving many terrestrial latent heat (LE) models that estimate global LE. However, the differences between different Rn products and their impact on global LE estimates still remain unclear. In this study, we evaluated two Rn products, Global LAnd Surface Satellite (GLASS) beta version Rn and Modern-Era Retrospective Analysis for Research and Applications-version 2 (MERRA-2) Rn, from 2007–2017 using ground-measured data from 240 globally distributed in-situ radiation measurements provided by FLUXNET projects. The GLASS Rn product had higher accuracy (R2 increased by 0.04–0.26, and RMSE decreased by 2–13.3 W/m2) than the MERRA-2 Rn product for all land cover types on a daily scale, and the two Rn products differed greatly in spatial distribution and variations. We then determined the resulting discrepancies in simulated annual global LE using a simple averaging model by merging five diagnostic LE models: RS-PM model, SW model, PT-JPL model, MS-PT model, and SIM model. The validation results showed that the estimated LE from the GLASS Rn had higher accuracy (R2 increased by 0.04–0.14, and RMSE decreased by 3–8.4 W/m2) than that from the MERRA-2 Rn for different land cover types at daily scale. Importantly, the mean annual global terrestrial LE from GLASS Rn was 2.1% lower than that from the MERRA-2 Rn. Our study showed that large differences in satellite and reanalysis Rn products could lead to substantial uncertainties in estimating global terrestrial LE.

Performance of GLASS and MODIS Satellite Albedo Products in Diagnosing Albedo Variations during Different Time Scales and Special Weather Conditions in the Tibetan Plateau

Surface albedo is a crucial parameter in accurately and quantitatively estimating energy and water budget on the Tibetan Plateau (TP) and is also one of the largest radiative uncertainties in land surface modelling attempts. Based on an 8-year ground-based observation of the surface albedo over typical alpine meadows at Maqu and Maduo sites in the eastern TP, the performance of surface albedo products of Global LAnd Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS) in describing albedo variations at daily, 8-day, seasonal timescales, and during different special weather conditions were analyzed. Compared with the ground-based observation in Maqu, the 8-day albedo products from GLASS and MCD43B3 present maximum negative biases of −0.030 and −0.027 at Maqu, respectively. The black-sky albedo (BSA) of GLASS product coincides well with the ground-based observation in Maduo, with root mean square error (RMSE) of 0.092 and correlation coefficient (R) of 0.833, whereas that of MCD43B3 had an RMSE of 0.072 and R of 0.752. However, they are underestimated when the albedo is greater than 0.4. At the seasonal timescale, the BSA of GLASS and MCD43B3 underestimated the ground-based observation of Maqu by 0.015 in summer, while their white-sky albedo (WSA) are slightly overestimated and closer to the ground-based observation. In daily timescale, the response of surface albedo to soil moisture is different in semihumid and semiarid areas in summer. For both sites, the blue-sky-albedo of MCD43A3 has better agreement with the ground-based observation than GLASS and MCD43B3, as it improves the temporal resolution and calculates the albedo by weighting multiple observations within 16 days to be closer to the actual surface. However, even MCD43A3 could not capture the slowdown processes of albedo changes resulted by small snowfall processes or the snow aging due to cloud cover and inversion algorithms.

Spatiotemporal Comparison and Validation of Three Global-Scale Fractional Vegetation Cover Products

Fractional vegetation cover (FVC) is an important parameter for many environmental and ecological models. Large-scale and long-term FVC products are critical for various applications. Currently, several global-scale FVC products have been generated with remote sensing data, such as VGT bioGEOphysical product Version 2 (GEOV2), PROBA-V bioGEOphysical product Version 3 (GEOV3) and Global LAnd Surface Satellite (GLASS) FVC products. However, studies comparing and validating these global-scale FVC products are rare. Therefore, in this study, the performances of three global-scale time series FVC products, including the GEOV2, GEOV3, and GLASS FVC products, are investigated to assess their spatial and temporal consistencies. Furthermore, reference FVC data generated from high-spatial-resolution data are used to directly evaluate the accuracy of these FVC products. The results show that these three FVC products achieve general agreements in terms of spatiotemporal consistencies over most regions. In addition, the GLASS and GEOV2 FVC products have reliable spatial and temporal completeness, whereas the GEOV3 FVC product contains much missing data over high-latitude regions, especially during wintertime. Furthermore, the GEOV3 FVC product presents higher FVC values than GEOV2 and GLASS FVC products over the equator. The main differences between the GEOV2 and GLASS FVC products occur over deciduous forests, for which the GLASS product presents slightly higher FVC values than the GEOV2 product during wintertime. Finally, temporal profiles of the GEOV2 and GLASS FVC products show better consistency than the GEOV3 FVC product, and the GLASS FVC product presents more reliable accuracy (R2 = 0.7878, RMSE = 0.1212) compared with the GEOV2 (R2 = 0.5798, RMSE = 0.1921) and GEOV3 (R2 = 0.7744, RMSE = 0.2224) FVC products over these reference FVC data.

Simultaneous Estimation of Multiple Land-Surface Parameters From VIIRS Optical-Thermal Data

Traditional methods for estimating land-surface parameters from remotely sensed data generally focus on a single parameter with a specific spectral region, resulting in physical and spatiotemporal inconsistencies in current satellite products. We recently proposed a unified inversion scheme to estimate a suite of parameters simultaneously from both visible and near-infrared and thermal-infrared MODIS data. In this letter, we implemented this scheme to estimate six time-series parameters [leaf area index, fraction of absorbed photosynthetically active radiation, surface albedo, land-surface emissivity, land-surface temperature (LST), and upwelling longwave radiation (LWUP)] from the Visible Infrared Imaging Radiometer Suite (VIIRS) data. Several components of these schemes are refined, including the incorporation of a snow bidirectional reflectance distribution function model, determination of the best band combination, and better estimation of the snow-covered surface emissivity by accounting for the snow-cover fraction. Validation using the measurements at 12 sites of SURFRAD, CarboEuropeIP, and FLUXNET, and intercomparisons with MODIS and Global Land-Surface Satellite products, are carried out: the retrieved albedo, LST, and LWUP achieved accuracies ( $R^{2}$ ) of 0.77, 0.96, and 0.95, root mean square errors of 0.06, 2.9 K, and 18.3 W/m2, and biases of 0.01, 0.09 K, and −0.08 W/m2, respectively. The retrieved parameters can achieve comparable or higher accuracy than existing products, which indicates that the unified algorithm can be applied effectively to the VIIRS data with high physical and temporal consistency and accuracy.

Analysis of Differences in Phenology Extracted from the Enhanced Vegetation Index and the Leaf Area Index.

Remote-sensing phenology detection can compensate for deficiencies in field observations and has the advantage of capturing the continuous expression of phenology on a large scale. However, there is some variability in the results of remote-sensing phenology detection derived from different vegetation parameters in satellite time-series data. Since the enhanced vegetation index (EVI) and the leaf area index (LAI) are the most widely used vegetation parameters for remote-sensing phenology extraction, this paper aims to assess the differences in phenological information extracted from EVI and LAI time series and to explore whether either index performs well for all vegetation types on a large scale. To this end, a GLASS (Global Land Surface Satellite Product)-LAI-based phenology product (GLP) was generated using the same algorithm as the MODIS (Moderate Resolution Imaging Spectroradiometer)-EVI phenology product (MLCD) over China from 2001 to 2012. The two phenology products were compared in China for different vegetation types and evaluated using ground observations. The results show that the ratio of missing data is 8.3% for the GLP, which is less than the 22.8% for the MLCD. The differences between the GLP and the MLCD become stronger as the latitude decreases, which also vary among different vegetation types. The start of the growing season (SOS) of the GLP is earlier than that of the MLCD in most vegetation types, and the end of the growing season (EOS) of the GLP is generally later than that of the MLCD. Based on ground observations, it can be suggested that the GLP performs better than the MLCD in evergreen needleleaved forests and croplands, while the MLCD performs better than the GLP in shrublands and grasslands.

Integrating ASTER and GLASS broadband emissivity products using a multi-resolution Kalman filter

ABSTRACTIn this study, the multi-resolution Kalman filter (MKF) algorithm, which can handle multi-resolution problems with high computational efficiency, was used to blend two emissivity products: the Global LAnd Surface Satellite (GLASS) (BBE) product and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) narrowband emissivity (NBE) product. The ASTER NBE product was first converted into a BBE product. A new detrending method was used to transfer the BBEs into a process suitable for the MKF. The new detrending method was superior to the two existing methods. Finally, both the de-trended GLASS and ASTER BBE products were incorporated into the MKF framework to obtain the optimal estimation at each scale. Field measurements collected in North America were used to validate the integrated BBEs. Visually, the fusion map showed good continuity, with the exception of the border areas, and the quality of the fusion map was better than that of the original maps. The validation results indicate that the MKF improved the BBE product accuracy at the coarse scale. In addition, the MKF was capable of recovering missing pixels at a finer scale.

Generating Global LAnd Surface Satellite incident shortwave radiation and photosynthetically active radiation products from multiple satellite data

Incident shortwave radiation (ISR) at the surface is an essential parameter in the land surface radiation budget and in many land surface process models. Incident photosynthetically active radiation (PAR) is required by nearly all terrestrial ecosystem models. Although several global radiation products from numeric models and satellite observations have been released, their coarse spatial resolutions and low accuracy, especially at high altitude regions, limit their applications in land community. In this study, the Global LAnd Surface Satellite (GLASS) ISR and PAR products are developed based on an improved look-up table method from 2008 to 2010 at a 5km spatial resolution and a 3hour temporal resolution, the first global radiation products at such high resolutions, from multiple polar-orbiting and geostationary satellite data, including the Moderate Resolution Imaging Spectroradiometer (MODIS), the Meteosat Second Generation (MSG2) SEVIRI, the Multi-functional Transport Satellite (MTSAT)-1R, and the Geostationary Operational Environmental Satellite (GOES) Imager. The look-up table algorithm primarily considers the influences of surface elevation, atmospheric water vapor because sensitivity experiments indicate that ISR and PAR are more sensitive to surface elevation, and less sensitive to atmospheric profiles and ozone amount. The water vapor absorption has strong impact on ISR but weak on PAR. Moreover, the surface bi-directional reflectance distribution function (BRDF) is taken into account to retrieve spatially and temporally continuous surface reflectances from the geostationary satellite observations, which is an input parameter in the revised look-up table method. Ground-based measurement data from 34 sites are used to validate the improved algorithm and the GLASS products. The validation results of the instantaneous ISR and PAR products at all validation sites are notably good with coefficient of determination values of 0.83 and 0.84, respectively, and root mean square error values of 115.0Wm−2 and 49.0Wm−2, respectively. We also aggregate the GLASS ISR product for comparing with the corresponding ISCCP and CERES data at 7 SURFRAD sites and demonstrate that the GLASS ISR product is more accurate. The GLASS ISR and PAR products have been made publicly available.

Validation of global land surface satellite (GLASS) downward shortwave radiation product in the rugged surface

The downward shortwave radiation (DSR) is an essential parameter of land surface radiation budget and many land surface models that characterize hydrological, ecological and biogeochemical processes. The new Global LAnd Surface Satellite (GLASS) DSR datasets have been generated recently using multiple satellite data in China. This study investigates the performances of direct comparison approach, which is mostly used for validation of surface insolation retrieved from satellite data over the plain area, and indirect comparison approach, which needs a fine resolution map of DSR as reference, for validation of GLASS DSR product in time-steps of 1 and 3 hours over three Chinese Ecosystem Research Network sites located in the rugged surface. Results suggest that it probably has a large uncertainty to assess GLASS DSR product using the direct comparison method between GLASS surface insolation and field measurements over complex terrain, especially at Mt. Gongga 3,000 m station with root mean square error of 279.04 and 229.06 W/m(2) in time-steps of 1 and 3 hours, respectively. Further improvement for validation of GLASS DSR product in the rugged surface is suggested by generation of a fine resolution map of surface insolation and comparison of the aggregated fine resolution map with GLASS product in the rugged surface. The validation experience demonstrates that the GLASS DSR algorithm is satisfactory with determination coefficient of 0.83 and root mean square error of 81.91W/m(2) over three Chinese Ecosystem Research Network sites, although GLASS product overestimates DSR compared to the aggregated fine resolution map of surface insolation.