Solar radiation incident at the Earth’s surface (R_s) is an essential component of the total energy exchange between the atmosphere and the surface. Reanalysis data have been widely used, but a comprehensive validation using surface measurements is still highly needed. In this study, we evaluated the R_s estimates from six current representative global reanalyses (NCEP–NCAR, NCEP-DOE; CFSR; ERA-Interim; MERRA; and JRA-55) using surface measurements from different observation networks [GEBA; BSRN; GC-NET; Buoy; and CMA] (674 sites in total) and the Earth’s Radiant Energy System (CERES) EBAF product from 2001 to 2009. The global mean biases between the reanalysis R_s and surface measurements at all sites ranged from 11.25 W/m² to 49.80 W/m². Comparing with the CERES-EBAF R_s product, all the reanalyses overestimate R_s, except for ERA-Interim, with the biases ranging from −2.98 W/m² to 21.97 W/m² over the globe. It was also found that the biases of cloud fraction (CF) in the reanalyses caused the overestimation of R_s. After removing the averaged bias of CERES-EBAF, weighted by the area of the latitudinal band, a global annual mean R_s values of 184.6 W/m², 180.0 W/m², and 182.9 W/m² were obtained over land, ocean, and the globe, respectively. Full article

Mapping surface all-wave net radiation (R_n) is critically needed for various applications. Several existing R_n products from numerical models and satellite observations have coarse spatial resolutions and their accuracies may not meet the requirements of land applications. In this study, we develop the Global LAnd Surface Satellite (GLASS) daytime R_n product at a 5 km spatial resolution. Its algorithm for converting shortwave radiation to all-wave net radiation using the Multivariate Adaptive Regression Splines (MARS) model is determined after comparison with three other algorithms. The validation of the GLASS R_n product based on high-quality in situ measurements in the United States shows a coefficient of determination value of 0.879, an average root mean square error value of 31.61 Wm⁻², and an average bias of −17.59 Wm⁻². We also compare our product/algorithm with another satellite product (CERES-SYN) and two reanalysis products (MERRA and JRA55), and find that the accuracy of the much higher spatial resolution GLASS R_n product is satisfactory. The GLASS R_n product from 2000 to the present is operational and freely available to the public. Full article

Accurate estimation of latent heat flux (LE) is critical in characterizing semiarid ecosystems. Many LE algorithms have been developed during the past few decades. However, the algorithms have not been directly compared, particularly over global semiarid ecosystems. In this paper, we evaluated the performance of five LE models over semiarid ecosystems such as grassland, shrub, and savanna using the Fluxnet dataset of 68 eddy covariance (EC) sites during the period 2000–2009. We also used a modern-era retrospective analysis for research and applications (MERRA) dataset, the Normalized Difference Vegetation Index (NDVI) and Fractional Photosynthetically Active Radiation (FPAR) from the moderate resolution imaging spectroradiometer (MODIS) products; the leaf area index (LAI) from the global land surface satellite (GLASS) products; and the digital elevation model (DEM) from shuttle radar topography mission (SRTM30) dataset to generate LE at region scale during the period 2003–2006. The models were the moderate resolution imaging spectroradiometer LE (MOD16) algorithm, revised remote sensing based Penman–Monteith LE algorithm (RRS), the Priestley–Taylor LE algorithm of the Jet Propulsion Laboratory (PT-JPL), the modified satellite-based Priestley–Taylor LE algorithm (MS-PT), and the semi-empirical Penman LE algorithm (UMD). Direct comparison with ground measured LE showed the PT-JPL and MS-PT algorithms had relative high performance over semiarid ecosystems with the coefficient of determination (R²) ranging from 0.6 to 0.8 and root mean squared error (RMSE) of approximately 20 W/m². Empirical parameters in the structure algorithms of MOD16 and RRS, and calibrated coefficients of the UMD algorithm may be the cause of the reduced performance of these LE algorithms with R² ranging from 0.5 to 0.7 and RMSE ranging from 20 to 35 W/m² for MOD16, RRS and UMD. Sensitivity analysis showed that radiation and vegetation terms were the dominating variables affecting LE Fluxes in global semiarid ecosystem. Full article

The temporal and spatial characteristics of meteorological drought have been investigated to provide a framework of methodologies for the analysis of drought in the Beijing-Tianjin-Hebei metropolitan area (BTHMA) in China. Using the Reconnaissance Drought Index (RDI) as an indicator of drought severity, the characteristics of droughts have been examined. The Beijing-Tianjin-Hebei metropolitan area was divided into 253 grid-cells of 27 × 27km and monthly precipitation data for the period of 1960–2010 from 33 meteorological stations were used for global interpolation of precipitation using spatial co-ordinate data. Drought severity was assessed from the estimated gridded RDI values at multiple time scales. Firstly, the temporal and spatial characteristics of droughts were analyzed, and then drought severity-areal extent-frequency (SAF) annual curves were developed. The analysis indicated that the frequency of moderate and severe droughts was about 9.10% in the BTHMA. Using the SAF curves, the return period of selected severe drought events was assessed. The identification of the temporal and spatial characteristics of droughts in the BTHMA will be useful for the development of a drought preparedness plan in the region. Full article

Temporal-related features are important for improving land cover classification accuracy using remote sensing data. This study investigated the efficacy of phenological features extracted from time series MODIS Normalized Difference Vegetation Index (NDVI) data in improving the land cover classification accuracy of Landsat data. The MODIS NDVI data were first fused with Landsat data via the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to obtain NDVI data at the Landsat spatial resolution. Next, phenological features, including the beginning and ending dates of the growing season, the length of the growing season, seasonal amplitude, and the maximum fitted NDVI value, were extracted from the fused time series NDVI data using the TIMESAT tool. The extracted data were integrated with the spectral data of the Landsat data to improve classification accuracy using a maximum likelihood classifier (MLC) and support vector machine (SVM) classifier. The results indicated that phenological features had a statistically significant effect on improving the land cover classification accuracy of single Landsat data (an approximately 3% increase in overall classification accuracy), especially for vegetation type discrimination. However, the phenological features did not improve on statistical measures including the maximum, the minimum, the mean, and the standard deviation values of the time series NDVI dataset, especially for human-managed vegetation types. Regarding different classifiers, SVM could achieve better classification accuracy than the traditional MLC classifier, but the improvement in accuracy obtained using advanced classifiers was inferior to that achieved by involving the temporally derived features for land cover classification. Full article

Although deforestation affects hydrological and climatic variables over tropical regions, its actual contributions to changes in evapotranspiration (ET) over subarctic China remain unknown. To establish a quantitative relationship between deforestation and terrestrial ET variations, we estimated ET using a semi-empirical Penman (SEMI-PM) algorithm driven by meteorological and satellite data at both local and regional scales. The results indicate that the estimated ET can be used to analyse the observed inter-annual variations. There is a statistically significant positive relationship between local-scale forest cover changes (∆F) and annual ET variations (∆ET) of the following form: ∆ET = 0.0377∆F – 2.11 (R² = 0.43, p < 0.05). This relationship may be due to deforestation-induced increases in surface albedo and a reduction in the fractional vegetation cover (FVC). However, the El Niño/Southern Oscillation (ENSO), rather than deforestation, dominates the multi-decadal ET variability due to regional-scale wind speed changes, but the exact effects of deforestation and ENSO on ET are challenging to quantify. Full article

Potential evapotranspiration (PET) is an important indicator of atmospheric evaporation demand and has been widely used to characterize hydrological change. However, sparse observations of pan evaporation (EP) prohibit the accurate characterization of the spatial and temporal patterns of PET over large spatial scales. In this study, we have estimated PET of China using the Penman-Monteith (PM) method driven by gridded reanalysis datasets to analyze the spatial and decadal variations of PET in China during 1982–2010. The results show that the estimated PET has decreased on average by 3.3 mm per year (p < 0.05) over China during 1982–1993, while PET began to increase since 1994 by 3.4 mm per year (p < 0.05). The spatial pattern of the linear trend in PET of China illustrates that a widely significant increasing trend in PET appears during 1982–2010 in Northwest China, Central China, Northeast China and South China while there are no obvious variations of PET in other regions. Our findings illustrate that incident solar radiation (Rs) is the largest contributor to the variation of PET in China, followed by vapor pressure deficit (VPD), air temperature (Tair) and wind speed (WS). However, WS is the primary factor controlling inter-annual variation of PET over Northwest China. Full article

Satellite-based vegetation indices (VIs) and Apparent Thermal Inertia (ATI) derived from temperature change provide valuable information for estimating evapotranspiration (LE) and detecting the onset and severity of drought. The modified satellite-based Priestley-Taylor (MS-PT) algorithm that we developed earlier, coupling both VI and ATI, is validated based on observed data from 40 flux towers distributed across the world on all continents. The validation results illustrate that the daily LE can be estimated with the Root Mean Square Error (RMSE) varying from 10.7 W/m² to 87.6 W/m², and with the square of correlation coefficient (R²) from 0.41 to 0.89 (p < 0.01). Compared with the Priestley-Taylor-based LE (PT-JPL) algorithm, the MS-PT algorithm improves the LE estimates at most flux tower sites. Importantly, the MS-PT algorithm is also satisfactory in reproducing the inter-annual variability at flux tower sites with at least five years of data. The R² between measured and predicted annual LE anomalies is 0.42 (p = 0.02). The MS-PT algorithm is then applied to detect the variations of long-term terrestrial LE over Three-North Shelter Forest Region of China and to monitor global land surface drought. The MS-PT algorithm described here demonstrates the ability to map regional terrestrial LE and identify global soil moisture stress, without requiring precipitation information. Full article

This study compared three broadband emissivity (BBE) datasets from satellite observations. The first is a new global land surface BBE dataset known as the Global Land Surface Satellite (GLASS) BBE. The other two are the North American ASTER Land Surface Emissivity Database (NAALSED) BBE and University of Wisconsin Global Infrared Land Surface Emissivity Database (UWIREMIS) BBE, which were derived from two independent narrowband emissivity products. Firstly, NAALSED BBE was taken as the reference to evaluate the GLASS BBE and UWIREMIS BBE. The GLASS BBE was more close to NAALSED BBE with a bias and root mean square error (RMSE) of −0.001 and 0.007 for the summer season, −0.001 and 0.008 for the winter season, respectively. Then, the spatial distribution and seasonal pattern of global GLASS BBE and UWIREMIS BBE for six dominant land cover types were compared. The BBE difference between vegetated areas and non-vegetated areas can be easily seen from two BBEs. The seasonal variation of GLASS BBE was more reasonable than that of UWIREMIS BBE. Finally, the time series were calculated from GLASS BBE and UWIREMIS BBE using the data from 2003 through 2010. The periodic variations of GLASS BBE were stronger than those of UWIREMIS BBE. The long time series high quality GLASS BBE can be incorporated in land surface models for improving their simulation results. Full article