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.

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.

Long-term global land surface fractional vegetation cover (FVC) data are essential for global climate modeling, earth surface process simulations, and related applications. However, high quality and long time series global FVC products remain scarce, although several FVC products have been generated using remote sensing data. This study aims to use the previously proposed Global LAnd Surface Satellite (GLASS) FVC product from Moderate Resolution Imaging Spectroradiometer (MODIS) data (denoted as GLASS-MODIS FVC) to generate a long term GLASS FVC product from advanced very high resolution radiometer (AVHRR) data (denoted as GLASS-AVHRR FVC) back to year 1981. The GLASS-AVHRR FVC algorithm adopted the multivariate adaptive regression splines method, which was trained using samples extracted from the GLASS-MODIS FVC product and the corresponding red and near-infrared band reflectances of the preprocessed AVHRR reflectance data from 2003 over the global sampling locations. The GLASS-AVHRR FVC product has a temporal resolution of eight days and a spatial resolution of 0.05°. Through comparison of the GLASS-AVHRR and GLASS-MODIS FVC products from 2013, good temporal and spatial consistencies were observed, which confirmed the reliability of the GLASS-AVHRR FVC product. Furthermore, direct validation using field FVC measurement based reference data indicated that the performance of the GLASS-AVHRR FVC product (R ² = 0.834, RMSE = 0.145) was slightly superior to that of the popular long term GEOV1 FVC product (R ² = 0.799, RMSE = 0.174).

The three‐north region in China (northeastern, northwestern, and northern China) is one of the most environmentally vulnerable regions in the country. To improve the local natural environment, the Chinese government launched the Three‐North Shelter Forest Program, one of the largest afforestation/reforestation programs in the world. This program has led to significant changes in vegetation. Although many studies have evaluated the impacts of vegetation changes on local climate in this region, their results are highly inconsistent. In this study, evidence for local monthly climate impacts of vegetation change was investigated using remotely sensed data and ground meteorological measurements during the growing season (May to September) from 1982 to 2011 using the bivariate Granger causality test. The results showed that the local near‐surface climate is sensitive mostly to vegetation changes characterized by the normalized difference vegetation index (NDVI) in arid and semiarid regions and that vegetation plays a more important role in influencing hydroclimate in the arid/semiarid zones than in other zones, which has great implications for water resources in this dry region. Moreover, NDVI changes in northeastern China have a significantly negative influence on air tembut no other climatic variables, whereas the test results in northern China is not as objective as the other zones due to the rapid urbanization. All these results suggest that the local climate is very sensitive to the variations in vegetation in arid and semiarid regions, so extra caution should be taken when planting trees in this area.

The Clouds and the Earth’s Radiant Energy System (CERES) generates one of the few global satellite radiation products. The CERES ARM Validation Experiment (CAVE) has been providing long-term in situ observations for the validation of the CERES products. However, the number of these sites is low and their distribution is globally sparse, and particularly the surface net radiation product has not been rigorously validated yet. Therefore, additional validation efforts are highly required to determine the accuracy of the CERES radiation products. In this study, global land surface measurements were comprehensively collected for use in the validation of the CERES net radiation (R_n) product on a daily (340 sites) and a monthly (260 sites) basis, respectively. The validation results demonstrated that the CERES R_n product was, overall, highly accurate. The daily validations had a Mean Bias Error (MBE) of 3.43 W·m⁻², Root Mean Square Error (RMSE) of 33.56 W·m⁻², and R² of 0.79, and the monthly validations had an MBE of 3.40 W·m⁻², RMSE of 25.57 W·m⁻², and R² of 0.84. The accuracy was slightly lower for the high latitudes. Following the validation, the monthly CERES R_n product, from March 2000 to July 2014, was used for a further analysis. The global spatiotemporal variation of the R_n, which occurred during the measurement period, was analyzed. In addition, two hot spot regions, the southern Great Plains and south-central Africa, were then selected for use in determining the driving factors or attribution of the R_n variation. We determined that R_n over the southern Great Plains decreased by −0.33 W·m⁻² per year, which was mainly driven by changes in surface green vegetation and precipitation. In south-central Africa, R_n decreased at a rate of −0.63 W·m⁻² per year, the major driving factor of which was surface green vegetation.

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.