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The Chinese GF-1 satellite, the first satellite of the China High-resolution Earth Observation System launched in 2013, can be used to help estimate evapotranspiration (LE), which is important for myriad hydroclimatic and ecosystem science and applications. We propose a novel approach to use the GF-1 visible and near-infrared (VNIR) measurements at 16 m and 4-day resolutions to estimate LE. The NIR (near-infrared)–red spectral-domain (NRSD) model is coupled to a perpendicular soil moisture index (PSI) and a perpendicular vegetation index (PVI). We applied the model to the Huailai agricultural region of China with 55 scenes of GF-1 imagery during 2013–2017 and validated using ground measurements with footprint models for two eddy-covariance (EC) flux tower sites and one large aperture scintillometer (LAS) site. The results illustrate that the terrestrial daily LE can be estimated with squared correlation coefficients ( $R^{2}$ ) of 0.77–0.84 ( $p ) and root-mean-square error (RMSE) values of 17.9–21.5 W/m2 among all three sites. The site-calibrated statistics are improved by 0.14–0.25 for $R^{2}$ and decreased by 4.2–8.3 W/m2 for RMSE as compared to the commonly used universal PT-JPL model. A satisfactory performance is achieved across all experimental conditions, encouraging the application of the NRSD model to estimate LE for other broad regions.

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.

Fractional vegetation cover (FVC) is a critical land surface parameter, and several large-scale FVC products have been generated based on remote sensing data. Among these existing products, the global land surface satellite (GLASS) FVC product, derived from moderate resolution imaging spectroradiometer (MODIS) 500 m reflectance data (MOD09A1), has achieved complete spatial-temporal continuity and satisfying accuracy. To further improve the spatial resolution of GLASS FVC product, this study developed a novel FVC estimation algorithm for MODIS 250 m reflectance data based on a recurrent neural network with the long short-term memory unit (RNN-LSTM). The RNN-LSTM was established using sequence training samples derived from the MODIS 250 m reflectance and GLASS FVC products, which were conducted over three vegetation types in mid-West China. Additionally, two machine learning methods, including the back propagation neural network (BPNN) and multivariate adaptive regression splines (MARS), were used to compare with the proposed method. The evaluation results showed that RNN-LSTM derived FVC had reliable spatial-temporal continuity and good consistency with the GLASS FVC product. Furthermore, the smooth temporal profiles of the RNN-LSTM FVC estimation indicated that the proposed method was capable of capturing the temporal characteristics of vegetation growth and reducing the uncertainties from the atmosphere and radiation. Finally, an independent validation case in the Heihe area indicated that the RNN-LSTM algorithm achieved the best accuracy (R2 = 0.8081, rmse = 0.0951) compared with the BPNN (R2 = 0.7320, rmse = 0.1127) and MARS (R2 = 0.7361, rmse = 0.1117). This study provides a new approach by showing the potential of the RNN-LSTM method for land surface parameter estimation and related research.

Fractional vegetation cover (FVC) is considered one of the most important vegetation parameters and is relevant to characterizing vegetation status and ecosystem function. An FVC with a fine spatial resolution of 30 m is essential for monitoring vegetation change and regional studies, while an FVC with a coarse spatial resolution of hundreds to thousands of metres plays an important role in global change studies. However, high spatial resolution data usually have low temporal resolution and are often affected by cloud cover. The objective of this study is to propose a practical way to generate spatiotemporally consistent FVC products at Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) scales, which are 30 m and 250 m, respectively. The geostatistical neighbourhood similar pixel interpolator (GNSPI) was first used to fill in the missing values caused by unscanned gaps and clouds/shadows on Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data and to generate spatially continuous Landsat reflectance. Then, the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was used to generate time series Landsat reflectance data with the same temporal resolution as that of Global LAnd Surface Satellite (GLASS) FVC generated from MODIS data. The high temporal resolution Landsat reflectance was preliminarily used to estimate FVC at the Landsat scale. Finally, MultiResolution Tree (MRT) was employed to fuse the Landsat FVC and GLASS FVC to generate spatiotemporally consistent FVC products at different scales. The results show that the missing Landsat-7 ETM+ data were filled well and spatial texture features were well preserved. The temporal resolutions of the Landsat and GLASS FVC products became consistent with an interval of one day at most. After MRT fusion, most of the root mean square error (RMSE) between the GLASS FVC and aggregated Landsat FVC dramatically decreased. The accuracy of the Landsat FVC validated by the ground-measured FVC improved after MRT fusion (before MRT: RMSE = 0.1031, R2 = 0.9172, bias = −0.0697; after MRT: RMSE = 0.0958, R2 = 0.9173, bias = −0.054). In addition, in the GNSPI-filled unscanned gaps and the ESTARFM-generated images, the Landsat FVC accuracy also improved slightly (before MRT: RMSE = 0.1065, R2 = 0.9011, bias = −0.0644; after MRT: RMSE = 0.1022, R2 = 0.9023, bias = −0.051). The accuracy of the GLASS FVC also improved (before MRT: RMSE = 0.0913, R2 = 0.884, bias = −0.0504; after MRT: RMSE = 0.0673, R2 = 0.9483, bias = −0.0444). Therefore, MRT could decrease the inconsistencies of different scales and reduce uncertainties in the FVC. In addition, MRT could fill in the missing data of the Landsat FVC directly, but there were a certain number of outliers in the fusion results, and the spatial transition was poor.

Accurate estimation of terrestrial latent heat flux (LE) at high spatial resolution is of great vital importance for energy balance and water resource management, especially for agricultural produc...

Surface shortwave net radiation (SSNR) and surface downward shortwave radiation (DSR) are the two surface shortwave radiation components in earth’s radiation budget and the fundamental quantities of energy available at the earth’s surface. Although several global radiation products from global circulation models, global reanalyses, and satellite observations have been released, their coarse spatial resolutions and low accuracies limit their application. In this paper, the Global LAnd Surface Satellite (GLASS) DSR product was generated from the Moderate Resolution Imaging Spectroradiometer top-of-atmosphere (TOA) spectral reflectance based on a direct-estimation method. First, the TOA reflectances were derived based on the atmospheric radiative transfer simulations under different solar/view geometries; second, a linear regression relationship between the TOA reflectance and SSNR was developed under various atmospheric conditions and surface properties for different solar/view geometries; third, the coefficients derived from the linear regression were used to compute the SSNR; and finally, the DSR was estimated using the SSNR estimates and broadband albedo at the surface. A 13-year (2003–2015) GLASS DSR product was generated at a 5-km spatial resolution and 1-day temporal resolution. Compared with the ground measurements collected from 525 stations from 2003 to 2005 around the world, the model-computed SSNR (DSR) had an overall bias of 8.82 (3.72) W/m2 and a root mean square error of 28.83 (32.84) W/m2 at the daily time scale. Moreover, the global land annual mean of the DSR was determined to be 184.8 W/m2 with a standard deviation of 0.8 W/m2 over a 13-year (2003–2015) period.

The newly launched GF-2 satellite is now the most advanced civil satellite in China to collect high spatial resolution remote sensing data. This study investigated the capability and strategy of GF-2 multispectral data for land use and land cover (LULC) classification in a region of the North China Plain. The pixel-based and object-based classifications using maximum likelihood (MLC) and support vector machine (SVM) classifiers were evaluated to determine the classification strategy that was suitable for GF-2 multispectral data. The validation results indicated that GF-2 multispectral data achieved satisfactory LULC classification performance, and object-based classification using the SVM classifier achieved the best classification accuracy with an overall classification accuracy of 94.33% and kappa coefficient of 0.911. Therefore, considering the LULC classification performance and data characteristics, GF-2 satellite data could serve as a valuable and reliable high-resolution data source for land surface monitoring. Future works should focus on improving LULC classification accuracy by exploring more classification features and exploring the potential applications of GF-2 data in related applications.

The accurate estimation of the terrestrial latent heat flux (LE) from satellite observations at high spatial and temporal scales plays an important role in the assessment of the water and heat exchange between the earth’s surface and the atmosphere. Although a variety of data fusion methods have been proposed to merge different LE products for more reliable estimates, most of them have ignored the spatiotemporal consistency of LE products across different resolutions. In this paper, we apply the multiresolution tree (MRT) method to improve the accuracy and reduce the inconsistency between the Moderate Resolution Imaging Spectroradiometer (MODIS) LE (MOD16) product and the Landsat-based LE product at different resolutions. Eddy covariance (EC) ground measurements at five sites, MODIS and Landsat images from January 2005 to December 2005 in the north central USA, are used to evaluate the performance of the MRT method. The results show that the MRT method can improve the accuracy of the original LE products (MOD16 and Landsat), and it has the potential to significantly reduce the uncertainty and inconsistency of these products. The bias decreased by 38.3% on average, and the root-mean-square error (RMSE) decreased by approximately 49.2% after the MRT was applied at each scale. Further studies are still required to make the MRT method more universal on a variety of land cover types for long-time periods.

The daytime surface net radiation ( $R_{n}$ ) product from version 4.0 Global LAnd Surface Satellite (GLASS) product suite was recently generated from Moderate Resolution Imaging Spectroradiometer data. It is the daytime average product of $R_{n}$ derived from 2000 to 2015 at a spatial resolution of 0.05°. This letter describes the results of validation of this new $R_{n}$ product using ground measurements collected from 142 sites distributed worldwide. The overall accuracy of the GLASS daytime $R_{n}$ product was satisfactory, with an $R^{2}$ of 0.80, root-mean-square error of 51.35 Wm $^{-2}$ , and mean bias error of 0.11 Wm $^{-2}$ . Its accuracy and quality were highly consistent for different land cover classes and elevation zones.

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 (R2 = 0.834, RMSE = 0.145) was slightly superior to that of the popular long term GEOV1 FVC product (R2 = 0.799, RMSE = 0.174).

Downward shortwave radiation (DSR) is one of the major driving forces of climate system. Knowledge of the Earth’s radiation budget is essential for improving our understanding of the Earth’s climate. Therefore, accurate estimation of DSR has great significance. Satellite remote sensing is a practical way to derive DSR with high spatial resolution and coverage. In this study, four machine learning methods, including gradient boosting regression tree (GBRT), random forest (RF), multivariate adaptive regression spline (MARS), and artificial neural network (ANN), were applied to estimate DSR at a spatial resolution of 5 km and a temporal resolution of 1 day using Advanced Very High Resolution Radiometer (AVHRR) data. The DSR estimates based on four machine learning methods were evaluated using ground measurements at 96 sites over China. The measurements were collected from the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA) from 2001 to 2003. The evaluation results showed that the GBRT method performed best at both daily and monthly time scales under both clear and cloudy sky conditions. The validation results at the daily time scale showed an overall root mean square error (RMSE) of 30.34 W m−2 and an R value of 0.90 under clear sky conditions, whereas these values were 42.03 W m−2 and 0.86, respectively, under cloudy sky conditions. The DSR estimates had an overall RMSE value of 16.93 W m−2 and an R value of 0.97 at the monthly time scale. The Clouds and Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) data sets were also used for comparison with the DSR estimates based on the GBRT method. The DSR estimates based on the GBRT method exhibited similar spatial distributions with those of the CERES-EBAF DSR product. Moreover, the DSR estimates based on the GBRT method did not show a clear overestimation tendency, as the CERES-EBAF DSR product did, at the CDC/CMA sites.

Validation of remotely sensed evapotranspiration (RS_ET) products is important because their accuracy is critical for various scientific applications. In this study, an integrated validation framework was proposed for evaluating RS_ET products with coarse spatial resolution extending from homogenous to heterogeneous land surfaces. This framework was applied at the pixel and river basin scales, using direct and indirect validation methods with multisource validation datasets, which solved the spatial mismatch between ground measurements and remotely sensed products. The accuracy, rationality of spatiotemporal variations, and error sources of RS_ET products and uncertainties during the validation process were the focuses in the framework. The application of this framework is exemplified by validating five widely used RS_ET products (i.e., GLEAM, DTD, MOD16, ETMonitor, and GLASS) in the Heihe River Basin from 2012 to 2016. Combined with the results from direct (as the priority method) and indirect validation (as the auxiliary method), DTD showed the highest accuracy (1-MAPE) in the vegetation growing season (75%), followed by ETMonitor (71%), GLASS (68%), GLEAM (54%), and MOD16 (44%). Each product reasonably reflected the spatiotemporal variations in the validation dataset. ETMonitor exhibited the highest consistency with the ground truth ET at the basin scale (ETMap) (R = 0.69), followed by GLASS (0.65), DTD (0.63), MOD16 (0.62), and GLEAM (0.57). Error sources of these RS_ET products were mainly due to the limitations of the algorithms and the coarse spatial resolution of the input data, while the uncertainties in the validation process amounted to 15–28%. This work is proposed to effectively validate and improve the RS_ET products over heterogeneous land surfaces.

Metropolis’ contribution (anthropogenic aerosols) to solar dimming and brightening remains a hot topic of special concern over the past several decades. However, urbanization effects on surface incident solar radiation (R s) have not been comprehensively investigated. In this study, the urbanization effects on solar dimming and brightening were addressed using the densely distributed reconstructed R s data at 375 stations and 92 urban–rural station pairs over the time period of 1960–2019 in China. The results indicate that the impacts of urbanization on the monthly mean R s is 0.86 ± 7.99 W m−2 during the study period, while the impact is 0.90 ± 8.30 W m−2 and 0.82 ± 8.26 W m−2 for the solar dimming (1960–1992) and brightening (1992–2019) phase, respectively. The urbanization effects on the trend of R s is −0.39 and 0.16 W m−2 per decade during dimming and brightening, respectively. It also found that urbanization effects on R s trend differs strikingly in magnitudes for specific regions in China. Generally, urbanization speeds up China’s dimming in the dimming phase and slows down China’s brightening in the brightening phase.

Advanced remote sensing technology has provided spatially distributed variables for estimating land–ocean heat fluxes, allowing for practical applications in drought monitoring, water resources management, and climate assessment. This Special Issue includes several research studies using state-of-the-art algorithms for estimating downward longwave radiation, surface net radiation, latent heat flux, columnar atmospheric water vapor, fractional vegetation cover, and grassland aboveground biomass. This Special Issue intends to help scientists involved in global change research and practices better comprehend the strengths and disadvantages of the application of remote sensing for monitoring surface energy, water, and carbon budgets. The studies published in this Special Issue can be applied by natural resource management communities to enhance the characterization and assessment of land–ocean biophysical variables, as well as for more accurately partitioning heat flux into soil and vegetation based on the existing and forthcoming remote sensing data.

Soil moisture is a key factor affecting crop growth and crop yield. Information on soil moisture is critical for crop growth monitoring and yield estimation. This study evaluated the OPtical TRApezoid Model (OPTRAM) on soil moisture estimates in the Sanjiang Plain, China, using Moderate Resolution Imaging Spectroradiometer (MODIS) data and in situ soil moisture data acquired from May to September in 2016 and 2017. This model was empirically calculated by parameterizing the relationship between the normalized difference vegetation index (NDVI) and shortwave infrared transformed reflectance (STR). In addition, the precipitation data were collected and used for verification of the results. According to the scatterplots of STR and NDVI, OPTRAM can estimate soil moisture from July to September, while it's inapplicable in May and June. Evaluation results indicated that OPTRAM‐based soil moisture estimates provide overall RMSE from 0.05 to 0.13 cm3/cm3, bias from −0.11 to 0.06 cm3/cm3, and R2 from 0.10 to 0.50, respectively, for all investigated sites. The performance of OPTRAM compared with the precipitation data showed good agreement. In conclusion, OPTRAM could be used to estimate soil moisture with reasonable accuracy in most areas from July to September in Sanjiang Plain.

The latent heat flux (LE) governs the associated heat flux from the interactions between the land surface and its atmosphere and plays an important role in the surface water and energy balance. The Qilian Mountains is the largest marginal mountain system in the northeast of the Qinghai-Tibet Plateau. An accurate representation of spatio-temporal patterns of LE over Qilian Mountains is essential in many terrestrial biosphere, hydrosphere, and atmosphere applications. Various satellite-derived LE products have been widely used to estimate terrestrial LE, yet each individual LE product exhibits large discrepancies. To reduce uncertainties from individual product and improve terrestrial LE estimation over Qilian Mountains, we produced five satellite-derived LE products using traditional algorithms based on Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI, LAI products and China Meteorological Forcing Dataset (CMFD), and implemented the fusion of these five LE products using Extremely Randomized Trees (Extra-Trees) combining information from ground-based measurements. A validation using ground-based measurements was applied at different plant function types and the validation results demonstrate that the fusion product using Extra-Trees outperformed all the LE products used in the fusion method. Comparing with three other machine learning fusion models such as Gradient Boosting Regression Tree (GBRT), Random Forest (RF) and Gaussian Process Regression (GPR), Extra-Trees exhibits the best performance in terms of both training and validation accuracy. This fusion LE product also outperformed when compared against two state-of-the-art global LE products such as Global Land Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS). The fusion LE product showed improvements in the linear correlation, bias and RMSE at different plant function types. Our results suggest that the fusion method using Extra-Trees could be successfully applied to other region and to enhance the estimation of other hydrometeorological variables.

The study of law of crop water consumption in small scale such as irrigation area requires remote sensing image data with high spatial and temporal resolution, however, remote sensing images that possess both high temporal and spatial resolution cannot be obtained for technical reasons. To solve the problem, this paper present a multisource remote sensing data spatial and temporal reflectance fusion method based on fuzzy C clustering model (FCMSTRFM) and multisource Vegetation index (VI) data spatial and temporal fusion model (VISTFM), the Landsat8 OLI and MOD09GA data are combined to generate high spatial and temporal resolution reflectance data and the landsat8 OLI, MOD09GA and MOD13Q1 data are combined to generate high spatial and temporal resolution normalized vegetation index (NDVI) and enhanced vegetation index (EVI) data.The rice area is mapped by spectral correlation similarity (SCS) between standard series EVI curve that based the EVI generated by VISTFM and average value of each EVI class that generated by classing Multiphase EVI into several class, the extraction results are verified by two methods: ground sample and Google Earth image. high spatial and temporal resolution Leaf area index (LAI) that covered the mainly rice growth and development stages is generated by higher precision method between artificial neural network and equation fitting that establish the relationship between NDVI, EVI and LAI. The yield of rice in the spatial scale is generated by establishing the relationship between yield and LAI of the mainly growth and development stages that has the maximum correlation with yield. Daily high spatial resolution evapotranspiration is generated by using multisource remote sensing data spatial and temporal reflectance fusion method to fusion the MODIS-like scale and Landsat-like scale evapotranspiration that generated by The Surface Energy Balance Algorithm for Land (SEBAL). Based on the data, the evapotranspiration, LAI and yield of rice, obtained by remote sensing methods, rice water growth function is established by Jensen, Blank, Stewart and Singh model.

Rocky desertification (RD) is a special process of land deterioration in karst topography, with a view of bedrock exposure and an effect of ecological degradation. Among the three largest karst regions in the world, southwest China boasts the largest RD area and highest diversity of karst landscapes. However, inefficient field surveying tends to restrict earlier studies of RD to local areas, and the high complexity of karst geomorphology in southwest China further lead to the shortage of the knowledge about its macroecological pattern so far. To address this gap, this study innovatively took county as the unit to statistically explore the links between the 2008-censused distributions of county-level RD in southwest China and its potential impact factors of three kinds (geologic, climatic, and anthropogenic), all transformed into the same mapping frame. Spatial pattern analyses based on spatial statistics and artificial interpretation unveiled the macroscopic characteristics of RD spatial patterns, and attribution analyses based on correlation analysis and dominance analysis exposed the links of the impact factors to RD and their contributions in deciding the macroscopic pattern of RD. The results suggested that geologic factors play a first role in drawing the macroecological pattern of RD, also for the slight-, moderate-, and severe-level RD scenarios, in southwest China. Despite this inference somehow collides with the popular awareness that anthropogenic factors like human activities are leadingly responsible for the RD-relevant losses, the findings are of practical implications in guiding making the macroscopic policies for mitigating RD degradation and advancing its environmental restoration.

To investigate the long-term characteristics of precipitation in Xinjiang, China, two long-term monthly satellite precipitation datasets called CHIRPS (Climate Hazards Group Infrared Precipitation with Stations data) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record) are evaluated and compared with in situ measurements from 105 meteorological stations for the period 1983–2014. The evaluation is performed at multiple temporal and spatial scales. Results based on comparisons with in situ measurements show that PERSIANN-CDR and CHIRPS have similar correlations. However, both of the BIAS and RMSE, CHIRPS outperformed PERSIANN-CDR with the smaller errors and bias. In terms of the long time-series comparison at temporal scale, CHIRPS is more accurate with gauge observations at monthly and annual scales while PERSIANN-CDR tends to overestimate the precipitation in the rain season (from May to September). Furthermore, compared with PERSIANN-CDR, results show that CHIRPS is more accurate in reflecting the spatial distribution of average monthly and annual precipitation. In summary, the study shows that CHIRPS is a valuable complement to gauge precipitation data and provides useful guidance when choosing satellite precipitation product for hydrometeorological applications in Xinjiang.

Fractional vegetation cover (FVC) is an important parameter for describing the land surface vegetation conditions and widely used for land surface process simulations and global change studies. Glo...

Climate Hazards Group Infrared Precipitation with Stations data (CHIRPS) rainfall dataset was early evaluated and compared with 29 meteorological stations over the Haihe River basin in China, for the period 1981–2015. Seven statistical and categorical metrics were applied to evaluate the performance of CHIRPS with gauge measurements at multi-time scales (monthly, seasonally and annually). Using the Standardized Precipitation Index (SPI) as the drought indicator, the applicability of this new long-term satellite precipitation product for drought monitoring was investigated in this study. Results indicate that the good performances were performed at multiple temporal scales (monthly, seasonally and annually). Although it tends to overestimate the higher precipitation in this region, CHIRPS demonstrated good agreement (R2 > 0.70) with gauge observations at monthly scale and greater agreements (R2 > 0.78) at seasonal and annual scales. Meanwhile, CHIRPS performed a good score of BIAS and lower error in a majority of months at multi-time scales. Because of its good performance at multi-time scales and the advantages of high spatial resolution and long-time record, CHIRPS was applied to derive the SPI over the Haihe River basin. It is evaluated and compared with stations observations to derive SPI at time scale of 1, 3 and 6 months. The results indicate that it performed good ability to monitor drought (R2 > 0.70) and successfully captured the historical drought years (1981, 1999, 2001 and 2012). Overall, this study concludes that CHIRPS can be a valuable complement to gauge precipitation data for estimating precipitation and drought monitoring in this region.

Evapotranspiration (ET) is the combination process of the surface evaporation and plant transpiration, which occur simultaneously, and it links the terrestrial water cycles, carbon cycles, and energy exchange. In this study, based on the observations from 242 global FLUXnet sites, with daily mean temperature, relative humidity, net radiation, wind speed, incoming shortwave radiation, maximum temperature, minimum temperature, normalized difference vegetation index, altitude, difference in temperature, and observed ET as input data, we used a support vector machine and a semiempirical algorithm to estimate the land surface daily ET at nine different vegetation-type sites. Subsequently, based on the meteorological reanalysis data combined with remote sensing data, we estimated regional land surface ET of China during 1982–2010. The results showed that, for all vegetation-type sites, when the predicted ET was validated with the eddy covariance measurements, the support vector machine algorithm undervalued ET while the semiempirical algorithm overvalued ET. When five indicators and the second classification method were selected, the semiempirical algorithm probably could explain 56%–76% of the land surface ET change, whereas the support vector machine algorithm probably could explain 71%–85%. The regional values of annual daily average ET varied from 5.8 to 110.5 W/m2, and the land surface ET overall trend decreased from the southeast to the northwest in China.

Accurate estimation of precipitation from satellites at high spatiotemporal scales over the Tibetan Plateau (TP) remains a challenge. In this study, we proposed a general framework for blending multiple satellite precipitation data using the dynamic Bayesian model averaging (BMA) algorithm. The blended experiment was performed at a daily 0.25° grid scale for 2007–2012 among Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT and 3B42V7, Climate Prediction Center MORPHing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). First, the BMA weights were optimized using the expectation-maximization (EM) method for each member on each day at 200 calibrated sites and then interpolated to the entire plateau using the ordinary kriging (OK) approach. Thus, the merging data were produced by weighted sums of the individuals over the plateau. The dynamic BMA approach showed better performance with a smaller root-mean-square error (RMSE) of 6.77 mm/day, higher correlation coefficient of 0.592, and closer Euclid value of 0.833, compared to the individuals at 15 validated sites. Moreover, BMA has proven to be more robust in terms of seasonality, topography, and other parameters than traditional ensemble methods including simple model averaging (SMA) and one-outlier removed (OOR). Error analysis between BMA and the state-of-the-art IMERG in the summer of 2014 further proved that the performance of BMA was superior with respect to multisatellite precipitation data merging. This study demonstrates that BMA provides a new solution for blending multiple satellite data in regions with limited gauges.

Advanced very high resolution radiometer (AVHRR) data provide the longest available time series of global satellite observations and have been extensively used. The Land Long-Term Data Record (LTDR) project has generated daily surface reflectance and normalized difference vegetation index (NDVI) products from AVHRR. However, residual cloud and aerosol contamination in the LTDR AVHRR surface reflectance and NDVI products significantly limits their applications and results in temporal and spatial inconsistencies in subsequent downstream products. Based on the LTDR AVHRR surface reflectance, a temporally continuous vegetation indices-based land-surface reflectance reconstruction (VIRR) method was refined in this study to generate Global LAnd Surface Satellite (GLASS) AVHRR NDVI and surface reflectance products from 1982 to 2015. The daily LTDR AVHRR surface reflectance data were first aggregated into eight-day intervals. The aggregated surface reflectance data were used to calculate NDVI, and a robust smoothing algorithm was used to reconstruct continuous and smooth NDVI upper envelopes, which were used to identify cloud-contaminated surface reflectance values. Then the surface reflectance time series was reconstructed from cloud-free surface reflectance values by incorporating the upper envelopes of the NDVI time series as constraints. The results show that the refined VIRR method successfully removes NDVI and surface reflectance values contaminated by clouds and can reconstruct temporally continuous NDVI and land-surface reflectance time series. Comparison of the GLASS AVHRR NDVI product with the third-generation Global Inventory Monitoring and Modeling System (GIMMS3g) and the moderate resolution imaging spectroradiometer (MODIS) NDVI products indicates that these NDVI products exhibit similar spatial patterns, but the GIMMS3g NDVI values were clearly higher than the GLASS AVHRR and MODIS NDVI values in tropical forest regions and the 50°N−60°N latitude band, particularly in July. Comparisons with the MODIS NDVI values over the BELMANIP (Benchmark Land Multisite Analysis and Intercomparison of Products) sites demonstrate that the GLASS AVHRR NDVI product provides better performance (RMSE = 0.1007 and Bias = 0.0518) than the GIMMS3g NDVI product (RMSE = 0.1288 and Bias = 0.0852). The temporal profiles of all these NDVI products exhibited consistent seasonal variations, but the temporal smoothness of the GLASS AVHRR NDVI product was superior to that of the GIMMS3g and MODIS NDVI products. The GLASS AVHRR and GIMMS3g NDVI products show consistent trends in most situations, but the trends of the GLASS AVHRR NDVI product were slightly more pronounced than those of the GIMMS3g NDVI product for each biome type. Comparison of the GLASS AVHRR surface reflectance product with MODIS surface reflectance product indicates the GLASS AVHRR and MODIS surface reflectance showed similar seasonal and interannual variations and the GLASS AVHRR surface reflectance was in good agreement with the MODIS surface reflectance, especially in the red band.

Ocean surface net radiation (Rn) is significant in research on the Earth’s heat balance systems, air–sea interactions, and other applications. However, there have been few studies on Rn until now. Based on radiative and meteorological measurements collected from 66 globally distributed moored buoys, it was found that Rn was dominated by downward shortwave radiation (Rg↓) when the length ratio of daytime (LRD) was greater than 0.4 but dominated by downward longwave radiation (Rl↓) for the other cases (LRD ≤ 0.4). Therefore, an empirical scheme that includes two conditional models named Case 1 (LRD > 0.4) utilizing Rg↓ as a major input and Case 2 (LRD ≤ 0.4) utilizing Rl↓ as a major input for Rn estimation was successfully developed. After validation against in situ Rn, the performance of the empirical scheme was satisfactory with an overall R2 value of 0.972, an RMSE of 9.768 Wm−2, and a bias of −0.092 Wm−2. Specifically, the accuracies of the two conditional models were also very good, with RMSEs of 9.805 and 2.824 Wm−2 and biases of −0.095 and 0.346 Wm−2 for the Case 1 and Case 2 models, respectively. However, due to the limited number of available samples, the performances of these new models were poor in coastal and high-latitude areas, and the models did not work when the LRD was too small (i.e., LRD < 0.3). Overall, the newly developed empirical scheme for Rn estimation has strong potential to be widely used in practical use because of its simple format and high accuracy.

Spatiotemporal fusion has provided a feasible way to generate fractional vegetation cover (FVC) data with high spatial and temporal resolution. However, when the currently available spatiotemporal fusion methods are applied over agricultural regions, they usually underestimate high FVC values at the peak vegetation growth stage with medium FVC values as base data. This mainly results from inconsistencies in the temporal variations between fine- and coarse-resolution data if substantial temporal changes occur in vegetation. Therefore, a Spatial and Temporal Fusion method combining with Vegetation Growth Models (STF-VGM) was proposed to address this problem in this study, which incorporates vegetation growth models into the fusion process. Unlike other spatiotemporal fusion methods that mainly rely on changes in coarse-resolution data for prediction, STF-VGM fully utilizes available coarse- and fine-resolution time series data, including uncontaminated information in cloud/cloud shadow contaminated images. By establishing vegetation growth models with time series data, a conversion relationship between coarse- and fine-resolution FVC that changes along with the nonlinear vegetation change process can be extracted. STF-VGM makes prediction based on this variable relationship. A typical agricultural region located in the North China Plain was selected as the study area. The validation results indicated that the prediction accuracy for high FVC values was significantly improved using STF-VGM compared to the commonly used Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and Flexible Spatiotemporal DAta Fusion (FSDAF) methods (STF-VGM: coefficient of determination (R2) = 0.9491, root mean square error (RMSE) = 0.0650, average difference (AD) = -0.0092; ESTARFM: R2 = 0.9341, RMSE = 0.1127, AD = -0.0631; FSDAF: R2 = 0.9224, RMSE = 0.1110, AD = -0.0599). The satisfactory performance of STF-VGM was also achieved in predicting FVC values at other vegetation growth stages (early growth stage: R2 = 0.8277, RMSE = 0.0440, AD = 0.0027; rapid growth stage: R2 = 0.9183, RMSE = 0.0844, AD = 0.0500). In addition, STF-VGM also has the potential to improve the spatiotemporal fusion accuracy of other vegetation parameters and vegetation indices, which will be evaluated in the future.

Fractional vegetation cover (FVC) is an important parameter for earth surface process simulations, climate modeling, and global change studies. Currently, several FVC products have been generated from coarse resolution (~1 km) remote sensing data, and have been widely used. However, coarse resolution FVC products are not appropriate for precise land surface monitoring at regional scales, and finer spatial resolution FVC products are needed. Time-series coarse spatial resolution FVC products at high temporal resolutions contain vegetation growth information. Incorporating such information into the finer spatial resolution FVC estimation may improve the accuracy of FVC estimation. Therefore, a method for estimating finer spatial resolution FVC from coarse resolution FVC products and finer spatial resolution satellite reflectance data is proposed in this paper. This method relies on the coupled PROSAIL radiative transfer model and a statistical crop growth model built from the coarse resolution FVC product. The performance of the proposed method is investigated using the time-series Global LAnd Surface Satellite FVC product and Landsat-7 Enhanced Thematic Mapper Plus reflectance data in a cropland area of the Heihe River Basin. The direct validation of the FVC estimated using the proposed method with the ground measured FVC data ( $R^{\mathrm {\mathbf {2}}} = 0.6942$ , RMSE =0.0884), compared with the widely used dimidiate pixel model ( $R^{\mathrm {\mathbf {2}}} = 0.7034$ , RMSE = 0.1575), shows that the proposed method is feasible for estimating finer spatial resolution FVC with satisfactory accuracy, and it has the potential to be applied at a large scale.

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 (R2) 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. (Resume d'auteur)

Plant phenotyping plays a critical role in grasping plant architectures and understanding plant responses to environment changes. Hence, various plant phenotyping techniques have kept being developed for different scenarios, via introducing diverse remote sensing (RS) techniques such as light detection and ranging (LIDAR). Now, one important trend of this field is developing low-cost 3D systems that are affordable by common users, whereas the often-used LIDAR sensors with high costs cannot satisfy this demand. To handle this issue, this study attempted to develop a low-cost 2D laser scanner based mobile terrestrial proximal sensing system for 3D plant structure phenotyping in indoor environment. Specifically, two RPLIDAR laser scanners, as one kind of the lowest-cost 2D LIDAR sensors in the contemporary market, are installed at the two far-ends of the scanner-fixing frame on the mobile platform, with their scan profiles set in an oblique-crossing way. Then, the movement of the platform, after accurate data georeferencing and calibration, can render the two series of 2D scanning profiles in parallel into a full 3D representation of each row of plants of interest. Based on the resulting 3D point clouds, detailed plant structure features can be derived. Tests showed that the proposed solution has been basically validated, in terms of the specific plant structure variables such as leaf area (R 2 = 0.92). Overall, this work has pushed forward the development of LIDAR-based plant phenotyping techniques into the real-sense low-cost stage, and this suggests that more and more practical applications of LIDAR for plant phenotyping may occur in the communities such as plant cultivation and precision agriculture.

A simple and robust satellite-based method for estimating agricultural field to regional surface energy fluxes at a high spatial resolution is important for many applications. We developed a simple temperature domain two-source energy balance (TD-TSEB) model within a hybrid two-source model scheme by coupling ‘layer’ and ‘patch’ models to estimate surface heat fluxes from Landsat TM/ETM+ imagery. For estimating latent heat flux (LE) of full soil, we proposed a temperature domain residual of the energy balance equation based on a simplified framework of total aerodynamic resistances, which provides a key link between thermal satellite temperature and sub-surface moisture status. Additionally, we used a modified Priestley-Taylor (PT) model for estimating LE of full vegetation. The proposed method was applied to TM/ETM+ imagery and was validated using the ground-measured data at five crop eddy covariance (EC) tower sites in China. The results show that TD-TSEB yielded root-mean-square-error (RMSE) values between 24.9 (8.9) and 77.3 (20.3) W/m2, and squared correlation coefficient (R2) values between 0.60 (0.51) and 0.97 (0.90), for the estimated instantaneous (daily) surface net radiation, soil, latent and sensible heat fluxes at all five sites. The TD-TSEB model shows good accuracy for partitioning LE into soil (LEsoil) and canopy (LEcanopy) components with an average bias of 11.1% for the estimated LEsoil/LE ratio at the Daman site. Importantly, the TD-TSEB model produced comparable accuracy but requires fewer forcing data (i.e. no wind speed and roughness length are needed) when compared with two other widely used surface energy balance models. Sensitivity analyses demonstrated that this accurate operational model provides an alternative method for mapping field surface heat fluxes with satisfactory performance.

Accurate estimates of terrestrial latent heat of evaporation (LE) for different biomes are essential to assess energy, water and carbon cycles. Different satellite- based Priestley-Taylor (PT) algorithms have been developed to estimate LE in different biomes. However, there are still large uncertainties in LE estimates for different PT algorithms. In this study, we evaluated differences in estimating terrestrial water flux in different biomes from three satellite-based PT algorithms using ground-observed data from eight eddy covariance (EC) flux towers of China. The results reveal that large differences in daily LE estimates exist based on EC measurements using three PT algorithms among eight ecosystem types. At the forest (CBS) site, all algorithms demonstrate high performance with low root mean square error (RMSE) (less than 16 W/m2) and high squared correlation coefficient (R2) (more than 0.9). At the village (HHV) site, the ATI-PT algorithm has the lowest RMSE (13.9 W/m2), with bias of 2.7 W/m2 and R2 of 0.66. At the irrigated crop (HHM) site, almost all models algorithms underestimate LE, indicating these algorithms may not capture wet soil evaporation by parameterization of the soil moisture. In contrast, the SM-PT algorithm shows high values of R2 (comparable to those of ATI-PT and VPD-PT) at most other (grass, wetland, desert and Gobi) biomes. There are no obvious differences in seasonal LE estimation using MODIS NDVI and LAI at most sites. However, all meteorological or satellite-based water-related parameters used in the PT algorithm have uncertainties for optimizing water constraints. This analysis highlights the need to improve PT algorithms with regard to water constraints.

Irrigation water is an important but missing hydrological cycle component in the region with intensive agricultural irrigation, due to the lack of monitoring facilities. The Hebei Plain, suffering the most severe groundwater depletion in China for agriculture production, provides an ideal background to study historical agricultural water consumption and its dependence on groundwater exploitation. This paper investigated the method of retrieving the spatial-temporal irrigation amount from multiple data sets of different sources and different scales. Comprehensive data including 21 years of satellite-based data, ground-based data, and four years of tracer experiment data are synthesized to implement the soil water balance. We proposed a modified soil water balance framework by relying on as much as possible of easily available data. Our results showed that the multi-mean annual irrigation amount in the Hebei Plain is 317 mm, and mean irrigation-to-evapotranspiration ratio reaches 50.8% in recent two decades. Moreover, the precipitation distribution, plant structure, and agricultural intensity result in significantly spatiotemporal variation in irrigation and irrigation-to-evapotranspiration ratio, while however has not been addressed by previous studies. Deep percolation, ignored by many soil water balance models, was shown to be unneglectable. The estimated actual irrigation amount, together with groundwater level data, are valuable to obtain a further understanding on groundwater depletion. The diverse groundwater depletion situation in the Hebei Plain indicated the importance of recognizing the groundwater utilization patterns at a smaller scale in the regional-scale groundwater resources management. This work showed the feasibility of estimating the irrigation amount using simultaneously different types of data and revealed the spatiotemporal characteristics of agriculture water consumption and associated groundwater depletion in the Hebei Plain.

Satellite-derived terrestrial latent heat flux (LE) models are useful tools to understand regional surface energy and water cycle processes for terrestrial ecosystems in the Heihe River basin (HRB) of Northwest China. This study developed a satellite-derived hybrid LE model parameterized by three soil moisture (SM) constraints: SM, relative humidity (RH), and diurnal air temperature range (DT); and assessed model performance and sensitivity. We used MODerate Resolution Imaging Spectroradiometer (MODIS) and eddy covariance (EC) data from 12 EC flux tower sites across the HRB. The hybrid model was trained using observed LE over 2012/2013–2014, and validated using observed LE for 2015 and leave-one-out cross-validation. The results show that the three SM constraints schemes exhibited some modeling differences at the flux tower site scale. LE estimation using SM achieved the highest correlation (R2 = 0.87, p

Fire is one of the most widespread and destructive disasters, which causes property losses, casualties, and disruption of the balance of ecosystems. Therefore, it is highly necessary for firefighting to study the variations in fire and its climatic attributions. This study analyzed the characteristics of fire-burned area (BA) and its response to climatic factors in seven subregions of China from 2001 to 2018 using satellite remote sensing BA products. The results show that the BA in China and most of its subregions shows a decreasing trend. In general, it is negatively correlated with precipitation and positively correlated with air temperature and wind speed based on the regression and correlation analyses. Based on Pearson correlation and random forest methods, it is also found that the temperature is commonly an important factor contributing to BA in China, except for R2 (Inner Mongolia region), where wind speed is more important, and R5 (South China), where precipitation is more important, which coexists at annual and seasonal scales. Besides temperature, precipitation in spring and summer is the main driving factor, such as in R1 (Northeast China), R5, R6 (Northwest China) and R7 (Qinghai–Tibet Plateau) in spring and R4 (Central China), R5 and R7 in summer; and wind speed in autumn and winter is the main driving factor, such as in R2 and R4 in autumn and R2, R3, R5, R6 and R7 in winter. Finally, the distributions of BA with respect to each climatic factor were also analyzed to quantify the range of climatic factors with maximum BA occurrence.

Terrestrial latent heat flux (LE) in the Three-River Headwaters region (TRHR) of China plays an essential role in quantifying the amount of water evaporation and carbon sink over the high altitude Tibetan Plateau (TP). Global warming is expected to accelerate terrestrial hydrological cycle and to increase evaporation. However, direct field observations are lacking in this region and the long-term variability in LE remains uncertain. In this study, we have revised a semi-empirical Penman LE algorithm based on ground eddy covariance (EC) observations from an alpine grass site and provided new satellite-based evidence to assess LE change in the TRHR during 1982–2010. Our results show that the average annual terrestrial LE in the TRHR is about 38.8 W/m 2 and there is no statistically significant change in annual LE from 1982 to 2010. We also found that during the same time period, terrestrial LE over the east region of the TRHR significantly decreased, on average, by 0.7 W/m 2 per decade, which was driven primarily by the surface incident solar radiation (R s ) limitation, offsetting the increased LE over the west region of the TRHR caused by the increased precipitation (P) and soil moisture (SM).

An automatic fractional vegetation cover (FVC) estimation method based on image characteristics in an agricultural region was proposed in this study to remove the empiricism in determining the key parameters of empirical methods. The proposed method automatically determined the soil and vegetation lines in the two-dimensional space of the red and blue band reflectances, which involved an iterative soil and vegetation pixels selection procedure, and then estimated FVC of a pixel based on its distances from the soil and vegetation lines. The accuracy assessment using field survey data indicated that the performance of the proposed method (R2 = 0.69, RMSE = 0.072, Bias = 0.014) was comparable with several commonly used empirical methods. Therefore, it was indicated that the proposed method could effectively estimate FVC in the corn-dominated region.

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.

Proximal sensing is increasingly highlighted, owing to its potential for various applications in precision agriculture. However, the uses of its related special devices like visible/near-infrared (VNIR) cameras for crop mapping are yet limited in practice, due to their high costs in market. To handle this issue, this case study was dedicated to modifying a low-cost RGB camera as a VNIR one in a do-it-yourself (DIY) way – replacing its infrared-blocking optical filter with a red-blocking one – for estimating leaf-level chlorophyll content. To enhance its performance, the angular effect that proved to impact the quality of imaging in conventional remote sensing was examined here. Specifically, in the cases of 120 individual leaves for four plant species, 24 vegetation indices (VIs) were proposed and derived from the three bands (blue, green, and near-infrared) of the collected images; then, the same operation was repeated for a set of leaf inclinations (θ, the angle between the optical axis of the camera and the normal vector of the leaf-fixed plate, set from 0°, 5°, …, to 50°). The results showed that for each of the four plant species, different optimal VIs for leaf chlorophyll content retrieval were detected for different geometries of leaf reflectance. This suggested that more modular retrieval models capable of integrally reflecting the angular effect best should be developed for accurately sensing of leaf biochemical content. Overall, the contribution of this study is of fundamental implications for advancing quantitative proximal sensing in plant biochemistry and precision agriculture.

Downward surface solar radiation (Rs) plays a dominant role in determining the climate and environment on the Earth. However, the densely distributed ground observations of Rs are usually insufficient to meet the increasing demand of the climate diagnosis and analysis well, so it is essential to build a long-term accurate Rs dataset. The extremely randomized trees (ERT) algorithm was used to generate Rs using routine meteorological observations (2000&ndash, 2015) from the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA). The estimated Rs values were validated against ground measurements at the national scale with an overall correlation coefficient value of 0.97, a mean bias of 0.04 Wm&minus, 2, a root-mean-square-error value of 23.12 Wm&minus, 2, and a mean relative error of 9.81%. It indicates that the estimated Rs from the ERT-based model is reasonably accurate. Moreover, the ERT-based model was used to generate a new daily Rs dataset at 756 CDC/CMA stations from 1958 to 2015. The long-term variation trends of Rs at 454 stations covering 46 consecutive years (1970&ndash, 2015) were also analyzed. The Rs in China showed a significant decline trend (&minus, 1.1 Wm&minus, 2 per decade) during 1970&ndash, 2015. A decreasing trend (&minus, 2.8 Wm&minus, 2 per decade) in Rs during 1970&ndash, 1992 was observed, followed by a recovery trend (0.23 Wm&minus, 2 per decade) during 1992&ndash, 2015. The recovery trends at individual stations were found at 233 out of 454 stations during 1970&ndash, 2015, which were mainly located in southern and northern China. The new Rs dataset would substantially provide basic data for the related studies in agriculture, ecology, and meteorology.

Downward shortwave radiation (RS) drives many processes related to atmosphere−surface interactions and has great influence on the earth’s climate system. However, ground-measured RS is still insufficient to represent the land surface, so it is still critical to generate high accuracy and spatially continuous RS data. This study tries to apply the random forest (RF) method to estimate the RS from the Himawari-8 Advanced Himawari Imager (AHI) data from February to May 2016 with a two-km spatial resolution and a one-day temporal resolution. The ground-measured RS at 86 stations of the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA) are collected to evaluate the estimated RS data from the RF method. The evaluation results indicate that the RF method is capable of estimating the RS well at both the daily and monthly time scales. For the daily time scale, the evaluation results based on validation data show an overall R value of 0.92, a root mean square error (RMSE) value of 35.38 (18.40%) Wm−2, and a mean bias error (MBE) value of 0.01 (0.01%) Wm−2. For the estimated monthly RS, the overall R was 0.99, the RMSE was 7.74 (4.09%) Wm−2, and the MBE was 0.03 (0.02%) Wm−2 at the selected stations. The comparison between the estimated RS data over China and the Clouds and Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) RS dataset was also conducted in this study. The comparison results indicate that the RS estimates from the RF method have comparable accuracy with the CERES-EBAF RS data over China but provide higher spatial and temporal resolution.

Different application conditions applied for different models used in satellite-based terrestrial latent heat estimation. Therefore, great uncertainties exist in large-scale application of such methods. BMA fusion algorithm, which has combined three commonly used models (including Penman Monteith LE algorithm, Priestly-Taylor LE algorithm and Semi-empirical Penman LE algorithm), is then carried out in this study. It can effectively reduce the uncertainty and improve the accuracy of terrestrial latent heat estimation comparing with single algorithm itself after testing with 190 eddy covariance tower site data (Fluxnet site data). The error of mean square root (RMSE) has decreased by $5\mathrm{W}/\mathrm{m}^{2}$ and the value of average correlation coefficient $(\mathrm{R}^{2})$ has increased by 0.05 for most of observation points in this test. The fusion model has applied in China to carry out a monthly-based latent heat estimation and mapping for data achieved from 1989 to 2006. The estimation result, after analyzed quantitatively, returns sound precision and stability, which can make up the shortage of current latent heat products. Meanwhile, the spatial distribution analysis shows that: latent heat spatial distribution is the combined contribution of temperature, precipitation and vegetation together. The temporal distribution of latent heat has obvious seasonal characteristic, which is low in winter and high in summer. The latent heat value is declined by 0.07 W/m2 per year for past 18 years.