Be aware of the significance of land surface net radiation ($R_{n}$), there is a need for accurate long-term and high spatial resolution global$R_{n}$estimates based on satellite data. Herein, we propose a novel globally applicable, highly effective algorithm for estimating daily$R_{n}$directly from Visible Infrared Imaging Radiometer Suite (VIIRS) top-of-atmosphere (TOA) observations ranging from 2011 to present, using the eXtreme Gradient Boosting (XGBoost) method. This algorithm, named the constraint conditional model (CCM), consists of five conditional models (namely, cases 1–5 model) divided by the combination of the length of daytime (dt), the instantaneous sky condition, and the surface broadband albedo, and the daily downward shortwave radiation (DSR) from ERA5-Land was introduced as a physical constraint when$dt \gt 9$, in which case$R_{n}$is dominated by$R_{\textit {si}}$(incoming solar radiation). The validation accuracy of CCM was satisfactory against the ground measurements, yielding a root-mean-square error (RMSE) of 18.95 Wm−2, a bias of 0.056 Wm−2, and an$R^{2}$of 0.89. The algorithm exhibited superior accuracy and robustness compared to GLASS-MODIS and ERA5-Land under spatiotemporally independent validation samples. This indicates the potential of VIIRS to extent MODIS$R_{n}$products for generating long-term global daily$R_{n}$data.

Spatiotemporal fusion algorithms have the ability to obtain high-resolution land surface temperature (LST). However, most existing fusion methods have difficulty obtaining LSTs with a high resolution corresponding to that of visible and near-infrared (VNIR) data and cannot adapt well to LSTs with complex nonlinear changes. Hence, this study proposes a local temperature unmixing-based fusion model (LTUBFM), which is designed for LSTs with complex variations at both spatial and temporal scales, to obtain high-resolution LSTs. The LTUBFM contains three parts. First, component temperatures are estimated based on a linear temperature mixing model (LTMM) from low-resolution images. Second, component temperatures are redistributed by neighboring similar pixels via a weighted function. Finally, model residuals are derived from redistributed component temperatures and interpolated using an inverse distance weighting (IDW) interpolator to obtain high-resolution LSTs. Compared to existing fusion algorithms, LTUBFM has the following strengths: 1) it can weaken the effect of land cover change on LST; 2) it can fuse LSTs to a high resolution corresponding to that of VNIR data; and 3) it is strongly robust even when the temperature changes greatly. The LTUBFM is tested with simulated data and satellite data and fuses 1000 m-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) LSTs into 30 m. Compared with STARFM, ESTARFM, and SPSTFM, LTUBFM reduces the RMSE by up to 3.5 K in areas covered with vegetation. These results indicate that the LTUBFM has the potential to generate satisfying high-spatiotemporal-resolution LSTs from multisource datasets with large temperature changes.

Remotely sensed soil moisture (SM) dataset with well accuracy and fine spatiotemporal resolution is very valuable in various fields. Downscaling is a promising way to obtain such an SM dataset. There are currently two basic methodologies in the downscaling with satellite datasets, i.e., the machine learning (ML) methods and the physical or semi-physical (PH) models. This study focuses on exploring feasible ways to integrate them for boosting the performance of downscaling. Here, three parallel modes i.e., arithmetic average (Ari), geometric average (Geo), and weighted average (Wei), and two series modes i.e., first PH then ML (PH-ML) and first ML then PH (ML-PH) were developed and evaluated. A representative of PH, DSCALE_mod16, and three ML algorithms Random Forest, XGBoost, and LightGBM were used in this study. Microwave SM datasets from Soil Moisture Active and Passive Mission (SMAP), satellite datasets from MODIS, and in-situ SM observations spanning from April 2015 to December 2018 were employed in the evaluation. Spatial dynamic range, energy conservation, and precision preservation analyses were conducted as evaluation methods. Results demonstrated the PH-ML series mode outperformed the other coupling modes, which was even better than the better one of the PH and ML. The PH can first generate a valuable initial estimate, and the initial estimate’s advantages can be preserved while its errors can be suppressed by the ML. Resultantly, the ideology of first using the PH to obtain an initial estimate and then putting the initial estimate into the ML is suggested for further microwave SM downscaling.

Dynamic land cover (LC) information is an essential part of environmental and ecological research. Therefore, acquiring dynamic LC data with high spatial resolution has attracted a great deal of attention in the remote sensing community. Nevertheless, the high-temporal resolution satellite data tend to have a coarse spatial resolution, and satellite data with high temporal resolution are often relatively low. Obtaining LC with high spatiotemporal resolution is extremely challenging. The super-resolution method can help researchers achieve this goal, and the recently developed neural-network-based deep learning algorithms have great potential for use as an alternative solution. This study proposes a focal loss temporal convolutional long short-term memory (FL-T-ConvLSTM) model for super-resolution LC classification research. It first trains the deep FL-T-ConvLSTM network to establish a transformation between low-resolution quantitative remote sensing parameters and high-resolution quantitative remote sensing parameters and then engages in nonlinear mapping with a high-resolution LC map. A long-term series 1-km super-resolution LC classification model based on deep learning was established and applied to the Beijing-Tianjin-Hebei region. Based on this method, a long-term series of 1-km LC maps from 1982 to 2019 can be obtained. The test accuracy and field validation accuracy of the model reached 90.1% and 86.8% when using reliable test samples and field test samples, respectively. This study provides a method for obtaining high-resolution LC classification products from low-resolution quantitative remote-sensing products.

The daytime surface net radiation (Rn) 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 Rnderived from 2000 to 2015 at a spatial resolution of 0.05°. This letter describes the results of validation of this new Rn product using ground measurements collected from 142 sites distributed worldwide. The overall accuracy of the GLASS daytime Rnproduct was satisfactory, with an R2of 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.

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/m2and a root mean square error of 28.83 (32.84) W/m2at the daily time scale. Moreover, the global land annual mean of the DSR was determined to be 184.8 W/m2with a standard deviation of 0.8 W/m2over a 13-year (2003-2015) period.

Leaf area index (LAI) is an important vegetation biophysical variable and has been widely used for crop growth monitoring and yield estimation, land-surface process simulation, and global change studies. Several LAI products currently exist, but most have limited temporal coverage. A long-term high-quality global LAI product is required for greatly expanded application of LAI data. In this paper, a method previously proposed was improved to generate a long time series of Global LAnd Surface Satellite (GLASS) LAI product from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MOD!S) reflectance data. The GLASS LAI product has a temporal resolution of eight days and spans from 1981 to 2014. During 1981-1999, the LAI product was generated from AVHRR reflectance data and was provided in a geographic latitude/longitude projection at a spatial resolution of 0.05°. During 2000-2014, the LAI product was derived from MODIS surface-reflectance data and was provided in a sinusoidal projection at a spatial resolution of 1 km. The GLASS LAI values derived from MODIS and AVHRR reflectance data form a consistent data set at a spatial resolution of 0.05°. Comparison of the GLASS LAI product with the MODIS LAI product (MOD15) and the first version of the Geoland2 (GEOV1) LAI product indicates that the global consistency of these LAI products is generally good. However, relatively large discrepancies among these LAI products were observed in tropical forest regions, where the GEOV1 LAI values were clearly lower than the GLASS and MOD15 LAI values, particularly in January. A quantitative comparison of temporal profiles shows that the temporal smoothness of the GLASS LAI product is superior to that of the GEOV1 and MODIS LAI products. Direct validation with the mean values of high-resolution LAI maps demonstrates that the GLASS LAI values were closer to the mean values of the high-resolution LAI maps (RMSE = 0.7848 and R2= 0.8095) than the GEOV1 LAI values (RMSE = 0.9084 and R2= 0.7939) and the MOD15 LAI values (RMSE = 1.1173 and R2= 0.6705).

Climate change has a great influence on vegetation growth and vegetation responds to Climate change with a certain time lag[1]. Considering the time-lag effects, this study applied MODIS-NDVI (Normalized Difference Vegetation Index) and CRU (Climate Research Unit) solar radiation data to analyze the vegetation responses to solar radiation in Amazon region. Based on the response mechanism, the variation of vegetation and solar radiation during the 2005 and 2010 Amazon droughts were investigated to identify the impact of the solar radiation change on Amazon vegetation growth. Result suggests that the response of Amazon forests vegetation to solar radiation vary with different local hydrothermal conditions. In the intensely affected areas of 2005 and 2010 Amazon droughts, a significant increase in pre-drought solar radiation was found, which means solar radiation has a significant contribution to the two extreme droughts.

Fractional vegetation cover (FVC) plays an important role in earth surface process simulations, climate modeling, and global change studies. Several global FVC products have been generated using medium spatial resolution satellite data. However, the validation results indicate inconsistencies, as well as spatial and temporal discontinuities of the current FVC products. The objective of this paper is to develop a reliable estimation algorithm to operationally produce a high-quality global FVC product from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance. The high-spatial-resolution FVC data were first generated using Landsat TM/ETM+ data at the global sampling locations, and then, the general regression neural networks (GRNNs) were trained using the high-spatial-resolution FVC data and the reprocessed MODIS surface reflectance data. The direct validation using ground reference data from validation of land European Remote Sensing instruments sites indicated that the performance of the proposed method (R2=0.809, RMSE =0.157) was comparable with that of the GEOV1 FVC product (R2=0.775, RMSE =0.166), which is currently considered to be the best global FVC product from SPOT VEGETATION data. Further comparison indicated that the spatial and temporal continuity of the estimates from the proposed method was superior to that of the GEOV1 FVC product.

The objective of this study is to retrieve a spatially-explicit map of forest biomass, which is not only an important parameter to evaluate carbon storage but also a necessary initial value for process-based carbon cycle models to simulate carbon dynamics within a region. In this study, we used the latest eighth national forest inventory statistics (2009-2013) and the MODIS Land Cover Type product (MCD12C1) to estimate current spatial distribution of forest biomass in China at 0.05° resolution using a straight-forward downscaling method. The results showed that the total stock of forest biomass in China has increased remarkably to 13.1Pg. The forest biomass in China has a clear spatial pattern, with the highest biomass values occurring in the Da Hinggan, Xiao Xing'an and Changbai mountains of the northeast, and the Hengduan mountains of the southwest. The relatively high values were widely distributed in mountain areas in Sichuan and Yunan provinces of the southwest, and Fujian province of the southeast.

Climate conditions have important influences on vegetation growth and there are also effects of temporal lags of plants growth to climate factors. This paper is based on GIMMS-NDVI (Normalized Difference Vegetation Index) and CRU (Climate Research Unit) climate datasets to analyze the relationship between vegetation and climate factors (temperature, precipitation and solar radiation). Results show that Western Australia responds to climate factors at long time-scales, however, Eastern Australia at short time-scales (with mean value of 6.6 months in totally areas). Moreover, vegetation growth in Australia's coastal areas is mainly influenced by solar radiation, while precipitation and solar radiation jointly play important roles in central Australia.

Cropland is one of the most important types of land surface. It provides the resources and environment on which human being relies for existence. This paper uses MODIS surface classification data and NDVI remote sensing data as data source to calculate the NDVI time series characteristic curve of cropland by histograms. We further obtain the 2001-2011 cropland distribution under 500m resolution with decision tree classification. Finally, the paper uses 2009 MERIS Globcover data as reference and calculates the producer's accuracy. The results show that: the NDVI time series characteristic curve based on histograms is more suitable for extracting cropland information. The extraction producer's accuracy shows an increase compared with cropland producer's accuracy of MODIS.

HJ-1/IRS is an infrared camera boarded on HJ-1B with four bands that includes 0.75~1.10μm, 1.55~1.75μm, 3.50~3.90μm and 10.5~12.5μm. It has a higher spatial resolution with 150m for the first three bands and 300m for the last thermal band. This paper first did research on the method of hot abnormal points by HJ-1/IRS camera, brought out the retrieval algorithm for detecting fire straw burning and straw burning for HJ-1/IRS, then did some test work to verify the work by comparing the result with that of MODIS, finally the paper taking use of AOD (aerosol optical depth) data and NO2data from Aura/OMI to do some analysis work to research on the effect of straw burning to the air quality. The results showed that Straw burning can lead to bad air condition.

A method for the retrieval of aerosol optical thickness on China-Brazil Earth Resources Satellite (CBERS) data has been introduced. This algorithm is designed to estimate precise Aerosol Optical Thickness (AOT) only using Visible to Near-infrared spectral bands (400-900nm) information. The first part of this paper is devoted to the description of this method; outline the main steps in this correction process. The validation task is detailed in the second part. Many groups of simulated data was used to validate this algorithm, the R-squared equals to 0.98 and the RMSE equals to 0.052 shows the method has a good performance. Then an image of CBERS was selected to validate this method. The surface reflectance after atmospheric correction is much closer to the real spectrum.

MODIS is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. It can view one place about two times a day and has been widely used for biomass burning monitoring. This paper took use of MODIS data to research on the air pollution impact of straw burning. Firstly, this paper introduced the detection method of the fire points of straw burning based on MODIS data. Then combining with GIS buffer tool and statistic method, it analyzed the relationship between the fire points of straw burning within butter area and the air quality. Finally, it studied the air pollution impact from the straw burning integrating with atmospheric data in the research area of North China. It showed that the fire points within 700 and 800 km buffer areas had better accordance with API values, and the air pollution had some connections with the unfavorable atmospheric diffusion factors.

On September 6, 2008, in Taiyuan Satellite Launch Center, HJ-1A/B satellites (HJ-1A/B), China's first two satellites of Environment & Disasters Monitoring and Predicting Microsatellite Constellations, were successfully launched with the technique of “one rocket, two satellites”. The HyperSpectral Imager(HSI) on-board HJ-1A satellite is a Fourier Transform HyperSpectral Imager(FTHSI) built by Xian Institute of Optics and Precision Mechanics(XIOPM) of Chinese Academy of Sciences(CAS). This paper briefly introduces the spectral and radiometric properties of HSI, basic imaging theories of the spatially modulated Fourier transform imaging spectrometer, and then discusses the algorithms of spectrum reconstruction. In the end, the result of the operational spectrum reconstruction for the raw data of the HJ-1A satellite Fourier transform HSI is presented.

The mine tailings, which were generated during the mine development, are normally piled along the bank of the river. While being exposed in the air and interacting with the precipitation, they ultimately inpour into the waters around the mine zone to cause river pollution and result in the environment degradation. The presented paper selects two indexes of AVRI and RPI for monitoring the pollution of the water and vegetation based on Remote Sensed method. Taking the De Xing copper mine, which is located in Jiang Xi province, as an example and using TM Remote Sensed data in different four periods from 1986 to 2000 as information sources, the research has been done. It shows that RS method contributed a lot to monitor the pollution conditions of the narrow inner land river and the relative vegetation.

This research focused on some soil samples in Jiang Su province, China. We have got the total ion content including Cu, Pb, Zn, Hg, As and Cd chemical components of these samples. At the same time, soil reflectance spectra in the visible-near infrared region (VNIR) were measured in the laboratory. A multivariate calibration procedure using partial least squares (PLS) regression was applied to establish a relationship between reflectance spectra in the visible-near infrared region and the chemical components. Results showed that hyperspectral remote sensing has the potential to the survey of metal concentrations in soils.

Chlorophyll and carotenoid, relating to the physiological function of leaves, are two pigments which can absorb the light energy during the process of plant photosynthesis. Among the pigments, the chlorophyll plays an important role in the photosynthesis, and its content, as a predictor of the nutritional status of vegetation, is one of the main factors to evaluate the environment and growth conditions for the winter wheat. This work, based on the reflectance spectra of wheat in Xiao Tangshan County, in China, took PLS regression as the quantitative inversion method to have established the hyperspectral inversion model between chlorophyll content and the reflectance spectra of wheat. Through analysis, it indicated that the chlorophyll content of wheat was highly relative to the reflectance of hyperspectral from 350 nm to 1060 nm. The correlation coefficient between the prediction value and the measured value is as high as 0.9, and the RMSEP is lower than 0.4. The research provided an effective method to estimate the chlorophyll content using the quantitative inversion technology of hyperspectral RS.

During oil-gas exploration, much information is collected concerning geo-physical exploration, geo-chemical exploration, remote-sensing and geology exploration. Depending on the traditional exploration methods, it is difficult to make further progress with the deep step in oil exploration, so new theories and methods are urgently needed. Geo-anomaly theory was first put forward by Zhao Pengda, a Chinese math geologist, in 1991. It has been widely applied in solid mineral exploration. In this research, it is promoted in oil and gas exploration. How to mine valuable oil and gas geo-anomaly information and knowledge from all collected data is a big problem. From this point of view, taking oil and gas exploration in one basin in east China as an example, research has been done on mining oil and gas geo-anomaly information using data mining technology. During the research, some new mining methods such as block convulsion filtering, BP neural network and grey prediction are applied besides entropy, and complex methods. Research shows that the block convulsion filtering method is beneficial in removing noise from the data, and grey prediction methods are better ways to extract geo-anomaly information from drilling and seismic data, the BP network method is used well in predicting reserve and cover layer parameters with high accuracy, complex and entropy methods make a great effect in predicting the oil and gas preferable area. Through the above research on oil and gas geo-anomaly information mining, good results have been achieved in quantity methods for oil and gas geo-anomaly information extraction. The research contributes a lot to get effective exploration knowledge and predict preferable objects for oil and gas exploration.

The Spectrum Knowledge Base System (SKBS) for Typical Object in China, built up by taking advantage of J2EE technology, is capable of providing the functionalities in spectrum analysis, query and comparison. More importantly, the spectrum scale effect, especially the scale extension, can be achieved in SKBS, which is based on the model-driven theory with the support of the prior knowledge.

The mineral environment of the DeXing Copper, in JiangXi Province in China, is monitored and analyzed by making use of the field spectral data and remote sensing images, TM data as well as ETM data, in different mineral developmental period. The location of the mine tailings is identified and its change in area and volume versus the time is calculated as well. A method to use DEM (Digital Elevation Model) for analyzing the change of the volume for the pollution source and its impact to the local environment is proposed in this paper. It provides the quantitative description for the mineral environmental pollution. This method can be used to monitor the open mineral environment, which has the same environmental problem like DeXing Copper Mine. It's beneficial for the local government to supervise the mineral environmental changes and be aware of the pollution status dynamically and lively.

The mine tailings, which are generated during the mine development, are normally piled along the bank of the river. They will produce the pollution of the river while being exposed in the air and interacting with the precipitation. They will ultimately inpour into the waters around the mine zone and result in the environment degradation. This paper summarizes the study on the remote sensing information model to describe the water pollution caused by the mine tailings considering the following critical parameters: the terrain factor, sand and metal ion, the impact on the spectrum of the typical surface features. A case study against the tailings piles in this De Xing Copper Mine, demonstrated the practicability and the maneuverability of this model.