A Joint Encryption and Compression Algorithm for Multiband Remote Sensing Image Transmission.

Due to the increasing capabilities of cybercriminals and the vast quantity of sensitive data, it is necessary to protect remote sensing images during data transmission with "Belt and Road" countries. Joint image compression and encryption techniques exhibit reliability and cost-effectiveness for data transmission. However, the existing methods for multiband remote sensing images have limitations, such as extensive preprocessing times, incompatibility with multiple bands, and insufficient security. To address the aforementioned issues, we propose a joint encryption and compression algorithm (JECA) for multiband remote sensing images, including a preprocessing encryption stage, crypto-compression stage, and decoding stage. In the first stage, multiple bands from an input image can be spliced together in order from left to right to generate a grayscale image, which is then scrambled at the block level by a chaotic system. In the second stage, we encrypt the DC coefficient and AC coefficient. In the final stage, we first decrypt the DC coefficient and AC coefficient, and then restore the out-of-order block through the chaotic system to get the correct grayscale image. Finally, we postprocess the grayscale image and reconstruct it into a remote sensing image. The experimental results show that JECA can reduce the preprocessing time of the sender by 50% compared to existing joint encryption and compression methods. It is also compatible with multiband remote sensing images. Furthermore, JECA improves security while maintaining the same compression ratio as existing methods, especially in terms of visual security and key sensitivity.

Remote Sensing Image Scene Classification in Hybrid Classical-Quantum Transferring CNN with Small Samples.

The scope of this research lies in the combination of pre-trained Convolutional Neural Networks (CNNs) and Quantum Convolutional Neural Networks (QCNN) in application to Remote Sensing Image Scene Classification(RSISC). Deep learning (RL) is improving by leaps and bounds pretrained CNNs in Remote Sensing Image (RSI) analysis, and pre-trained CNNs have shown remarkable performance in remote sensing image scene classification (RSISC). Nonetheless, CNNs training require massive, annotated data as samples. When labeled samples are not sufficient, the most common solution is using pre-trained CNNs with a great deal of natural image datasets (e.g., ImageNet). However, these pre-trained CNNs require a large quantity of labelled data for training, which is often not feasible in RSISC, especially when the target RSIs have different imaging mechanisms from RGB natural images. In this paper, we proposed an improved hybrid classical-quantum transfer learning CNNs composed of classical and quantum elements to classify open-source RSI dataset. The classical part of the model is made up of a ResNet network which extracts useful features from RSI datasets. To further refine the network performance, a tensor quantum circuit is subsequently employed by tuning parameters on near-term quantum processors. We tested our models on the open-source RSI dataset. In our comparative study, we have concluded that the hybrid classical-quantum transferring CNN has achieved better performance than other pre-trained CNNs based RSISC methods with small training samples. Moreover, it has been proven that the proposed algorithm improves the classification accuracy while greatly decreasing the amount of model parameters and the sum of training data.

Spatio-Temporal Changes in Ecosystem Quality across the Belt and Road Region.

The Silk Road Economic Belt and the 21st Century Maritime Silk Road Initiative (BRI) proposed in 2013 by China has greatly accelerated the social and economic development of the countries along the Belt and Road (B&R) region. However, the international community has questioned its impact on the ecological environment and a comprehensive assessment of ecosystem quality changes is lacking. Therefore, this study proposes an objective and automatic method to assess ecosystem quality and analyzes the spatiotemporal changes in the B&R region. First, an ecosystem quality index (EQI) is established by integrating the vegetation status derived from three remote sensing ecological parameters including the leaf area index, fractional vegetation cover and gross primary productivity. Then, the EQI values are automatically categorized into five ecosystem quality levels including excellent, good, moderate, low and poor to illustrate their spatiotemporal changes from the years 2016 to 2020. The results indicate that the spatial distributions of the EQIs across the B&R region exhibited similar patterns in the years 2016 and 2020. The regions with excellent levels accounted for the lowest proportion of less than 12%, while regions with moderate, low and poor levels accounted for more than 68% of the study area. Moreover, based on the EQI pattern analysis between the years 2016 and 2020, the regions with no significant EQI change accounted for up to 99.33% and approximately 0.45% experienced a significantly decreased EQI. Therefore, this study indicates that the ecosystem quality of the B&R region was relatively poor and experienced no significant change in the five years after the implementation of the "Vision and Action to Promote the Joint Construction of the Silk Road Economic Belt and the 21st Century Maritime Silk Road". This study can provide useful information for decision support on the future ecological environment management and sustainable development of the B&R region.

Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization.

Soil moisture content (SMC) plays an essential role in geoscience research. The SMC can be retrieved using an artificial neural network (ANN) based on remote sensing data. The quantity and quality of samples for ANN training and testing are two critical factors that affect the SMC retrieving results. This study focused on sample optimization in both quantity and quality. On the one hand, a sparse sample exploitation (SSE) method was developed to solve the problem of sample scarcity, resultant from cloud obstruction in optical images and the malfunction of in situ SMC-measuring instruments. With this method, data typically excluded in conventional approaches can be adequately employed. On the other hand, apart from the basic input parameters commonly discussed in previous studies, a couple of new parameters were optimized to improve the feature description. The Sentinel-1 SAR and Landsat-8 images were adopted to retrieve SMC in the study area in eastern Austria. By the SSE method, the number of available samples increased from 264 to 635 for ANN training and testing, and the retrieval accuracy could be markedly improved. Furthermore, the optimized parameters also improve the inversion effect, and the elevation was the most influential input parameter.

An Improved Spatiotemporal Data Fusion Method for Snow-Covered Mountain Areas Using Snow Index and Elevation Information.

Remote sensing images with high spatial and temporal resolution in snow-covered areas are important for forecasting avalanches and studying the local weather. However, it is difficult to obtain images with high spatial and temporal resolution by a single sensor due to the limitations of technology and atmospheric conditions. The enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) can fill in the time-series gap of remote sensing images, and it is widely used in spatiotemporal fusion. However, this method cannot accurately predict the change when there is a change in surface types. For example, a snow-covered surface will be revealed as the snow melts, or the surface will be covered with snow as snow falls. These sudden changes in surface type may not be predicted by this method. Thus, this study develops an improved spatiotemporal method ESTARFM (iESTARFM) for the snow-covered mountain areas in Nepal by introducing NDSI and DEM information to simulate the snow-covered change to improve the accuracy of selecting similar pixels. Firstly, the change in snow cover is simulated according to NDSI and DEM. Then, similar pixels are selected according to the change in snow cover. Finally, NDSI is added to calculate the weights to predict the pixels at the target time. Experimental results show that iESTARFM can reduce the bright abnormal patches in the land area compared to ESTARFM. For spectral accuracy, iESTARFM performs better than ESTARFM with the root mean square error (RMSE) being reduced by 0.017, the correlation coefficient (r) being increased by 0.013, and the Structural Similarity Index Measure (SSIM) being increased by 0.013. For spatial accuracy, iESTARFM can generate clearer textures, with Robert's edge (Edge) being reduced by 0.026. These results indicate that iESTARFM can obtain higher prediction results and maintain more spatial details, which can be used to generate dense time series images for snow-covered mountain areas.

QUantitative and Automatic Atmospheric Correction (QUAAC): Application and Validation.

The difficulty of atmospheric correction based on a radiative transfer model lies in the acquisition of synchronized atmospheric parameters, especially the aerosol optical depth (AOD). At the moment, there is no fully automatic and high-efficiency atmospheric correction method to make full use of the advantages of geostationary meteorological satellites in large-scale and efficient atmospheric monitoring. Therefore, a QUantitative and Automatic Atmospheric Correction (QUAAC) method is proposed which can efficiently correct high-spatial-resolution (HSR) satellite images. QUAAC uses the atmospheric aerosol products of geostationary satellites to match the synchronized AOD according to the temporal and spatial information of HSR satellite images. This method solves the problem that the AOD is difficult to obtain or the accuracy is not high enough to meet the demand of atmospheric correction. By using the obtained atmospheric parameters, atmospheric correction is performed to obtain the surface reflectance (SR). The whole process can achieve fully automatic operation without manual intervention. After QUAAC applied to Gaofen-2 (GF-2) HSR satellite and Himawari-8 (H-8) geostationary satellite, the results show that the effect of QUAAC correction is slightly better than that of the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) correction, and the QUAAC-corrected surface spectral curves have good coherence to that of the synchronously measured by field experiments.

Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review.

Green fractional vegetation cover (fc ) is an important phenotypic factor in the fields of agriculture, forestry, and ecology. Spatially explicit monitoring of fc via relative vegetation abundance (RA) algorithms, especially those based on scaled maximum/minimum vegetation index (VI) values, has been widely investigated in remote sensing research. Although many studies have explored the effectiveness of RA algorithms over the past 30 years, a literature review summarizing the corresponding theoretical background, issues, current state-of-the-art techniques, challenges, and prospects has not yet been published. The overall objective of the present study was to accomplish a comprehensive and systematic review of RA algorithms considering these factors based on the scientific papers published from January 1990 to November 2019. This review revealed that the key issues related to RA algorithms is the determination of the appropriate normalized difference vegetation index (NDVI) values of the full vegetation cover and bare soil (denoted hereafter by NDVI∞ and NDVIS, respectively). The existing methods used to correct for these issues were investigated, and their advantages and disadvantages are discussed in depth. In literature trends, we found that the number of reported studies in which RA algorithms were used has increased consistently over time, and that most authors tend to utilize the linear NDVI model, rather than other models in the RA algorithm family. We also found that RA algorithms have been utilized to analyze the images with spatial resolutions ranging from the sub-meter to kilometer, most commonly, using images of 30-m spatial resolution. Finally, current challenges and forward-looking insights in remote estimation of fc using RA algorithms are discussed to guide future research and directions.

Leaf Area Index Estimation Using Chinese GF-1 Wide Field View Data in an Agriculture Region.

Leaf area index (LAI) is an important vegetation parameter that characterizes leaf density and canopy structure, and plays an important role in global change study, land surface process simulation and agriculture monitoring. The wide field view (WFV) sensor on board the Chinese GF-1 satellite can acquire multi-spectral data with decametric spatial resolution, high temporal resolution and wide coverage, which are valuable data sources for dynamic monitoring of LAI. Therefore, an automatic LAI estimation algorithm for GF-1 WFV data was developed based on the radiative transfer model and LAI estimation accuracy of the developed algorithm was assessed in an agriculture region with maize as the dominated crop type. The radiative transfer model was firstly used to simulate the physical relationship between canopy reflectance and LAI under different soil and vegetation conditions, and then the training sample dataset was formed. Then, neural networks (NNs) were used to develop the LAI estimation algorithm using the training sample dataset. Green, red and near-infrared band reflectances of GF-1 WFV data were used as the input variables of the NNs, as well as the corresponding LAI was the output variable. The validation results using field LAI measurements in the agriculture region indicated that the LAI estimation algorithm could achieve satisfactory results (such as R² = 0.818, RMSE = 0.50). In addition, the developed LAI estimation algorithm had potential to operationally generate LAI datasets using GF-1 WFV land surface reflectance data, which could provide high spatial and temporal resolution LAI data for agriculture, ecosystem and environmental management researches.

Effects of mixing states on the multiple-scattering properties of soot aerosols.

The radiative properties of soot aerosols are highly sensitive to the mixing states of black carbon particles and other aerosol components. Light absorption properties are enhanced by the mixing state of soot aerosols. Quantification of the effects of mixing states on the scattering properties of soot aerosol are still not completely resolved, especially for multiple-scattering properties. This study focuses on the effects of the mixing state on the multiple scattering of soot aerosols using the vector radiative transfer model. Two types of soot aerosols with different mixing states such as external mixture soot aerosols and internal mixture soot aerosols are studied. Upward radiance/polarization and hemispheric flux are studied with variable soot aerosol loadings for clear and haze scenarios. Our study showed dramatic changes in upward radiance/polarization due to the effects of the mixing state on the multiple scattering of soot aerosols. The relative difference in upward radiance due to the different mixing states can reach 16%, whereas the relative difference of upward polarization can reach 200%. The effects of the mixing state on the multiple-scattering properties of soot aerosols increase with increasing soot aerosol loading. The effects of the soot aerosol mixing state on upwelling hemispheric flux are much smaller than in upward radiance/polarization, which increase with increasing solar zenith angle. The relative difference in upwelling hemispheric flux due to the different soot aerosol mixing states can reach 18% when the solar zenith angle is 75°. The findings should improve our understanding of the effects of mixing states on the optical properties of soot aerosols and their effects on climate. The mixing mechanism of soot aerosols is of critical importance in evaluating the climate effects of soot aerosols, which should be explicitly included in radiative forcing models and aerosol remote sensing.

[In-Flight Radiometric Calibration for ZY-3 Satellite Multispectral Sensor by Modified Reflectance-Based Method].

Through integrating multi-spectral sensor characteristics of ZY-3 satellite, a modified reflectance-based method is proposed and used to achieve ZY-3 satellite multispectral sensor in-flight radiometric calibration. This method chooses level 1A image as data source and establishes geometric model to get an accurate observation geometric parameters at calibration site according to the information provided in image auxiliary documentation, which can reduce the influences on the calibration accuracy from image resampling and observation geometry errors. We use two-point and multi-points methods to calculate the absolute radiometric calibration coefficients of ZY-3 satellite multispectral sensor based on the large campaign at Dongying city, Shan Dong province. Compared with ZY-3 official calibration coefficients, multi-points method has higher accuracy than two-point method. Through analyzing the dispersion between each calibration point and the fitting line, we find that the residual error of water calibration site is larger than others, which of green band is approximately 67.39%. Treating water calibration site as an error, we filter it out using 95.4% confidence level as standard and recalculate the calibration coefficients with multi-points method. The final calibration coefficients show that the relative differences of the first three bands are less than 2% and the last band is less than 5%, which manifests that the proposed radiometric calibration method can obtain accurate and reliable calibration coefficients and is useful for other similar satellites in future.

[A review of atmospheric aerosol research by using polarization remote sensing].

In the present paper, aerosol research by using polarization remote sensing in last two decades (1993-2013) was reviewed, including aerosol researches based on POLDER/PARASOL, APS(Aerosol Polarimetry Sensor), Polarized Airborne camera and Ground-based measurements. We emphasize the following three aspects: (1) The retrieval algorithms developed for land and marine aerosol by using POLDER/PARASOL; The validation and application of POLDER/PARASOL AOD, and cross-comparison with AOD of other satellites, such as MODIS AOD. (2) The retrieval algorithms developed for land and marine aerosol by using MICROPOL and RSP/APS. We also introduce the new progress in aerosol research based on The Directional Polarimetric Camera (DPC), which was produced by Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences (CAS). (3) The aerosol retrieval algorithms by using measurements from ground-based instruments, such as CE318-2 and CE318-DP. The retrieval results from spaceborne sensors, airborne camera and ground-based measurements include total AOD, fine-mode AOD, coarse-mode AOD, size distribution, particle shape, complex refractive indices, single scattering albedo, scattering phase function, polarization phase function and AOD above cloud. Finally, based on the research, the authors present the problems and prospects of atmospheric aerosol research by using polarization remote sensing, and provide a valuable reference for the future studies of atmospheric aerosol.

[Effects of aerosol optical thickness on the optical remote sensing imaging quality].

In recent years, due to changes in atmospheric environment, atmospheric aerosol affection on optical sensor imaging quality is increasingly considered by the load developed departments. Space-based remote sensing system imaging process, atmospheric aerosol makes optical sensor imaging quality deterioration. Atmospheric medium causing image degradation is mainly forward light scattering effect caused by the aerosol turbid medium. Based on the turbid medium radiation transfer equation, the point spread function models were derived contained aerosol optical properties of atmosphere in order to analyze and evaluate the atmospheric blurring effect on optical sensor imaging system. It was found that atmospheric aerosol medium have effect on not only energy decay of atmospheric transmittance, but also the degradation of image quality due to the scattering effect. Increase of atmospheric aerosol optical thickness makes aerosol scattering intensity enhanced, variation of aerosol optical thickness is also strongly influences the point spread function of the spatial distribution. it is because the degradation of aerosol in spatial domain, which reduces the quality of remote sensing image, in particularly reduction of the sharpness of image. Meanwhile, it would provide a method to optimize and improve simulation of atmospheric chain.

[Evaluation of four dark object atmospheric correction methods based on ZY-3 CCD data].

The present paper performed the evaluation of four dark-object subtraction(DOS) atmospheric correction methods based on 2012 Inner Mongolia experimental data The authors analyzed the impacts of key parameters of four DOS methods when they were applied to ZY-3 CCD data The results showed that (1) All four DOS methods have significant atmospheric correction effect at band 1, 2 and 3. But as for band 4, the atmospheric correction effect of DOS4 is the best while DOS2 is the worst; both DOS1 and DOS3 has no obvious atmospheric correction effect. (2) The relative error (RE) of DOS1 atmospheric correction method is larger than 10% at four bands; The atmospheric correction effect of DOS2 works the best at band 1(AE (absolute error)=0.0019 and RE=4.32%) and the worst error appears at band 4(AE=0.0464 and RE=19.12%); The RE of DOS3 is about 10% for all bands. (3) The AE of atmospheric correction results for DOS4 method is less than 0. 02 and the RE is less than 10% for all bands. Therefore, the DOS4 method provides the best accuracy of atmospheric correction results for ZY-3 image.

DPIF-Net: a dual path network for rural road extraction based on the fusion of global and local information.

Background: Automatic extraction of roads from remote sensing images can facilitate many practical applications. However, thus far, thousands of kilometers or more of roads worldwide have not been recorded, especially low-grade roads in rural areas. Moreover, rural roads have different shapes and are influenced by complex environments and other interference factors, which has led to a scarcity of dedicated low level category road datasets. Methods: To address these issues, based on convolutional neural networks (CNNs) and tranformers, this article proposes the Dual Path Information Fusion Network (DPIF-Net). In addition, given the severe lack of low-grade road datasets, we constructed the GaoFen-2 (GF-2) rural road dataset to address this challenge, which spans three regions in China and covers an area of over 2,300 km, almost entirely composed of low-grade roads. To comprehensively test the low-grade road extraction performance and generalization ability of the model, comparative experiments are carried out on the DeepGlobe, and Massachusetts regular road datasets. Results: The results show that DPIF-Net achieves the highest IoU and F1 score on three datasets compared with methods such as U-Net, SegNet, DeepLabv3+, and D-LinkNet, with notable performance on the GF-2 dataset, reaching 0.6104 and 0.7608, respectively. Furthermore, multiple validation experiments demonstrate that DPIF-Net effectively preserves improved connectivity in low-grade road extraction with a modest parameter count of 63.9 MB. The constructed low-grade road dataset and proposed methods will facilitate further research on rural roads, which holds promise for assisting governmental authorities in making informed decisions and strategies to enhance rural road infrastructure.

Spatiotemporally continuous PM2.5 dataset in the Mekong River Basin from 2015 to 2022 using a stacking model.

With the potential to cause millions of deaths, PM2.5 pollution has become a global concern. In Southeast Asia, the Mekong River Basin (MRB) is experiencing heavy PM2.5 pollution and the existing PM2.5 studies in the MRB are limited in terms of accuracy and spatiotemporal coverage. To achieve high-accuracy and long-term PM2.5 monitoring of the MRB, fused aerosol optical depth (AOD) data and multi-source auxiliary data are fed into a stacking model to estimate PM2.5 concentrations. The proposed stacking model takes advantage of convolutional neural network (CNN) and Light Gradient Boosting Machine (LightGBM) models and can well represent the spatiotemporal heterogeneity of the PM2.5-AOD relationship. In the cross-validation (CV), comparison with CNN and LightGBM models shows that the stacking model can better suppress overfitting, with a higher coefficient of determination (R2) of 0.92, a lower root mean square error (RMSE) of 5.58 µg/m3, and a lower mean absolute error (MAE) of 3.44 µg/m3. For the first time, the high-accuracy PM2.5 dataset reveals spatially and temporally continuous PM2.5 pollution and variations in the MRB from 2015 to 2022. Moreover, the spatiotemporal variations of annual and monthly PM2.5 pollution are also investigated at the regional and national scales. The dataset will contribute to the analysis of the causes of PM2.5 pollution and the development of mitigation policies in the MRB.

Method to intercalibrate sunphotometer constants using an integrating sphere as a light source in the laboratory.

A calibration method is introduced to transfer calibration constants from the reference to secondary sunphotometers using a laboratory integrating sphere as a light source, instead of the traditional transferring approach performed at specific calibration sites based on sunlight. The viewing solid angle and spectral response effects of the photometer are taken into account in the transfer, and thus the method can be applied to different types of sunphotometers widely used in the field of atmospheric observation. A laboratory experiment is performed to illustrate this approach for four types of CIMEL CE318 sunphotometers belonging to the aerosol robotic network (AERONET). The laboratory calibration method shows an average difference of 1.4% from the AERONET operational calibration results, while a detailed error analysis suggests that the uncertainty agrees with the estimation and could be further improved. Using this laboratory calibration approach is expected to avoid weather influences and decrease data interruption due to operationally required periodic calibration operations. It also provides a basis for establishing a network including different sunphotometers for worldwide aerosol measurements, based on a single standard calibration reference.

[Influence of measurement errors of radiation in NIR bands on water atmospheric correction].

For standard algorithm of atmospheric correction of water, the ratio of two near-infrared (NIR) channels is selected to determine an aerosol model, and then aerosol radiation at every wavelength is accordingly estimated by extrapolation. The uncertainty of radiation measurement in NIR bands will play important part in the accuracy of water-leaving reflectance. In the present research, erroneous expressions were derived mathematically in order to see the error propagation from NIR bands. The errors distribution of water-leaving reflectance was thoroughly studied. The results show that the bigger the errors of measurement are made, the bigger the errors of water-leaving reflectance are retrieved, with sometimes the NIR band errors canceling out. Moreover, the higher the values of aerosol optical depth or the more the component of small particles in aerosol, the bigger the errors that appear during retrieval.

[The research on remote sensing dust aerosol by using split window emissivity].

Dust aerosol can cause the change in the land surface emissivity in split window by radiative forcing (RF). Firstly, the present paper explained from the microscopic point of view the extinction properties of dust aerosols in the 11 and 12 microm channels, and their influence on the land surface emissivity. Secondly, on April 29, 2011, in the northern region of Inner Mongolia a strong sandstorm outbroke, and based on the analysis of the changes in land surface emissivity, this paper proposed a dust identification method by using the variation of emissivity. At last, the dust identification result was evaluated by the dust monitoring product provided by the National Satellite Meteorological Center. The result shows that under the assumption that the 12 microm emissivity equals to 1, using 11 microm relative emissivity could identify dust cover region effectively, and the 11 microm relative emissivity to a certain extent represented the intensity information of dust aerosol.

Influences of environmental factors on the dissipation of green tides in the Yellow Sea, China.

Green tides attack the Yellow Sea every year since 2007 and have caused substantial financial loss. Based on Haiyang-1C/Coastal zone imager (HY-1C/CZI) and Terra/MODIS satellite images, the temporal and spatial distribution of green tides floating in the Yellow Sea during 2019 was extracted. The relationships between the growth rate of the green tides and the environmental factors including sea surface temperature (SST), photosynthetically active radiation (PAR), sea surface salinity (SSS), nitrate and phosphate during the green tides' dissipation phase has been detected. Based on the maximum likelihood estimation, a regression model that includes SST, PAR and phosphate was recommended to predict the growth rate of the green tides in the dissipation phase (R2 = 0.63), and this model was also examined using Bayesian information criterion and Akaike information criterion. When the average SST in the study area was above 23.6 °C, the coverage of green tides began to decrease with the increase in temperature under the influence of PAR. The growth rate of the green tides was related to SST (R = -0.38), PAR (R = -0.67) and phosphate (R = 0.40) in the dissipation phase. Compared with HY-1C/CZI, the green tide area extracted using Terra/MODIS tended to be underestimated when the green tide patches were smaller than 11.2 km2. Otherwise, the lower spatial resolution of MODIS resulted in larger mixed pixels of water and algae, which would overestimate the total area of the green tides.

Chinese Soil Moisture Observation Network and Time Series Data Set for High Resolution Satellite Applications.

High-quality ground observation networks are an important basis for scientific research. Here, an automatic soil observation network for high-resolution satellite applications in China (SONTE-China) was established to measure both pixel- and multilayer-based soil moisture and temperature. SONTE-China is distributed across 17 field observation stations with a variety of ecosystems, covering both dry and wet zones. In this paper, the average root mean squared error (RMSE) of station-based soil moisture for well-characterized SONTE-China sites is 0.027 m3/m3 (0.014~0.057 m3/m3) following calibration for specific soil properties. The temporal and spatial characteristics of the observed soil moisture and temperature in SONTE-China conform to the geographical location, seasonality and rainfall of each station. The time series Sentinel-1 C-band radar signal and soil moisture show strong correlations, and the RMSE of the estimated soil moisture from radar data was lower than 0.05 m3/m3 for the Guyuan and Minqin stations. SONTE-China is a soil moisture retrieval algorithm that can validate soil moisture products and provide basic data for weather forecasting, flood forecasting, agricultural drought monitoring and water resource management.