The Soil Moisture Active Passive (SMAP) satellite makes coincident global measurements of soil moisture using an L-band radar instrument and an L-band radiometer. It is crucial to evaluate the errors in the newest L-band SMAP satellite-derived soil moisture products, before they are routinely used in scientific research and applications. This study represents the first evaluation of the SMAP radiometer soil moisture product over China. In this paper, a preliminary evaluation was performed using sparse in situ measurements from 655 China Meteorological Administration (CMA) monitoring stations between 1 April 2015 and 31 August 2016. The SMAP radiometer-derived soil moisture product was evaluated against two schemes of original soil moisture and the soil moisture anomaly in different geographical zones and land cover types. Four performance metrics, i.e., bias, root mean square error (RMSE), unbiased root mean square error (ubRMSE), and the correlation coefficient (R), were used in the accuracy evaluation. The results indicated that the SMAP radiometer-derived soil moisture product agreed relatively well with the in situ measurements, with ubRMSE values of 0.058 cm³·cm⁻³ and 0.039 cm³·cm⁻³ based on original data and anomaly data, respectively. The values of the SMAP radiometer-based soil moisture product were overestimated in wet areas, especially in the Southwest China, South China, Southeast China, East China, and Central China zones. The accuracies over croplands and in Northeast China were the worst. Soil moisture, surface roughness, and vegetation are crucial factors contributing to the error in the soil moisture product. Moreover, radio frequency interference contributes to the overestimation over the northern portion of the East China zone. This study provides guidelines for the application of the SMAP-derived soil moisture product in China and acts as a reference for improving the retrieval algorithm.

Relative humidity (RH) is one of the key variables for understanding the water, energy, and carbon exchange between the Earth and the atmosphere. Traditional methods for deriving RH from remotely sensed data usually require ground meteorological observations or are limited to clear-sky conditions, thereby making it a significant challenge to obtain spatially complete RH under all-sky conditions, especially over the regions with sparse meteorological instruments for observation. To this end, a new approach for deriving all-sky RH entirely based on Moderate Resolution Imaging Spectroradiometer (MODIS) data was proposed in the present study. Two key assumptions in the approach under cloudy conditions are that the actual water vapor is linearly related to the total precipitable water vapor (PWV) and that air temperature is linearly related to land surface temperature (LST). Results from a total of 30 AmeriFlux stations proved the aforementioned assumptions based on MODIS data collected over a study period of three years from 2009 to 2011. For different aridity conditions, RH retrieval revealed reasonable accuracy with a root-mean-square error (RMSE) of approximately 15.3% over an arid and semiarid region, whereas a comparable RMSE of 17.0% was obtained over a humid area. Further results also indicated that the aforementioned linear relationships were generally temporally stable, thereby indicating that the proposed method can be used to obtain all-sky RH at a regional or global scale entirely based on MOD06_L2-derived LST and MOD05_L2-derived PWV data given that the assumed linear relationships can be easily determined by historical MOD07_L2-derived atmospheric profiles.

Accurately mapping the surface water bodies through remote sensing technology is of great significance for water resources management, flood monitoring, and drought monitoring. At present, many scholars at home and abroad carry out research on deep learning image recognition algorithms based on convolutional neural networks, and a variety of variant-based convolutional neural networks are proposed to be applied to extract water bodies from remote sensing images. However, due to the low depth of convolutional layers employed and underutilization of water spectral feature information, most of the water body extraction methods based on convolutional neural networks (CNNs) for remote sensing images are limited in accuracy. In this study, we propose a novel surface water automatic extraction method based on the convolutional neural network (CRAUnet++) for Sentinel-2 images. The proposed method includes three parts: (1) substituting the feature extractor of the original Unet++ with ResNet34 to enhance the network’s complexity by increasing its depth; (2) Embedding the Spatial and Channel ‘Squeeze and Excitation’ (SCSE) module into the up-sampling stage of the network to suppress background features and amplify water body features; (3) adding the vegetation red edge-based water index (RWI) into the input data to maximize the utilization of water body spectral information of Sentinel-2 images without increasing the data processing time. To verify the performance and accuracy of the proposed algorithm, the ablation experiment under four different strategies and comparison experiment with different algorithms of RWI, FCN, SegNet, Unet, and DeepLab v3+ were conducted on Sentinel-2 images of the Poyang Lake. The experimental result shows that the precision, recall, F1, and IoU of CRAUnet++ are 95.99%, 96.41%, 96.19%, and 92.67%, respectively. CRAUnet++ has a good performance in extracting various types of water bodies and suppressing noises because it introduces SCSE attention mechanisms and combines surface water spectral features from RWI, exceeding that of the other five algorithms. The result demonstrates that CRAUnet++ has high validity and reliability in extracting surface water bodies based on Sentinel-2 images.

Global gridded population data can be applied to a broad range of fields, such as government planning and decision, natural disaster assessment, and resource allocation. Evaluation of the accuracy of the global gridded population data has been recognized as a critical and challenging task. This study aims to evaluate the data accuracy of two population datasets in China, including LandScan with a 1 km spatial resolution and the WorldPop project (WorldPop) with a 100m spatial resolution. These datasets at 2018 are evaluated at three spatial scales based on the population data of the 2018 Statistical Yearbook. The results indicate that WorldPop has the highest estimation accuracy in the spatial detail and comparison with the census dataset. Specifically, both LandScan and WorldPop perform well in estimating the total population and population density of the whole of China at province scale, with R2 great than 0.99. When it comes to city scales in Guangdong province, the accuracy of the two data still performs well with R2 great than 0.98 in the total population and population density. However, when it comes to the population density of county scale in Shenzhen city, WorldPop still maintains R2 great than 0.94, which is better than LandScan data with R2 of about 0.88. This study provides a reference for the selection and use of two famous global population data.

Heilongjiang is China’s first major grain-producing province, which is typical in dry farming. Drought is one of the major natural disasters in the agricultural production area. Soil moisture is an important index of drought monitoring. Remote sensing has advantages such as a wide range of observation, high instantaneity and low cost, which can be widely applied to soil moisture monitoring. In this paper, we analyze the relationship among soil moisture and NDVI (Normalized Difference Vegetation Index) and LST (Land Surface Temperature), and built remote sensing estimating model of soil moisture based on NDVI and LST. According to this model, we dynamically monitored the drought in Heilongjiang province from April to June in 2009 based on NDVI and LST product of MODIS data and the ground measured soil moisture. The monitoring results show that the drought in Heilongjiang Province gradually relieved at first stage, then worsened significantly and eventually reached the mitigation, and the development of drought exhibits good agreement with the practical situation. The research indicates that the soil moisture monitoring method based on satellite remote sensing and the ground measured soil moisture can be applied to large-scale drought monitoring in Heilongjiang province.