Soil moisture is an important variable in ecological, hydrological, and meteorological studies. An effective method for improving the accuracy of soil moisture retrieval is the mutual supplementation of multi-source data. The sensor configuration and band settings of different optical sensors lead to differences in band reflectivity in the inter-data, further resulting in the differences between vegetation indices. The combination of synthetic aperture radar (SAR) data with multi-source optical data has been widely used for soil moisture retrieval. However, the influence of vegetation indices derived from different sources of optical data on retrieval accuracy has not been comparatively analyzed thus far. Therefore, the suitability of vegetation parameters derived from different sources of optical data for accurate soil moisture retrieval requires further investigation. In this study, vegetation indices derived from GF-1, Landsat-8, and Sentinel-2 were compared. Based on Sentinel-1 SAR and three optical data, combined with the water cloud model (WCM) and the advanced integral equation model (AIEM), the accuracy of soil moisture retrieval was investigated. The results indicate that, Sentinel-2 data were more sensitive to vegetation characteristics and had a stronger capability for vegetation signal detection. The ranking of normalized difference vegetation index (NDVI) values from the three sensors was as follows: the largest was in Sentinel-2, followed by Landsat-8, and the value of GF-1 was the smallest. The normalized difference water index (NDWI) value of Landsat-8 was larger than that of Sentinel-2. With reference to the relative components in the WCM model, the contribution of vegetation scattering exceeded that of soil scattering within a vegetation index range of approximately 0.55–0.6 in NDVI-based models and all ranges in NDWI1-based models. The threshold value of NDWI2 for calculating vegetation water content (VWC) was approximately an NDVI value of 0.4–0.55. In the soil moisture retrieval, Sentinel-2 data achieved higher accuracy than data from the other sources and thus was more suitable for the study for combination with SAR in soil moisture retrieval. Furthermore, compared with NDVI, higher accuracy of soil moisture could be retrieved by using NDWI1 (R² = 0.623, RMSE = 4.73%). This study provides a reference for the selection of optical data for combination with SAR in soil moisture retrieval.

The GaoFen-7 (GF-7) satellite, which was launched on November 3, 2019, is China's first civilian submeter stereo mapping satellite. The satellite is equipped with the first laser altimeter officially in China for earth observation. Except for the laser altimeter, the GF-7 spaceborne laser altimeter system also includes two laser footprint cameras and a laser optical axis surveillance camera. The laser altimeter system is designed and used to assist improving the elevation accuracy without Ground Control Points (GCPs) of the two line-array stereo mapping cameras. This paper details the design of the GF-7 spaceborne laser altimeter system, its ranging performance in the laboratory and its data processing method. The type of data products is also released. These data will play a vital role in the application of geography, glaciology, forestry and other industries.

Quantitative estimates of the groundwater depletion and droughts in the Tigris‐Euphrates Basin (TEB) can be useful for sustainably managing its water resources. Here, data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are used to infer the monthly changes in the total water storage of the TEB from January 2003 to December 2015. Additionally, the data of altimetry and output from land surface models are used to remove the contributions from lake water changes and other hydrological factors to obtain the total groundwater depletion (TGWD), human‐driven groundwater depletion (HGWD), and climate‐driven groundwater depletion. We conclude that an alarming rate of decrease in the total water storage and the loss of TGWD have an “accelerating” trend, as the trend during 2007 to 2015 was 3.6 times that during 2003 to 2006. Moreover, the HGWD is 116.09 Gt, which accounts for 98% of the TGWD. Finally, the total storage deficit index (TSDI) is derived from the GRACE data to characterize the drought of the TEB. The results show that TSDI agrees well with the actual drought rather than the Palmer drought severity index (PDSI) and that the TEB has been undergoing a severe drought since September 2007 according to both the TSDI and PDSI. The research in this study provides an effective and unique method for understanding the hydrological processes and sustainable use of water resources in regions or countries with little data, which is essential for more efficient, sustainable, and cross‐boundary cooperative water resource management.