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The Soil Moisture Active Passive (SMAP) satellite can no longer directly deliver high-resolution (9 km) soil moisture products with the failure of the onboard L-band radar. Thus, an appropriate replacement sensor and new algorithms are urgently needed to compensate for the lost radar and estimate soil moisture at high-resolution. This paper presents a new downscaling approach, the Downscaling basEd oN gradient boosting deciSion trEe (DENSE) method to solve this problem. We analyzed 26 soil moisture related indices, derived from MODIS and a digital elevation model, to identify proxy variables for soil moisture variability. A gradient boosting decision tree regression links the aggregated soil moisture proxies and SMAP observations at a coarse scale to express the nonlinear relationships between them. High-resolution soil moisture products were generated by applying this built regression model to the optimal soil moisture proxies at a fine scale over the entire Tibetan Plateau during the years 2015–2017. In situ measurements were collected from the Ngari, Naqu, and Maqu networks, which represent different climatic and vegetation conditions. We evaluated the downscaled soil moisture against ground observations at the daily, point, and network scales. The results indicated that the DENSE method effectively infers the spatio-temporal variability of soil moisture; and at the same time, preserves SMAP soil moisture accuracy. The performance of the proposed method failed in the Maqu network as this area is covered by dense vegetation. Although further improvements are still needed to correct vegetation effects, nevertheless, the DENSE method improved the spatial resolution of SMAP soil moisture estimates, from 36 km to 1 km over the Tibetan plateau where high-resolution soil moisture products are necessary to support local and global hydrological applications. • A novel method named DENSE is proposed to downscale SMAP soil moisture (SM). • Nine SM proxies is used for the downscaling scheme. • High-resolution SM maps are generated for the entire Tibetan Plateau. • Downscaled SM was evaluated against in situ measurements at three scales. [ABSTRACT FROM AUTHOR]

• Our method has the potential to extract water with high accuracy on a large scale. • Our method realizes the conversion from unlabeled learning to noisy label learning. • The model performance is significantly higher than traditional methods. • Our method provides a new idea for unsupervised learning. Large-scale monitoring of surface water bodies is of great significance to the sustainable development of regional ecosystems. Remote sensing is currently the main method of global earth observation. On the one hand, the traditional water index is simple and efficient, but it relies on a fixed global threshold, which leads to low accuracy for water extraction. On the other hand, deep learning has achieved state-of-the-art results in classification of spectral data, but it consumes a substantial amount of manpower and time to label sufficient high-quality samples. Furthermore, spectral characteristics of water bodies in different areas vary greatly due to changes in atmospheric conditions and viewing geometry. In this paper, we propose a new accurate water extraction framework based on unsupervised deep learning and NDWI of multispectral images. Binarized NDWI images are used to identify potential water bodies, and deep learning training is performed using these pseudo samples and labels. This process realizes the conversion from unlabeled learning to noisy label learning. First, we proposed a simple and fast binarization algorithm to segment as many real water bodies as possible from NDWI images. Then a set of water confidence assessment rules was constructed from the four aspects of the spectrum, shape, agglomeration, and range. The water segments were scored and sorted to make the model start learning from easy samples and gradually advance to complex samples. Finally, the adjusted co-teaching learning strategy is adopted to filter errors introduced by noisy labels through peer networks with different learning capabilities during training. We tested the accuracy of our method using Gaofen Image Dataset (GID), Sentinel-2 and Landsat images of several water bodies in China. Compared with other methods, our method improved the F1 score by 18.1–40.3% and 6.8–22.2% for GID and Sentinel-2 images, respectively. In addition, our method is more stable in long-term water monitoring. The proposed method has the potential to be used for extracting water bodies with high accuracy on a large scale, especially in areas with complex environments and a lack of deep learning samples. And it provides a new idea for unsupervised learning in the current remote sensing field by fully combining remote sensing expertise and spectral information of ground objects. [ABSTRACT FROM AUTHOR]

Drought as a complex natural hazard encompassing comprehensive information on the occurrence and development of drought using a single variable alone is difficult. In the present study, a composite multivariate drought index (CMDI) was developed based on a copula combining three univariate drought indices (standardized precipitation index [SPI], standardized runoff index [SRI], and water storage deficit index [WSDI]) and four timescales (monthly, quarterly, semi-annually, and annually). The new index combines information on terrestrial water storage, precipitation, and runoff to capture drought comprehensively. Based on the CMDI, drought trends and characteristics were assessed for 10 major basins in China from 2003 to 2020 using the seasonal Mann–Kendall test and the run theory. The results showed that the developed CMDI 1) integrated meteorological and hydrological drought information using the SPI, SRI, and WSDI; 2) captured overall drought development more completely and frequently than traditional drought indices; and 3) monitored an increasing trend of drought in the Qinghai-Tibet Plateau and North China Plain, in addition to a more substantial drought in northern China than in southern China. [Display omitted] • We developed Combined Multivariate Drought Index (CMDI). • CMDI monitors drought from multiple time scales and factors. • CMDI allows robust and reliable drought characterization. [ABSTRACT FROM AUTHOR]