The Tibetan Plateau (TP) plays a significant role in the Earth's climate system. This letter examines the nighttime lake surface water temperature (LSWT) of 374 lakes (≥10 km² each) over the TP for the past 15 years (2001–2015). An overall warming trend (0.037 °C/year) is found and it is consistent with the warming air temperature (0.036 °C/year) over the TP, with the vast majority of the lakes (70%, with 28% of which are significant) showing warming (0.076 °C/year) and the rest (30%, with 37% of which are significant) showing cooling (−0.053 °C/year). This astonishing contrast was controlled by different water sources recharging these lakes: (1) warming LSWT due to increased precipitation (central and northern TP) or permafrost degradation (northwestern TP) and (2) cooling LSWT due to increased glacier meltwater (southwestern TP), revealing LSWT a sensitive indicator to climate change that triggered different regional responses in precipitation, permafrost, and glacier changes over the TP. This study helps improve our understanding of high‐altitude lakes and their changing mechanisms under the warming climate.

Man-made reservoirs are key components of terrestrial hydrological systems. Identifying the location and number of reservoirs is the premise for studying the impact of human activities on water resources and environmental changes. While complete bottom-up censuses can provide a comprehensive view of the reservoir landscape, they are time-consuming and laborious and are thus infeasible on a global scale. Moreover, it is challenging to distinguish man-made reservoirs from natural lakes in remote sensing images. This study proposes a convolutional neural network (CNN)-based framework to recognize global reservoirs from Landsat 8 imageries. On the basis of the HydroLAKES dataset, a Landsat 8 cloud-free mosaic of 2017 was clipped for each feature (reservoir or lake) and was resized into 224 × 224 patches, which were collected as training and testing samples. Compared to other deep learning methods (Alexnet and VGG) and state-of-the-art traditional machine learning methods (support vector machine, random forest, gradient boosting, and bag-of-visual-words), we found that fine-tuning the pretrained CNN model, ResNet-50, could reach the highest accuracy (91.45%). Application cases in Kansas (USA, North America), Mpumalanga (South Africa, Africa), and Kostanay (Kazakhstan, Asia) resulted in classification accuracies of better than 99%, which showed the applicability of the proposed ResNet-50 model to the extraction of reservoirs from a vast amount of moderate resolution images. The framework that was developed in this paper is the first attempt to combine remote sensing big data and the deep learning technique to the recognition of reservoirs at a global scale.

NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission, launched in 2016, is a small satellite constellation designed to measure the ocean surface wind speed in hurricanes and tropical cyclones. To explore its additional capabilities for applications on the land surface, this study investigated the advantages and limitations of using CYGNSS data to monitor flood inundation during typhoon and extreme precipitation events in southeast China in 2017. The results showed that despite the lack of quantitative evaluation, the CYGNSS-derived surface reflectivity (SR) and flood inundation area was qualitatively consistent with the Global Precipitation Measurement (GPM)-derived precipitation and Soil Moisture Active Passive (SMAP)/Soil Moisture and Ocean Salinity (SMOS)-derived total brightness temperature at circular polarization ( ${\mathrm{T}}_{{\mathrm{b}}_{\mathrm{C}}}$ ). The results provide supporting evidence for further designation of Global Navigation Satellite System (GNSS) reflectometry (GNSS-R) constellations to monitor land surface hydrology.

The availability of lake bathymetry maps is imperative for estimating lake water volumes and their variability, which is a sensitive indicator of climate. It is difficult, if not impossible, to obtain bathymetric measurements from all of the thousands of lakes across the globe due to costly labor and/or harsh topographic regions. In this study, we develop a new digital lake bathymetry model (DLBM) using the step-wise water recession method (WRM) to generate 3-dimensional lake bathymetric maps based on the digital elevation model (DEM) alone, with two assumptions: (1) typically, the lake’s bathymetry is formed and shaped by geological processes similar to those that shaped the surrounding landmasses, and (2) the agent rate of water (the thickness of the sedimentary deposit proportional to the lake water depth) is uniform. Lake Ontario and Lake Namco are used as examples to demonstrate the development, calibration, and refinement of the model. Compared to some other methods, the estimated 3D bathymetric maps using the proposed DLBM could overcome the discontinuity problem to adopt the complex topography of lake boundaries. This study provides a mathematically robust yet cost-effective approach for estimating lake volumes and their changes in regions lacking field measurements of bathymetry, for example, the remote Tibetan Plateau, which contains thousands of lakes.

Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) provides a new opportunity for land observation. This study is the first to compare and evaluate the performance of the only two spaceborne GNSS-R satellite missions whose data are publicly available, i.e., the UK’s TechdemoSat-1 (TDS-1) and the US’s Cyclone Global Navigation Satellite System (CYGNSS), for sensitivity analysis with SMAP SM on a daily basis and soil moisture (SM) estimates on a monthly basis over Mainland China. For daily sensitivity analysis, the two data were matched up and compared for the period (i.e., May 2017 through April 2018) when they coexisted (R = 0.561 vs. R = 0.613). For monthly SM estimates, a back-propagation artificial neural network (BP-ANN) was used to construct a model using data from more than two years. The model was subsequently used to derive long-term and continuous SM maps over Mainland China. The results showed that TDS-1 and CYGNSS agree and correlate very well with the SMAP SM in Mainland China (R = 0.676, MAE = 0.052 m³m⁻³, and ubRMSE = 0.060 m³m⁻³ for TDS-1; R = 0.798, MAE = 0.040 m³m⁻³, and ubRMSE = 0.062 m³m⁻³ for CYGNSS). The retrieved results were further validated using monthly in situ SM data from dense sites across Mainland China. It was found that the SM derived from the TDS-1/CYGNSS also correlated well with in situ SM (R = 0.687, MAE = 0.066 m³m⁻³, and ubRMSE = 0.056 m³m⁻³ for TDS-1; R = 0.724, MAE = 0.052 m³m⁻³, and ubRMSE = 0.053 m³m⁻³ for CYGNSS). The results in this study suggested that TDS-1/CYGNSS and the upcoming spaceborne GNSS-R mission could be new and powerful data sources to produce SM data set at a large scale and with relatively high precision.

With the launch of the Ice, Cloud and Land Elevation Satellite‐2 (ICESat‐2), densely measuring the Antarctic ocean all year long, monthly sea ice thickness changes can be inspected. However, the consistency between ICESat‐2 and its predecessor ICESat remains unknown if a long‐term change of the Antarctic sea ice thickness is to be examined. Due to the complex physical structure of Antarctic sea ice, an improved One‐Layer Method (OLMi) for sea‐ice thickness retrieval, with an ∼0.3 m uncertainty, is proposed in this study. Using this method, the monthly sea ice variation in each sea sector of Antarctica from ICESat‐2 is inspected, revealing detailed characteristics of Antarctic sea ice over sector scale such as bi‐modal distributions. The different retrieving methods for the total freeboard from ICESat are found to be ∼0.036 m lower than that from ICESat‐2. The circum‐Antarctic sea ice thickness shows a decreasing tendency (2003–2020), especially in summer with −0.014 m/yr.