Soil moisture (SM) is an important index of soil drought, and it directly controls the energy balance and water cycle of the land surface. As an indicator and amplifier of global warming, the Tibetan Plateau (TP) is becoming warmer and wetter. Because of its particular geographical environment, large‐scale measurements of SM on the TP can only be achieved by satellite remote sensing. The resolution of current SM product of the Soil Moisture Active Passive (SMAP) satellite is 36 km, which is insufficient for many practical applications. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) and Digital Elevation Model (DEM) are applied to increase the resolution of SM down to 1 km using the Random Forest (RF) algorithm. The preliminary results of the proposed algorithm are evaluated by station observations and other reanalysis products. The downscaled results are more consistent with the in situ observations, the Land Data Assimilation System (CLDAS) from China Meteorological Administration (CMA), and the Global Land Data Assimilation System (GLDAS) from National Aeronautics and Space Administration (NASA) than the original SMAP product. The downscaling algorithm is most effective for grasslands. It is demonstrated that high‐resolution SM products can be generated by fusing various features using machine‐learning algorithms.

Reversible color-to-grayscale conversion (RCGC) is a method that embeds the chromatic information of a full color image into its grayscale version such that the original color image can be reconstructed in the future when necessary. In practical applications, it is required to provide a means to authenticate an information-embedded image such that its integrity can be guaranteed. However, none of the current RCGC algorithms take this factor into account. In this paper, to address this issue, we develop an information-embedding framework based on a vector quantization-based (VQ-based) RCGC algorithm recently proposed by us. Under this framework, we propose a RCGC algorithm that can embed both chromatic information and fragile watermark simultaneously into a grayscale image with the same technique to reduce the complexity and improve the efficiency. Like other VQ-based RCGC algorithms, the performance of the proposed RCGC algorithm highly relies on the palette it uses. We also propose a palette generation algorithm in this paper to support the information embedding process such that the visual quality of the color-embedded grayscale images and the reconstructed color images can be significantly improved.

In recent years, unmanned aerial vehicles (UAVs) have undergoing experienced remarkable advancements. Nevertheless, the growing utilization of UAVs brings forth potential security threats to the public, particularly in private and sensitive locales. To address these emerging hazards, we introduce a low-cost, three-stage UAV detection framework for monitoring invading UAVs in vulnerable zones. This framework devised through an exhaustive investigation of the Chinese UAV market. Various scenarios were examined to evaluate the effectiveness of the framework, and it was subsequently implemented on a portable board. Experiments demonstrated that the proposed framework can detect invading UAVs at an early stage, even in stealthy mode. As such, the framework has the potential to be applied in the formulation of a portable surveillance system for a UAV-restricted region.