Incorporated with digital beam-forming processing, multichannel spaceborne synthetic aperture radar (SAR) systems are able to overcome the minimum antenna area constraint and yield high resolution and wide swath (HRWS) images. This letter mainly investigates the performance of the space-time adaptive processing (STAP) approach applied to HRWS SAR imaging. The analytic expressions for the signal-to-noise ratio (SNR) scaling factor and azimuth ambiguity to signal ratio (AASR) are derived and confirmed by the simulated results. Then, the influence of channel errors on HRWS imaging is analyzed in detail.

Extensive studies on hydrological responses to forest change have been published for centuries, yet partitioning the hydrological effects of forest change, climate variability and other factors in a large watershed remains a challenge. In this study, we developed a single watershed approach combining the modified double mass curve (MDMC) and the time series multivariate autoregressive integrated moving average model (ARIMAX) to separate the impact of forest change, climate variability and other factors on dry season runoff variation in two large watersheds in China. The Zagunao watershed was examined for the deforestation effect, while the Meijiang watershed was examined to study the hydrological impact of reforestation. The key findings are: (1) both deforestation and reforestation led to significant reductions in dry season runoff, while climate variability yielded positive effects in the studied watersheds; (2) the hydrological response to forest change varied over time due to changes in soil infiltration and evapotranspiration after vegetation regeneration; (3) changes of subalpine natural forests produced greater impact on dry season runoff than alteration of planted forests. These findings are beneficial to water resource and forest management under climate change and highlight a better planning of forest operations and management incorporated trade-off between carbon and water in different forests.

For a target illuminated by a high-resolution and wide-swath synthetic aperture radar system, the velocity is estimated based on sparse direction-of-arrival estimation. Then, the space-time adaptive processing algorithm is utilized to suppress the clutter and reconstruct the spectra of the target, followed by the traditional target imaging. The performance of the proposed algorithm is analyzed in detail. The proposed algorithm can retain the power of the moving target, and is free of searching.

In this study, a new approach is presented to detect ground moving targets and estimate their motion parameters for high‐resolution and wide‐swath (HRWS) synthetic aperture radar (SAR) systems. First, the space‐time adaptive processing is adopted for clutter suppression, followed by the traditional SAR imaging and constant‐false‐alarm‐rate detection for obtaining the ambiguous images of ground moving targets. Then, the real targets can be distinguished from all the ambiguous ones based on the space relationship between ambiguous images. Finally, according to the shift amount of moving targets in azimuth, which can be obtained by utilising the range histories of moving targets, the motion parameter can be achieved. The spaceborne HRWS simulation data confirm the validity of the method.

The sewer/water leakage has become a worldwide concern. Conventional field measurement method is cost-consuming and time-inefficient, and deep learning (DL)-based method is not readily to be interpreted. Under this condition, we propose an interpretable principal component analysis (PCA) and support vector machine (SVM)-based leak detection algorithm (PSLDA) for identifying the sewer/water leakage. The PSLDA takes synthetic aperture radar (SAR)-derived moisture content and SAR interferometry (InSAR) closure phase as inputs, conducts feature extraction and dimensionality reduction with PCA, and implements binary classification with SVM, finally identifying leaks or non-leaks. The main advantage of PSLDA lies in that it respectively adopts the mathematically-derived PCA and SVM instead of the convolutional and pooling layer and the loss-function layer in the DL network, ensuring the validity and interpretability simultaneously. With 1222 in-situ leak points and 1206 non-leak points in Beijing and Tianjin of China, the PSLDA is trained. The well-trained PSLDA is subsequently applied to detect leak/non-leaks in real-world scenarios, and achieves a leak-detection accuracy of 91.86% in the fifth ring road of Beijing, a non-leak-detection accuracy of 88.10% in the Huizhou City of Guangdong Province. The proposed PSLDA demonstrates to be efficacious and credible, potentially offering explicit guidance for pipeline maintenance.

Tropical forests are frequently disturbed by extreme weather events including tropical cyclones and cold waves, which can not only yield direct impact on hydrological processes but also produce indirect effect on hydrology by disturbing growth and structures of tropical forests. However, the hydrological response to extreme weather-induced forest disturbances especially in tropical forested watersheds has been less evaluated. In this study, a tropical experimental watershed in Hainan Province, China, was selected to investigate the hydrological responses to extreme weather-induced forest disturbances by use of a single watershed approach and the paired-year approach. Key results are: (1) extreme weather-induced forest disturbances (e.g., typhoon and cold wave) generally had a positive effect on streamflow in the study watershed, while climate variability either yielded a negative effect or a positive effect in different periods; (2) the response of low flows to forest discussion was more pronounced; (3) the relative contribution of forest disturbances to annual streamflow (48.6%) was higher than that of climate variability (43.0%) from 1995 to 2005. Given the increasing extreme weather with climate change and their possible catastrophic effects on tropical forests and hydrology in recent decades, these findings are essential for future adaptive water resources and forest management in the tropical forested watersheds.