Active microwave remote sensing data, such as Sentinel-1 Synthetic Aperture Radar (SAR), is indispensable for flooding process monitoring and emergency response, as it possesses the all-weather Earth's surface imaging capabilities and provides global coverage freely. Nevertheless, flood monitoring based on SAR still faces great challenges stemming from the inherent noise and interference present in background, which are further complicated by the dynamic and urgent nature of floods. To address these challenges, this study utilizes Sentinel-1 SAR data, complemented by Sentinel-2 multispectral data and Digital Elevation Model (DEM), to construct a knowledge-driven flood intelligent monitoring (KDFIM) method consisting of three progressive modules. Firstly, KDFIM employs a spatiotemporal SAR feature fusion module, designed to mitigate the noise and interference present in background, effectively extracting multi-temporal SAR features for flood detection. Subsequently, a flood knowledge implanting module is constructed to facilitate the adaptive extraction of flood extent by integrating flood-related knowledge extracted into multi-temporal SAR features. Finally, a knowledge-driven flood multi-parameters calculating module is developed to enhance three-dimensional dynamic flood analysis by integrating multiple remote sensing data. The KDFIM is validated with the flood event caused by the destruction of the Kahovka Dam being a case study, which presented that the flood inundation extraction accuracy reaching 98.46 ± 0.39% and a Kappa coefficient of 0.9691 ± 0.08. Additionally, analysis of flood inundation extent, water depth, and inundation duration based on KDFIM reveals the peak flood on June 9, 2023, with water depth exceeding 6 meters and inundation lasting 8 days in certain areas. Overall, following a comprehensive multi-parameters analysis to the flooding process reveals a significant and far-reaching impact on both the watershed and local cities.

Damage to terrain and the collapse of buildings present significant challenges to human society due to earthquakes, floods, and other natural disasters. Rapid and reliable remote sensing identification of disaster area damage and building collapses is crucial for emergency response and disaster mitigation. However, disaster areas are often affected by clouds, rain, fog, smoke, etc., making SAR-based remote sensing an essential tool for ground observation. Leveraging the scale invariance of texture information features in SAR images, the SAR Texture Fractal Change-Based Model (STFCM) enables a swift evaluation of post-disaster surface damage conditions. To address the challenge of coordinating the use of pre- and post-disaster images, the proposed SAR image preprocessing of STFCM effectively overcomes the difficulties arising from significant differences between pre- and post-disaster SAR images. Furthermore, considering the issue of weak SAR image texture information and severe noise interference, STFCM introduces the concept of a neighborhood and proposes a multi-scale multi-order neighborhood SAR texture fractal dimension calculation module based on nested windows. This module effectively enhances the texture information of buildings in SAR images and reduces the interference of noise in SAR image information extraction. Finally, to address the uncertainty caused by chaotic post-disaster surface structures, STFCM proposes a multiscale fractal dimensional consistency assessment module. The consistency analysis based on multiscale fractal change monotonicity provides more accurate and reliable results. The STFCM operates independently on computers, conducts automatic computations, and efficiently identifies surface damage across disaster-stricken areas as well as critical local regions with speed and accuracy, providing technical support for remote sensing mapping of damaged areas, rapid assessment of disaster situations, and decision-making in emergency response and relief efforts.

Vegetation indices (VIs) are valuable in numerous remote sensing fields. Nevertheless, it is difficult to accurately discern vegetation using VIs, causing by the effects of the shaded vegetation, saturation vegetation, synthetic turf stadiums, and color steel tile. Measuring the spectral curves of shadows, synthetic turf stadiums, and color steel tiles, we found that the interfering features can be suppressed by a formula composition, named the Original Multiple Spectrum-based Vegetation Index (OMSVI). In addition, we integrated the anti-saturation function into OMSVI to solve the band saturation issue, resulting in the Improved Multiple Spectrum-based Vegetation Index (IMSVI). To address the structure saturation issue, the blue and red edge 1 bands could be used to alter the denominator structure of IMSVI, thus obtaining the final Modified Multiple Spectrum-based Vegetation Index (MMSVI). To demonstrate the generalization of the MMSVI, nine common VIs were compared in three large cities with diverse environments. The results of MMSVI showed that the vegetation extraction accuracy was stabilized at more than 90%, and the saturated LAI position continued to exceed 5.0, both of which were superior to the current VIs. In combination with a conventional optical sensor, it provided an innovative solution for monitoring vegetation in high-dynamic spaces, particularly in subtropical cities where saturation issues are more prevalent.

Soil salinization has degraded a large number of land resources, and is one of the most serious environmental issues occurring on a global scale, particularly in arid and semi-arid regions and coastal areas where evaporation is mainly used to supply precipitation. Monitoring these regions' salinization, due to the numerous distribution of saline and complex form factors, is difficult. The existing spectral index structure only uses the sensitive band for simple calculation, does not consider the impact of the relationship between bands on soil salinization, and can not accurately extract saline soil and characterize the degree of salinization. In this paper, we constructed a new soil salinity index, namely the Baseline-based Soil Salinity Index (BSSI), by introducing the concept of the virtual-baseline. In the configuration of the BSSI, we selected three new bands that are sensitive to saline soil to improve its recognition. BSSI uses the infrared and short-wave infrared-2 bands as the virtual-baseline to measure the height of the short-wave infrared-1 reflectance. Validated by the measured data in the coastal areas of the Yellow River Delta, BSSI is significantly better than other salinity indices, with a correlation coefficient of up to 90%. Meanwhile, the BSSI also was applied to the arid region of Xinjiang, and the results showed that the effect was still the best, which correlation can reach more than 96%. Therefore, BSSI can be used as a reliable indicator of soil salinization monitoring.

Synergetic use of both Synthetic Aperture Radar (SAR) and optical data has been applied for mapping impervious surface (IS) in recent years. However, researchers use only descending or ascending SAR to cooperate with optical data, which cannot avoid the problems caused by side looking of SAR, including layover and SAR shadow. This research explored and analyzed the impact of mapping IS by cooperating descending, ascending SAR and both of them with optical data respectively. To obtain a credible result, support vector machine (SVM) is employed to conduct the classification. The results indicated that the combined use of both descending and ascending SAR with optical data achieved the highest accuracy on IS mapping.

As the source of urban ecology, urban green space (UGS) has always been the focus of urban ecological research. The complex urban surface structure causes great interference to UGS extraction. In areas with high vegetation density, the vegetation index becomes rapidly saturated. Existing vegetation indices are not effective for the two problems due to that these indices do not make full use of the rich spectral information contained in hyperspectral image. To remedy these issues, a hyperspectral image based vegetation index (HSVI) is proposed. In the formulation of the HSVI numerator, we chose four new bands combination sensitive to vegetation to improve the identification of vegetation. We opt the sum of the red edge and green bands as the HSVI denominator and reconstruct the easily saturable band (760nm) in the form of exponential function to weaken the saturation problem. We use the hyperspectral image from Shanghai Theatre Academy and University of Houston with different geomorphological features to verify the effect of HSVI. The performance of HSVI is compared with three widely adopted vegetation indices, i.e., the normalized difference vegetation index (NDVI), the optimized soil-adjusted vegetation index (OSVAI) and the wide-dynamic-range vegetation index (WDRVI). The results show that the UGS extraction accuracy of HSVI is more than 90%, which is significantly better than the other indices. Meanwhile, HSVI can also solve the problem of vegetation index saturation. It can be proved that HSVI can fulfill the requirements of urban ecological research on a fine scale.