Accurate change detection continues to pose challenges due to the continuous renovation of old urban areas and the emergence of cloud cover in coastal areas. There have been numerous methods proposed to detect land-cover changes from optical images. However, there are still many flaws in many existing deep learning methods. In response to the problems of unpredictable change details and the lack of global semantic information in deep learning-based change detection models, a change detection model based on multi-scale and attention is proposed. Firstly, a multi-scale attention module is proposed to effectively obtain multi-scale semantic information to build an end-to-end dual multi-scale attention building change detection model. Secondly, an efficient double-threshold automatic data equalization rule is proposed to address the imbalance of data categories existing in the building change detection dataset, which effectively alleviates the severely skewed data distribution and facilitates the training and convergence of the model. The validation experiments are conducted on three open-source high-resolution building change detection datasets. The experimental results show that the proposed method in this paper can detect the location and area of the actual building changes more accurately and has better results in the detail detection part. This verifies the effectiveness and accuracy of the proposed method.

Waterlogging harms winter wheat growth. To enable accurate monitoring of agricultural waterlogging, this paper conducts a winter wheat waterlogging monitoring study using multi-source data in Guzhen County, Anhui Province, China. The hydrological model HYDRUS-1D is coupled with the crop growth model WOFOST, and the Ensemble Kalman Filter is used to assimilate Sentinel-1 inversion soil moisture data. According to the precision and continuity of soil moisture, the damage of winter wheat waterlogging were obtained. The experimental results show that the accuracy of the soil moisture is improved after data assimilation compared with that before data assimilation, and the Nash–Sutcliffe efficiency (NSE) of the simulated soil moisture values at three monitoring sites increased from 0.528, 0.541 and 0.575 to 0.752, 0.692 and 0.731, respectively. A new waterlogging identification criterion has been proposed based on the growth periods and probability distribution of soil moisture. The proportion, calculated from this identification criterion, of the waterlogging wheat farmland in total farmland shows a high correlation with the yield reduction rate. The correlation coefficient of the waterlogging farmland proportion and the yield reduction rate in 11 towns of Guzhen County reaches 0.78. Through the synchronization of geography, agriculture and meteorology, the framework shows great potential in waterlogging monitoring.

This study investigates waterlogging disasters in winter wheat using the Agricultural Production Systems Simulator (APSIM) model. This research explores the effects of soil hypoxia on wheat root systems and the tolerance of wheat at different growth stages to waterlogging, proposing a model to quantify the degree of waterlogging in wheat. Remote sensing data on soil moisture and wheat distribution are utilized to establish a monitoring system for waterlogging disasters specific to winter wheat. The analysis focused on affected areas in Bengbu and Jingzhou. Experimental results from 2017 to 2022 indicate that the predominant levels of waterlogging disasters in Bengbu and Jingzhou were moderate and mild, with the proportion of mild waterlogging ranging from 30.1% to 39.3% and moderate waterlogging from 14.8% to 25.6%. A combined analysis of multi-source remote sensing data reveals the key roles of precipitation, evapotranspiration, and altitude in waterlogging disasters. This study highlights regional disparities in the distribution of waterlogging disaster risks, providing new strategies and tools for precise assessment of waterlogging disasters.