Accurate lake depth mapping and estimation of changes in water level and water storage are fundamental significance for understanding the lake water resources on the Tibetan Plateau. In this study, combined with satellite images and bathymetric data, we comprehensively evaluate the accuracy of a multi-factor combined linear regression model (MLR) and machine learning models, create a depth distribution map and compare it with the spatial interpolation, and estimate the change of water level and water storage based on the inverted depth. The results indicated that the precision of the random forest (RF) was the highest with a coefficient of determination (R²) value (0.9311) and mean absolute error (MAE) values (1.13 m) in the test dataset and had high reliability in the overall depth distribution. The water level increased by 9.36 m at a rate of 0.47 m/y, and the water storage increased by 1.811 km³ from 1998 to 2018 based on inversion depth. The water level change was consistent with that of the Shuttle Radar Topography Mission (SRTM) method. Our work shows that this method may be employed to study the water depth distribution and its changes by combining with bathymetric data and satellite imagery in shallow lakes.

Synthetic aperture radar (SAR) and optical images provide highly complementary ground information. The fusion of SAR and optical data can significantly enhance semantic segmentation inference results. However, the fusion methods for multimodal data remains a challenge for current research due to significant disparities in imaging mechanisms from diverse sources. Our goal was to bridge the significant gaps between optical and SAR images by developing a dual-input model that utilizes image-level fusion. To improve most existing state-of-the-art image fusion methods, which often assign equal weights to multiple modalities, we employed the principal component analysis (PCA) transform approach. Subsequently, we performed feature-level fusion on shallow feature maps, which retain rich geometric information. We also incorporated a channel attention module to highlight channels rich in features and suppress irrelevant information. This step is crucial due to the substantial similarity between SAR and optical images in shallow layers such as geometric features. In summary, we propose a generic multimodal fusion strategy that can be attached to most encoding–decoding structures for feature classification tasks, designed with two inputs. One input is the optical image, and the other is the three-band fusion data obtained by combining the PCA component of the optical image with the SAR. Our feature-level fusion method effectively integrates multimodal data. The efficiency of our approach was validated using various public datasets, and the results showed significant improvements when applied to several land cover classification models.

Rapid urban development in China has aggravated land subsidence, which poses a potential threat to sustainable urban development. It is imperative to monitor and predict land subsidence over large areas. To address these issues, we chose Henan Province as the study area and applied small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) technology to obtain land deformation information for monitoring land subsidence from November 2019 to February 2022 with 364 multitrack Sentinel-1A satellite images. The current traditional time-series deep learning models suffer from the problems of (1) poor results in extracting a sequence of information that is too long and (2) the inability to extract the feature information between the influence factor and the land subsidence well. Therefore, a long short-term memory-temporal convolutional network (LSTM-TCN) deep learning model was proposed in order to predict land subsidence and explore the influence of environmental factors, such as the volumetric soil water layer and monthly precipitation, on land subsidence in this study. We used leveling data to verify the effectiveness of SBAS-InSAR in land subsidence monitoring. The results of SBAS-InSAR showed that the land subsidence in Henan Province was obvious and uneven in spatial distribution. The maximum subsidence velocity was −94.54 mm/a, and the uplift velocity was 41.23 mm/a during the monitoring period. Simultaneously, the land subsidence in the study area presented seasonal changes. The rate of land subsidence in spring and summer was greater than that in autumn and winter. The prediction accuracy of the LSTM-TCN model was significantly better than that of the individual LSTM and TCN models because it fully combined their advantages. In addition, the prediction accuracies, with the addition of environmental factors, were improved compared with those using only time-series subsidence information.

Soil organic matter (SOM) is an essential nutrient for crop growth and development. Hyperspectral satellite images with comprehensive spectral band coverage and high spectral resolution can be used to estimate and draw a spatial distribution map of SOM content in the region, which can provide a scientific management basis for precision agriculture. This study takes Xinzheng City, Henan Province’s agricultural area, as the research object. Based on ZY1-02D hyperspectral satellite image data, the first derivative of reflectance (FDR) was processed on the original reflectance (OR). The SOM characteristic spectral bands were extracted using the correlation coefficient (CC) and least absolute shrinkage and selection operator (Lasso) methods. The prediction model of SOM content was established by multiple linear regression (MLR), partial least squares regression (PLSR), and random forest (RF) algorithms. The results showed that: (1) FDR processing can enhance SOM spectral features and reduce noise; (2) the Lasso feature band extraction method can reduce the model’s input variables and raise the estimation precision; (3) the SOM content prediction ability of the RF model was significantly better than that of the MLR and PLSR models. The FDR-Lasso-RF model was the best SOM content prediction model, and the validation set R² = 0.921, MAE_V = 0.512 g/kg, RMSE_V = 0.645 g/kg; (4) compared with laboratory hyperspectral data-SOM prediction methods, hyperspectral satellite data can achieve accurate, rapid, and large-scale SOM content prediction and mapping. This study provides an efficient, accurate, and feasible method for predicting and mapping SOM content in an agricultural region.

The non-stationarity, nonlinearity and complexity of the PM_2.5 series have caused difficulties in PM_2.5 prediction. To improve prediction accuracy, many forecasting methods have been developed. However, these methods usually do not consider the importance of data preprocessing and have limitations only using a single forecasting model. Therefore, this paper proposed a new hybrid decomposition–ensemble learning paradigm based on variation mode decomposition (VMD) and improved whale-optimization algorithm (IWOA) to address complex nonlinear environmental data. First, the VMD is employed to decompose the PM_2.5 sequences into a set of variational modes (VMs) with different frequencies. Then, an ensemble method based on four individual forecasting approaches is applied to forecast all the VMs. With regard to ensemble weight coefficients, the IWOA is applied to optimize the weight coefficients, and the final forecasting results were obtained by reconstructing the refined sequences. To verify and validate the proposed learning paradigm, four daily PM_2.5 datasets collected from the Jing-Jin-Ji area of China are chosen as the test cases to conduct the empirical research. The experimental results indicated that the proposed learning paradigm has the best results in all cases and metrics.