The main aim of this study was to utilize remote sensing data to establish regression models through machine learning to predict locust density in the upcoming year. First, a dataset for monitoring grassland locust density was constructed based on meteorological data and multi-source remote sensing data in the study area. Subsequently, an SVR (support vector regression) model, BP neural network regression model, random forest regression model, BP neural network regression model with the PCA (principal component analysis), and deep belief network regression model were built on the dataset. The experimental results show that the random forest regression model had the best prediction performance among the five models. Specifically, the model achieved a coefficient of determination (R²) of 0.9685 and a root mean square error (RMSE) of 1.0144 on the test set, which were the optimal values achieved among all the models tested. Finally, the locust density in the study area for 2023 was predicted and, by comparing the predicted results with actual measured data, it was found that the prediction accuracy was high. This is of great significance for local grassland ecological management, disaster warning, scientific decision-making support, scientific research progress, and sustainable agricultural development.

As one of the important lakes in the “One Lake and Two Seas” of the Inner Mongolia Autonomous Region, the monitoring of water quality in Lake Daihai has attracted increasing attention, and the concentration of chlorophyll-a directly affects the water quality, making the monitoring of chlorophyll-a concentration in Lake Daihai particularly crucial. Traditional methods of monitoring chlorophyll-a concentration are not only inefficient but also require significant human and material resources. Remote sensing technology has the advantages of wide coverage and short update cycles. For lakes such as Daihai with a high salinity content, salinity is considered a key factor when inverting the concentration of chlorophyll-a. In this study, machine learning models, including model stacking from ensemble learning, a ridge regression model, and a random forest model, were constructed. After comparing the training accuracy of the three models on Zhuhai-1 satellite data, the random forest model, which had the highest accuracy, was selected as the final training model. By comparing the accuracy changes before and after adding salinity factors to the random forest model, a high-precision model for inverting chlorophyll-a concentration in hypersaline lakes was obtained. The research results show that, without considering the salinity factor, the root mean square error (RMSE) of the model was 0.056, and the coefficient of determination (R²) was 0.64, indicating moderate model performance. After adding the salinity factor, the model accuracy significantly improved: the RMSE decreased to 0.047, and the R² increased to 0.92. This study provides a solid basis for the application of remote sensing technology in hypersaline aquatic environments, confirming the importance of considering salinity when estimating chlorophyll-a concentration in hypersaline waters. This research helps us gain a deeper understanding of the water quality and ecosystem evolution in Daihai Lake.

Above-ground biomass (AGB) is a core indicator for evaluating grassland ecosystem health and carbon storage. Traditional ground-based AGB measurements are labor-intensive and ill suited for large-scale monitoring. This study addresses this gap by developing a Multilayer Perceptron (MLP) model integrating Landsat 9 OLI/TIRS imagery acquired on 15 August 2024, with ground data from 78 sampling points (62 training, 16 testing). Incorporating fourteen multi-source features (seven vegetation indices, e.g., Modified Vegetation Index (MVI) and Green Chlorophyll Index (CIg); four meteorological variables; three soil properties), all data were standardized via z-score normalization before training. The MLP model, optimized via six-fold cross-validation, achieved an R² of 0.765 and RMSE of 38.066 g/m², outperforming XGBoost (R² = 0.723, RMSE = 41.354 g/m²) with a statistically significant 5.8% accuracy improvement (p < 0.05). Spatial analysis revealed a north-to-south AGB gradient, strongly correlated with precipitation gradients (250–350 mm/year) and soil organic carbon (R = 0.428). These findings provide a robust framework for climate-adaptive grassland management and carbon assessment in semi-arid regions.

The desertified ecological restoration vegetation of Wuzhumuqin grassland plays an important role in the ecological restoration and protection of the region. However, there are few studies on the monitoring of the changes in ecological restoration vegetation in grassland sandy land in the past. In order to improve the low efficiency of ecological restoration vegetation monitoring, this study used Gaofen-6 (GF-6) remote sensing data to calculate the kernel Normalized Difference Vegetation Index (kNDVI) and vegetation coverage of ecological restoration vegetation and analyze their spatial and temporal trends. At the same time, a transform three-branch network structure based on deep learning is proposed to extract visual features. The kernel Normalized Difference Vegetation Index-position-temporal awareness transformer (kNDVI-PT-Former) model monitoring method based on two-phase remote sensing image features combined with kNDVI for spatio-temporal feature extraction can accurately obtain the vegetation changes in desertification ecological restoration in Wuzhumuqin grassland. The results show that the kNDVI of the study area shows an increasing trend from 2019 to 2024. The kNDVI value is 0.4086 in 2019 and 0.4927 in 2024. From the perspective of the change trend of vegetation coverage, the overall vegetation coverage of the Wuzhumuqin desertification restoration study area showed a gradual increase trend from 2019 to 2024, and the vegetation coverage increased by 19% in 2024 compared with 2019. The transformation of vegetation coverage from low level to high level in the study area is more prominent. Based on the self-built monitoring dataset of more than 5.2 million pairs of grassland vegetation changes, through model comparison and analysis, the kNDVI-PT-Former model obtains that the Class Pixel Accuracy (CPA) is 0.7295, the Intersection over Union (IoU) is 0.7228, and the overall monitoring accuracy of the model is improved by 11%. Furthermore, the stability of the model’s performance was confirmed through evaluation with five-fold cross-validation.

Phase unwrapping in interferometric synthetic aperture radar (InSAR) aims to recover a continuous phase field from wrapped observations, which enable accurate topographic reconstruction and surface deformation measurements. With the recent advances in deep learning (DL), several DL-based unwrapping approaches have shown promising performance. However, deep learning networks suffer from inconsistent feature representations between encoder and decoder stages. This leads to incompatible skip connections that provide limited benefits and even degrade reconstruction quality. To overcome this limitation, we propose ResUCTransNet that integrates residual learning with transformer-based feature modeling. The network employs a multi-scale residual backbone derived from Res_UNet to extract stable deep features. Then, to replace conventional skip connections, a channel transformer (CTrans) module is introduced that composed of channel-wise cross fusion transformer (CCT) and channel-wise cross attention (CCA). This design effectively reduces the semantic gap in different network stages, which allows adaptive integration of local CNN features and global transformer representations. Experiments on the public InSAR-DLPU dataset demonstrate that ResUCTransNet effectively reduces model complexity and achieves substantial improvements over existing deep learning models and classical unwrapping algorithms. Specifically, the proposed method attains the best performance in terms of RMSE and SSIM (RMSE = 1.6247, SSIM = 0.7741). Compared with the second-best model, Res_Unet (RMSE = 2.8409, SSIM = 0.7733), ResUCTransNet achieves an approximately 42.8% reduction in RMSE while maintaining nearly identical structural similarity. The proposed method provides higher reconstruction accuracy and better structural fidelity, while maintaining strong robustness and generalization in complex terrain or severe noise conditions.

The Hunshandake Sandy Land is located in the transition zone between typical steppe and desertified grassland in Inner Mongolia, where grassland and sandy land are the predominant land-cover types, accompanied by scattered buildings, roads, and lakes. Its ecosystem is fragile and exhibits significant spatiotemporal dynamics. Given the need for reliable land-cover monitoring in such a fragile and dynamically changing ecosystem, spaceborne polarimetric synthetic aperture radar (PolSAR) is adopted for typical land-cover classification to support grassland ecological monitoring and desertification control. However, high-dimensional PolSAR features derived from multiple decomposition strategies usually contain redundancy and correlation, which may weaken the representation of critical discriminative scattering information. To address this issue, we propose a DeepLassoNet-based feature selection method for typical land-cover classification in the Hunshandake Sandy Land. First, a set of conventional polarimetric decomposition features derived from PolSARpro software is extracted to construct an initial high-dimensional feature pool. Next, a dual-module DeepLassoNet is designed by integrating a nonlinear spatial representation module and an explicit feature–response module, so as to learn discriminative spatial representations while preserving direct channel-level relationships between polarimetric features and classification responses. Finally, by introducing hierarchical sparsity constraints and combining gradient updating with proximal mapping, redundant and irrelevant features are progressively eliminated along a sparse feature screening path. Experimental results demonstrate that the proposed method can effectively select discriminative PolSAR features, reduce feature redundancy interference, and improve the classification accuracy and robustness in complex sandy land–grassland transition scenarios.