Various drought indices have been developed to monitor drought conditions. Each index has typical characteristics that make it applicable to a specific environment. In this study, six popular drought indices, namely, precipitation condition index (PCI), temperature condition index (TCI), vegetation condition index (VCI), vegetation health index (VHI), scaled drought condition index (SDCI), and temperature–vegetation dryness index (TVDI), have been used to monitor droughts in the Greater Changbai Mountains(GCM) in recent years. The spatial pattern and temporal trend of droughts in this area in the period 2001–2018 were explored by calculating these indices from multi-source remote sensing data. Significant spatial–temporal variations were identified. The results of a slope analysis along with the F-statistic test showed that up to 20% of the study area showed a significant increasing or decreasing trend in drought. It was found that some drought indices cannot be explained by meteorological observations because of the time lag between meteorological drought and vegetation response. The drought condition and its changing pattern differ from various land cover types and indices, but the relative drought situation of different landforms is consistent among all indices. This work provides a basic reference for reasonably choosing drought indices for monitoring drought in the GCM to gain a better understanding of the ecosystem conditions and environment.

Various drought indices are developed to monitor drought conditions. Each drought index applies to specific environments given its own characteristics. This article aims to compare the drought conditions of continental China detected by remote sensing. We compare diverse types of remote sensing-based indices for studying drought situation in 2017. The Station-based precipitation data with different scales are used to evaluate drought phenomena. The drought indices compared in this article include the precipitation condition index (PCI) derived from tropical rainfall measuring mission data, vegetation condition index (VCI), temperature condition index (TCI) derived from moderate-resolution imaging spectra-radiometer (MODIS) data, and other integrated drought indices, which combine VCI, TCI, and PCI. Vegetation health index (VHI), scaled drought condition index (SDCI), and synthesized drought index (SDI) constitute the integrated drought indices. The results indicate that numerous indices have different applicability across continental China. VHI has the optimal correlation with short-term and long-term precipitation. The SDI, SDCI, VCI, TCI, and temperature-vegetation dryness index (TVDI) are also reliable for monitoring drought conditions and are more suitable for long-term than short-term precipitation. Furthermore, combined indices (VHI, SDI, and SDCI) perform better than single drought indices (PCI, TCI, and VCI). VHI, SDI, and SDCI did not show good consistency in croplands when monitoring agricultural drought.

Soil salinization is one of the most critical threats to land degradation in arid and semiarid regions. This study is based on machine learning using Landsat 8 operational land imager (OLI) imagery to estimate soil salinity in Da'an City. A total of 19 spectral indexes, including 15 salinity indexes, 3 vegetation indexes, and a brightness index, were calculated using the blue, green, red, and near-infrared bands of Landsat 8 OLI images. Four machine learning regression algorithms, namely Cubist, support vector regression, random forest regression, and extreme gradient boosting regression, were used on the basis of the 19 aforementioned indexes to estimate soil salinity. Results demonstrated that the Cubist model has the highest prediction accuracy (RMSE=0.31mS/cm). Thus, the spatial distribution of soil salinity based on the Cubist model best meets the expectations of the authors. Moreover, the canopy salinity index (CRSI) correlated the most with the measured electrical conductivity, demonstrating a correlation coefficient of −0.44. After using the random forest method for variable screening, the Cubist model based on 9 spectral indexes still achieved satisfactory prediction accuracy with the RMSE of 0.34 mS/cm. Thus, the Cubist method is recommended for soil salinity monitoring in arid and semiarid areas.

Soil salinization has become one of the major environmental problems threatening food security. The identification and knowledge of the spatial distributions of soil salinization are key in addressing this problem. This study assumes that a good saline land identification effect can be obtained with the help of deep learning methods. Therefore, this study used the OLI sensor data from the Landsat-8, based on the U²-Network, and proposes a method to extract saline land from remote sensing images. The study also adds different salinity indices (SI, SI1, and SI2) to explore its impact on classification accuracy. Through our method, accurate saline soil distribution information were obtained, and several verification indicators (the Intersection-over-Union (IoU), recall, precision, and F1-score) were all measured above 0.8. In addition, compared with the multi-spectral training results, the classification accuracy increased after adding a specific salinity index, and most of the accuracy indices increased by about 2% (the IoU increased by 3.70%, recall increased by 1.50%, precision increased by 2.81%, and F1-score increased by 2.13%). In addition, we also included a case study based on our methodology to analyze the distribution characteristics and changes of saline soil in the Zhenlai area of Northeast China from 2016 to 2020. We found that the area of saline land in the Zhenlai area has reduced, which shows that the extraction method proposed in this study is feasible. Overall, this paper indicates that deep learning-based methods can efficiently extract the salinity of soil and enhance the mapping of its spatial distribution. The study has the broad impact of supplementing satellite imagery for salinity modeling and helping to guide agricultural land management practices for northeastern China and other salinized regions.

Jilin Province is located in the northeast of China, and has fragile ecosystems, and a vulnerable environment. Large-scale, long time series, high-precision land-use/cover change (LU/CC) data are important for spatial planning and environmental protection in areas with high surface heterogeneity. In this paper, based on the high temporal and spatial fusion data of Landsat and MODIS and the Google Earth Engine (GEE), long time series LU/CC mapping and spatio-temporal analysis for the period 2000–2023 were realized using the random forest remote sensing image classification method, which integrates remote sensing indices. The prediction results using the OL-STARFM method were very close to the real images and better contained the spatial image information, allowing its application to the subsequent classification. The average overall accuracy and kappa coefficient of the random forest classification products obtained using the fused remote sensing index were 95.11% and 0.9394, respectively. During the study period, the area of cultivated land and unused land decreased as a whole. The area of grassland, forest, and water fluctuated, while building land increased to 13,442.27 km² in 2023. In terms of land transfer, cultivated land was the most important source of transfers, and the total area share decreased from 42.98% to 38.39%. Cultivated land was mainly transferred to grassland, forest land, and building land, with transfer areas of 7682.48 km², 8374.11 km², and 7244.52 km², respectively. Grassland was the largest source of land transfer into cultivated land, and the land transfer among other feature types was relatively small, at less than 3300 km². This study provides data support for the scientific management of land resources in Jilin Province, and the resulting LU/CC dataset is of great significance for regional sustainable development.

Currently, soil salinization is a serious problem affecting agricultural production and human settlements. Remote sensing techniques have the advantages of a large monitoring range, rapid acquisition of information, implementation of dynamic monitoring, and low impact on the ground surface. Over the past two decades, many semi-empirical bidirectional polarized distribution function (BPDF) models have been proposed to accurately calculate the polarized reflectance (Rp) on the soil surface. Although there have been some studies on the BPDF model based on traditional machine learning methods, there is a lack of research on the BPDF model based on deep learning, especially using laboratory measurement spectrum data as the processing object, with limited research results. In this paper, we collected saline-alkaline soil in the field as the observation object and measured the Rp at multiple angles in the laboratory environment. We used semi-empirical models (the Nadal–Bréon model, Litvinov model, and Xie–Cheng model) and machine learning methods (support vector regression, random forest, and deep neural networks regression) to simulate and predict the surface Rp of saline-alkaline soils and compare them with experimental results. The measured values of the laboratory are compared and fitted, and the root mean squared error, R-squared, and correlation coefficient are calculated to express the prediction effect. The results show that the predictions of the BPDF model based on machine learning methods are generally better than those of the semi-empirical BPDF model, which is improved by 3.06% at 670 nm and 19.75% at 865 nm. The results of this study also provide new ideas and methods based on deep learning for the prediction of Rp on the surface of saline-alkaline soils.