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.

Annually, hundreds of individuals tragically lose their lives at sea due to shipwrecks or aircraft accidents. For search and rescue personnel, the task of locating the debris of a downed aircraft in the vastness of the ocean presents a formidable challenge. A primary task these teams face is determining the search area, which is a critical step in the rescue operation. The movement of aircraft wreckage on the ocean surface is extremely complex, influenced by the combined effects of surface winds, waves, and currents. Establishing an appropriate drift motion prediction model is instrumental in accurately determining the search area for the wreckage. This article initially conducts maritime drift observation experiments on wreckage, and based on the results of these experiments, analyzes the drift characteristics and patterns of the debris. Subsequently, employing a wealth of observational experimental data, three types of drift prediction models for the wreckage are established using the least squares method. These models include the AP98 model, the dynamics model, and an improved model. In conclusion, the effectiveness and accuracy of the three models is evaluated and analyzed using Monte Carlo techniques. The results indicate that the probability of positive crosswind leeway (CWL) is 47.4%, while the probability of negative crosswind leeway (CWL) is 52.6%. The jibing frequency is 7.7% per hour, and the maximum leeway divergence angle observed is 40.4 degrees. Among the three drift prediction models, the refined AP98 drift model demonstrates the highest forecasting precision. The findings of this study offer a more accurate drift prediction model for the search of an aircraft lost at sea. These results hold significant guiding importance for maritime search and rescue operations in the South China Sea.

The necessity of combining multi-source remote sensing sensors for multi-temporal observation of the Earth, arises from the influence of cloud cover and the limited operational period of single satellite. However, significant differences exist among satellites and among sensors within the same satellite system. In this case, conducting a cross-comparison analysis is crucial for reliable change detection. The Jilin-1 satellite constellation has a total of 108 satellites in orbit, forming the largest commercial remote sensing satellites constellation in China. Firstly, the top of atmosphere (TOA) data from the Jilin-1GF03B series satellites (Jilin-1GF03B04, Jilin-1GF03B05, and Jilin-1GF03B06), following radiometric calibration processing, were selected to evaluate their performance in a single phase by comparison to Sentinel-2 satellites. Subsequently, we compared four spectrum bands and four commonly used biophysical indices, and the results indicated a higher radiation consistency between Jilin-1GF03B and Sentinel-2 satellites, suggested by correlation coefficient (over 0.953), and R2 (over 0.907). Moreover, we assessed the band sensitivity of the two satellites across different land use classes. It was found that Sentinel-2 exhibited greater sensitivity in bare land, vegetation, and forest, whereas the near-infrared band of Jilin-1GF03B proved more sensitive in water areas. Finally, to mitigate the risk of randomness in a single temporal phase, we constructed two long-term vegetation index datasets for phenological analysis. The close performance alignment observed for both single-phase and phenological variations indicated that Jilin-1GF03B satellites and Sentinel-2 can be effectively utilized together for long-term monitoring of Earth surface attributes.

In recent years, the advancement of CubeSat technology has led to the emergence of high-resolution, flexible imaging satellites as a pivotal source of information for the efficient and precise monitoring of crops. However, the dynamic geometry inherent in flexible side-view imaging poses challenges in acquiring the high-precision reflectance data necessary to accurately retrieve crop parameters. This study aimed to develop an angular correction method designed to generate nadir reflectance from high-resolution satellite side-swing imaging data. The method utilized the Anisotropic Flat Index (AFX) in conjunction with a fixed set of Bidirectional Reflectance Distribution Function (BRDF) parameters to compute the nadir reflectance for the Jilin-1 GP01/02 multispectral imager (PMS). Crop parameter retrieval was executed using regression models based on vegetation indices, the leaf area index (LAI), fractional vegetation cover (FVC), and chlorophyll (T850 nm/T720 nm) values estimated based on angle corrected reflectance compared with field measurements taken in the Inner Mongolia Autonomous Region. The findings demonstrate that the proposed angular correction method significantly enhances the retrieval accuracy of the LAI, FVC, and chlorophyll from Jilin-1 GP01/02 PMS data. Notably, the retrieval accuracy for the LAI and FVC improved by over 25%. We expect that this approach will exhibit considerable potential to improve crop monitoring accuracy from high-resolution satellite side-view imaging data.

hange detection (CD) in remote sensing images plays a vital role in dynamic Earth measurement.hange detection (CD) in remote sensing images plays a vital role in dynamic Earth measurement.C However, the scarcity of high-resolution, comprehensive open-source datasets and the difficulty in achieving robust performance across varying change types remain major challenges. To address these issues, we introduce JL1-CD, a large-scale, sub-meter CD dataset consisting of 5,000 image pairs. We further propose a novel Origin-Partition (O-P) strategy and integrate it into a Multi-Teacher Knowledge Distillation (MTKD) framework to enhance CD performance. The O-P strategy partitions the training set by Change Area Ratio (CAR) and trains specialized teacher models on each subset. The MTKD framework then distills complementary knowledge from these teachers into a single student model, enabling improved detection results across diverse CAR scenarios without additional inference cost. Our MTKD approach demonstrated strong performance in the 2024 “Jilin-1” Cup challenge, ranking first in the preliminary and second in the final rounds. Extensive experiments on the JL1-CD and SYSU-CD datasets show that the MTKD framework consistently improves the performance of CD models with various network architectures and parameter sizes, establishing new state-of-the-art results. Code and dataset are available at https://github.com/circleLZY/MTKD-CD.

As a critical component of ice-snow tourism, land cover classification for ski resorts is crucial to ice-snow resource management. However, there is currently a scarcity of datasets and methods capable of high-precision mapping for such fine-grained scenarios. Although Transformers with long-sequence interactions and convolutional neural networks (CNNs) have emerged as mainstream solutions, their performance remains limited on high-resolution remote sensing data characterized by small datasets and high heterogeneity. Targeting land cover classification in ski resort areas, this study proposes a triple-branch segmentation framework integrating CNNs and Transformers to extract global, detail and boundary features (GDBNet), and constructs the first high-resolution ski resort land cover dataset with a resolution of 0.75 m using JiLin-1 satellite constellation (LULC_SKI). The framework employs a backbone combining SegFormer with dual CNN branches. SegFormer captures global semantic context, while dual ResNet-18 branches extract local semantics and edge details respectively. The neck integrates two specialized feature interaction modules, the proposed Pixel-Guided Feature Attention (PG-AFM) and Boundary-Guided Feature Attention (BG-AFM), which synergistically fuse these heterogeneous feature representations for enhanced multi-scale modeling. For the segmentation head, a multi-task learning approach supervises both semantic and edge outputs. LULC_SKI covers seven representative ski resorts in Jilin Province, China, comprising 10,000 multi-seasonal images annotated with six land cover classes, including roads, vegetation, built-up areas, ski runs, water bodies, and cropland. Experiments demonstrate GDBNet achieves 85.44% mIoU and 91.84% mF1 on LULC_SKI, outperforming other advanced models with particularly significant improvements for linear objects like roads and ski runs. Extensive experimental comparisons show that GDBNet delivers consistently excellent performance on both the iSAID and LoveDA datasets, underscoring the superiority of our proposed method. Ablation studies validate the effectiveness of the triple-branch architecture, attention modules, and multi-task supervision. This work proposes a modular framework for land cover classification in complex ski resort scenarios.