With the development of hyperspectral imaging technology, the potential for utilizing hyperspectral images to accurately estimate heavy metal concentrations in regional soil has emerged. Currently, soil heavy metal inversion based on laboratory hyperspectral data has demonstrated a commendable level of accuracy. However, satellite images are susceptible to environmental factors such as atmospheric and soil background, presenting a significant challenge in the accurate estimation of soil heavy metal concentrations. In this study, typical chromium (Cr)-contaminated agricultural land in Shaoguan City, Guangdong Province, China, was taken as the study area. Soil sample collection, Cr content determination, laboratory spectral measurements, and hyperspectral satellite image collection were carried out simultaneously. The Zhuhai-1 hyperspectral satellite image spectra were corrected to match laboratory spectra using the direct standardization (DS) algorithm. Then, the corrected spectra were integrated into an optimal model based on laboratory spectral data and sample Cr content data for regional inversion of soil heavy metal Cr content in agricultural land. The results indicated that the combination of standard normal variate (SNV)+ uninformative variable elimination (UVE)+ support vector regression (SVR) model performed best with laboratory spectral data, achieving a high accuracy with an R² of 0.97, RMSE of 5.87, MAE of 4.72, and RPD of 4.04. The DS algorithm effectively transformed satellite hyperspectral image data into spectra resembling laboratory measurements, mitigating the impact of environmental factors. Therefore, it can be applied for regional inversion of soil heavy metal content. Overall, the study area exhibited a low-risk level of Cr content in the soil, with the majority of Cr content values falling within the range of 36.21–76.23 mg/kg. Higher concentrations were primarily observed in the southeastern part of the study area. This study can provide useful exploration for the promotion and application of Zhuhai-1 image data in the regional inversion of soil heavy metals.

In order to comprehensively and accurately evaluate the adsorption effect of Eichhornia crassipes planting on algae in water, this study used 131 cloudless Sentinel-2 images to monitor the scale and growth dynamics of Eichhornia crassipes planting in Caohai and its surrounding waters. Based on the single and multi-peak histogram characteristics of the NDWI image in the study area, the empirical threshold and the optimized histogram slope adaptive threshold (OHSAT) segmentation methods were respectively used and manual inspection was added to accurately segment the water bodies and Eichhornia crassipes. The results of the analysis of Eichhornia crassipes area, growth status (NDVI), water body algae density (NDVI), and their spatial–temporal co-variation and trends show that the Eichhornia crassipes has a significant enrichment effect on algae, showing a significant decreasing trend of water bloom centered on it. The size and growth status of Eichhornia crassipes are inversely proportional to the water body NDVI, and reasonable harvesting plans based on these findings are expected to achieve optimized pollution control effects.

Specialty agriculture can provide an important foundation for rural economic diversification, but its sustained development does not necessarily depend on the continuous expansion of cultivated land. This study examines the development of the rose-based specialty agricultural economy in Xiaohebian Village, Mile City, Yunnan Province, Southwest China, using a longitudinal case-study design. Multi-temporal high-resolution satellite imagery, centimeter-resolution unmanned aerial vehicle (UAV) orthophotos, field surveys, and industry statistics from 2020 to 2025 were integrated to examine changes in cultivation space, agricultural production infrastructure, processing space, and rose-related activities. The results show that within the village-scale study area, rose cultivation increased from 16.01 ha in 2020 to 36.94 ha in 2023 and 39.4 ha in 2025, indicating rapid expansion during 2020–2023 followed by a marked slowdown during 2023–2025. Meanwhile, within the overlapping UAV survey area in 2023 and 2025, the length of drip-irrigation networks increased from 3.46 km to 5.67 km. The area of processing facilities within the village increased from 2852.34 m² in 2020 to 19,436.42 m² in 2025, with the most rapid expansion occurring after 2023. Industry statistics further indicate that the total rose cultivation area, including land leased outside the village, increased from 133.34 ha in 2020 to 233.35 ha in 2023, suggesting that rose production expanded not only within the village but also outward through the leasing of land beyond the village boundary. The range of processed products and related business activities continued to increase, while tourism, catering, and accommodation activities also gradually developed. Taken together, the trajectories of these indicators reveal clear stage-specific differences in the Xiaohebian rose economy during the study period: the earlier stage was characterized primarily by rapid expansion of cultivation space within the village, whereas the later stage was increasingly characterized by the expansion of production infrastructure and processing space, together with the diversification of related economic functions. The findings suggest that understanding the economic development of specialty agricultural villages requires attention not only to changes in cultivation scale and output value, but also to the differentiated changes in cultivation space, production infrastructure, processing activities, and related economic functions across temporal and spatial scales. By integrating remote-sensing, UAV, and socioeconomic data, this study provides village-scale case evidence for identifying the spatial restructuring and changes in economic functions associated with the development of specialty agriculture.