The essence of the 2030 Agenda for Sustainable Development adopted by the United Nations is described in 17 Sustainable Development Goals (SDGs). SDG 15 focuses on Life on Land, in other words, terrestrial biodiversity and ecosystems, as well as their services. Land degradation is a severe anthropic and natural phenomenon that is affecting land use/cover globally; therefore, a dedicated target of the SDG 15 (the indicator 15.3.1) was proposed. The identification of the areas where land degradation has occurred and the analysis of its drivers allow for the design of solutions to prevent further degradation in the studied areas. We followed the methodology proposed by the United Nations Convention to Combat Desertification (UNCCD) to study the land degradation in the Honghe Prefecture in southwest China between 2005 and 2015. Through spatial analysis, we found that the degraded areas were consistent with the areas of active human activities (such as urban centers), while the impact of natural factors (such as disasters) on land degradation existed in small areas at high altitudes. Land degradation was affected primarily by the loss of land productivity and secondly by land cover changes caused by the growth of artificial areas. Changes in the soil organic carbon were not significant. We concluded that human activity was the main driver of land degradation in Honghe Prefecture. Decision makers should work to find a balance between economic development and environmental protection to restore degraded land and strive to achieve a land degradation-neutral prefecture to defend all ecosystem services.

Tobacco growth monitoring and yield estimation are very important for tobacco planting control and allocation. However, traditional tobacco yield estimation models have not considered the light energy utilization, carbon cycle, and ecological processes, resulting in the mechanisms poorly explained and the reduced monitoring accuracy. To address these limitations, a tobacco yield remote sensing monitoring model based on ecological process and carbon cycle was proposed. The model couples the Carbon Exchange between Vegetation, Soil, and Atmosphere (CEVSA) ecosystem process model and the global production efficiency model (GLO-PEM), to simulate effective solar radiation and tobacco light energy utilization, stress effects of surface air temperature, water vapour pressure deficit and photosynthetic effective radiation. Then the tobacco gross primary production (GPP), net primary productivity (NPP), tobacco biomass were estimated. Finally, the tobacco yield estimation model based on the correlation between tobacco yield and NPP was proposed. The results showed that there was a significant correlation (correlation= 0.94) between NPP and tobacco leaves weight at 99% confidence level, and the yield estimated by the remote sensing monitoring model was in good agreement with the measured results, with errors of Class I, II, III were 9.644%, 4.316%, and 8.495% respectively. In conclusion, the proposed model can be used to estimate tobacco yield, support decision-making of tobacco planting plan, and strengthen purchasing management.

Estimating city–scale emissions using gridded inventories lacks direct, precise measurements, resulting in significant uncertainty. A Kalman filter integrates diverse, uncertain information sources to deliver a reliable, accurate estimate of the true system state. By leveraging multiple gridded inventories and a Kalman filter fusion method, we developed an optimal city–scale (3 km) FFCO₂ emission product that incorporates quantified uncertainties and connects global–regional–city scales. Our findings reveal the following: (1) Kalman fusion post–reconstruction reduces estimate uncertainties for 2000–2014 and 2015–2021 to ±9.77% and ±11.39%, respectively, outperforming other inventories and improving accuracy to 73% compared to ODIAC and EDGAR (57%, 65%). (2) Long–term trends in the Greater Bay Area (GBA) show an upward trajectory, with a 2.8% rise during the global financial crisis and a −0.19% decline during the COVID-19 pandemic. Spatial analysis uncovers a “core–subcore–periphery” emission pattern. (3) The core city GZ consistently contributes the largest emissions, followed by DG as the second–largest emitter, and HK as the seventh–highest emitter. Factors influencing the center–shift of the pattern include the urban form of cities, population migration, GDP contribution, but not electricity consumption. The reconstructed method and product offer a reliable solution for the lack of directly observed emissions, enhancing decision–making accuracy for policymakers.

Regional image coverage retrieval is a critical problem in large-scale remote sensing data processing. However, traditional vector topology-based methods suffer from rapidly increasing computational costs when handling massive and highly overlapping datasets. This paper proposes a coverage retrieval approach based on the Discrete Global Grid System (DGGS), which transforms geometric topological operations into grid-encoding set operations, thereby reconstructing the coverage computation process under a discrete spatial indexing framework. A heuristic greedy strategy is integrated to achieve efficient coverage selection. Experimental results demonstrate that the proposed DGGS-based method achieves speedups ranging from approximately 1.5× to 11×, depending on dataset scale and coverage. Grid-level analysis indicates that level 7 grids generally provide a favorable balance between spatial accuracy and computational efficiency for near-complete coverage retrieval, whereas level 6 grids offer a more computationally efficient alternative for rapid coverage estimation and sparse-coverage scenarios, with only marginal accuracy loss. Furthermore, the method exhibits near-linear scalability with increasing data volume and maintains stable performance under incomplete coverage scenarios. The results confirm that DGGS-based discrete modeling significantly reduces computational complexity while preserving spatial reliability, providing an efficient and scalable solution for PB-scale remote sensing data processing.