Effects of optical and radar satellite observations within Google Earth Engine on soil organic carbon prediction models in Spain.

The modeling and mapping of soil organic carbon (SOC) has advanced through the rapid growth of Earth observation data (e.g., Sentinel) collection and the advent of appropriate tools such as the Google Earth Engine (GEE). However, the effects of differing optical and radar sensors on SOC prediction models remain uncertain. This research aims to investigate the effects of different optical and radar sensors (Sentinel-1/2/3 and ALOS-2) on SOC prediction models based on long-term satellite observations on the GEE platform. We also evaluate the relative impact of four synthetic aperture radar (SAR) acquisition configurations (polarization mode, band frequency, orbital direction and time window) on SOC mapping with multiband SAR data from Spain. Twelve experiments involving different satellite data configurations, combined with 4027 soil samples, were used for building SOC random forest regression models. The results show that the synthesis mode and choice of satellite images, as well as the SAR acquisition configurations, influenced the model accuracy to varying degrees. Models based on SAR data involving cross-polarization, multiple time periods and "ASCENDING" orbits outperformed those involving copolarization, a single time period and "DESCENDING" orbits. Moreover, combining information from different orbital directions and polarization modes improved the soil prediction models. Among the SOC models based on long-term satellite observations, the Sentinel-3-based models (R2 = 0.40) performed the best, while the ALOS-2-based model performed the worst. In addition, the predictive performance of MSI/Sentinel-2 (R2 = 0.35) was comparable with that of SAR/Sentinel-1 (R2 = 0.35); however, the combination (R2 = 0.39) of the two improved the model performance. All the predicted maps involving Sentinel satellites had similar spatial patterns that were higher in northwest Spain and lower in the south. Overall, this study provides insights into the effects of different optical and radar sensors and radar system parameters on soil prediction models and improves our understanding of the potential of Sentinels in developing soil carbon mapping.

Review on supply and demand of ecosystem service and the construction of systematic framework.

The supply and demand of ecosystem services are related to both natural ecosystems and socio-economic systems. The research on the supply and demand of ecosystem services would help enhance ecosystem management and achieve optimal allocation of resources, which ensures regional ecological security and sustainable development of socio-economic. Based on a systematic review of international literature, we comprehensively reviewed the conceptual connotation, evaluation metho-ds, and practical application of ecosystem service supply and demand. Although relatively abundant investigations have been conducted from the perspective of theoretical development, they are still scattered and lacking a coherent research framework. Based on expanding the scope of research on the supply and demand of ecosystem service, we constructued a research framework that referred to "qualitative-positioning-quantitative-policymaking" in accordance with the research pattern of "theory-methodology-practice". To promote the theoretical and practical research on the supply and demand of ecosystem service, future research needs to focus on the spatial delivery mechanism, strengthen the research on quantitative methods, deepen the management and application practice, and establish the evaluation mechanism of ecosystem service supply and demand application.