Detecting Temporal Trends in Straw Incorporation Using Sentinel-2 Imagery: A Mann-Kendall Test Approach in Household Mode

Straw incorporation (SI) is a key strategy for promoting sustainable agriculture. It aims to mitigate environmental pollution caused by straw burning and enhances soil organic matter content, which increases crop yields. Consequently, the accurate and efficient monitoring of SI is crucial for promoting sustainable agricultural practices and effective management. In this study, we employed the Google Earth Engine (GEE) to analyze time-series Sentinel-2 data with the Mann–Kendall (MK) algorithm. This approach enabled the extraction and spatial distribution retrieval of SI regions in a representative household mode area in Northeast China. Among the eight tillage indices analyzed, the simple tillage index (STI) exhibited the highest inversion accuracy, with an overall accuracy (OA) of 0.85. Additionally, the bare soil index (BSI) achieved an overall accuracy of 0.84. In contrast, the OA of the remaining indices ranged from 0.28 to 0.47, which were significantly lower than those of the STI and BSI. This difference indicated the limited performance of the other indices in retrieving SI. The high accuracy of the STI is primarily attributed to its reliance on the bands B11 and B12, thereby avoiding potential interference from other spectral bands. The geostatistical analysis of the SI distribution revealed that the SI rate in the household mode area was 36.10% in 2022 in the household mode area. Regions A, B, C, and D exhibited SI rates of 34.76%, 33.05%, 57.88%, and 22.08%, respectively, with SI mainly concentrated in the eastern area of Gongzhuling City. Furthermore, the study investigated the potential impacts of household farming practices and national policies on the outcomes of SI implementation. Regarding state subsidies, the potential returns from SI per hectare of cropland in the study area varied from RMB −65 to 589. This variation indicates the importance of higher subsidies in motivating farmers to adopt SI practices. Sentinel-2 satellite imagery and the MK test were used to effectively monitor SI practices across a large area. Future studies will aim to integrate deep learning techniques to improve retrieval accuracy. Overall, this research presents a novel perspective and approach for monitoring SI practices and provides theoretical insights and data support to promote sustainable agriculture.