Inversion of large-scale citrus soil moisture using multi-temporal Sentinel-1 and Landsat-8 data

作者：Zongjun Wu, Ningbo Cui, Wenjiang Zhang, Daozhi Gong, Chunwei Liu, Quanshan Liu, Shunsheng Zheng, Zhihui Wang, Lu Zhao, Yenan Yang · 发表于：Agricultural Water Management · 年份：2024 · DOI：10.1016/j.agwat.2024.108718 · 被引用次数：32 · 研究领域：Soil Moisture and Remote Sensing、Precipitation Measurement and Analysis、Soil erosion and sediment transport

Soil moisture is a significant variable in agricultural study and precision irrigation decision-making. It determines the soil water availability for plants, directly influencing plant growth, yield and quality. Owing to the variations in regional microclimate, landform difference, soil type and vegetation coverage, the soil moisture has strong spatial-temporal heterogeneity on a large regional scale. Micro-wave remote sensing can be used to invert soil moisture based on the dielectric constant under different weather conditions, while optical remote sensing utilizes spectral characteristics to estimate the physiological and ecological information of vegetation. In this study, two new hybrid models (ACO-RF and SSA-RF) were structured by optimizing the standalone random forest (RF) based on the ant colony optimization algorithm (ACO) and sparrow search algorithm (SSA), and six input combinations based on the multi-temporal Sentinel-1 and Landsat-8 remote sensing data from different sensors (optical, thermal and radar sensors) were used. The standalone RF, ACO-RF, and SSA-RF models with different combinations of inputs were employed to predict the soil moisture at different depths (5 cm, 10 cm, 20 cm, 40 cm) in a large-scale drip-irrigated citrus orchard. The results showed that the ACO-RF and SSA-RF outperformed the standalone RF model in terms of prediction accuracy at a depth of 0–40 cm, with R2 of 0.800–0.921 and 0.504–0.798, RRMSE of 7.214–16.284% and 11.124–22.214%, respe...