Exploring the Potential of Gaofen-1/6 for Crop Monitoring: Generating Daily Decametric-Resolution Leaf Area Index Time Series

作者：Baodong Xu, Haodong Wei, Zhiwen Cai, Jingya Yang, Zhewei Zhang, Cong Wang, Jing Li, Jing Zhao, Yonghua Qu, Gaofei Yin, Aleixandre Verger · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2023 · DOI：10.1109/tgrs.2023.3257290 · 被引用次数：13 · 研究领域：Remote Sensing in Agriculture、Leaf Properties and Growth Measurement、Plant Water Relations and Carbon Dynamics

High spatiotemporal resolution time series of leaf area index (LAI) are essential for monitoring crop dynamics and validating coarse-resolution LAI products. The optical satellite sensors at decametric-resolution have historically suffered from a long revisit cycle and cloud contamination issues that hampered the acquisition of frequent and high-quality observations. The 16-m/4-day resolution of the new generation Gaofen-1 (GF-1) and Gaofen-6 (GF-6) satellites provide an unprecedented opportunity to address these limitations. Here we developed an effective strategy to generate daily 16-m LAI maps combing GF-1/6 data and ground LAINet measurements. All high-quality GF-1/6 observations were utilized first to derive smoothed time series of vegetation indices (VIs). Second, a random forest regression (RF-r) model was trained to link the VIs with corresponding field LAI measurements. The trained RF-r was finally employed to generate the LAI maps. Results demonstrated the reliability of the reconstructed daily VIs (relative error2of 0.05, 0.59 and 0.75, respectively. The LAI time series well captured the spatiotemporal variation of crop growth. Furthermore, the continuous GF-1/6 LAI maps outperformed Sentinel-2 LAI estimates both in terms of temporal frequency and accuracy. Our study indicates the potential of GF-1/6 to generate continuous decametric-resolution LAI maps for fine-scale agricultural monitoring.