Retrieving decametric-resolution leaf chlorophyll content from GF-6 WFV by assessing the applicability of red-edge vegetation indices

Abstract

Accurate and timely retrieval of leaf chlorophyll content (LCC) at fine spatial scales is crucial for monitoring plants' photosynthetic capacity and nutritional status. The wide field-of-view camera (WFV) onboard Gaofen-6 (GF-6) satellite has two red-edge bands at 16-meter spatial resolution, providing an opportunity to derive decametric-resolution LCC. However, the potential of GF-6 WFV data in LCC estimation has yet to be studied. Vegetation indices (VIs) are widely used in LCC estimation due to their simplicity and efficiency, and the accuracy of LCC inversion can be improved with red-edge VIs. This study focused on exploring the retrieval of LCC with GF-6 WFV using the VI-based method by assessing the applicability of nine red-edge VIs derived from GF-6 WFV. Our results revealed that (1) the newly developed canopy structure-insensitive CSI (chlorophyll sensitive index) achieved comparable accuracy to the physically-based method and reduced the limitation of the VI-based method being species- and time-specific. Validation results showed that the root-mean-square error (RMSE) of the estimated LCC from the CSI-based model was 7.91–11.38 μg/cm2 for four plant functional types (similar accuracy to Croft et al. (2020) based on the lookup table method with RMSE of 9.25–13.18 μg/cm2). The accuracy of the CSI-based model remained stable across species and growth conditions, indicating its feasibility for large-scale applications. (2) GF-6 WFV provided accurate LCC estimates and well-captured the spatial and seasonal variations in LCC. GF-6 LCC maps showed comparable accuracy (R2 = 0.53, RMSE = 9.78 μg/cm2) and similar spatiotemporal distribution to the 30-m Sentinel-2 LCC products. Additionally, GF-6 LCC maps outperformed Sentinel-2 LCC products regarding observation frequency and spatial continuity. Our study highlights the potential of GF-6 WFV in retrieving decametric-resolution LCC and provides further support for applications of GF-6 WFV data in precision agriculture and forestry.