Evaluating the potential of very high-resolution satellite data for the enhanced estimation of rice aboveground biomass by combining spectral and spatial information

Abstract

Monitoring aboveground biomass (AGB) using high spatial and temporal resolution remote sensing data is important for smart agriculture. Significant technological advances have been made in developing satellites with very high spatial resolution, delivering a promising avenue for vegetation observations. However, the high costs and limited revisit periods of high-resolution satellites hinder their widespread use, leaving the feasibility of combining vegetation indices (VIs) and textures derived from satellite images for AGB estimation uncertain and the quantitative improvements achieved by incorporating textures into estimation unclear. Airborne hyperspectral imaging with high spectral and spatial resolution offers a fresh opportunity to simulate the satellite imaging process objectively and realistically across both spectral and spatial dimensions. The study first evaluated the potential benefits of combining textures and VIs derived from different high-resolution satellites to enhance AGB retrieval. Rice samples and UAV hyperspectral data were collected throughout the rice growth cycle over three consecutive years. Each hyperspectral image was resampled in spectral and spatial dimensions to simulate nine multispectral satellites with sub-meter spatial resolution (WorldView-3, WorldView-2, GeoEye-1, SuperView-1C, GaoFen-2, Beijing-2, Jilin-1, GeoSat-2, KomPast-2). VIs, textures, and their combinations were employed to establish AGB models for the pre-heading, post-heading, and the entire growth stage, respectively. The results showed that combining VIs and textures always achieved the greatest rice AGB estimations, with the integration of multiple satellite data always yielding the best outcomes (overall validation rRMSE ≤ 0.35). For the texture-based monitoring, the impact of satellite spatial resolution was more pronounced on influencing the estimation effectiveness than spectral bands. The monitoring accuracy of rice AGB demonstrated a nonlinear decreasing trend as the spatial resolution dropped, and combining VIs and textures mitigated the negative impact of reduced spatial resolution on the monitoring accuracy of rice AGB. The combination of VIs and textures showed a compensatory effect and combining VIs and textures derived from red-edge band could offset the impact of the reduced spatial resolution on AGB estimation. The involvement of textures in modelling exerted an overall bigger impact on rice AGB estimation than the inclusion of red-edge variables. Satellites with higher spatial resolution and a red-edge band always performed the best in AGB estimation. This study facilitates the optimization of sensor design and farmland management.