Estimation of Rice Plant Coverage Using Sentinel-2 Based on UAV-Observed Data

Abstract

Vegetation coverage is a crucial parameter in agriculture, as it offers essential insight into crop growth and health conditions. The spatial resolution of spaceborne sensors is limited, hindering the precise measurement of vegetation coverage. Consequently, fine-resolution ground observation data are indispensable for establishing correlations between remotely sensed reflectance and plant coverage. We estimated rice plant coverage per pixel using time-series Sentinel-2 Multispectral Instrument (MSI) data, enabling the monitoring of rice growth conditions over a wide area. Coverage was calculated using unmanned aerial vehicle (UAV) data with a spatial resolution of 3 cm with the spectral unmixing method. Coverage maps were generated every 2–3 weeks throughout the rice-growing season. Subsequently, crop growth was estimated at 10 m resolution through multiple linear regression utilizing Sentinel-2 MSI reflectance data and coverage maps. In this process, a geometric registration of MSI and UAV data was conducted to improve their spatial agreement. The coefficients of determination (R2) of the multiple linear regression models were 0.92 and 0.94 for the Level-1C and Level-2A products of Sentinel-2 MSI, respectively. The root mean square errors of estimated rice plant coverage were 10.77% and 9.34%, respectively. This study highlights the promise of satellite time-series models for accurate estimation of rice plant coverage.

Plant phenotyping relevance

UAVとSentinel-2を用いてイネの植物被覆率を推定し、回帰精度と誤差を評価している。植物形質の取得・推定手法が研究の中心であるため含める。

Code and data availability

The paper's UAV multispectral images, Sentinel-2 data, coverage maps, and analysis code are not deposited in any public repository. The Data Availability Statement only offers contact with the corresponding author; all cited URLs are generic tools, government statistics, or vendor pages, not paper-specific assets.

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