Remote sensing-based maize growth process parameters revel the maize yield: a comparison of field- and regional-scale - Minghan Cheng; Xiuliang Jin; Chenwei Nie; Kaihua Liu; Tianao Wu; Yuping Lv; Shuaibing Liu; Xun Yu; Yi Bai; Yadong Liu; Lin Meng; Xiao Jia; et al. (BMC Plant Biology)
Abstract The accurate estimation of regional crop yields holds significant importance for optimizing subsequent resource allocation and maximizing economic returns in agriculture. Crop yield can be effectively estimated by assessing the overall growth status through long-term remote sensing observations. However, most previous studies have relied on remote sensing data from one or a few periods for yield estimation, thus lacking a comprehensive description of entire crop growth. Furthermore, past algorithms have not considered their applicability across different observational scales (e.g., unmanned aerial vehicle (UAV)- and satellite-observed). Considering this, we extracted four maize growth process parameters using Leaf Area Index (LAI) obtained from UAV (equipped with multispectral sensor, centimeter-level) and satellite (MODIS, 1 km) observations: PP_a (representing the duration of the crop growth period), PP_b (representing the peak growth stage of the crop), PP_c (representing the initial state of the crop), and LAImax (maximum LAI). These parameters were used to construct a maize yield estimation model applicable at both regional and field scales. The results indicate that the four process parameters extracted in this study can accurately estimate crop yields, with rRMSE = 14.08% at the field-scale and rRMSE = 17.75% at the regional-scale. Among these parameters, PP_a, representing the duration of the crop growth period, and the maximum LAI, are the parameters that individually contribute the most to the estimation accuracy. Moreover, the proposed method exhibited good spatial applicability (field-scale: Moran Index (MI) = -0.18; regional-scale: MI = 0.19). In conclusion, the parameters describing maize growth process derived from long-term-series observations can effectively estimate maize yield across different observation scales. This method not only facilitates the optimization of agronomic practices based on UAV observations but also supports the decision of regional agricultural policies based on satellite observations. Furthermore, crop yield estimation utilizing process-based parameters provides a new perspective for related studies.