Estimation of aboveground biomass in non-uniformly planted rice using multi-temporal UAV imagery data

Abstract

Estimating rice aboveground biomass (AGB) in unevenly planted areas using sowing parameters is challenging, mainly due to row spacing errors caused by the subjectivity of operators, as well as problems such as missing seeding and multiple seedlings per hill. Estimating regional rice AGB in actual agricultural production under the assumption of uniform planting density significantly increases errors. This study innovatively proposes a Rice Pixel Ratio (RPR) based on high-resolution Unmanned Aerial Vehicle (UAV) imagery to characterize the spatial distribution pattern of rice at the pixel scale. During 2023–2024, this study set up a total of 6 experimental treatments: 4 uniform planting density treatments (15, 18, 21, and 24 plants/m², sequentially denoted as K1-K4), 1 uneven density treatment (D1), and 1 composite uniform density treatment (D2) integrating K1-K4. The study revealed: (i) Eight VIs extracted from high-resolution remote sensing imagery exhibited significant correlations with real-time rice growth parameters; (ii) During the vegetative phase, after RPR calibration, the non-uniform density plot D1 showed significantly improved AGB estimation accuracy, with R² increasing from 0.76 to 0.82; (ii) When RPR>0.985 in test plots, and no model accuracy degradation occurred, confirming RPR’s broad applicability and strong robustness; (iii) For the composite density model D2 incorporating all four densities, RPR calibration elevated the reproductive-phase stem-leaf model’s R² from 0.28 to 0.71, representing a 158.18 % improvement. Results demonstrate RPR’s capacity to significantly enhance the generalizability and robustness of AGB models under high vegetation coverage, mixed-density, and non-uniform planting conditions.