Work

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

Year: 2025

Type: article

Abstract: • A new method for improving the accuracy of biomass estimation in non-uniformly planted rice crops. • Use drones to obtain multispectral images and achieve dynamic monitoring of rice biomass. • Improve uniform density and monitoring accuracy of biomass models in multiple uniform density mixing zones. 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/m 2 , 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 R 2 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 R 2 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.

Source: Smart Agricultural Technology

DOI: 10.1016/j.atech.2025.101680

Authors: Yong Su, Jinyan Tian, Bo Yang, Jing Yang

Institutions: Southwest Petroleum University, Guangyuan China Nuclear Vocational and Technical College

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $1,770