Unverified paper record
UAV-based spatial sampling bridges ground measurements and satellite data for multi-scale estimation of sugar beet aboveground biomass.
Plant phenomics (Washington, D.C.) · 12 Feb 2026 · 10.1016/j.plaphe.2026.100173
Abstract
Aboveground biomass (AGB) is a critical indicator for assessing crop growth status and productivity, yet accurately linking fine-scale ground measurements with coarse-resolution satellite imagery remains challenging. Here, we propose an integrated ground-UAV-satellite framework that combines high-resolution UAV observations with an optimized systematic sampling-Global Moran's I (SS-GMI) procedure and a simple allometric growth model. Multi-variety sugar beet cultivated across heterogeneous habitats was used as a case study. Results indicate that a power-law model effectively captures the allometric relationships between AGB, plant height, and the Dreg vegetation index in sugar beet, achieving high accuracy and strong transferability. Incorporating phenological information from Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) codes and a thermal index further enhanced model robustness across independent habitat trials, yielding coefficients of determination ( R 2 ) of 0.80 and 0.83. The SS-GMI sampling procedure integrates systematic sampling with Global Moran's I to reduce spatial autocorrelation while ensuring uniform spatial coverage, thereby enabling the acquisition of representative and spatially independent samples from UAV-derived AGB maps. These samples were used to develop satellite-based AGB estimation models for PlanetScope and Sentinel-2A imagery, achieving R 2 values of 0.83 and 0.73, respectively. This study provides a practical and scalable framework for field-to-satellite AGB upscaling, offering new insights for the scale conversion of multi-source data in agricultural remote sensing.
Plant phenotyping relevance
UAV・衛星観測とSS-GMIサンプリング、モデル化を組み合わせ、サトウダイコンの地上部バイオマスという植物形質を推定する統合手法が研究の中心である。
abstractHere, we propose an integrated ground-UAV-satellite framework that combines high-resolution UAV observations with an optimized systematic sampling-Global Moran's I (SS-GMI) procedure and a simple allometric growth model.
abstractThe SS-GMI sampling procedure integrates systematic sampling with Global Moran's I to reduce spatial autocorrelation while ensuring uniform spatial coverage, thereby enabling the acquisition of representative and spatially independent samples from UAV-derived AGB maps.
Code and data availability
The paper's core phenotyping assets (UAV RGB/multispectral imagery, DOM/DSM/MOM products, and ground-measured plant height/AFW/ADW/AGB data) are not publicly deposited; the authors state they are available from the corresponding author upon request. Sentinel-2A and PlanetScope imagery are cited via generic public/com平台
No evidence-backed public reproduction asset is currently recorded.
This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.