.png){kind=icon}
.png){kind=icon}
Return
Pasture Biomass Monitoring in Queensland Rangelands with UAV and Satellite Cascades
.png){kind=icon}
OA
AI
J
H
G
DOI：10.3390/agriengineering8080317.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The operational satellite monitoring of pasture biomass requires models that transfer beyond the properties on which they were calibrated. We present a hierarchical, open-source cascade that scales in situ clip-and-weigh biomass (n = 1120 samples across eleven sites on five Queensland properties) through UAV digital-surface-model imagery to Sentinel-2 predictions, using TabPFN—a pre-trained transformer foundation model for small tabular data—as the regressor at all three nested spatial scales. Under a leave-one-site-out (LOSO) protocol on twenty site–date aggregates across nine sites, spectral-only Sentinel-2 models failed to transfer (best R 2 = − 0.15 , RMSE 4.62 t ha−1). Appending open climate (Open-Meteo ERA5) and topsoil (SoilGrids 2.0) covariates and evaluating five learners (GBM, RF, XGBoost, TabPFN, and GBM + TabPFN stack) on log-transformed biomass increased LOSO R 2 to − 0.05 and reduced RMSE to 4.43 t ha−1; a leaf-nitrogen growth trajectory predicted by the TabPFN nitrogen regressor from our earlier pasture-chemistry work reduced pixel-level LOSO RMSE by a further 4%. Three alternative covariate classes—BARRA-R2 reanalysis climate, three independent fractional-cover products, and Sentinel-1 C-band SAR backscatter—were tested and rejected, all hitting the same RMSE floor. The symmetric negative results indicate that the residual LOSO ceiling on the current nine-property footprint is a sample-size and optical-saturation limit rather than a feature-engineering one; the most tractable operational path forward is to stratify the production model by climatic zone and Queensland Land Type rather than pursuing further covariates within a single global learner.
Keywords:
pasture biomass
TabPFN
transformer
foundation model
UAV
digital surface model
Sentinel-2
random forest
time-series
rangeland monitoring
hierarchical scaling
Australia
Journal
A
IF:
3
Papers:
1.3K
Citations:
1.3K
Organization
Cited Papers