Almond yield prediction at orchard scale using satellite-derived biophysical traits and crop evapotranspiration combined with machine learning

Abstract

Accurate almond yield prediction is essential for supporting decision-making across multiple scales, from individual growers to international markets. This is crucial in the Mediterranean region, where diminishing water resources pose significant challenges to the almond industry. In this study, remote sensing-based evapotranspiration estimates were evaluated for predicting almond yield at the orchard scale using machine learning (ML) algorithms. The almond prediction models were calibrated and validated using data provided by commercial growers, along with meteorological reanalysis and remote sensing products. The remote sensing products included: i) spectral indices, ii) vegetation biophysical traits retrieved from Sentinel-2, and iii) actual evapotranspiration (ET a ) estimated using the Priestley-Taylor two-source energy balance (TSEB-PT) model driven by Copernicus-based data. Almond yield data were collected from commercial orchards located in Spain’s Ebro and Guadalquivir basins from 2017 to 2022. Data collected from growers enables the establishment of almond water production functions at the orchard scale, yielding results comparable to those reported in experimental study sites. Almond yield prediction models calibrated with remote sensing data demonstrated predictive accuracy comparable to that of models relying on ground-truth variables provided by farmers, such as irrigation, orchard age, tree density, and cultivar. Among them, the PM CRS model—which integrates the fraction of absorbed photosynthetically active radiation (fAPAR), the normalized difference moisture index (NDMI), canopy chlorophyll content (C ab ), ETa, and meteorological data—achieved a RMSE of 399.1 kg ha - ¹ in July. These findings highlight the potential of remote sensing-based models for accurately estimating almond yield. Furthermore, the PM CRS model proved scalable and effective when applied across four almond-producing regions in the Ebro basin. Future improvements may be realized through enhanced ET a retrieval using upcoming thermal satellite missions, integration of irrigation estimates, and the adoption of advanced machine learning and deep learning algorithms.

Plant phenotyping relevance

衛星リモートセンシング由来の植物生理・生物物理形質と蒸発散を用いて果樹園単位のアーモンド収量を推定し、機械学習モデルを較正・検証している。収量形質の取得・推定ワークフローが研究の中心である。

Code and data availability

The paper's almond yield data are grower-provided and only available on request ('The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.'). All public URLs cited (MSWEP, CREODIAS, SNAP, pyDMS, scikit-learn, DUN-SIGPAC, FAOSTAT) are generic third-party数据

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