Improving the estimation of alfalfa yield based on multi-source satellite data and the synthetic minority oversampling strategy

Abstract

Alfalfa is the most important forage crop in grassland agriculture. Efficient regional-scale estimates of alfalfa yields are crucial for the precision management of cultivated alfalfa production; however, obtaining rapid and accurate yield assessments remains challenging due to the scarcity of in situ sample data and limited integration of multi-source satellite remote sensing data. To address these issues, this study utilized a yield dataset from 78 sample plots collected during the alfalfa growing season (May–October) and multi-source heterogeneous satellite remote sensing data (Landsat 8, Sentinel-2, and Sentinel-1). By integrating simulated Landsat 8 red-edge bands and applying sample augmentation via the synthetic minority over-sampling technique for regression with Gaussian noise (SMOGN), a high-accuracy framework for estimating cultivated alfalfa yields is proposed based on multi-source remote sensing data and sample augmentation. The study’s key outcomes are as follows. 1) Compared to alfalfa yield estimation models constructed using only Sentinel-2 or Landsat 8 data, incorporating simulated Landsat 8 red-edge bands or Sentinel-1 variables slightly improves the estimation accuracy, with an R² increase of 0.01–0.04, an RMSE decrease of 3.67–8.00 g/m², an RPD increase of 0.02–0.05, and an MAE decrease of 3.34–8.25 g/m². 2) Using the SMOGN algorithm to augment the sample data significantly enhances estimation accuracy, with R² increases of 0.09–0.28 and RMSE decreases of 0.30–38.57 g/m². 3) Integrating spectral bands and vegetation indices derived from the Sentinel-2, Landsat 8, and Sentinel-1 data improves the accuracy of alfalfa yield estimates, with the optimal model constructed using RF algorithm explaining 66 % of yield variation. Overall, multi-source satellite data and sample augmentation techniques represent an extremely promising approach for remote sensing-based alfalfa yield estimation. This study’s findings provide technical support for an innovative method framework for the precision management of cultivated alfalfa production.

Plant phenotyping relevance

衛星リモートセンシングと機械学習により、アルファルファの圃場収量を推定する方法枠組みを開発・比較検証しており、収量という植物形質の取得が中心である。

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