Work

Object-based image analysis of sentinel-1/2 time series using deep learning for field-scale alfalfa yield mapping

Year: 2025

Type: article

Abstract: • Object-based analysis (OBIA) surpasses pixel-based methods for yield estimation. • Fusing Sentinel-1 and Sentinel-2 data enhances model accuracy and robustness. • An explainable LSTM model precisely estimates yield and identifies key drivers. • Flowering-period imagery is the key window for alfalfa yield estimation. Accurate alfalfa yield estimation is essential for efficient forage management and feed planning. We compiled a three-year (2021–2023) dataset of 61 fields, constructing matched Sentinel-2 (S2) and Sentinel-1 (S1) time-series features for each cutting stage (greening, branching, budding, flowering). We benchmarked seven models (long short-term memory (LSTM) networks, Convolutional LSTM (ConvLSTM), convolutional neural networks (CNN), Random Forest (RF), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Least Absolute Shrinkage and Selection Operator (LASSO)) under two remote sensing image analysis methods (object-based image analysis (OBIA) and pixel-based image analysis (PBIA)) and three input data combinations (S1+S2, S2, and S1). Our results consistently show that OBIA out-performs PBIA for field-scale yield estimation. The S1+S2 fusion also demonstrated superior performance across all metrics compared to single-sensor inputs, highlighting the complementarity of optical and radar data. Additionally, deep learning outperformed machine learning, with LSTM best overall. An annual evaluation using the optimal settings (OBIA, S1+S2, LSTM) revealed small total yield errors (3.97%–5.90%), indicating robust generalization across seasonal and inter-annual variations. integrated gradients analysis identified Sentinel-2 SWIR (B11, B12) and Sentinel-1 VV/VH as the most influential variables, with the flowering stage contributing the most. The object-level “sum” statistic improved stability by reflecting with-in-field yield accumulation. Overall, this work presents an accurate, interpretable, and cost-effective framework for mapping total alfalfa yield.

Source: Smart Agricultural Technology

DOI: 10.1016/j.atech.2025.101588