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@article{Li:2026:10.3390/rs18081157,
author = {Li, H-Y and Lawrence, JA and Mason, PJ and Ghail, RC},
doi = {10.3390/rs18081157},
journal = {Remote Sensing},
title = {A framework for accurate annual regional crop yield prediction},
url = {http://dx.doi.org/10.3390/rs18081157},
volume = {18},
year = {2026}
}
TY - JOUR
AB - Food insecurity occurs due to the impact of climate change and intense global conditions. Thus, understanding crop farming plans and monitoring crop yields have become major tasks for decision makers. Previous work has applied remote sensing techniques and empirical methods to predict the yields and analyse the relationships between spectral indices and historical crop yield data. However, a limitation of these studies is that they do not extract the values of spectral indices by crop types when the testing area is regional with multiple farmlands and requires a crop classification process. This can cause inaccurate results when investigating the correlations between the yield and the spectral indices. This research develops a yield prediction framework with historical crop maps by means of unsupervised classification with zero ground truth using Sentinel-2 imagery to retrieve the values of spectral indices of winter barley. The extracted spectral indices and the meteorological and historical yield data in North Norfolk, UK, are implemented in 1D Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and CNN–LSTM for winter barley yield predictions. LSTM has outstanding performance overall and the best result approaches a Root Mean Square Error (RMSE) of 0.406 kg/hectare, a Mean Square Error (MSE) of 0.165 kg/hectare and a Mean Absolute Error (MAE) of 10.495 kg/hectare. The EVI in April, May and June is the most important feature in the LSTM model and shows strong positive correlation with the yield of winter barley. The developed framework with unsupervised crop classification and LSTM can be applied to multiple crop types and in different regions using opensource datasets, historical yields, spectral indices and meteorological data. Correlations between these datasets indicate that higher EVI and maximum and minimum temperature and sun hours at the germination and seedling growth stages increase the yields of winter barley, but excess Water Conten
AU - Li,H-Y
AU - Lawrence,JA
AU - Mason,PJ
AU - Ghail,RC
DO - 10.3390/rs18081157
PY - 2026///
SN - 2072-4292
TI - A framework for accurate annual regional crop yield prediction
T2 - Remote Sensing
UR - http://dx.doi.org/10.3390/rs18081157
VL - 18
ER -
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