Predicting wheat yield from 2001 to 2020 in Hebei Province at county and pixel levels based on synthesized time series images of Landsat and MODIS
Guanjin Zhang, Siti Nur Aliaa Binti Roslan, Helmi Zulhaidi Mohd Shafri, Yanxi Zhao, Ci Wang, Ling Quan

TL;DR
This study uses satellite images and deep learning to predict wheat yields in Hebei Province with high accuracy and resolution over 20 years.
Contribution
A novel deep learning model combining LSTM and NIRv for high-resolution wheat yield prediction at county and pixel levels.
Findings
The model combining LSTM and NIRv achieved the best prediction performance and stability.
Synthesized Landsat and MODIS images improved the completeness and quality of yield prediction data.
April was identified as the optimal time for prediction due to performance and lead time considerations.
Abstract
To obtain seasonable and precise crop yield information with fine resolution is very important for ensuring the food security. However, the quantity and quality of available images and the selection of prediction variables often limit the performance of yield prediction. In our study, the synthesized images of Landsat and MODIS were used to provide remote sensing (RS) variables, which can fill the missing values of Landsat images well and cover the study area completely. The deep learning (DL) was used to combine different vegetation index (VI) with climate data to build wheat yield prediction model in Hebei Province (HB). The results showed that kernel NDVI (kNDVI) and near-infrared reflectance (NIRv) slightly outperform normalized difference vegetation index (NDVI) in yield prediction. And the regression algorithm had a more prominent effect on yield prediction, while the yield…
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Taxonomy
TopicsRemote Sensing in Agriculture · Remote Sensing and Land Use · Smart Agriculture and AI
