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An LSTM neural network for improving wheat yield estimates by integrating remote sensing data and meteorological data in the Guanzhong Plain, PR China
journal contributionposted on 2022-01-19, 09:47 authored by Huiren Tian, Pengxin Wang, Kevin Tansey, Jingqi Zhang, Shuyu Zhang, Hongmei Li
Crop growth condition and production play an important role in food management and economic development. Therefore, estimating yield accurately and timely is of vital importance for regional food security. The long short-term memory (LSTM) model represents a deep network structure to incorporating crop growth processes, which has been proven to accommodate different types and representations of data, recognize sequential patterns over long time spans, and capture complex nonlinear relationships. The LSTM model was developed to estimate wheat yield in the Guanzhong Plain by integrating meteorological data and two remotely sensed indices, vegetation temperature condition index (VTCI) and leaf area index (LAI) at the main growth stages. Considering the LSTM model has characteristics of memorizing time series information, we adopted different time steps to estimate wheat yield. The results showed that the accuracy of yield estimation was highest (RMSE = 357.77 kg/ha and R2 = 0.83) under two time steps and the input combination (meteorological data and two remotely sensed indices). We evaluated the yield estimation accuracy of the optimal LSTM model performance compared with the back propagation neural network (BPNN) and support vector machine (SVM). As a result, the LSTM model outperformed BPNN (R2 = 0.42 and RMSE = 812.83 kg/ha) and SVM (R2 = 0.41 and RMSE = 867.70 kg/ha), since its recurrent neural network structure that can incorporate nonlinear relationships between multi-features inputs and yield. To further validate the robustness of the optimal LSTM method, the correlations between estimated yield and measured yield at the irrigation sites and the rain-fed sites from 2008 to 2016 were analyzed, and the results demonstrated that the proposed model can serve as an effective approach for different type sampling sites and has better adaptability to interannual fluctuations of climate. Our findings demonstrated a reliable and promising approach for improving yield estimation.
This work was supported by the National Natural Science Foundation of China under Grant 41871336. This work was supported by UK Research and Innovation (UKRI) funding from a Science & Technology Facilities Council grant administered through Rothamsted Research (No. SM008 CAU). The work was further supported by a Royal Society-Newton Mobility Grant (UK).
CitationHuiren Tian, Pengxin Wang, Kevin Tansey, Jingqi Zhang, Shuyu Zhang, Hongmei Li. An LSTM neural network for improving wheat yield estimates by integrating remote sensing data and meteorological data in the Guanzhong Plain, PR China, Agricultural and Forest Meteorology, Volume 310, 2021, 108629,https://doi.org/10.1016/j.agrformet.2021.108629.
Author affiliationSchool of Geography
- VoR (Version of Record)
Published inAgricultural and Forest Meteorology
Science & TechnologyLife Sciences & BiomedicinePhysical SciencesAgronomyForestryMeteorology & Atmospheric SciencesAgricultureVegetation temperature condition index (VTCI)Leaf area index (LAI)Meteorological dataLong short-term memory (LSTM)Yield estimationLEAF-AREA INDEXTEMPERATURE CONDITION INDEXSENSED VEGETATION INDEXESCORN-YIELDMAIZE GROWTHMODELDROUGHTASSIMILATIONPREDICTIONCLIMATE