Integration of a Crop Growth Model and Deep Learning Methods to Improve Satellite-Based Yield Estimation of Winter Wheat in Henan Province, China

Timely and accurate regional crop-yield estimates are crucial for guiding agronomic practices and policies to improve food security. In this study, a crop-growth model was integrated with time series of remotely sensed data through deep learning (DL) methods to improve the accuracy of regional wheat...

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Autores principales:	Yi Xie, Jianxi Huang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	winter wheat yield estimation remote sensing deep learning CERES-Wheat Science Q
Acceso en línea:	https://doaj.org/article/f72474d9738e4b80beff48f6cb603b10
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spelling	oai:doaj.org-article:f72474d9738e4b80beff48f6cb603b102021-11-11T18:54:54ZIntegration of a Crop Growth Model and Deep Learning Methods to Improve Satellite-Based Yield Estimation of Winter Wheat in Henan Province, China10.3390/rs132143722072-4292https://doaj.org/article/f72474d9738e4b80beff48f6cb603b102021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4372https://doaj.org/toc/2072-4292Timely and accurate regional crop-yield estimates are crucial for guiding agronomic practices and policies to improve food security. In this study, a crop-growth model was integrated with time series of remotely sensed data through deep learning (DL) methods to improve the accuracy of regional wheat-yield estimations in Henan Province, China. Firstly, the time series of moderate-resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) were input into the long short-term memory network (LSTM) model to identify the wheat-growing region, which was further used to estimate wheat areas at the municipal and county levels. Then, the leaf area index (LAI) and grain-yield time series simulated by the Crop Environment REsource Synthesis for Wheat (CERES-Wheat) model were used to train and evaluate the LSTM, one-dimensional convolutional neural network (1-D CNN) and random forest (RF) models, respectively. Finally, an exponential model of the relationship between the field-measured LAI and MODIS NDVI was applied to obtain the regional LAI, which was input into the trained LSTM, 1-D CNN and RF models to estimate wheat yields within the wheat-growing region. The results showed that the linear correlations between the estimated wheat areas and the statistical areas were significant at both the municipal and county levels. The LSTM model provided more accurate estimates of wheat yields, with higher <i>R</i><sup>2</sup> values and lower root mean square error (RMSE) and mean relative error (MRE) values than the 1-D CNN and RF models. The LSTM model has an inherent advantage in capturing phenological information contained in the time series of the MODIS-derived LAI, which is important for satellite-based crop-yield estimates.Yi XieJianxi HuangMDPI AGarticlewinter wheatyield estimationremote sensingdeep learningCERES-WheatScienceQENRemote Sensing, Vol 13, Iss 4372, p 4372 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	winter wheat yield estimation remote sensing deep learning CERES-Wheat Science Q
spellingShingle	winter wheat yield estimation remote sensing deep learning CERES-Wheat Science Q Yi Xie Jianxi Huang Integration of a Crop Growth Model and Deep Learning Methods to Improve Satellite-Based Yield Estimation of Winter Wheat in Henan Province, China
description	Timely and accurate regional crop-yield estimates are crucial for guiding agronomic practices and policies to improve food security. In this study, a crop-growth model was integrated with time series of remotely sensed data through deep learning (DL) methods to improve the accuracy of regional wheat-yield estimations in Henan Province, China. Firstly, the time series of moderate-resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) were input into the long short-term memory network (LSTM) model to identify the wheat-growing region, which was further used to estimate wheat areas at the municipal and county levels. Then, the leaf area index (LAI) and grain-yield time series simulated by the Crop Environment REsource Synthesis for Wheat (CERES-Wheat) model were used to train and evaluate the LSTM, one-dimensional convolutional neural network (1-D CNN) and random forest (RF) models, respectively. Finally, an exponential model of the relationship between the field-measured LAI and MODIS NDVI was applied to obtain the regional LAI, which was input into the trained LSTM, 1-D CNN and RF models to estimate wheat yields within the wheat-growing region. The results showed that the linear correlations between the estimated wheat areas and the statistical areas were significant at both the municipal and county levels. The LSTM model provided more accurate estimates of wheat yields, with higher <i>R</i><sup>2</sup> values and lower root mean square error (RMSE) and mean relative error (MRE) values than the 1-D CNN and RF models. The LSTM model has an inherent advantage in capturing phenological information contained in the time series of the MODIS-derived LAI, which is important for satellite-based crop-yield estimates.
format	article
author	Yi Xie Jianxi Huang
author_facet	Yi Xie Jianxi Huang
author_sort	Yi Xie
title	Integration of a Crop Growth Model and Deep Learning Methods to Improve Satellite-Based Yield Estimation of Winter Wheat in Henan Province, China
title_short	Integration of a Crop Growth Model and Deep Learning Methods to Improve Satellite-Based Yield Estimation of Winter Wheat in Henan Province, China
title_full	Integration of a Crop Growth Model and Deep Learning Methods to Improve Satellite-Based Yield Estimation of Winter Wheat in Henan Province, China
title_fullStr	Integration of a Crop Growth Model and Deep Learning Methods to Improve Satellite-Based Yield Estimation of Winter Wheat in Henan Province, China
title_full_unstemmed	Integration of a Crop Growth Model and Deep Learning Methods to Improve Satellite-Based Yield Estimation of Winter Wheat in Henan Province, China
title_sort	integration of a crop growth model and deep learning methods to improve satellite-based yield estimation of winter wheat in henan province, china
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f72474d9738e4b80beff48f6cb603b10
work_keys_str_mv	AT yixie integrationofacropgrowthmodelanddeeplearningmethodstoimprovesatellitebasedyieldestimationofwinterwheatinhenanprovincechina AT jianxihuang integrationofacropgrowthmodelanddeeplearningmethodstoimprovesatellitebasedyieldestimationofwinterwheatinhenanprovincechina
_version_	1718431681567784960

Integration of a Crop Growth Model and Deep Learning Methods to Improve Satellite-Based Yield Estimation of Winter Wheat in Henan Province, China

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