Joint Retrieval of Growing Season Corn Canopy LAI and Leaf Chlorophyll Content by Fusing Sentinel-2 and MODIS Images

Continuous and accurate estimates of crop canopy leaf area index (LAI) and chlorophyll content are of great importance for crop growth monitoring. These estimates can be useful for precision agricultural management and agricultural planning. Our objectives were to investigate the joint retrieval of...

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Main Authors: Wei Su, Zhongping Sun, Wen-hua Chen, Xiaodong Zhang, Chan Yao, Jiayu Wu, Jianxi Huang, Dehai Zhu
Format: Article
Language:English
Published: MDPI AG 2019-10-01
Series:Remote Sensing
Subjects:
lai
Online Access:https://www.mdpi.com/2072-4292/11/20/2409
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spelling doaj-5fe28558072845f69767d61f8410e96b2020-11-24T21:41:24ZengMDPI AGRemote Sensing2072-42922019-10-011120240910.3390/rs11202409rs11202409Joint Retrieval of Growing Season Corn Canopy LAI and Leaf Chlorophyll Content by Fusing Sentinel-2 and MODIS ImagesWei Su0Zhongping Sun1Wen-hua Chen2Xiaodong Zhang3Chan Yao4Jiayu Wu5Jianxi Huang6Dehai Zhu7College of Land Science and Technology, China Agriculture University, Beijing 100083, ChinaSatellite Environment Center, Ministry of Environmental Protection, Beijing 100094, ChinaDepartment of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, UKCollege of Land Science and Technology, China Agriculture University, Beijing 100083, ChinaCollege of Land Science and Technology, China Agriculture University, Beijing 100083, ChinaCollege of Land Science and Technology, China Agriculture University, Beijing 100083, ChinaCollege of Land Science and Technology, China Agriculture University, Beijing 100083, ChinaCollege of Land Science and Technology, China Agriculture University, Beijing 100083, ChinaContinuous and accurate estimates of crop canopy leaf area index (LAI) and chlorophyll content are of great importance for crop growth monitoring. These estimates can be useful for precision agricultural management and agricultural planning. Our objectives were to investigate the joint retrieval of corn canopy LAI and chlorophyll content using filtered reflectances from Sentinel-2 and MODIS data acquired during the corn growing season, which, being generally hot and rainy, results in few cloud-free Sentinel-2 images. In addition, the retrieved time series of LAI and chlorophyll content results were used to monitor the corn growth behavior in the study area. Our results showed that: (1) the joint retrieval of LAI and chlorophyll content using the proposed joint probability distribution method improved the estimation accuracy of both corn canopy LAI and chlorophyll content. Corn canopy LAI and chlorophyll content were retrieved jointly and accurately using the PROSAIL model with fused Kalman filtered (KF) reflectance images. The relation between retrieved and field measured LAI and chlorophyll content of four corn-growing stages had a coefficient of determination (R<sup>2</sup>) of about 0.6, and root mean square errors (RMSEs) ranges of mainly 0.1&#8722;0.2 and 0.0&#8722;0.3, respectively. (2) Kalman filtering is a good way to produce continuous high-resolution reflectance images by synthesizing Sentinel-2 and MODIS reflectances. The correlation between fused KF and Sentinel-2 reflectances had an R<sup>2</sup> value of 0.98 and RMSE of 0.0133, and the correlation between KF and field-measured reflectances had an R<sup>2</sup> value of 0.8598 and RMSE of 0.0404. (3) The derived continuous KF reflectances captured the crop behavior well. Our analysis showed that the LAI increased from day of year (DOY) 181 (trefoil stage) to DOY 236 (filling stage), and then increased continuously until harvest, while the chlorophyll content first also increased from DOY 181 to DOY 236, and then remained stable until harvest. These results revealed that the jointly retrieved continuous LAI and chlorophyll content could be used to monitor corn growth conditions.https://www.mdpi.com/2072-4292/11/20/2409joint retrievallaichlorophyll contentdata fusionkalman filter
collection DOAJ
language English
format Article
sources DOAJ
author Wei Su
Zhongping Sun
Wen-hua Chen
Xiaodong Zhang
Chan Yao
Jiayu Wu
Jianxi Huang
Dehai Zhu
spellingShingle Wei Su
Zhongping Sun
Wen-hua Chen
Xiaodong Zhang
Chan Yao
Jiayu Wu
Jianxi Huang
Dehai Zhu
Joint Retrieval of Growing Season Corn Canopy LAI and Leaf Chlorophyll Content by Fusing Sentinel-2 and MODIS Images
Remote Sensing
joint retrieval
lai
chlorophyll content
data fusion
kalman filter
author_facet Wei Su
Zhongping Sun
Wen-hua Chen
Xiaodong Zhang
Chan Yao
Jiayu Wu
Jianxi Huang
Dehai Zhu
author_sort Wei Su
title Joint Retrieval of Growing Season Corn Canopy LAI and Leaf Chlorophyll Content by Fusing Sentinel-2 and MODIS Images
title_short Joint Retrieval of Growing Season Corn Canopy LAI and Leaf Chlorophyll Content by Fusing Sentinel-2 and MODIS Images
title_full Joint Retrieval of Growing Season Corn Canopy LAI and Leaf Chlorophyll Content by Fusing Sentinel-2 and MODIS Images
title_fullStr Joint Retrieval of Growing Season Corn Canopy LAI and Leaf Chlorophyll Content by Fusing Sentinel-2 and MODIS Images
title_full_unstemmed Joint Retrieval of Growing Season Corn Canopy LAI and Leaf Chlorophyll Content by Fusing Sentinel-2 and MODIS Images
title_sort joint retrieval of growing season corn canopy lai and leaf chlorophyll content by fusing sentinel-2 and modis images
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2019-10-01
description Continuous and accurate estimates of crop canopy leaf area index (LAI) and chlorophyll content are of great importance for crop growth monitoring. These estimates can be useful for precision agricultural management and agricultural planning. Our objectives were to investigate the joint retrieval of corn canopy LAI and chlorophyll content using filtered reflectances from Sentinel-2 and MODIS data acquired during the corn growing season, which, being generally hot and rainy, results in few cloud-free Sentinel-2 images. In addition, the retrieved time series of LAI and chlorophyll content results were used to monitor the corn growth behavior in the study area. Our results showed that: (1) the joint retrieval of LAI and chlorophyll content using the proposed joint probability distribution method improved the estimation accuracy of both corn canopy LAI and chlorophyll content. Corn canopy LAI and chlorophyll content were retrieved jointly and accurately using the PROSAIL model with fused Kalman filtered (KF) reflectance images. The relation between retrieved and field measured LAI and chlorophyll content of four corn-growing stages had a coefficient of determination (R<sup>2</sup>) of about 0.6, and root mean square errors (RMSEs) ranges of mainly 0.1&#8722;0.2 and 0.0&#8722;0.3, respectively. (2) Kalman filtering is a good way to produce continuous high-resolution reflectance images by synthesizing Sentinel-2 and MODIS reflectances. The correlation between fused KF and Sentinel-2 reflectances had an R<sup>2</sup> value of 0.98 and RMSE of 0.0133, and the correlation between KF and field-measured reflectances had an R<sup>2</sup> value of 0.8598 and RMSE of 0.0404. (3) The derived continuous KF reflectances captured the crop behavior well. Our analysis showed that the LAI increased from day of year (DOY) 181 (trefoil stage) to DOY 236 (filling stage), and then increased continuously until harvest, while the chlorophyll content first also increased from DOY 181 to DOY 236, and then remained stable until harvest. These results revealed that the jointly retrieved continuous LAI and chlorophyll content could be used to monitor corn growth conditions.
topic joint retrieval
lai
chlorophyll content
data fusion
kalman filter
url https://www.mdpi.com/2072-4292/11/20/2409
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