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

Sun Sun,Zhu Zhu,Zhang Zhang,Yao Yao,Wu Wu,Su Su,Chen Chen,Huang Huang

doi:10.3390/rs11202409

Sun Sun, Zhu Zhu + Show 6 more

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https://doi.org/10.3390/rs11202409

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Abstract

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 (R2) of about 0.6, and root mean square errors (RMSEs) ranges of mainly 0.1–0.2 and 0.0–0.3, 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 R2 value of 0.98 and RMSE of 0.0133, and the correlation between KF and field-measured reflectances had an R2 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.

Highlights

Corn (Zea mays) is planted widely around the world, and is vital for food security both as food and fodder
Our analysis showed that the Leaf area index (LAI) increased from day of year (DOY) 181 to DOY 236, and increased continuously until harvest, while the chlorophyll content first increased from DOY 181 to DOY 236, and remained stable until harvest
A similar trend can be seen for the NIR band, except that there is a bias on the Y-axis, which indicates that the Sentinel-2 reflectance is systematically higher than the Kalman filtered (KF) reflectance

Summary

Introduction

Corn (Zea mays) is planted widely around the world, and is vital for food security both as food and fodder. Sensed spectral reflectances have already been shown to provide precise and timely information of corn plants status [2,3]. Remote sensing reflectance from the corn canopy is affected by corn canopy LAI and chlorophyll content [8,9,10,11], which gives an indirect estimate of nutrient status, plant stress, and productivity [12]. Both the LAI and chlorophyll content of corn are retrieved using the reflectance of remote sensing images in this study

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