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The Ability of Sun-Induced Chlorophyll Fluorescence From OCO-2 and MODIS-EVI to Monitor Spatial Variations of Soybean and Maize Yields in the Midwestern USA

by 1,2,3, 1,2,3, 4,5, 6, 7,8, 1,2,3 and 1,2,3,*
1
International Institute for Earth System Sciences, Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China
2
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
3
Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China
4
College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
5
National Center for Supercomputing Applications, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
6
Section 1.4 Remote Sensing, GFZ German Research Centre for Geosciences, Helmholtz-Centre, 14473 Potsdam, Germany
7
Environment and Production Technology Division (EPTD), International Food Policy Research Institute, Washington, DC 20005, USA
8
Macro Agriculture Research Institute, College of Economics and Management, Huazhong Agricultural University, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(7), 1111; https://doi.org/10.3390/rs12071111
Received: 7 February 2020 / Revised: 22 March 2020 / Accepted: 27 March 2020 / Published: 31 March 2020
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Satellite sun-induced chlorophyll fluorescence (SIF) has emerged as a promising tool for monitoring growing conditions and productivity of vegetation. However, it still remains unclear the ability of satellite SIF data to predict crop yields at the regional scale, comparing to widely used satellite vegetation index (VI), such as the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, few attempts have been made to verify if SIF products from the new Orbiting Carbon Observatory-2 (OCO-2) satellite could be applied for regional corn and soybean yield estimates. With the deep neural networks (DNN) approach, this study investigated the ability of OCO-2 SIF, MODIS EVI, and climate data to estimate county-level corn and soybean yields in the U.S. Corn Belt. Monthly mean and maximum SIF and MODIS EVI during the peak growing season showed similar correlations with corn and soybean yields. The DNNs with SIF as predictors were able to estimate corn and soybean yields well but performed poorer than MODIS EVI and climate variables-based DNNs. The performance of SIF and MODIS EVI-based DNNs varied with the areal dominance of crops while that of climate-based DNNs exhibited less spatial variability. SIF data could provide useful supplementary information to MODIS EVI and climatic variables for improving estimates of crop yields. MODIS EVI and climate predictors (e.g., VPD and temperature) during the peak growing season (from June to August) played important roles in predicting yields of corn and soybean in the Midwestern 12 states in the U.S. The results highlighted the benefit of combining data from both satellite and climate sources in crop yield estimation. Additionally, this study showed the potential of adding SIF in crop yield prediction despite the small improvement of model performances, which might result from the limitation of current available SIF products. The framework of this study could be applied to different regions and other types of crops to employ deep learning for crop yield forecasting by combining different types of remote sensing data (such as OCO-2 SIF and MODIS EVI) and climate data. View Full-Text
Keywords: sun-induced chlorophyll fluorescence; crop yield; soybean; corn; deep neural network sun-induced chlorophyll fluorescence; crop yield; soybean; corn; deep neural network
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MDPI and ACS Style

Gao, Y.; Wang, S.; Guan, K.; Wolanin, A.; You, L.; Ju, W.; Zhang, Y. The Ability of Sun-Induced Chlorophyll Fluorescence From OCO-2 and MODIS-EVI to Monitor Spatial Variations of Soybean and Maize Yields in the Midwestern USA. Remote Sens. 2020, 12, 1111. https://doi.org/10.3390/rs12071111

AMA Style

Gao Y, Wang S, Guan K, Wolanin A, You L, Ju W, Zhang Y. The Ability of Sun-Induced Chlorophyll Fluorescence From OCO-2 and MODIS-EVI to Monitor Spatial Variations of Soybean and Maize Yields in the Midwestern USA. Remote Sensing. 2020; 12(7):1111. https://doi.org/10.3390/rs12071111

Chicago/Turabian Style

Gao, Yun, Songhan Wang, Kaiyu Guan, Aleksandra Wolanin, Liangzhi You, Weimin Ju, and Yongguang Zhang. 2020. "The Ability of Sun-Induced Chlorophyll Fluorescence From OCO-2 and MODIS-EVI to Monitor Spatial Variations of Soybean and Maize Yields in the Midwestern USA" Remote Sensing 12, no. 7: 1111. https://doi.org/10.3390/rs12071111

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