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Editorial for the Special Issue “Frontiers in Spectral Imaging and 3D Technologies for Geospatial Solutions”
Article

Using Solar-Induced Chlorophyll Fluorescence Observed by OCO-2 to Predict Autumn Crop Production in China

by 1,2,3, 1,2,3,4,*, 1,2,3, 1,2,3, 1,2,3 and 1,2,3
1
State Cultivation Base of Eco-agriculture for Southwest Mountainous Land, Southwest University, Chongqing 400715, China
2
Research Base of Karst Eco-environments at Nanchuan in Chongqing, Ministry of Nature Resources, School of Geographical Sciences, Southwest University, Chongqing 400715, China
3
Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China
4
Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(14), 1715; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11141715
Received: 15 May 2019 / Revised: 6 July 2019 / Accepted: 17 July 2019 / Published: 19 July 2019
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
The remote sensing of solar-induced chlorophyll fluorescence (SIF) has attracted considerable attention as a new monitor of vegetation photosynthesis. Previous studies have revealed the close correlation between SIF and terrestrial gross primary productivity (GPP), and have used SIF to estimate vegetation GPP. This study investigated the relationship between the Orbiting Carbon Observatory-2 (OCO-2) SIF products at two retrieval bands (SIF757, SIF771) and the autumn crop production in China during the summer of 2015 on different timescales. Subsequently, we evaluated the performance to estimate the autumn crop production of 2016 by using the optimal model developed in 2015. In addition, the OCO-2 SIF was compared with the moderate resolution imaging spectroradiometer (MODIS) vegetation indices (VIs) (normalized difference vegetation index, NDVI; enhanced vegetation index, EVI) for predicting the crop production. All the remotely sensed products exhibited the strongest correlation with autumn crop production in July. The OCO-2 SIF757 estimated autumn crop production best (R2 = 0.678, p < 0.01; RMSE = 748.901 ten kilotons; MAE = 567.629 ten kilotons). SIF monitored the crop dynamics better than VIs, although the performances of VIs were similar to SIF. The estimation accuracy was limited by the spatial resolution and discreteness of the OCO-2 SIF products. Our findings demonstrate that SIF is a feasible approach for the crop production estimation and is not inferior to VIs, and suggest that accurate autumn crop production forecasts while using the SIF-based model can be obtained one to two months before the harvest. Furthermore, the proposed method can be widely applied with the development of satellite-based SIF observation technology. View Full-Text
Keywords: solar-induced chlorophyll fluorescence; OCO-2; EVI; NDVI; crop production solar-induced chlorophyll fluorescence; OCO-2; EVI; NDVI; crop production
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MDPI and ACS Style

Wei, J.; Tang, X.; Gu, Q.; Wang, M.; Ma, M.; Han, X. Using Solar-Induced Chlorophyll Fluorescence Observed by OCO-2 to Predict Autumn Crop Production in China. Remote Sens. 2019, 11, 1715. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11141715

AMA Style

Wei J, Tang X, Gu Q, Wang M, Ma M, Han X. Using Solar-Induced Chlorophyll Fluorescence Observed by OCO-2 to Predict Autumn Crop Production in China. Remote Sensing. 2019; 11(14):1715. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11141715

Chicago/Turabian Style

Wei, Jin, Xuguang Tang, Qing Gu, Min Wang, Mingguo Ma, and Xujun Han. 2019. "Using Solar-Induced Chlorophyll Fluorescence Observed by OCO-2 to Predict Autumn Crop Production in China" Remote Sensing 11, no. 14: 1715. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11141715

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