Abstract
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.
Highlights
China is an agricultural country with the largest population in the world, which only accounts for 7% of the earth’s cropland resources but it needs to feed 22% of the world population [1]
solar-induced chlorophyll fluorescence (SIF) is generated during the photosynthesis process, regardless of the region or crop, which provides a possibility for SIF to estimate crop production
This study explored the potential of Orbiting Carbon Observatory-2 (OCO-2) SIF product in estimating autumn crop production in China
Summary
China is an agricultural country with the largest population in the world, which only accounts for 7% of the earth’s cropland resources but it needs to feed 22% of the world population [1]. Precise prediction of crop production in China and any other countries with the same situations is very significant [2,3]. Satellite remote sensing has been widely used to manage cropland [7,8,9,10,11] and estimate crop production [5,12,13,14,15]. Vegetation indices (VIs) (e.g., normalized difference vegetation index, NDVI; enhanced vegetation index, EVI), chlorophyll content, leaf area index (LAI), and the fraction of absorbed photosynthetically active radiation by vegetation (fAPAR) have been used to estimate the production of corn, rice, and other crops [16,17,18,19]. Crop production was forecasted by using VIs that were derived from satellite data and machine learning methods on the Canadian Prairies [5], and by inputting NDVI and daytime land surface temperature (LST) to build a regression tree-based model in America [18]
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