ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume X-3/W2-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-3/W2-2022, 43–49, 2022
https://doi.org/10.5194/isprs-annals-X-3-W2-2022-43-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-3/W2-2022, 43–49, 2022
https://doi.org/10.5194/isprs-annals-X-3-W2-2022-43-2022
 
27 Oct 2022
27 Oct 2022

CROP YIELD ESTIMATION IN THE NORTH CHINA PLAIN FROM 2001 TO 2016 USING MULTI-SOURCE REMOTE SENSING DATA AND PROCESS-BASED FGM MODEL

Q. Wu1,2, X. Wang1,2, J. Jiang1,2, and S. Chen3 Q. Wu et al.
  • 1School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture(BUCEA), Beijing, 100044, China
  • 2Key Laboratory of Urban Spatial Information, Ministry of Natural Resources of the People's Republic of China, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
  • 3Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, 100091, China

Keywords: Gross primary productivity (GPP), crop yield, process-based model, North China Plain

Abstract. Gross primary productivity (GPP) is an essential indicator of vegetation growth that reflects ecosystem function. GPP is the original source of energy entering cropland ecosystem and thus could serve as a direct indicator of crop yield. In the context of increasing population, changing climate, and decreasing available resources, accurate monitoring and forecasting of food and crop yields play an essential role in sustainable human development. In this study, the process-based Farquhar GPP model (FGM) driven by multisource remote sensing data was implemented to estimate the spatial and temporal dynamics of GPP in crop-growing areas of the North China Plain from 2001 to 2016. We found that the GPP of crops in the North China Plain is relatively high in the southern provinces while lower in the northern part. The GPP values showed a significant increasing trend from 2001 to 2016 (+2.19 Mt C yr−1, P<0.05). Based on crop yield statistical yearbook, we found that GPP is well correlated with crop yield (R2 = 0.98, RMSE = 10.4 Mt yr−1). Thus, we constructed an empirical regression model between GPP and crop yield (i.e., ‘GPP-yield’ empirical model). Finally, time-series GPP data and the ‘ GPP-yield ’ model were applied the crop yield in the North China Plain with spatial and temporal continuity. We found that the crop yield in the North China Plain changed in accordance with GPP, and also showed a significant increasing trend from 2001 to 2016, with a mean increasing rate of +2.84 Mt yr−1 (P<0.05, R2 = 0.16, RMSE = 31.73 Mt yr−1). This study proved an example of large-scale crop yield estimation using multi-source remote sensing data.