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Remote Sens. 2017, 9(10), 1082; https://doi.org/10.3390/rs9101082

A 33-Year NPP Monitoring Study in Southwest China by the Fusion of Multi-Source Remote Sensing and Station Data

1
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, Hubei, China
2
Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, Hubei, China
3
School of Resource and Civil Engineering, Wuhan Institute of Technology, Wuhan 430205, Hubei, China
4
Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, Zhejiang, China
5
Laboratory of Critical Zone Evolution, School of Earth Sciences, China University of Geosciences, Wuhan 430074, Hubei, China
6
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, Hubei, China
7
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, Hubei, China
*
Author to whom correspondence should be addressed.
Received: 24 July 2017 / Revised: 7 October 2017 / Accepted: 20 October 2017 / Published: 24 October 2017
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Abstract

Knowledge of regional net primary productivity (NPP) is important for the systematic understanding of the global carbon cycle. In this study, multi-source data were employed to conduct a regional NPP study in southwest China, with a 33-year time span and a 1-km scale. A multi-sensor fusion framework was applied to obtain a new normalized difference vegetation index (NDVI) time series from 1982 to 2014, combining the advantages of different remote sensing datasets. As another key parameter for NPP modeling, the total solar radiation was calculated utilizing the improved Yang hybrid model (YHM), based on meteorological station data. The accuracy of the data processes is proved reliable by verification experiments. Moreover, NPP estimated by fused NDVI shows an obvious improved accuracy than that based on the original data. The spatio-temporal analysis results indicated that 67% of the study area showed an increasing NPP trend over the past three decades. The correlation between NPP and precipitation was significant heterogeneous at the monthly scale; specifically, the correlation is negative in the growing season and positive in the dry season. Meanwhile, the lagged positive correlation in the growing season and no lag in the dry season indicated the important impacts of precipitation on NPP. What is more, we found that there are three distinct stages during the variation of NPP, which were driven by different climatic factors. Significant climate warming led to a great increase of NPP from 1992 to 2002, while NPP clearly decreased during 1982–1992 and 2002–2014 due to the frequent droughts caused by the precipitation decrease. View Full-Text
Keywords: net primary productivity; multi-sensor information fusion; regional scale; long-term time series; spatio-temporal analysis; climate control net primary productivity; multi-sensor information fusion; regional scale; long-term time series; spatio-temporal analysis; climate control
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Guan, X.; Shen, H.; Gan, W.; Yang, G.; Wang, L.; Li, X.; Zhang, L. A 33-Year NPP Monitoring Study in Southwest China by the Fusion of Multi-Source Remote Sensing and Station Data. Remote Sens. 2017, 9, 1082.

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