Next Article in Journal
An Operational Framework for Land Cover Classification in the Context of REDD+ Mechanisms. A Case Study from Costa Rica
Previous Article in Journal
Analysis and Mapping of the Spectral Characteristics of Fractional Green Cover in Saline Wetlands (NE Spain) Using Field and Remote Sensing Data
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(7), 592; doi:10.3390/rs8070592

Tower-Based Validation and Improvement of MODIS Gross Primary Production in an Alpine Swamp Meadow on the Tibetan Plateau

1
Lhasa Plateau Ecosystem Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, 3-1-3 Kannondai, Tsukuba, Ibaraki 305-8604, Japan
*
Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra and Prasad S. Thenkabail
Received: 26 May 2016 / Revised: 8 July 2016 / Accepted: 11 July 2016 / Published: 13 July 2016
View Full-Text   |   Download PDF [2514 KB, uploaded 13 July 2016]   |  

Abstract

Alpine swamp meadow on the Tibetan Plateau is among the most sensitive areas to climate change. Accurate quantification of the GPP in alpine swamp meadow can benefit our understanding of the global carbon cycle. The 8-day MODerate resolution Imaging Spectroradiometer (MODIS) gross primary production (GPP) products (GPP_MOD) provide a pathway to estimate GPP in this remote ecosystem. However, the accuracy of the GPP_MOD estimation in this representative alpine swamp meadow is still unknown. Here five years GPP_MOD was validated using GPP derived from the eddy covariance flux measurements (GPP_EC) from 2009 to 2013. Our results indicated that the GPP_EC was strongly underestimated by GPP_MOD with a daily mean less than 40% of EC measurements. To reduce this error, the ground meteorological and vegetation leaf area index (LAIG) measurements were used to revise the key inputs, the maximum light use efficiency (εmax) and the fractional photosynthetically active radiation (FPARM) in the MOD17 algorithm. Using two approaches to determine the site-specific εmax value, we suggested that the suitable εmax was about 1.61 g C MJ−1 for this alpine swamp meadow which was considerably larger than the default 0.68 g C MJ−1 for grassland. The FPARM underestimated 22.2% of the actual FPAR (FPARG) simulated from the LAIG during the whole study period. Model comparisons showed that the large inaccuracies of GPP_MOD were mainly caused by the underestimation of the εmax and followed by that of the undervalued FPAR. However, the DAO meteorology data in the MOD17 algorithm did not exert a significant affection in the MODIS GPP underestimations. Therefore, site-specific optimized parameters inputs, especially the εmax and FPARG, are necessary to improve the performance of the MOD17 algorithm in GPP estimation, in which the calibrated MOD17A2 algorithm (GPP_MODR3) could explain 91.6% of GPP_EC variance for the alpine swamp meadow. View Full-Text
Keywords: alpine swamp meadow; MOD17A2 algorithm; eddy covariance (EC); light use efficiency (LUE); gross primary production (GPP); Tibetan Plateau alpine swamp meadow; MOD17A2 algorithm; eddy covariance (EC); light use efficiency (LUE); gross primary production (GPP); Tibetan Plateau
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Niu, B.; He, Y.; Zhang, X.; Fu, G.; Shi, P.; Du, M.; Zhang, Y.; Zong, N. Tower-Based Validation and Improvement of MODIS Gross Primary Production in an Alpine Swamp Meadow on the Tibetan Plateau. Remote Sens. 2016, 8, 592.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top