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Remote Sens. 2016, 8(1), 10; doi:10.3390/rs8010010

Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation

1
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China
2
Hainan Key Laboratory of Earth Observation, Hainan 572029, China
3
College of Resources and Environment, University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
4
Department of Mathematics, The George Washington University, 2115 G St. NW, Washington, DC 20052, USA
5
College of Information Science and Engineering, Shandong Agricultural University, No. 61 Daizong Road, Taian 271018, China
*
Author to whom correspondence should be addressed.
Academic Editors: Clement Atzberger and Prasad S. Thenkabail
Received: 10 September 2015 / Revised: 20 November 2015 / Accepted: 6 December 2015 / Published: 24 December 2015
View Full-Text   |   Download PDF [6758 KB, uploaded 24 December 2015]   |  

Abstract

Accurate monitoring of grassland biomass at high spatial and temporal resolutions is important for the effective utilization of grasslands in ecological and agricultural applications. However, current remote sensing data cannot simultaneously provide accurate monitoring of vegetation changes with fine temporal and spatial resolutions. We used a data-fusion approach, namely the spatial and temporal adaptive reflectance fusion model (STARFM), to generate synthetic normalized difference vegetation index (NDVI) data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat data sets. This provided observations at fine temporal (8-d) and medium spatial (30 m) resolutions. Based on field-sampled aboveground biomass (AGB), synthetic NDVI and support vector machine (SVM) techniques were integrated to develop an AGB estimation model (SVM-AGB) for Xilinhot in Inner Mongolia, China. Compared with model generated from MODIS-NDVI (R2 = 0.73, root-mean-square error (RMSE) = 30.61 g/m2), the SVM-AGB model we developed can not only ensure the accuracy of estimation (R2 = 0.77, RMSE = 17.22 g/m2), but also produce higher spatial (30 m) and temporal resolution (8-d) biomass maps. We then generated the time-series biomass to detect biomass anomalies for grassland regions. We found that the synthetic NDVI-derived estimations contained more details on the distribution and severity of vegetation anomalies compared with MODIS NDVI-derived AGB estimations. This is the first time that we have generated time series of grassland biomass with 30-m and 8-d intervals data through combined use of a data-fusion method and the SVM-AGB model. Our study will be useful for near real-time and accurate (improved resolutions) monitoring of grassland conditions, and the data have implications for arid and semi-arid grasslands management. View Full-Text
Keywords: biomass; data fusion; STARFM; MODIS; Landsat; support vector machine (SVM) biomass; data fusion; STARFM; MODIS; Landsat; support vector machine (SVM)
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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).

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Zhang, B.; Zhang, L.; Xie, D.; Yin, X.; Liu, C.; Liu, G. Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation. Remote Sens. 2016, 8, 10.

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