Downscaling 250-m MODIS Growing Season NDVI Based on Multiple-Date Landsat Images and Data Mining Approaches

Remote Sensing

Yingxin Gu¹  Bruce K. Wylie²  Chandra Giri³  Clement Atzberger³ 

[1] ASRC InuTeq, Contractor to U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, 47914 252nd Street, Sioux Falls, SD 57198, USA;USGS EROS, 47914 252nd Street, Sioux Falls, SD 57198, USA; E-Mail:;id="af1-remotesensing-07-03489">1ASRC InuTeq, Contractor to U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, 47914 252nd Street, Sioux Falls, SD 57198, U

关键词: Landsat;    MODIS;    growing season averaged NDVI;    biomass;    data mining;    cubist regression models;    downscaling;    multi-sensor;   

DOI  :  10.3390/rs70403489

来源: mdpi

【 摘 要 】

The satellite-derived growing season time-integrated Normalized Difference Vegetation Index (GSN) has been used as a proxy for vegetation biomass productivity. The 250-m GSN data estimated from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have been used for terrestrial ecosystem modeling and monitoring. High temporal resolution with a wide range of wavelengths make the MODIS land surface products robust and reliable. The long-term 30-m Landsat data provide spatial detailed information for characterizing human-scale processes and have been used for land cover and land change studies. The main goal of this study is to combine 250-m MODIS GSN and 30-m Landsat observations to generate a quality-improved high spatial resolution (30-m) GSN database. A rule-based piecewise regression GSN model based on MODIS and Landsat data was developed. Results show a strong correlation between predicted GSN and actual GSN (r = 0.97, average error = 0.026). The most important Landsat variables in the GSN model are Normalized Difference Vegetation Indices (NDVIs) in May and August. The derived MODIS-Landsat-based 30-m GSN map provides biophysical information for moderate-scale ecological features. This multiple sensor study retains the detailed seasonal dynamic information captured by MODIS and leverages the high-resolution information from Landsat, which will be useful for regional ecosystem studies.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.