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

Yingxin Gu,Bruce Wylie

doi:10.3390/rs70403489

Abstract

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.

Highlights

Satellite remote sensing has become an essential tool for measuring and monitoring the dynamics of terrestrial ecosystems over large areas because of its wide coverage, high spatial and temporal resolutions, and consistency [1,2,3,4,5,6,7,8]
One limitation of using GSN to estimate vegetation productivity is that Normalized Difference Vegetation Indices (NDVIs) can reach saturation in dense vegetation canopies (i.e., NDVI becomes insensitive at high values of leaf area index) [14,21,22,23,24,25], which may lead to an underestimation of vegetation productivity in high biomass regions
The correlation coefficient (r) between the upscaled Landsat NDVI and the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI is 0.77, which is much lower than the correlation coefficients between the predicted GSN and the MODIS GSN shown in Tables 1 and 3 (r > 0.91)

Summary

Introduction

Satellite remote sensing has become an essential tool for measuring and monitoring the dynamics of terrestrial ecosystems over large areas because of its wide coverage, high spatial and temporal resolutions, and consistency [1,2,3,4,5,6,7,8]. The satellite-derived Normalized Difference Vegetation Index (NDVI) is the normalized reflectance difference between the near-infrared (NIR) band and the visible red band [9,10]. Higher NDVI values usually reflect greater vigor and greenness of the vegetation [10,11,12]. One limitation of using GSN to estimate vegetation productivity is that NDVI can reach saturation in dense vegetation canopies (i.e., NDVI becomes insensitive at high values of leaf area index) [14,21,22,23,24,25], which may lead to an underestimation of vegetation productivity in high (dense) biomass regions. Gu et al developed an approach that adjusted NDVI (and GSN) pixel values that were near saturation to better characterize the cropland productivity in the Greater Platte

Objectives

Results

Discussion

Conclusion