Next Article in Journal
Assessing the Pattern Differences between Satellite-Observed Upper Tropospheric Humidity and Total Column Water Vapor during Major El Niño Events
Next Article in Special Issue
Effects of Forest Canopy Vertical Stratification on the Estimation of Gross Primary Production by Remote Sensing
Previous Article in Journal
Application of Multi-Sensor Satellite Data for Exploration of Zn–Pb Sulfide Mineralization in the Franklinian Basin, North Greenland
Previous Article in Special Issue
Developing an Integrated Remote Sensing Based Biodiversity Index for Predicting Animal Species Richness
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle

The Retrieval of 30-m Resolution LAI from Landsat Data by Combining MODIS Products

1
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2
USDA (United States Department of Agriculture), Agricultural Research Service, Hydrology and Remote Sensing Laboratory, 10300 Baltimore Avenue, Beltsville, MD 20705, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(8), 1187; https://doi.org/10.3390/rs10081187
Received: 11 June 2018 / Revised: 17 July 2018 / Accepted: 24 July 2018 / Published: 27 July 2018
  |  
PDF [19325 KB, uploaded 1 August 2018]
  |  

Abstract

Leaf area index (LAI) is a critical vegetation structural parameter in biogeochemical and biophysical ecosystems. High-resolution LAI products play an essential role in regional studies. Empirical methods, which normally use field measurements as their training samples and have been identified as the most commonly used approaches to retrieve structural parameters of vegetation from high-resolution remote-sensing data, are limited by the quality of training samples. Few efforts have been made to generate training samples from existing global LAI products. In this study, two methods (a homogeneous and pure pixel filter method (method A) and a pixel unmixing method (method B)) were developed to extract training samples from moderate-resolution imaging spectroradiometer (MODIS) surface reflectance and LAI products, and a support vector regression (SVR) algorithm trained by the samples was used to retrieve the high-resolution LAI from Landsat data at Baoding, situated in the Hebei Province in China, and Des Moines, situated in Iowa, United States. For the homogeneous and pure pixel filter method, two different sets of training samples were designed. One was composed of upscaled Landsat reflectance at the 500-m resolution and MODIS LAI products (dataset A1); the other was composed of MODIS reflectance and LAI products (dataset A2). With them, two inversion models were developed using SVR. For the pixel unmixing method, the training samples (dataset B) were extracted from unmixed MODIS surface reflectance and LAI products at 30-m resolution, and the third inversion model was obtained with them. LAI inversion results showed that good agreement with field measurements was achieved using these three inversion models. The R2 (coefficient of determination) value and the root mean square error (RMSE) value were computed to assess the results. For all tests, the R2 values are higher than 0.74 and RMSE values are less than 0.73. These tests showed that three models for the two methods combined with MODIS products can retrieve 30-m resolution LAI from Landsat data. The results of the pixel unmixing method was slightly better than that of the homogeneous and pure pixel filter method. View Full-Text
Keywords: leaf area index; MODIS products; Landsat; high resolution; homogeneous and pure pixel filter; pixel unmixing leaf area index; MODIS products; Landsat; high resolution; homogeneous and pure pixel filter; pixel unmixing
Figures

Graphical abstract

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
Printed Edition Available!
A printed edition of this Special Issue is available here.

Share & Cite This Article

MDPI and ACS Style

Zhou, J.; Zhang, S.; Yang, H.; Xiao, Z.; Gao, F. The Retrieval of 30-m Resolution LAI from Landsat Data by Combining MODIS Products. Remote Sens. 2018, 10, 1187.

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