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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #354665

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: The retrieval of high-resolution LAI from Landsat data by combining MODIS products

Author
item Zhou, J. - Beijing Normal University
item Zhang, S - Beijing Normal University
item Yang, H. - Beijing Normal University
item Xiao, Z. - Beijing Normal University
item Gao, Feng

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/24/2018
Publication Date: 7/27/2018
Citation: Zhou, J., Zhang, S., Yang, H., Xiao, Z., Gao, F.N. 2018. The retrieval of high-resolution LAI from Landsat data by combining MODIS products. Remote Sensing. 10(8):1187. https://doi.org/10.3390/rs10081187.
DOI: https://doi.org/10.3390/rs10081187

Interpretive Summary: Leaf area index (LAI) is a key biophysical parameter used in most land surface models. Operationally, the LAI product currently used comes from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor. However, MODIS spatial resolution (500-m) is too coarse to capture spatial variability at field scales. Landsat observation (30-m) is good for mapping at the field level. To make a MODIS-consistent LAI product from Landsat imagery, two Landsat LAI retrieval approaches (conventional pure pixel approach and a new mixed pixel approach) were evaluated using three LAI datasets generated from Landsat surface reflectance and MODIS LAI products. The experiments were conducted over two different landscapes (Baoding in China and Central Iowa in the USA). Results show that both approaches produced good agreement with field measurements. The mixed pixel approach performed slightly better than the pure pixel approach. The MODIS-consistent LAI from Landsat data is important for crop yield modeling and water use monitoring at field scales.

Technical 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 approachs 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 up-scaled Landsat reflectance at the 500-m resolution and MODIS LAI products (datasets A1), the other was composed of MODIS reflectance and LAI products (datasets A2). With them, two inversion models were developed using SVR. For the pixel unmixing method, the training samples (datasets 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. In Des Moines, the R2 value was 0.79 and the root mean square error (RMSE) value was 0.73 with the datasets A1; the R2 value was 0.81 and the RMSE value was 0.69 with the datasets A2. The R2 value was 0.82 and the RMSE value was 0.65 for the pixel unmixing method (method B). In the Hebei Province, the R2 value was 0.74 and the RMSE value was 0.45 with the datasets A1; the R2 value was 0.80 and the RMSE value was 0.37 with the datasets A2; and the R2 value was 0.77 and the RMSE value was 0.41 in general for the homogeneous and pure pixel filter method. The R2 value was 0.79 and the RMSE value was 0.49 for the pixel unmixing method. These tests showed that three models for the two methods combined with MODIS products can retrieve high-resolution LAI from Landsat data. And the results of the pixel unmixing method was slightly higher than that of the homogeneous and pure pixel filter method.