Location: Hydrology and Remote Sensing Laboratory
Title: Retrieving leaf area index from landsat using MODIS LAI products and field measurements Authors
Submitted to: Geoscience and Remote Sensing Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: February 27, 2013
Publication Date: August 14, 2013
Citation: Gao, F.N., Anderson, M.C., Kustas, W.P., Houborg, R. 2013. Retrieving leaf area index from landsat using MODIS LAI products and field measurements. Geoscience and Remote Sensing Letters. 11:773-777. Interpretive Summary: Leaf area index (LAI) is a key biophysical parameter used in most land surface models. While coarse resolution LAI at the kilometer scale are available from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor and often sufficient for global, continental and regional scale applications, field scale applications require finer pixel resolution data such as from Landsat (30m). A simple reference based approach has been developed for retrieving leaf area index (LAI) from Landsat imagery using MODIS LAI products as a reference in previous work. However, there is underestimation for the medium to high LAI (>3) due to a paucity of high quality MODIS LAI samples in this range available for training. This paper improves the approach by combing MODIS LAI samples with ground LAI measurements. Results from the Soil Moisture Experiment of 2002 (SMEX02) field campaign show that use of ground LAI measurements improved field scale estimations of LAI but did not degrade the agreement with MODIS LAI products. The paper demonstrates an effective approach for producing LAI from Landsat imagery for crop modeling at field scales which is required by the National Agricultural Statistics Service and Foreign Agricultural Service for crop yield monitoring.
Technical Abstract: A simple reference-based approach has been developed for retrieving leaf area index (LAI) from Landsat imagery. The approach uses homogeneous and high quality LAI retrievals from the moderate resolution imaging spectroradiometer (MODIS) as reference to develop a regression tree relating these MODIS LAI samples to Landsat surface reflectance. The LAI maps retrieved from Landsat are consistent with the MODIS estimates. However, there is underestimation (about 0.4) for the medium to high LAI (>3) due to a paucity of high quality MODIS LAI samples in this range available for training. LAI measurements acquired on the ground may help to fill the gap by providing LAI information over a full data range of LAI expected at the Landsat scale. This paper tests this hypothesis by combining MODIS LAI samples with field measurements to train the Landsat-scale reconstruction. An effective approach has been developed to combine LAI samples from MODIS data products and field measurements, which considers the quality and spatial distribution of the samples. Results from the Soil Moisture Experiment of 2002 (SMEX02) field campaign show that use of ground LAI measurements improved field scale estimation of LAI but did not degrade consistency with MODIS LAI products. The approach and results are useful for models that require consistent LAI inputs at different spatial resolutions.