Retrieving leaf area index from landsat using MODIS LAI products and field measurements

Feng Gao, Martha C. Anderson, William P. Kustas, Rasmus Houborg

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


Land surface models that operate at multiple spatial resolutions require consistent leaf area index (LAI) inputs at each scale. In order to produce LAI from Landsat imagery that is consistent with the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product and with in situ measurements, an improved regression tree mapping approach has been developed, which uses homogeneous and high-quality LAI retrievals from MODIS and existing LAI measurements acquired on the ground as samples to train a rule-based model based on Landsat surface reflectance data in visible and infrared bands. The methodology employs a simple geostatistical approach for determining sample weight, considering the spatial distribution of the samples and data quality. Results from the Soil Moisture Experiment of 2002 field campaign show that incorporation of available ground-based LAI measurements into the training sample collection improved field-scale estimation of LAI, especially in areas of very high vegetation cover fraction, but did not degrade consistency with MODIS LAI products.

Original languageEnglish (US)
Article number6595584
Pages (from-to)773-777
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number4
StatePublished - Apr 2014


  • Biophysical parameters
  • Landsat
  • Moderate Resolution Imaging Spectroradiometer (MODIS)
  • Soil Moisture Experiment of 2002 (SMEX02) field campaign
  • disaggregation
  • downscaling
  • leaf area index (LAI)
  • spatial statistics

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering


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