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Satellite retrieval of vineyard leaf area index and impact of vine canopy structure and interrow cover crop on retrieval algorithm
 
 
 
 

Satellite retrieval of vineyard leaf area index and impact of vine canopy structure and interrow cover crop on retrieval algorithm

 
Dr. Feng Gao
 
United States Department of Agriculture (USDA), Agricultural Research Service, Hydrology and Remote Sensing Lab, USA
 
 

Leaf area index (LAI) is an essential biophysical parameter in most land-surface models, governing the partitioning of energy, carbon, and water fluxes between the soil and canopy components of the land-surface system. Ground measurement of LAI is labor-intensive and time-consuming. LAI retrieval using routine remote-sensing data provides an indirect estimation of LAI with spatial and temporal variability. The remote-sensing LAI retrieval can be conducted using the empirical relationship of LAI and vegetation indices or through the inversion of a physical radiative transfer model. In the previous study, we have developed an approach that uses the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data product as a reference to retrieve Landsat LAI. This method has been successfully incorporated into the disaggregated ALEXI (DisALEXI) evapotranspiration model to map energy fluxes at 30 m resolution over vineyards as part of the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project. The approach produces Landsat LAI data product that is consistent with MODIS. Recently, the approach has been applied to the Harmonized Landsat and Sentinel-2 (HLS) data to generate a more frequent (3-4-day) 30-m resolution LAI. Comparing to LAI field measurements over the GRAPEX experimental sites from 2017 to 2019, HLS LAI captures the spatial and temporal variability. However, the agreement varies on measurement dates and sites. This could be due to different vine plant structure, interrow cover crop, or clumping factor. In this presentation, we will assess HLS LAI across different sites and years. The causes of differences will be analyzed. Sampling size and spatial heterogeneity will be assessed using Unmanned Aerial Vehicle (UAV) and the Vegetation and Environment monitoring New MicroSatellite (VENµS) imagery. An improved method that combines MODIS LAI samples and field measurements will be presented. The challenges of satellite retrieval of vineyard leaf area index will be discussed.