Data for: Crop specific algorithms trained over ground measurements provide the best performance for GAI and fAPAR estimates from Landsat-8 observations

Published: 25 April 2021| Version 1 | DOI: 10.17632/nzrdbn8g2d.1
Contributors:
Fernando Camacho,
,

Description

This dataset encompasses a large number (> 700) of in-situ observations over elemantary sampling units (about 20m2) of LAI (effective and actual), fAPAR and fraction of vegetation cover (fCover) collected over a network of agricultural sites during the ImagineS project (http://fp7-imagines.eu/) in the period 2013-2016. The ground dataset was collected with digital hemispherical photography (DHP), LAI2200, and AccuPAR devices following well stablished protocols in agreement with CEOS LPV good practices. The ground data is complemented with concomitant Landsat-8/OLI observations, the sun zenith angle at the acquisition and the NDVI. This results in a unique database to calibrate and validate algorithms for retrieval of biophysical variables over crops.

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Categories

Remote Sensing, Remote Sensing in Agriculture, Algorithm Development for Remote Sensing

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