Paper
24 February 2004 Estimation of crop coefficients by means of optimized vegetation indices for corn
Jose Gonzalez-Piqueras, Alfonso Calera, Maria Amparo Gilabert, Andres Cuesta, Fernando De la Cruz Tercero
Author Affiliations +
Abstract
A linear relationship between NDVI and basal crop coefficient (Kcb) allows to compute the spectral crop coefficient (Krcb). Due to the influence of soil variations varying surface humidity on NDVI, five soil optimized indices have been used to obtain a linear relationship normalized for soil background effect (SAVI, OSAVI, TSAVI, MSAVI and GESAVI). Data used on this work have been obtained from a field campaign for corn in the area of Barrax (Spain), describing crop growth stages with green fraction cover (GFC), and leaf area index (LAI). SAVI with optimized factor L set to 0.5 is a good estimator of Krcb from sparse to dense vegetation, nevertheless the soil line based index ( GESAVI) due to a wider range of variation are more sensitive to leaf variations at high levels of vegetation amount. Spectral crop coefficients obtained from SAVI and soil line based GESAVI are sensitive to crop hazards by weather anomalies and estimates in real time the basal crop coefficients to estimate the amount of water removed by the crop from the active root zone.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jose Gonzalez-Piqueras, Alfonso Calera, Maria Amparo Gilabert, Andres Cuesta, and Fernando De la Cruz Tercero "Estimation of crop coefficients by means of optimized vegetation indices for corn", Proc. SPIE 5232, Remote Sensing for Agriculture, Ecosystems, and Hydrology V, (24 February 2004); https://doi.org/10.1117/12.511317
Lens.org Logo
CITATIONS
Cited by 22 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Vegetation

Near infrared

Reflectivity

Humidity

Agriculture

Meteorology

Remote sensing

Back to Top