Translator Disclaimer
Paper
24 November 1995 Mapping crop coefficients in irrigated areas from Landsat TM images
Author Affiliations +
Abstract
It is well known that reflectance of Earth surface largely depends upon amount of biomass, crop type, development stage, ground coverage. The knowledge of these parameters -- together with groundbased meteorological data -- allows for the estimate of crop water requirements and their spatial distribution. Recent research has shown the possibility of using multispectral satellite images in combination with other information for mapping crop coefficients in irrigated areas. This approach is based on the assumption that crop coefficients (Kc) are greatly influenced by canopy development and vegetation fractional ground cover; since these parameters directly affect the reflectance of cropped areas, it is possible to establish a correlation between multispectral measurements of canopies reflectance and the corresponding Kc values. Within this frame, two different approaches may be applied: (1) definition of spectral classes corresponding to different crop coefficient values and successive supervised classification for the derivation of crop coefficients maps; (2) use of analytical relationships between the surface reflectance and the corresponding values of vegetation parameters, i.e., the leaf area index, the albedo and the surface roughness, needed for the calculation of the potential evapotranspiration according to the combination type equation. The two different techniques are discussed with reference to the results of their application to specific case-studies. The aim of this report is to illustrate the suitability of remote sensing techniques as an operational tool for assessing crop water demand at regional scale.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Guido D'Urso and Massimo Menenti "Mapping crop coefficients in irrigated areas from Landsat TM images", Proc. SPIE 2585, Remote Sensing for Agriculture, Forestry, and Natural Resources, (24 November 1995); https://doi.org/10.1117/12.227167
PROCEEDINGS
7 PAGES


SHARE
Advertisement
Advertisement
Back to Top