Mapping Latent Heat Flux in the Western Forest Covered Regions of Algeria Using Remote Sensing Data and a Spatialized Model

• Published in: Remote sensing (Basel, Switzerland) Vol. 1; no. 4; pp. 795 - 817
• Main Authors: Zahira, Souidi, Abderrahmane, Hamimed, Mederbal, Khalladi, Frederic, Donze
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2009
• Subjects: Air temperature; Albedo; Data acquisition; Drought; Energy balance; energy fluxes; Fluctuations; forest cover; Landsat; Latent heat; latent heat flux; Remote sensing; S-SEBI model; Sensible heat; sensible heat flux; Surface temperature
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs1040795

The present paper reports on an investigation to monitor the drought status in Algerian forest covered areas with satellite Earth observations because ground data are scarce and hard to collect. The main goal of this study is to map surface energy fluxes with remote sensing data, based on a simplified algorithm to solve the energy balance equation on each data pixel. Cultivated areas, forest cover and a large water surface were included in the investigated surfaces. The input parameters involve remotely sensed data in the visible, near infrared and thermal infrared. The surface energy fluxes are estimated by expressing the partitioning of energy available at the surface between the sensible heat flux (H) and the latent heat flux (LE) through the evaporative fraction (Λ) according to the S-SEBI (Simplified Surface Energy Balance Index) concept. The method is applicable under the assumptions of constant atmospheric conditions and sufficient wet and dry pixels over a Landsat 7 image. The results are analyzed and discussed considering instantaneous latent heat flux at the data acquisition time. The results confirm the relationships between albedo (r0), the surface temperature (T0) and the evaporative fraction. The method provides estimates of air temperature and LE close to reference measurements. The estimate of latent heat flux and other variables are comparable to those of previous studies. Their comparison with other methods shows reasonable agreement. This approach has demonstrated its simplicity and the fact that remote sensing data alone is sufficient; it could be very promising in areas where data are scarce and difficult to collect.