|COURAULT, D - Institut National De La Recherche Agronomique (INRA)
|JACOB, F - Institut National De La Recherche Agronomique (INRA)
|BENOIT, V - Institut National De La Recherche Agronomique (INRA)
|WEISS, M - Institut National De La Recherche Agronomique (INRA)
|MARLOIE, O - Institut National De La Recherche Agronomique (INRA)
|HANOCQ, J - Institut National De La Recherche Agronomique (INRA)
|FILLOL, E - Institut National De La Recherche Agronomique (INRA)
|OLIOSO, A - Institut National De La Recherche Agronomique (INRA)
|DEDIEU, G - Center For The Study Of The Biosphère From Space(CESBIO)
|GOUAUX, P - Institut National De La Recherche Agronomique (INRA)
|GAY, M - Ecole Polytechnique Fédérale De Lausanne
Submitted to: International Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/1/2008
Publication Date: 3/10/2009
Citation: Courault, D., Jacob, F., Benoit, V., Weiss, M., Marloie, O., Hanocq, J.F., Fillol, E., Olioso, A., Dedieu, G., Gouaux, P., Gay, M., French, A.N. 2009. Influence of Agricultural Practices on Micrometerological Spatial Variations at Local and Regional Scales. International Journal of Remote Sensing. 30(5):1183-1205.
Interpretive Summary: Quantifying the spatial distribution of evapotranspiration (ET) from agricultural lands is critical for monitoring water use, managing irrigation resources, and understanding the effect of land use practices upon both local and regional climate. The only way to provide accurate spatial ET estimates is to observe land cover with remote sensing in combination with weather models. However, these ET estimates might not be accurate and need to be tested before others can use them. This research investigated the accuracy of one of the ET methods known as the Surface Energy Balance Model for Land (SEBAL). The model used 15-90 m resolution data from the ASTER remote sensing instrument. Analysis of data collected over agricultural lands in south-western France in 2003 showed that ET effects over a 3 km x 3 km region were dominated by irrigation practices and strongly affected near-surface air temperatures. These results show that land surface remote sensing is important for detecting spatial changes in vegetation water use and are important for agronomists, hydrologists, and water managers seeking ways to conserve water.
Technical Abstract: Soil-vegetation-atmosphere transfers significantly influence interactions and feedbacks between vegetation and boundary layer in relation with plant phenology and water status. The current study focused on linking micrometeorological conditions to cultural practices at the local and regional scales (lower than 100 km2) over an agricultural region in South Western France. This was achieved considering observation and modeling tools designed for characterizing spatial variabilities over land surfaces. These tools were the ASTER high spatial resolution optical remote sensing data, and the SEBAL spatialised surface energy balance model. Surface bidirectional reflectance and brightness temperature were first derived from ASTER data through solar and thermal atmospheric radiative transfer codes, and next used to infer surface radiative properties required for model simulations. Assessing model consistency in terms of air temperature simulations gave satisfactory results when intercomparing against weather station data, although basic model assumptions were not systematically verified in terms of spatial variability. Next, spatialised simulations of evapotranspiration and air temperature were analyzed at the regional and local scales in relation with pedology, land use, and cultural practices. It was shown that model estimates were consistent with the considered crops and the related cultural practices. Irrigation appeared as the main factor among others (soil, land use, sowing date…) explaining the micrometeorological variability. Although interesting and promising in terms of linking micrometeorological conditions to cultural practices, the results reported here emphasized several difficulties, especially about capturing subfield scale variability and monitoring the considered processes at an appropriate temporal sampling.