RESUMO
This paper aimed to support the rational crop expansion in agricultural-growing regions. MODIS satellite images are used together with gridded weather data to model biometeorological parameters at the Fruit Circuit region, state of São Paulo, Southeast Brazil. This region has experienced some cases of drought, while arising rainfall water excess in some periods, demanding large-scale water and energy balance studies to subsidize water resource policies. The SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm was applied together with the radiation-use efficiency (RUE) model for biometeorological index assessments. The highest latent heat fluxes (λE), above 8.0 MJ m-2 d-1, at the end of the year, coincide with the progressive increases on both rainfall and global solar radiation (RG) levels, and drop to below 5.0 MJ m-2 d-1 in the middle of the year, during the driest conditions. The same tendencies along the year are verified for sensible heat fluxes (H), for which mean pixel values are above 3.5 MJ m-2 d-1 at the end of the year. On the one hand, the highest values for water productivity (WP), which is considered the ratio of actual evapotranspiration (ET) to biomass production (BIO), above 4.0 kg m-3, are verified in April, period under increasing BIO and low ET rates. On the other hand, the lowest WP values (below 2.0 kg m-3) occur between August and October, when BIO is low, and ET is high. Although the area featuring good WP levels under high precipitation (P), with rainfalls generally above ET, supplementary irrigation may benefit agriculture in some periods of the year. The results of the large-scale modeling showed applicability of the models for monitoring water and vegetation dynamics over 16-day timescale and at a 250-m spatial resolution in areas experiencing climate and land-use changes by combining climate data and MODIS images. Application of these tools enables to indicate the best options for expanding the agriculture activities, being of great potential for rational natural resources management, in regions under environmental vulnerability conditions.