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.

中文翻译：

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模拟大规模生物气象指数以监测农业种植区：在巴西圣保罗水果循环区的应用

本文旨在支持农业产区合理种植作物。MODIS 卫星图像与网格化天气数据一起用于对巴西东南部圣保罗州水果赛道地区的生物气象参数进行建模。该地区经历了一些干旱，同时出现了一些时期的降雨水量过剩，需要大规模的水能平衡研究来补贴水资源政策。SAFER（蒸散检索简单算法）算法与辐射利用效率（RUE）模型一起应用于生物气象指标评估。最高潜热通量 (λE) 在年底时高于 8.0 MJ m-2 d-1，与降雨量和全球太阳辐射 (RG) 水平的逐渐增加一致，并降至 5 以下。0 MJ m−2 d−1 在年中，在最干燥的条件下。对于显热通量 (H) 验证了一年中的相同趋势，年末平均像素值高于 3.5 MJ m-2 d-1。一方面，水生产力 (WP) 的最高值被认为是实际蒸散量 (ET) 与生物量生产 (BIO) 的比率，高于 4.0 kg m-3，在 4 月份得到验证，期间 BIO 和低 ET 率。另一方面，最低 WP 值（低于 2.0 kg m-3）出现在 8 月和 10 月之间，此时 BIO 低，ET 高。尽管该地区在高降水 (P) 下具有良好的 WP 水平，降雨量普遍高于 ET，但补充灌溉可能在一年中的某些时期有益于农业。大规模建模的结果表明，通过结合气候数据和 MODIS 图像，这些模型适用于在 16 天时间尺度上以 250 米空间分辨率监测气候和土地利用变化地区的水和植被动态。这些工具的应用能够表明在环境脆弱条件下的地区扩大农业活动的最佳选择，对合理的自然资源管理具有巨大潜力。