25 October 2016 Water productivity mapping using Landsat 8 satellite together with weather stations
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Abstract
The use of remote sensing satellite in conjunction with models and meteorological data enable the mapping of biophysical properties of agroecosystems with satisfactory accuracy. The main goal of this research was to determine the spatial-temporal agro-ecological indicators of water productivity in watersheds with different types of land use and occupation, using Landsat 8 images, agro-meteorological stations and application of Monteith and SAFER (Simple Algorithm for Retrieving Evapotranspiration) models to estimate the production biomass (BIO) and the actual evapotranspiration (ET), respectively. Incident global solar radiation (RS ↓) is observed seasonality of radiation during the year. Higher RS ↓levels happen during the first and the last four months, when the Sun is around its zenith positions in the study region. During the natural dry period in the region, the RS↓ is lower because winter solstice time for the Southern Hemisphere, this condition it is verified the reducing in the values of ET and BIO. Average values of biophysical properties for the study period were 0.54, 0.16 and 301 K for Normalized Difference Vegetation Index, albedo and surface temperature, respectively. The highest value of BIO was 105 kg ha-1d-1 and occurred in July 2013. The lowest value was 15.9 kg ha-1d-1 and occurred in October 2014. ET showed a value of 1.65 mm d-1 in the rainy period and 0.64 during the dry period in the study area. The highest average ET occurred in the irrigated area (June 2014), with a value of 1.89 mm d-1 and a maximum of 2.46 mm d-1. WP average for the evaluated period was 3.06 Kg m-3, with the largest value of 4.91 Kg m-3 in June 2013 and a minimum value of 2.45 Kg m-3 in September 2013.
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Renato A. M. Franco, Fernando B. T. Hernandez, Antônio H. de C. Teixeira, Janice Freitas Leivas, Daniel Noe Coaguila, Christopher M. Neale, "Water productivity mapping using Landsat 8 satellite together with weather stations", Proc. SPIE 9998, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, 99981H (25 October 2016); doi: 10.1117/12.2242003; https://doi.org/10.1117/12.2242003
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