Evapotranspiration mapping of commercial corn fields in Brazil using SAFER algorithm

Abstract

SAFER (Simple Algorithm for Evapotranspiration Retrieving) is a relatively new algorithm applied successfully to estimate actual crop evapotranspiration (ET) at different spatial scales of different crops in Brazil. However, its use for monitoring irrigated crops is scarce and needs further investigation. This study assessed the performance of SAFER to estimate ET of irrigated corn in a Brazilian semiarid region. The study was conducted in São Desidério, Bahia State, Brazil, in corn-cropped areas in no-tillage systems and irrigated by central pivots. SAFER algorithm with original regression coefficients (a = 1.8 and b =-0.008) was initially tested during the growing seasons of 2014, 2015, and 2016. SAFER performed very poorly for estimating corn ET, with RMSD values greater than 1.18 mm d-1 for 12 fields analyzed and NSE values < 0 in most fields. To improve estimates, SAFER regression coefficients were calibrated (using 2014 and 2015 data) and validated with 2016 data, with the resulting coefficients a and b equal to 0.32 and-0.0013, respectively. SAFER performed well for ET estimation after calibration, with r 2 and NSE values equal to 0.91 and RMSD = 0.469 mm d-1. SAFER also showed good performance (r 2 = 0.86) after validation, with the lowest RMSD (0.58 mm d-1) values for the set of 14 center pivots in this growing season. The results support the use of calibrated SAFER algorithm as a tool for estimating water consumption in irrigated corn fields in semiarid conditions.

Remote sensing environmental indicators for monitoring spatial and temporal dynamics of water and vegetation conditions: applications to the Brazilian biomes

“…In this paper a correction factor is applied to the evaporative fraction (ETf) for considering distinct atmospheric demands for actual evapotranspiration (ET) acquirements. In this way, the SAFER algorithm has been validated in several Brazilian agroecosystems (Araujo et al 2019;Leivas et al 2015;Nuñez et al 2017;Rampazo et al 2020;Santos et al 2020;Silva et al 2019;Teixeira et al 2020a, b;2021a, b;Venancio et al 2021). Besides the corrected ETf values were satisfactorily checked with literature.…”

Orbital Remote Sensing for the Management of Areas Irrigated With a Central Pivot System

“…The crop coefficient (Kc) represents the integration of the effects of three characteristics (crop height, surface resistance, and crop-soil surface albedo) (Pokorny, 2019), which varies according to the phenological stages of the crop, with the possibility of being affected by the duration of the phenological stages (Venancio et al, 2020), and is applied in irrigation management and water allocation (Rozenstein et al, 2019). Therefore, the Kc applied by IRRIGER for irrigation management in commercial areas is based on the recommendations of FAO-56 (Allen et al, 1998); in this sense, the Kc values for the initial, medium, and final stages were 0.38, 1.2, and 0.8, respectively (Table 1).…”

SAFER applications for water productivity assessments with aerial camera onboard a remotely piloted aircraft (RPA). A rainfed corn study in Northeast Brazil

“…ET can be partitioned into transpiration and soil evaporation, and the magnitude of these components depend on the root-zone moisture and crop stages (Fandiño et al, 2012;Consoli and Vanella, 2014;Rosa et al, 2016;Longo-Minnolo et al, 2020). The ratio ET/ET 0 , under optimum root-zone moisture conditions is known as the crop coefficient -K c and can be used to estimate ET p (Mateos et al, 2013;Venancio et al, 2021), while under non-optimum root-zone moisture conditions, this ratio can characterize crop water stress (Lu et al, 2011).…”

“…High WP levels in precision agriculture require maximizing farm inputs by using the best practices at good both spatial and temporal scales, but field measurements of relevant biophysical parameters through destructive sampling are time and cost intensive. Remote sensing methods offer rapid and cost-effective ways to support farmer decisions to optimize the WP components, offering a range of possibilities their assessments at suitable scales, when aiming to insure good yields minimizing negative environmental effects (Mulla, 2013;Marino et al, 2015;McShane et al, 2017;Campos et al, 2018;Silva et al, 2019;Teixeira et al, 2020a,b;Venancio et al, 2021).…”

“…Currently, precision agriculture research using remotely sensed data can help N fertilizing strategies (Sharma and Bali, 2018;Colaço and Bramley, 2018), plant protection managements (Mahlein et al, 2012;Sedina et al, 2017), biomass estimations (Teixeira et al, 2019;Togeiro de Alckmin et al, 2020); and to retrieve physical (Ballabio et al, 2016), biological (Yigini and Panagos, 2016), and chemical (Ballabio et al, 2019) properties of soils. However, there are still lacks of remote sensing studies for corn WP modelling at the level of precision agriculture in Brazil, with few ones done at irrigation pivot parcels with Landsat 8 images at spatial resolutions ranging from 30 to 120 m (Teixeira et al, 2014b;Venancio et al, 2021).…”