Simple Algorithm For Evapotranspiration Retrieving Research Articles

Monitoring anomalies on large-scale energy and water balance components by coupling remote sensing parameters and gridded weather data.

The SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm was applied with MODIS images and gridded weather data from 2007 to 2021, to monitor the energy balance components and their anomalies, in the Atlantic Forest (AF) and Caatinga (CT) biomes inside the coastal agricultural growing zone, Northeast Brazil. Considering the long-term data, the Rn values between the biomes are not significantly different, however presenting distinct Rn partitions into latent (λE), sensible (H), and ground (G) heat fluxes between biomes. The Rn values annual averages are 9.40 ± 0.21 and 9.50 ± 0.23MJm-2 d-1, for AF and CT, respectively. However, for respectively AF and CT, they are respectively 5.10 ± 1.14MJm-2 d-1 and 4.00 ± 0.99MJm-2 d-1 for λE; 3.80 ± 1.12MJm-2 d-1 and 5.00 ± 1.00MJm-2 d-1 for H; 0.50 ± 0.12MJm-2 d-1 and 0.40 ± 0.10MJm-2 d-1 for G, yielding respective mean evaporative fraction (Ef = λE/(Rn - G) values of 0.60 ± 0.12 and 0.50 ± 0.15. Anomalies on λE, H, and Ef were detected through standardized index for these energy balance components by comparing the results for the years 2018 to 2021 with the long-term values from 2007 to each of these years, showing that the energy fluxes between surfaces and the lower atmosphere, and then the root-zone moisture conditions for both biomes, may strongly vary along seasons and years, with alternate positive and negative anomalies. These assessments are important for water policies as they can picture suitable periods and places for rainfed agriculture as well as the irrigation needs in irrigated agriculture, allowing rational agricultural environmental management while minimizing water competitions among other water users, under climate and land-use changes conditions.

Large-scale water balance modeling using remote sensing and weather data: Application in an agricultural growing region of the coastal northeast Brazil

The SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm was applied to test water balance (WB) monitoring in the agricultural growing region of SEALBA, an acronym for the states Sergipe (SE), Alagoas (AL), and Bahia (BA), coastal Northeast Brazil, classifying the biomes Atlantic Forest and Caatinga. The MODIS MOD13Q1 reflectance product and meteorological data were used to calculate actual (ET) and reference (ET0) evapotranspiration at 16-day timescale along the years 2007–2021. The period with the highest precipitation (P) is in May, when the 16-day values are above 90 mm in Atlantic Forest and higher than 70 mm in Caatinga. For ET, the largest rates occur between July and September, when the 16-day average surpass 2.70 mm d−1 in the Atlantic Forest and 2.90 mm d−1 in Caatinga. Larger P together with lower ET, concentrates the positive water balance - WB (P – ET) from March to August in both biomes, bringing suitability for the rainfed agriculture. However, from July to December occur the negative WB values, indicating the need of irrigation for critical crop stages. Inferring the root-zone moisture conditions throughout the evaporative fraction values (ETf = ET/ET0) it is noticed that their highest values occur from April to October, evidencing a delay between the moment in which rainfall attend evapotranspiration with that in which the root-zone moisture recovers the optimum levels after the driest periods, what was also confirmed by the Pearson correlation coefficient values. The large-scale modelling carried out in SEALBA, presented viability to subsidize public policies regarding the water resources, with potential for replication of the methods in other regions.

SAFER-ET based assessment of irrigation patterns and impacts on groundwater use in the central Punjab, Pakistan

The excessive pumping of groundwater during the dry season in Pakistan is identified as a significant concern for groundwater sustainability. Farmers encounter difficulties in efficiently managing irrigation, and adopting the outputs of Irrigation Advisory System (IAS) to improve irrigation practices. Upgrading the existing IAS can help to identify the regions with over-irrigation issues and optimize outreach efforts. To solve these issues, we define the new concept of integrating Gravity Recovery and Climate Experiment (GRACE) total water storage anomalies (TWSA) and Landsat 8 imageries, which can enhance the IAS efficiency by accurately identifying regions facing groundwater depletion by estimating actual crop water consumption through application of the Simple Algorithm For Evapotranspiration Retrieving (SAFER) on Landsat 8 data and Penman-Monteith (PM) method was used to calculate crop water demand using meteorological forcing data from Global Land Data Assimilation System (GLDAS) over the five selected districts of Punjab during the dry seasons of year 2013–2020. Comparing actual water consumption (SAFER evapotranspiration (ET)) with in-situ groundwater depth, strong correlations were found between them that are supporting the use of Landsat 8 data for irrigation monitoring. Further, compared SAFER ET was with PM ET to calculate the percentage of over/under irrigated regions, and the results revealed that most of the selected districts were over-irrigated, indicating the potential for excessive irrigation water savings. Our results show that the integration of GRACE TWSA and Landsat 8 data enhance the efficiency of operational IAS and can lead to potential savings of 81% (equivalent to 155 million m3) of groundwater during the dry season in the Central Punjab, Pakistan. Satellite data integration globally enhances IAS implementation, aiding regions with unsustainable dry season irrigation. This empowers farmers to optimize agriculture through precise IAS outputs.

Actual evapotranspiration by machine learning and remote sensing without the thermal spectrum.

The objectives of this study were to use machine learning algorithms to establish a model for estimating the evapotranspiration fraction (ETf) using two data input scenarios from the spectral information of the Sentinel-2 constellation, and to analyze the temporal and spatial applicability of the models to estimate the actual evapotranspiration (ETr) in agricultural crops irrigated by center pivots. The spectral bands of Sentinel 2A and 2B satellite and vegetation indices formed the first scenario. The second scenario was formed by performing the normalized ratio procedure between bands (NRPB) and joining the variables applied in the first scenario. The models were generated to predict the ETf using six regression algorithms and then compared with ETf calculated by the Simple Algorithm For Evapotranspiration Retrieving (SAFER) algorithm, was considered as the standard. The results possible to select the best model, which in both scenarios was Cubist. Subsequently, ETf was estimated only for the center pivots present in the study area and the classification of land use and cover was accessed through the MapBiomas product. Land use was necessary to enable the calculation of ETr in each scenario, in the center pivots with sugarcane and soybean crops. ETr was estimated using two ETo approaches (EToBrazil and Hargreaves-Samani). It was found that the Hargreaves-Samani equation overestimated ETr with higher errors mainly for center pivots with sugarcane, where systematic error (MBE) ranged from 0.89 to 2.02 mm d-1. The EToBrazil product, on the other hand, presented statistical errors with MBE values ranging from 0.00 to 1.26 mm d-1 for both agricultural crops. Based on the results obtained, it is observed that the ETr can be monitored spatially and temporally without the use of the thermal band, which causes the estimation of this parameter to be performed with greater temporal frequency.

Performance of the SAFER model in estimating peanut maturation

The most widespread method for obtaining Peanut Maturity Index (PMI), the Hull-Scrape, is time-consuming and highly subjective, which makes its application on a large scale difficult and does not represent the variability of the production area. Seeking more accurate PMI estimates, this research uses a combination of weather and spectral data. Therefore, this study aimed to evaluate the performance of the Simple Algorithm for Evapotranspiration Retrieving (SAFER) model to calculate evapotranspiration and estimate PMI, indicating the optimal timing for crop digging. The experiment was conducted in three commercial peanut fields (A, B, and C) in Georgia, USA, in the 2020 and 2021 growing seasons. Pods were collected on different dates and classified according to maturity using the Hull-Scrape method. Weather data and PlanetScope images were used to calculate actual evapotranspiration from the SAFER model, which was correlated with the PMI collected in situ and used to generate linear regression models. Maturity in Fields A and B showed a stronger correlation with evapotranspiration estimated by SAFER (0.757 and 0.796, respectively), which led to the development of a model using data from these two fields. This model presented a relative error of 13.16% and proved to be the most suitable for estimating peanut maturity by integrating different field conditions. The SAFER model proved to be promising for estimating PMI, as it reduces the subjectivity of the traditional method by eliminating the need for a person to identify the color of pod mesocarp. Additionally, the model does not require images from the given day PMI is estimated, allowing for the estimation even in regions highly affected by the presence of clouds and shadows.

Forage Mass Estimation in Silvopastoral and Full Sun Systems: Evaluation through Proximal Remote Sensing Applied to the SAFER Model

The operational slowness in the execution of direct methods for estimating forage mass, an important variable for defining the animal stocking rate, gave rise to the need for methods with faster responses and greater territorial coverage. In this context, the aim of this study was to evaluate a method to estimate the mass of Urochloa brizantha cv. BRS Piatã in shaded and full sun systems, through proximal sensing applied to the Simple Algorithm for Evapotranspiration Retrieving (SAFER) model, applied with the Monteith Radiation Use Efficiency (RUE) model. The study was carried out in the experimental area of Fazenda Canchim, a research center of Embrapa Pecuária Sudeste, São Carlos, SP, Brazil (21°57′S, 47°50′W, 860 m), with collections of forage mass and reflectance in the silvopastoral systems animal production and full sun. Reflectance data, as well as meteorological data obtained by a weather station installed in the study area, were used as input for the SAFER model and, later, for the radiation use efficiency model to calculate the fresh mass of forage. The forage collected in the field was sent to the laboratory, separated, weighed and dried, generating the variables of pasture total dry mass), total leaf dry mass, leaf and stalk dry mass and leaf area index. With the variables of pasture, in situ, and fresh mass, obtained from SAFER, the training regression model, in which 80% were used for training and 20% for testing the models. The SAFER was able to promisingly express the behavior of forage variables, with a significant correlation with all of them. The variables that obtained the best estimation performance model were the dry mass of leaves and stems and the dry mass of leaves in silvopastoral and full sun systems, respectively. It was concluded that the association of the SAFER model with the proximal sensor allowed us to obtain a fast, precise and accurate forage estimation method.

Orbital remote sensing to estimate evapotranspiration in a cotton cropping system irrigated by a centre pivot

Evapotranspiration (ET) estimation is essential for adequate management of water resources. The main ways to quantify ET are based on the use of field sensors, class A tanks, Bowen's ratio method, turbulent vortex correlation analysis, use of lysimeters, and through Remote Sensing (RS), which allows estimating biophysical parameters based on satellite images. The objective of the research was to determine the effective evapotranspiration (ETe) of cotton plantations irrigated with a centre pivot, using the Surface Energy Balance Algorithm for Land (SEBAL), Mapping Evapotranspiration at High Resolution and with Internalized Calibration (METRIC) and Simple Algorithm for Evapotranspiration Retrieving (SAFER) methods. Comparisons were made for three agricultural years, using the R², RMSE, MAE, MBE and R statistics. The best performances in the crops were obtained with the SEBAL vs. SAFER models, which presented mean values of 0.54, -0.30 and 0.31 mm d-1 in RMSE, MBE and MAE, respectively. The data obtained by all three models can be applied to estimate ETe in irrigated cotton plantations and, consequently, their results can assist in irrigation management and in crop treatments

PERFORMANCE OF THE SAFER MODEL IN ESTIMATING ACTUAL EVAPOTRANSPIRATION OF WHEAT AND BEAN IRRIGATED CROPS IN THE CENTRAL BRAZILIAN SAVANNAH

Meeting the ever-growing demand for food depends tightly on irrigation. In this scenario, it is essential to manage the scarce water resources available and so to understand the spatial and temporal variation of actual evapotranspiration (ETa). In the past decades, models for monitoring and mapping ETa applying remote sensing data were developed and assessed. In this work, the performance of the Simple Algorithm for Evapotranspiration Retrieving (SAFER) for estimating the evapotranspiration of two annual winter irrigated crops in the Cerrado (Brazilians Savannah) was evaluated. Multispectral and thermal images from the sensors ETM+/Landsat 7 and OLI-TIRS/Landsat 8 were used for this purpose. Initially, the model was calibrated for the wheat crop, then this calibration was tested for the bean crop, using Bowen ration ETa data as reference. After calibration, a coefficient of determination (r2) equal to 0.91 and the root mean square error (RMSE) equal to 0.58 mm d-1 were found for the wheat. For the bean, the r2 was equal to 0.65 and RMSE, equal to 0.77 mm d-1. The results are satisfactory, although they suggest that the model may present an improved performance with specific calibration for each crop and/or region, or adopting calibration in two stages, for lower and higher NDVI values.

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

Aerial images taken with a camera onboard a remotely piloted aircraft (ARP) at 4-cm resolution were used for rainfed corn water productivity (WP) assessments by applying the Simple Algorithm for Evapotranspiration Retrieving (SAFER) and the radiation use efficiency (RUE) Monteith's model in Northeast Brazil. We present a methodology based on the use of the visible and near infrared images from a Sequoia camera together with weather and actual yield data (Ya), to model actual evapotranspiration (ET) and biomass production (BIO), retrieving water productivity based on both BIO (WPBIO) and actual yield (WPYa). Different nitrogen (N) fertilizing cover levels (0–250 kg ha−1) and sources (nitrate – Nt and urea – Ur) were analyzed for supporting precision agriculture, aiming a fertilizing recommendation for good corn yield while reducing water use and N leaching problems. According to our statistical analyzes, there were no significant difference on ET values for the N treatments inside the analyzed phenological stages (PS) due to the proportional partition into soil evaporation and transpiration from seeding to harvest stages, but as BIO values is related to transpiration which in turn varied with PS until a certain limit, they affected WP till N cover fertilizing level of 150 kg ha−1 for both Nt and Ur N sources. Regarding the growing season (GS) WP components, no significant differences were verified between Nt and Ur N sources, being the average values for ET, BIO, and Ya, of 394 mm GS−1, 17.3 t GS−1, and 8.8 t GS−1, which yielded mean WPBIO andWPYaof 4.4 kg m−3 and 2.2 kg m−3, corresponding to a harvest index (HI) around 0.50 considering all N cover fertilizing treatments. The most important finding of the current research is that the SAFER algorithm can be applied to estimate ET with high resolution aerial cameras without the thermal bands, and together with modelled BIO or Ya data, WP assessments can be carried out following the principles of precision agriculture. The slightly lower WP values for Ur N source and the advantage of its lower both price and N leaching problems, Ur applications at 150 kg ha−1 is recommended to save money while avoiding N leaching to the ground water, when compared with Nt source. For replication of the tested methods in other regions, simple calibrations of the modelling equations may be done with field or/and remote sensing measurements, to infer the specific environmental conditions.

Energy balance and irrigation performance assessments in lemon orchards by applying the SAFER algorithm to Landsat 8 images

In the semiarid conditions of the São Francisco River Basin, Brazil, irrigated fruit crops are replacing natural vegetation, being lemon highlighted by incentives for the national and international markets. This paper aimed to support the rational water management of lemon orchards under different irrigation systems under these conditions. We present a methodology based on the use of the visible and near infrared images from Landsat 8 (L8) satellite together with weather and actual yield data (Ya), to assess the energy balance components and irrigation performance indicators (IPI) by applying the SAFER (Simple Algorithm for Evapotranspiration Retrieving) in six commercial farms under different irrigation systems inside the basin in the northern Minas Gerais state, Southeast Brazil. The ET rates averaged 2.7 mm d−1, 2.9 mm d−1, and 3.7 mm d−1, for drip, micro sprinkler, and pivot irrigated orchards, respectively. The evaporative fraction (latent heat flux by the available energy) reached above 1.00 for localized irrigation (drip and micro sprinkler), and 1.30 for pivots, during the lemon phenological stages from fruit growth to harvest peaks. Pivot irrigation systems were not recommended under the semi-arid conditions, due to large water direct evaporated from the soil and air close to the surface. For drip and micro sprinkler irrigated orchards, crop coefficient curves were modeled based on the accumulated degree-days (DDac) to estimate lemon crop water requirements. Drip irrigated orchards presented better water productivity levels being recommended together with deficit irrigation strategies which could allow good lemon yields with saving water savings. The most important findings of the current research are that the SAFER algorithm can be applied to estimate crop ET with satellite images without the thermal bands which together with modelled Ya data, irrigation assessments can be carried out at high spatial resolution following the principles of precision agriculture. For replication of the methods in other regions, simple calibrations of the modelling equations can be performed to infer the specific environmental conditions.

Evapotranspiration mapping of commercial corn fields in Brazil using SAFER algorithm

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.

Water stress coefficient determined by orbital remote sensing techniques

ABSTRACT In regions where the irrigated area is increasing and water availability is reduced, such as the West of the Bahia state, Brazil, the use of techniques that contribute to improving water use efficiency is paramount. One of the ways to improve irrigation is by improving the calculation of actual evapotranspiration (ETa), which among other factors is influenced by soil drying, so it is important to understand this relationship, which is usually accounted for in irrigation management models through the water stress coefficient (Ks). This study aimed to estimate the water stress coefficient (Ks) through information obtained via remote sensing, combined with field data. For this, a study was carried out in the municipality of São Desidério, an area located in western Bahia, using images of the Landsat-8 satellite. Ks was calculated by the relationship between crop evapotranspiration and ETa, calculated by the Simple Algorithm for Evapotranspiration Retrieving (SAFER). The Ks estimated by remote sensing showed, for the development and medium stages, average errors on the order of 5.50%. In the final stage of maize development, the errors obtained were of 23.2%.

Modeling large-scale biometeorological indices to monitor agricultural-growing areas: applications in the fruit circuit region, São Paulo, Brazil.

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 8.0MJm-2d-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 5.0MJm-2d-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 3.5MJm-2d-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 4.0kgm-3, are verified in April, period under increasing BIO and low ET rates. On the other hand, the lowest WP values (below 2.0kgm-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.

Performance of SAFER evapotranspiration using missing meteorological data

The Simple Algorithm for Evapotranspiration Retrieving (SAFER) can be used to estimate surface evapotranspiration. In the process of SAFER parameterization, the reference evapotranspiration (ETo) calculated by the Penman-Monteith (PM) method was included. However, methodologies for estimating ETo that use fewer meteorological variables should be sought, mainly to increase the applicability of the method in regions where there are no complete weather stations. Thus, the objective of this study was to evaluate the impact of the ETo calculated with estimated data on the calculation of surface evapotranspiration using SAFER (ETSAFER) in the Brazilian semiarid region. The study was carried out in areas of centre pivots cultivated with sugarcane located in the public irrigated perimeter of Jaíba, MG, Brazil. ETo and its impact on ETSAFER were analysed using meteorological data from a station belonging to the National Institute of Meteorology (INMET) as well as data from 19 images of the Landsat 8 satellite from 2013, 2014 and 2016. The ETSAFER responses were analysed from the calculations of ETo estimated by the Hargreaves-Samani (HS) and PM methods with missing data including single absences of solar radiation (RS), wind speed (U2), relative humidity (RH) and combined absences of U2 and RH, U2, RH and RS, RS and U2, and RS and RH. The temporal and spatial variability of ETSAFER for sugarcane crops was assessed. RS was the parameter that most affected ETo and, consequently, ETSAFER. ETSAFER was underestimated when calculated using the ETo estimated by PM with missing RS data and overestimated when the ETo was calculated by HS. In the absence of single or combined data of U2 and RH, it is recommended to estimate ETo by PM with missing data for the calculation of ETSAFER. It is not advised to use the ETo obtained by the HS method for calculating ETSAFER in semiarid regions.

Water productivity monitoring by using geotechnological tools in contrasting social and environmental conditions: Applications in the São Francisco River basin, Brazil

We used the SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm together with the Monteith's RUE (Radiation Use Efficiency) model, for large-scale water productivity assessments in different social and environmental conditions of the São Francisco River sub basins, Brazil, by coupling remote sensing parameters from MODIS images and a net of agrometeorological stations, during the year 2016. Variations on actual evapotranspiration (ET) and biomass production (BIO) among ecosystems and sub basins were strongly detected, affecting the water productivity based on ET (WP = BIO/ET). The mean ET daily rates were 2.1 mm d−1 for both, Forest Zone (FZ) and Savanna (SV), and 1.3 mm d−1 for Caatinga (CA), while regarding BIO they were 67, 68 e 33 kg ha−1 d−1, for FZ, SV, and CA, respectively resulting in corresponding average WP values of 2.6; 2.5, and 1.7 kg m−3. The modelling by using the MODIS reflectance product together with gridded weather data presented operational applicability for monitoring water and vegetation dynamics at a 16-day period and at a 250 m spatial resolution along the year, under conditions of distinct water availability. The satisfactory results highlighted the suitability of geotechnological tools to subsidize the rational natural resources management on large scales in societies with different environmental vulnerabilities.

Evapotranspiration and biomass modelling in the Pontal Sul Irrigation Scheme

ABSTRACTIn order to make feasible the use of irrigated areas of the Pontal Sul Irrigation Scheme in Petrolina, State of Pernambuco, Brazil, modelling of real evapotranspiration (ET) and plant biomass production (BIO) at a large scale was performed with associated agro-meteorological data and the algorithm SAFER (Simple Algorithm For Evapotranspiration Retrieving) using MODIS images for the years 2010–2017. The year 2012 presented the highest ET average with 3.15 mm day−1 in the rainy season, reaching a maximum of 6 mm day−1. The lowest mean ET was recorded in the year 2013, with 0.08 mm day−1 in the dry season, and the maximum recorded for the period was 3.40 mm day−1 in the irrigated agricultural area. For BIO, the highest average was 104.82 kg ha−1 day−1, during the rainy season of 2012, reaching a maximum value of 252 kg ha−1 day−1. The lowest average was for the dry period of 2013, with a value of 0.93 kg ha−1 day−1, and a maximum of 112.51 kg ha−1 day−1 in the irrigated agricultural area. The higher ET results represent moisture in the root zone, provided by rainfall and/or irrigation, showing maximum values in the rainy season. The results obtained for BIO suffer greatly from perimeter water availability. The present study becomes very important to the management of water resources, as it provides an increase in water productivity for food production.

COMPARISON OF SAFER AND METRIC-BASED ACTUAL EVAPOTRANSPIRATION MODELS IN A SUBTROPICAL AREA OF BRAZIL

COMPARISON OF SAFER AND METRIC-BASED ACTUAL EVAPOTRANSPIRATION MODELS IN A SUBTROPICAL AREA OF BRAZIL&#x0D; &#x0D; &#x0D; CÉSAR DE OLIVEIRA FERREIRA SILVA1; RODRIGO LILLA MANZIONE2 E JOSÉ LUIZ ALBUQUERQUE FILHO3&#x0D; &#x0D; 1Agronomical Sciences Faculty, Universidade Estadual Paulista, Av. Universitária, 3780, Altos do Paraíso, 18610-034, São Paulo/SP, Brazil, cesaroliveira.f.silva@gmail.com. &#x0D; 2School of Sciences and Engineering, Universidade Estadual Paulista, Rua Domingos da Costa Lopes,780, Jd. Itaipu, 17602-496, Tupã/SP, Brazil, rlmanzione@gmail.com. &#x0D; 3Department of Hydrogeology, Institute of Technological Research (IPT), Av. Prof. Almeida Prado, 532, Cid. Universitária – Butantã, 05508-901, São Paulo/SP, Brazil, albuze@ipt.br. &#x0D; &#x0D; &#x0D; 1 ABSTRACT&#x0D; &#x0D; Remote sensing algorithms are well known to estimate surface energy fluxes in regional to global scales with low cost. The remote sensing approach has an advantage of estimating evapotranspiration (ET) on larger spatiotemporal scales when compared with traditional methods. This study compared the result of ET estimates from the “Simple Algorithm for Evapotranspiration Retrieving” (SAFER) and “Mapping Evapotranspiration at high Resolution with Internal Calibration” (METRIC) models on varied land uses of a subtropical area located in Southeast Brazil by using a image from the sensor OLI of LANDSAT-8. The results showed similarity of ET estimate from both models, although slight deviation especially at high ET values. It happened due differences as the need of anchor pixel in METRIC, which requires two points with extrem thermohydrological conditions in the same area. Minimum ground data requirement is the major advantage of the METRIC over the SAFER model. The maximum value, the sum and ET range by METRIC was higher than SAFER. This study has considered both models feasible for estimation of ET from satellite data in the study area.&#x0D; &#x0D; Keywords: remote sensing, modelling, superficial temperature, Landsat-8, agriwater.&#x0D; &#x0D; &#x0D; SILVA, C. O. F.; MANZIONE, R. L.; ALBUQUERQUE FILHO, J. L. &#x0D; COMPARAÇÃO DE MODELOS DE EVAPOTRANSPIRAÇÃO REAL SAFER E METRIC EM UMA ÁREA SUBTROPICAL DO BRASIL&#x0D; &#x0D; &#x0D; 2 RESUMO&#x0D; &#x0D; Algoritmos de sensoriamento remoto são conhecidos por estimar fluxos de energia de superfície em escalas regionais a globais com baixo custo. A abordagem de sensoriamento remoto tem a vantagem de estimar a evapotranspiração (ET) em escalas espaço-temporais maiores que os métodos tradicionais. Este estudo compara o resultado da estimativa de ET do “Simple Algorithm for Evapotranspiration Retrieving” (SAFER) e “Mapping Evapotranspiration at high Resolution with Internal Calibration” (METRIC) em variados usos da terra de uma área subtropical localizada no Sudeste do Brasil usando uma imagem do sensor OLI do satélite LANDSAT-8. Os resultados mostraram similaridade da estimativa da ET em ambos os modelos, embora houvesse desvio, especialmente em altos valores de ET. Isto é devido a diferenças como a necessidade de âncora de pixel no modelo METRIC, que necessita de dois pontos com condições termohidrológicas extremas em uma mesma área. A exigência mínima de dados terrestres é a principal vantagem do METRIC sobre o modelo SAFER. Esse estudo considerou ambos os modelos viáveis ​​para a estimativa de ET a partir de dados de satélite na área de estudo. Neste estudo, o valor máximo, a soma e a variação do ET pelo METRIC foram maiores que o do SAFER.&#x0D; &#x0D; Palavras-chave: sensoriamento remoto, modelagem, temperatura de superfície, Landsat-8, agriwater.

SENSITIVITY OF EVAPOTRANSPIRATION ESTIMATED BY ORBITAL IMAGES UNDER INFLUENCE OF SURFACE TEMPERATURE

Surface temperature (Ts) is a determining factor to obtain energy balance parameters, being relevant to understand the influence of this variable on the estimation of evapotranspiration. Thus, the objective of this study was to simulate errors in Ts estimation to verify the consequences of actual evapotranspiration (ETa) estimated by the SAFER (Simple Algorithm for Evapotranspiration Retrieving) model. For this, an image of the Landsat-8 satellite was used to induce errors from 0.2K to 10K in the variable Ts, allowing verifying the consequences in the ETa data. After the estimations of Ts and ETa, the quantitative consequences and dynamics of Ts impact on the ETa data were verified along the different land uses in the study area. The results showed that the precise estimation of Ts is essential to obtain ETa accurately. The image of ETa errors presented the highest relative errors on the surface with exposed soils and with high Ts values. However, the highest residuals of ETa images occurred on the surfaces with milder Ts and higher evapotranspiration rates (irrigated surfaces).

A geostatistical approach for multi-source data fusion to predict water table depth

Accurate water table depth mapping is important for water management and activity planning. The joint use of exhausted geospatial raster data with sparse field measurements could improve predictions. The aim of this work was to fuse different support data, collected with remote sensors, with point soil field observations to improve water table depth prediction. A method for multi-source data fusion is described in detail, based on multivariate geostatistics and exemplified with a case study in a conservation area of 5700 ha in the state of São Paulo, Brazil. TanDEM-X digital surface model with 90 m resolution and SAFER (Simple Algorithm for Evapotranspiration Retrieving) data calculated from Sentinel-2 images with 20 m resolution, were jointly used with water table depth and soil physical variables measured at 56 locations to predict water table depth in two hydrological years (2015–16 and 2016–17). Data were transformed to normal distributions using the Gaussian anamorphosis approach. A Linear Model of Coregionalization (LMC), calculated for all direct and cross-variograms of the eleven variables of study, was regularized at block support for multi-collocated block cokriging predictions. Support change correction was made to reduce punctual variance to block variances. Univariate and multivariate geostatistical interpolation methods were compared through cross validation. The uncertainty associated to the water table depths estimated by multivariate approach was lower than those by the univariate approach. Moreover, multivariate predictions incorporated the influences induced by local relief, vegetation and soil properties. Confidence interval maps, presented as uncertainty measure, reveal areas with higher and lower precision of groundwater level prediction that could be effectively used as support in land use management.

Biophysical Parameters and Actual Evapotranspiration of Bean Culture by Means of Remote Sensing

Agricultural irrigation is involved in an important chain that involves all sectors of the economy, either directly, by increasing food production, or indirectly, by withdrawing large amounts of fresh water. The relevance of this theme forces the search for alternatives to make water use as rational as possible. Evapotranspiration estimation methods based in remote sensing, such as the SAFER (Simple Algorithm for Evapotranspiration Retrieving) model, become extremely relevant in these scenarios, since it is possible to estimate this parameter in large scales. Therefore, the aim of this research was to apply the SAFER model in the estimation of bean crop actual evapotranspiration using Landsat-8 satellite image data. One of the parameters used as input in the SAFER model is the NDVI (Normalized Difference Vegetation Index), which presented a coefficient of determination (r&amp;sup2;) equal to 0.80 when compared to the crop coefficient. The actual evapotranspiration (ETa) estimated by the SAFER model were compared to the FAO 56 model estimates for later correlation between the models. This information is expected to assist the producer in a better management of water resources used in irrigation. The correlation between the two models presented a relevant coefficient of determination (r2 = 0.73), representing the potential of the SAFER model in relation to the FAO model 56.