Spatial variability quantification of maize water consumption based on Google EEflux tool

Costa, Jéfferson de Oliveira; José, Jefferson Vieira; Wolff, Wagner; Oliveira, Niclene Ponce Rodrigues de; Oliveira, Rafaella Conceição; Ribeiro, Nathália Lopes; Coelho, Rubens Duarte; Silva, Tonny José Araújo da; Bonfim-Silva, Edna Maria; Schlichting, Alessana Franciele

doi:10.1016/j.agwat.2020.106037

Cited by 21 publications

(15 citation statements)

References 26 publications

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“…Another point is that the MFAO evapotranspiration was based on grass reference evapotranspiration, while EEFlux was calculated using the alfalfa reference, which can also contribute to the difference that was verified. Last, there are also limitations related to the number of samples available within the crop cycle, where the small number of samples tends to decrease the precision and accuracy of ET estimation models [17].…”

Section: Comparison Of the Eta And Kc Eeflux With The Mfaomentioning

confidence: 99%

“…Costa et al [17], in a very similar study, compared K c EEFlux to K c derived from FAO56 for maize crops, and according to them, the difference between K c values is related to the temporal variability. Once the K c FAO is obtained from multiday data, the K c EEFlux is obtained from the passing time data of the satellite, which makes these methodologies considerably different.…”

Section: Comparison Of the Eta And Kc Eeflux With The Mfaomentioning

confidence: 99%

“…METRIC users, for example, need to accumulate and assemble a variety of layers, including satellite images, land cover maps, terrain, local climate and soil maps from different sources and platforms, and there might be a significant amount of preprocessing required for the different layers before applying the algorithm [16]. Data entry and manipulation can be the most timeconsuming phase of this process [17]. However, the Earth Engine Evapotranspiration Flux (EEFlux) platform has recently developed a METRIC version that operates on the Google Earth Engine (GEE) system [16,18].…”

Section: Introductionmentioning

confidence: 99%

“…Some works have been conducted recently using EEFlux products [17,[22][23][24][25]. Costa et al [17] for example, found results satisfactory for estimate the spatial variability quantification of maize water consumption in Brazil [17]. Ayyad et al [22], evaluated ETa from EEFlux in Egyptian agricultural areas of the Nile Delta and the Nile Valley and verified that their estimations produce overestimations for the ETa values.…”

Section: Introductionmentioning

confidence: 99%

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Mapping within‑field variability of soybean evapotranspiration and crop coefficient using the Earth Engine Evaporation Flux (EEFlux) application

et al. 2020

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Accurate information about the spatiotemporal variability of actual crop evapotranspiration (ETa), crop coefficient (K c) and water productivity (WP) is crucial for water efficient management in the agriculture. The Earth Engine Evapotranspiration Flux (EEFlux) application has become a popular approach for providing spatiotemporal information on ETa and Kc worldwide. The aim of this study was to quantify the variability of water consumption (ETa) and the K c for an irrigated commercial planting of soybeans based on the EEFlux application in the western region of the state of Bahia, Brazil. The water productivity (WP) for the fields was also obtained. Six cloud-free images from Landsat 7 and 8 satellites, acquired during the 2016/17 soybean growing season were used and processed on the EEFlux platform. The ETa from EEFlux was compared to that of the modified FAO (MFAO) approach using the following statistical metrics: Willmot's index of agreement (d-index), root mean square error (RMSE), mean absolute error (MAE) and mean bias error (MBE). The K c from EEFlux was compared to the K c used in the soybean field (K c FAO-based) and to the K c values obtained in different scientific studies using the d-index. A similar procedure was performed for WP. Our results reveal that EEFlux is able to provide accurate information about the variability of ETa and the K c of soybean fields. The comparison between ETa EEFlux and ETa MFAO showed good agreement based on the d-index, with values of 0.85, 0.83 and 0.89 for central pivots 1, 2 and 3, respectively. However, EEFlux tends to slightly underestimate ETa. The K c EEFlux showed good accordance with the K c values considered in this study, except in phase II, where a larger difference was observed; the average WP of the three fields (1.14 kg m-3) was higher than that in the majority of the previous studies, which is a strong indicator of the efficient use of water in the studied soybean fields. The study showed that EEFlux, an innovative and free tool for access spatiotemporal variability of ETa and Kc at global scale is very efficient to estimate the ETa and Kc on different growth stages of soybean crop.

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Section: Comparison Of the Eta And Kc Eeflux With The Mfaomentioning

confidence: 99%

Section: Comparison Of the Eta And Kc Eeflux With The Mfaomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mapping within‑field variability of soybean evapotranspiration and crop coefficient using the Earth Engine Evaporation Flux (EEFlux) application

et al. 2020

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“…Broadband thermal imaging is a potential mean to measure soil moisture via the water stress of the leaves [48]. Estimation of soil water evaporation using broad-band thermal imagery acquired from the ground was also possible [49,50].…”

Section: Introductionmentioning

confidence: 99%

An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources

Zeng

Romano

et al. 2020

Water

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The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m–1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results.

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Assessing the water use efficiency of irrigated fruit crops in semi‐arid regions of Brazil using remote sensing and meteorological data

Costa,

Coelho,

Guimarães

et al. 2024

Irrigation and Drainage

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The irrigated perimeter of Jaíba‐MG is located in a semi‐arid region of Brazil, which makes irrigation practices necessary in order to guarantee agricultural production. This need occurs in the context of limited water resources and, therefore, it is necessary to study ways to optimize water use efficiency (WUE). For this, it is necessary to obtain local and precise estimates of the crop coefficient (Kc) and the reference evapotranspiration (ET0). The objective of this study was to evaluate the water requirements and WUE of banana, lemon and mango crops in the irrigated perimeter region of Jaíba using satellite images and meteorological data. Landsat 8 satellite images from 2020 and 2021 were used to estimate the local Kc of these crops. Three methodologies for estimating local Kc were adopted: METRIC, NDVI and NDVI 2. The Kc NDVI method stood out when compared with the standard method (FAO Bulletin 56), with RMSEs of 0.11, 0.16 and 0.20 for banana, lemon and mango, respectively, showing the potential of this methodology. The overall average WUE values for banana, lemon and mango crops were 2.19, 1.58 and 1.58 kg m−3, respectively. The results could allow important advances in water management for fruit crops under semi‐arid conditions.

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Spatial variability quantification of maize water consumption based on Google EEflux tool

Cited by 21 publications

References 26 publications

Mapping within‑field variability of soybean evapotranspiration and crop coefficient using the Earth Engine Evaporation Flux (EEFlux) application

Mapping within‑field variability of soybean evapotranspiration and crop coefficient using the Earth Engine Evaporation Flux (EEFlux) application

An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources

Assessing the water use efficiency of irrigated fruit crops in semi‐arid regions of Brazil using remote sensing and meteorological data

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