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Cropland evapotranspiration (ET) is the major source of water consumption in agricultural systems. The precise management of agricultural ET helps optimize water resource usage in arid and semiarid regions and requires field-scale ET data support. Due to the combined limitations of satellite sensors and ET mechanisms, the current high-resolution ET models need further refinement to meet the demands of field-scale ET management. In this research, we proposed a new field-scale ET estimation method by developing an allocation factor to quantify field-level ET variations and allocate coarse ET to the field scale. By regarding the agricultural field as the object of the ET parcel, the allocation factor is calculated with combined high-resolution remote sensing indexes indicating the field-level ET variations under different crop growth and land-surface water conditions. The allocation ET results are validated at two ground observation stations and show improved accuracy compared with that of the original coarse data. This allocated ET model provides reasonable spatial results of field-level ET and is adequate for precise agricultural ET management. This allocation method provides new insight into calculating field-level ET from coarse ET datasets and meets the demands of wide application for controlling regional water consumption, supporting the ET management theory in addressing the impacts of water scarcity on social and economic developments. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is one of the components in the water cycle and the surface energy balance systems. It is fundamental information for agriculture, water resource management, and climate change research. This study presents a scheme for regional actual evapotranspiration estimation using multi-source satellite data to compute key land and meteorological variables characterizing land surface, soil, vegetation, and the atmospheric boundary layer. The algorithms are validated using ground observations from the Heihe River Basin of northwest China. Monthly data estimates at a resolution of 1 km from the proposed algorithms compared well with ground observation data, with a root mean square error (RMSE) of 0.80 mm and a mean relative error (MRE) of −7.11%. The overall deviation between the average yearly ET derived from the proposed algorithms and ground-based water balance measurements was 9.44% for a small watershed and 1% for the entire basin. This study demonstrates that both accuracy and spatial depiction of actual evapotranspiration estimation can be significantly improved by using multi-source satellite data to measure the required land surface and meteorological variables. This reduces dependence on spatial interpolation of ground-derived meteorological variables which can be problematic, especially in data-sparse regions, and allows the production of region-wide ET datasets. [ABSTRACT FROM AUTHOR]

Remote sensing allows regional evapotranspiration (ET) values to be obtained. Surface conductance is a key variable in estimating ET and controls surface flux interactions between the underlying surface and atmosphere. Limited by the influence of clouds, ET can only be estimated on cloud-free days. In this study, a gap-filling method is proposed to acquire daily surface conductance, which was coupled into a Penman-Monteith (P-M) equation, to estimate the regional daily ET over the Hai River Basin. The gap-filling method is coupled with the canopy conductance, surface conductance and a simple time extension method, which provides more mechanisms and is more comprehensive. Field observations, including eddy covariance (EC) fluxes and meteorological elements from automatic weather station (AWS), were collected from two sites for calibration and validation. One site is located in Guantao County, which is cropped in a circular pattern with winter wheat and summer maize. The other site is located in Miyun County, which has orchard and summer maize crops. The P-M equation was inverted to the computed surface conductance at the field scale, and latent heat fluxes from EC were processed and converted to daily ET. The results show that the surface conductance model used in the gap-filling method performs well compared with the inverted surface conductance, which suggests that the model used here is reasonable. In addition, the relationship between the results estimated from the gap-filling method and EC measurements is more pronounced than that between the other method and the EC measurements. The R 2 values improve from 0.68 to 0.75 at the Guantao site and from 0.79 to 0.88 at the Miyun site. The improvement mainly occurs during the growing crop season, according to the temporal variations in the results. [ABSTRACT FROM AUTHOR]

Increased model complexity and data quantities have raised the computing power requirement for efficient evapotranspiration (ET) estimation. A cloud-based service is presented to encapsulate and publish the ETWatch modeling algorithms as web application programming interfaces (APIs) in a consistent style to provide extensible model calculation service and data storage service in a cloud platform for water managers and stakeholders. The prototype system, named ETWatch Cloud allows users to rapidly and easily set up an ET generation project for any region of interest by invoking APIs directly to produce ET data using a web browser or local integrated development environment. The case study demonstrates that ETWatch Cloud can provide a highly scalable and interoperable ET generation tool for stakeholders from ET community, helping to facilitate the application of remote sensing-based ET algorithms for water management in hydrology sector. • A new high scalability, easy interoperability, cloud-based for ET generation at reginal scale. • Encapsule and publish ETWatch modeling algorithm to web API in a consistent style. • A general solution for the publishing, running and management of modules in the cloud. • Provides stakeholders in the ET community and hydrology sector with an innovative tool. • Generate ET data without additional computing, storage, or algorithm development. [ABSTRACT FROM AUTHOR]

• A field-scale 10 m daily ET estimation model was developed with Sentinel-2 data. • The plant optimization theory was applied to the remote sensing-based ET model. • ET sensitivity towards warming and elevated CO2 concentration was analyzed based on the proposed model. • The remote sensing spatial scale effect on ET models was quantified. Crop evapotranspiration (ET) is an essential part of agricultural water consumption, and robust monitoring of remote sensing (RS)-based ET at the field scale improves agricultural water management against water shortages. In this study, we propose a high-resolution optical RS-driven daily ET estimation framework coupling water vaporization and carbon assimilation based on Sentinel-2 satellite data. To determine if the proposed framework is accurate compared with flux observations, three tower sites are chosen (Guantao and Huailai from the Haihe Basin; Daman from the Heihe Basin), with a total of four years of observations adopted for model validation. The correlation coefficient R ranges from 0.870 to 0.912, and the RMSE ranges from 0.89 to 1.21 mm/day. Sensitivity analyses indicate that ET is most sensitive to air temperature, followed by ambient CO 2 concentration and absorbed shortwave radiation, which provides indications into potential future farming strategies to confront global climate change. Finally, we discuss the scale effects on the proposed model at the field scale. Results from three sites show that for a larger area of interest (AOI) the impact of scales increases. This research provides insights into ET calculations across several spatial scales and application potential in precision agricultural water management. [ABSTRACT FROM AUTHOR]