Remote Estimation of a Managed Pine Forest Evapotranspiration with Geospatial Technology

ABSTRACT. Evapotranspiration (ET) processes (1) reference ET (REF-ET) method development and applications, including crop management and irrigation scheduling, limitations due to sensor inaccuracies and variability, and sensitivity to climatic drivers (three articles); (2) ET process and pathway characterization, including canopy interception, transpiration, and soil evaporation measured using various state-of-the-art techniques on crop lands and plantation forests, and effects of soil moisture on grassland water balance (three articles); (3) ET simulation within hydrological models (SWAT, MIKE SHE, RZWQM, RZ-SHAW, RegCM-BATS, DRAINMOD-FOREST, and Thornthwaite water balance) as well as related processes, such as crop growth and ET/PET ratios, for grass, crop, and forest lands (four articles); and (4) geospatial technology applications, such as using remote sensing to estimate ET and its components (soil evaporation and transpiration) for various land uses (two articles). Study sites represent a range of spatial scales and ecohydrological settings, including grasslands in Inner Mongolia dry lands in northern China, semiarid high plains in Texas, corn production regions from Iowa to Colorado, forest plantations on the humid Atlantic Coastal Plain, developed coastal areas on the island of Taiwan, and the continental U.S. Results from these studies will help guide current development and assessment of REF-ET, ET, and monitoring and modeling of their components in multiple scales and ecosystems. The studies also establish a platform for addressing potential inaccuracies in data from weather sensors and algorithms used in remote sensing products for estimating ET and its parameters, including uncertainties in REF-ET estimates, for tall forest vegetation in particular.Furthermore, the studies offer insights into the interactions between climatic variability and change and vegetation through the ET process.

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Automated Geospatial Models of Varying Complexities for Pine Forest Evapotranspiration Estimation with Advanced Data Mining

Panda

Amatya

Jackson

et al. 2018

Water

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The study goal was to develop automated user-friendly remote-sensing based evapotranspiration (ET) estimation tools: (i) artificial neural network (ANN) based models, (ii) ArcGIS-based automated geospatial model, and (iii) executable software to predict pine forest daily ET flux on a pixel- or plot average-scale. Study site has had long-term eddy-flux towers for ET measurements since 2006. Cloud-free Landsat images of 2006−2014 were processed using advanced data mining to obtain Principal Component bands to correlate with ET data. The regression model’s r2 was 0.58. The backpropagation neural network (BPNN) and radial basis function network (RBFN) models provided a testing/validation average absolute error of 0.18 and 0.15 Wm−2 and average accuracy of 81% and 85%, respectively. ANN models though robust, require special ANN software and skill to operate; therefore, automated geospatial model (toolbox) was developed on ArcGIS ModelBuilder as user-friendly alternative. ET flux map developed with model tool provided consistent ET patterns for landuses. The software was developed for lay-users for ET estimation.

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Remote Estimation of a Managed Pine Forest Evapotranspiration with Geospatial Technology

Abstract: ABSTRACT. Remote sensing has increasingly been used to estimate evapotranspiration (ET) and its supporting parameters in a rapid

Cited by 3 publications

References 41 publications

Estimation of evapotranspiration and its parameters for pine, switchgrass, and intercropping with remotely-sensed images based geospatial modeling

Estimation of evapotranspiration and its parameters for pine, switchgrass, and intercropping with remotely-sensed images based geospatial modeling

Ecosystem Evapotranspiration: Challenges in Measurements, Estimates, and Modeling

Automated Geospatial Models of Varying Complexities for Pine Forest Evapotranspiration Estimation with Advanced Data Mining

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