Sensitivity and uncertainty quantification for the ECOSTRESS evapotranspiration algorithm - DisALEXI

Authors: CAWSE-NICHOLSON - Jet Propulsion Laboratory; BRAVERMAN, A. - Jet Propulsion Laboratory; KANG, E. - University Of Cincinnati; LI, M. - University Of Cincinnati; JOHNSON, M. - Jet Propulsion Laboratory; HALVERSON, G. - Jet Propulsion Laboratory; Anderson, Martha; HAIN, C. - Nasa Marshall Space Flight Center; GUNSON, M. - Jet Propulsion Laboratory; HOOKS, S. - Jet Propulsion Laboratory

Submitted to: International Journal of Applied Earth Observation and Geoinformation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/1/2020
Publication Date: 7/1/2020
Publication URL: https://handle.nal.usda.gov/10113/7071234
Citation: Cawse-Nicholson, Braverman, A., Kang, E., Li, M., Johnson, M., Halverson, G., Anderson, M.C., Hain, C., Gunson, M., Hooks, S. 2020. Sensitivity and uncertainty quantification for the ECOSTRESS evapotranspiration algorithm - DisALEXI. International Journal of Applied Earth Observation and Geoinformation. https://doi.org/10.1016/j.jag.2020.102088.
DOI: https://doi.org/10.1016/j.jag.2020.102088

Interpretive Summary: The ability to map evapotranspiration (ET), or consumptive water use, using satellite remote sensing data will benefit a range of agricultural water management applications – from irrigation decision-making, to detecting drought-induced crop stress, to regional water use accounting. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission is providing high-resolution land-surface temperature data critical to ET mapping systems, enabling quantification of water use at sub-field spatial scales. Also critical for decision-making, however, is an estimate the accuracy of these ET estimates and whether they are reliable enough to support specific applications. This paper explores statistical methods for mapping the uncertainty associated with ET estimates derived from ECOSTRESS imagery. These accuracy assessments will be delivered to end-users along with the ET products themselves in support of the full decision-making chain.

Technical Abstract: Evapotranspiration (ET) is a measure of plant stress that is utilized regionally for drought detection and monitoring, and locally for agricultural water resource management. We quantify the uncertainty in disALEXI; an ET algorithm that utilizes land surface temperature (LST) derived from ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station), as well ancillary inputs for landcover, elevation, vegetation parameters, and meteorological inputs. Since each of these inputs has an associated, and potentially unknown, uncertainty, in this study we have used a Monte Carlo simulation based on a spatial statistical model to determine the algorithms sensitivity to each of its inputs, and to quantify the probability distribution of algorithm outputs. We found that the algorithm is most sensitive to LST (the input derived from ECOSTRESS). Significantly, the output distribution is non-Gaussian, due to the non-linear nature of the algorithm. This means that ET uncertainty cannot be prescribed by accuracy and precision alone. We quantify prediction uncertainty using five quantiles of the output distribution. The distribution was consistent across five different datasets (mean offset is 0.01 mm/day, and 95% of the data is contained within 0.3 mm/day). An additional two datasets with low ET, showed higher uncertainty (95% of the data is within 1 mm/day), and a positive bias (i.e. ET was overestimated by an average of 0.12 mm/day when ET was low).