OpenET uses publicly available field boundary datasets. In some locations, these datasets are incomplete or missing large numbers of fields. The OpenET team is currently working with partners to improve and update these datasets. The Data Explorer ‘Raster View’ currently allows users to define any location of interest up to 1000 hectares (2,470 acres) and request data for the user-defined region.  The API allows users to define locations of interest and upload boundaries (shapefile, geojson, etc.) to obtain data summaries for those regions.

This is a known issue and the OpenET team is currently collaborating with other scientists to implement a method specifically for calculation of open water evaporation from satellite data and meteorological data. OpenET data over open water currently provided through the OpenET Data Explorer are provisional and likely to change in future updates.

Evaluation of initial results from application of the Mean Absolute Deviation (MAD) approach for outlier detection highlighted a limitation in sparse vegetation desert environments during the warm season, where ET rates are very low. In these cases, the majority of models within the ensemble have a tendency to consistently estimate ET values of zero, while one or two other models provide estimates of low, but non-zero ET values. Requiring a minimum of four models allows non-zero values to be included in the ensemble average in most cases.  However, in some locations, at least four models are providing ET estimates of zero.

There are some time periods and locations where data is missing for a month for one or more models. This is usually due to the lack of high quality satellite observations due to persistent cloud cover or snow cover during the missing month. Ongoing work by the OpenET team is focused on addressing these data gaps through data fusion techniques and increased use of data from Sentinel-2. Moving forward, we plan to reduce these gaps by using more satellite observations. 

From the limited number of cropland in-situ flux stations located in very arid environments, it is evident that some models have a systematic low bias for smaller agricultural areas in very arid regions, and the MAD outlier filtering approach does not filter outliers as desired due to the large range is model estimates. This may result in a low bias in the ensemble average. These locations are often indicated by fields with a wide range of ET values across the ensemble of ET models. Over the coming months, the OpenET team will continue to conduct additional research in these more challenging settings and develop a Best Practices Manual that will provide more region and application specific guidance. The ensemble value is likely to evolve in the coming year as the team conducts additional research and designs more region-specific approaches for calculation of the ensemble ET value. We encourage users to rely upon their knowledge of local conditions in applying the ensemble ET value, or selecting a single model or subset of models for use in their application.

The SIMS model generally performs well over irrigated agricultural areas and shows strong agreement with the ground-based ET datasets used in the OpenET Intercomparison and Accuracy Assessment. However, a positive bias in SIMS ET estimates for rainfed croplands, deficit irrigated fields, and fallow agricultural lands is a known issue. The SIMS model is primarily responsive to satellite measurements of green vegetative cover and assumes well-watered conditions. As a result, SIMS can overestimate ET from partially irrigated or deficit irrigated fields and long-term fallow areas, especially when grasses or weeds are present but not irrigated. Following the Phase I accuracy assessment, the team made improvements to the SIMS equations to address over-estimation of ET during periods with sparse vegetative cover by adjusting the Kcb minimum values. This improvement reduced the modest overall positive bias observed in the SIMS data at monthly and annual timesteps. The team has also implemented a gridded soil water balance model on Earth Engine to improve estimation of evaporation from bare soil and identify periods when available soil water in the root zone is likely to serve as a constraint on ET for non-irrigated lands.  

SIMS was developed to support ET estimation and irrigation management over agricultural lands. For the purposes of providing continuous ET estimates across the western U.S., a simple reflectance-based model was implemented for non-agricultural land cover types. This approach was based on a linear transformation of NDVI, and did not incorporate the full density-coefficient approach used for agricultural lands. As a result, it frequently produced ET estimates with a very high positive bias for non-agricultural land cover types and these data were excluded from inclusion in the OpenET ensemble value. The data is included in the SIMS archive and accessible via Data Explorer queries in ‘raster view’ or the OpenET API. For non-agricultural areas, SIMS ET data should be interpreted as an upper limit on ET and a measure of potential ET that accounts for the vegetation density and condition. Future work by the SIMS team will extend the density coefficient approach used by SIMS to non-agricultural land cover types, and incorporate the soil water balance model to account for soil water limitations on ET. 

geeSEBAL performance is dependent on (i) the domain area of application, including topography and climate conditions, and (ii) the endmembers selection for automated calibration. Overall, some underestimation is observed in agricultural areas (especially in annual crops) and in arid and temperate climates with dry summers. Most of the uncertainties are related to the endmembers selection for automated calibration.The team is currently working to improve these limitations to increase model accuracy at multiple climate conditions and complex topographic landscapes. Some of the limitations are discussed below.

Arid areas and climates with dry summers

In desert areas and arid or semiarid climates, including areas with dry summers, geeSEBAL tends to yield lower ET estimates, producing ET estimates of zero or nearly zero over sparse vegetation areas. On the other hand, some vegetated areas can yield higher values, overestimating evapotranspiration in these conditions. This is possibly caused by the elevated temperatures in some land cover classes, such as bare soils or sparsely vegetated areas.

Endmembers selection for automated calibration

The geeSEBAL team is currently working on improving the endmembers selection algorithm to provide more accurate ET estimations, especially in arid climates and sparsely vegetated areas. The selection of accurate hot and cold endmembers will determine the accuracy of sensible heat estimates and therefore latent heat. Searching accurate endmembers over large and complex areas, with multiple climate and land cover conditions is challenging, so our solution is based on multiple strategies, including search algorithms and additional filters (available energy, slope, albedo, soil type, NDVI and LST) to select the endmembers over homogeneous areas and to accurately estimate sensible and latent heat fluxes.

Water bodies

The geeSEBAL team members are currently conducting an assessment and validation of evaporation estimates over water bodies, to investigate and characterize model uncertainties in these areas. Future improvements to geeSEBAL will include separate solutions for open water evaporation, including a better representation of the heat transferred to the water column. 

The following known issues, or causes of SSEBop ET model error, have been identified for user interpretation and awareness. Future improvements will address any known biases and utilize improved input data availability and data quality where available.

Cloud contaminants and mountain shadows

The SSEBop model uses Landsat Collection 2 (C2) source QA_PIXEL flags for masking unusable pixels (cloud, cloud shadow, snow, etc.) during image processing. SSEBop does not currently apply additional cloud screening or cloud/shadow-buffering techniques during processing. In some instances, model estimates may result in outlier ET values due to cloud mask errors. Similarly, valid pixels for mountain shadows in complex terrain have potential to create abnormal (low) land surface temperature (ST) values, leading to ET overestimation bias on shaded hillslopes. 

Missing ST auxiliary data  

Both reflective and thermal Landsat bands are required for the successful processing of SSEBop ET. Therefore, OLI-only or TIRS-only Landsat 8 or Landsat 9 scenes (e.g., “Tier 2”) cannot be used. Please note SSEBop ET may contain NoData pixels in images due to missing emissivity auxiliary data required for C2 ST processing. For additional information please visit Landsat C2 Level-2 ST Data Caveats.