1. Only NDVI and ET are available as raster views
The requirements for visualization of satellite data products developed by the OpenET partners and working group members only included NDVI and ET for the raster view. Additional data layers will be added to the raster layer viewer in the future.
2. Model selection for monthly timeseries graphs resets for each field and each time the timeseries graphs are loaded
Model selection for the monthly timeseries graphs is reset with each new query to avoid overloading the server with unnecessary queries and to increase response time for individual queries. Model selection may be changed in the future to be “sticky” based on query response times and user feedback. Queries for multiple models for large numbers of fields should primarily be handled through the API in the future.
3. Only monthly data are available
At this time, only monthly data are available through the Data Explorer. The OpenET is currently finalizing implementation of the daily data products, and these will be added to the OpenET Data Explorer and API in the near future.
1. EToF values do not agree well, even when ET values do agree across the ensemble
The fraction of reference ET (EToF) is calculated as the ratio of ET to ETo (grass reference ET). When ETo is low, small variations in estimated ET will translate to large differences in EToF. In general, EToF values from different models should agree well during the growing season. Large differences in EToF during the winter are expected, even when there appears to be generally good agreement among the ET values.
2. The range of the model ensemble (the difference between the lowest and highest model) appears large for fallow fields, pasture, and other polygons with low ET.
The OpenET team has observed greater disagreement across the ensemble of models for low ET conditions that include fallow fields, partially irrigated pasture lands, and other fields with low ET. The OpenET team is currently working to explain and resolve these differences. Because the y-axis in the graphing windows scales based on the maximum ET values for a given field, fields with lower ET values will have a smaller range in the y-axis and, as a result, smaller differences in ET among the models will be exaggerated in the graphs relative to fields with higher monthly and annual ET values.
3. Crop type information for the current year is incorrect, or crop type information for California appears to be incorrect for years other than 2016 and 2018.
Crop type information for the current year is derived from the previous year, and is provided to assist with interpretation of ET values. Any inaccuracies or inconsistencies may be ignored. Outside of California, all crop type information is calculated from the USDA Cropland Data Layer.
Crop type information for California for 2016 and 2018 is based on the CA Department of Water Resources (DWR) Land Cover database developed by LandIQ and appears to be generally accurate. Data for California for all other years are from the USDA Cropland Data Layer which has lower classification accuracies for many of the specialty crops grown in California. Crop type information will be updated for California as additional data updates are made available by CA DWR.
4. Field boundaries / data appear to be missing
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 locations of interest up to 1000 acres and request data for the user-defined region. In the near future, the API and the Custom Reporting interface will allow users to define locations of interest and upload shapefiles to obtain data summaries for regions of interest.
5. ET estimates do not agree well over open water bodies
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.
6. DisALEXI data is not available outside of California
Implementation of the ALEXI/DisALEXI model within the OpenET framework was only recently completed and data for DisALEXI is only available at this time for California. Data for DisALEXI will not load for other states.
Implementation of the ALEXI/DisALEXI model within the OpenET framework was only recently completed and data for DisALEXI is only available at this time for California. Data for DisALEXI will not load for other states. The team is currently reviewing the initial results for DisALEXI and additional known issues will be added soon.
- Coastal Areas
DisALEXI ET is observed to be biased high along the Pacific coastline. DisALEXI is based on disaggregation of 4km continental-scale ALEXI fluxes, which are driven by morning time changes in land-surface temperature from GOES acquired at 4km spatial resolution. This coarse signal incorporates ocean temperatures close to the coast, leading to anomalously high ET artifacts.
Potential solution: Flag DisALEXI ET within a buffer zone 4-8 km from coast.
- High Elevation Bias
DisALEXI ET is biased high at high elevations. Most notable in the current release are biases in the Sierra Nevada peaks.
Potential solution: Elevation-based corrections are being implemented in the ALEXI model. Revised ALEXI assets will be used in the next updates.
- Low ET in Northern Coastal States
DisALEXI is biased low in northern California and parts of Oregon and Washington. This has been traced to issues with terrain corrections to solar radiation inputs used in ALEXI.
Potential solution: Terrain corrections will be fixed in next updates to ALEXI/DisALEXI.
- Use of Different Meteorological Inputs
To be consistent with continental-scale ALEXI execution, DisALEXI is currently using meteorological inputs from the Climate Forecast System Reanalysis (CFSR) – a coarse-resolution global dataset. Differences between the meteorological forcings (insolation in particular) will lead to differences in model output – for example, DisALEXI may be significantly lower than other models on days when CFSR has mis-forecast cloud conditions.
Potential solution: In the next updates, DisALEXI will migrate to the insolation dataset used in the other models. This will help to reduce non-model related differences with respect to the ensemble.
There are currently no known major issues with eeMETRIC. ET estimates for the foothill region of the Sierras of California may tend to run high, as with most of the ensemble models, due to the aridity of that region during summer months and the effect on gridded weather data. This causes overstatement of the grass reference ET that is used during time integration of individual Landsat images into monthly values. This problem is outside the eeMETRIC process, but does impact the estimation of monthly ET. The OpenET team is working on advancing bias correction of reference ET estimates for the region. We welcome all indications of over- or under-estimation by eeMETRIC. This provides valuable feedback to the way that we automate the calibration and operation processes for the eeMETRIC system.
For croplands, wetlands, and riparian areas in arid and semiarid environments where advection is prevalent, a PT alpha adjustment layer was developed based on the ratio of ASCE reference ET and Priestley-Taylor ETw, following principles of the complementary relationship of evaporation (Kahler and Brutsaert, 2006; Szilagyi, 2007; Huntington et al., 2011). ASCE reference ET and ETw estimates were developed using the gridMET dataset. The alpha adjustment layer was calculated as the ratio of the growing average bias corrected ASCE reference ET to the growing season average ETw. Growing seasons were defined for each gridMET grid cell based on cumulative growing degree days of daily average air temperature from January 1 and killing frost thresholds of 300 C and -2 C, respectively. Adjustment factors were limited to a minimum of 0.79 and maximum of 2 to account for uncertainty in weather data and model performance in locations with extreme climate and weather (e.g. Death Valley, coastal regions). Adjusted alpha values range from 1 to 2.5, and fall within the range of alpha values previously reported across arid and humid settings (Priestley and Taylor, 1972; McAneney and Itier, 1996; Weiß and Menzel, 2008; Yang et al., 2009; Tabari and Talaee, 2011). Application of adjusted alpha values were limited to cropland, wetland, and riparian areas as defined by the USDA cropland data layer (CDL).
Results from Phase II of the OpenET Accuracy Assessment and Intercomparison study indicated that application of the PT alpha adjustment layer increased overall accuracy of PT-JPL for cropland, wetland, and riparian areas. In cases where land cover information is incorrect, the correction may not have been applied, resulting in a low bias in estimates from PT-JPL. Future enhancements to PT-JPL will include the slope and aspect corrections to short and longwave radiation and LST inputs, similar to eeMETRIC, and further improvements to the PT alpha adjustment layer.
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 fallowed areas, especially when grasses or weeds are present but not irrigated. The team has 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 this data was masked out of the SIMS data archive and excluded from inclusion in the OpenET ensemble value. 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.
Implementation of geeSEBAL within the OpenET framework was just recently completed. Overall, geeSEBAL performance is dependent on the domain area, including topography and climate conditions, and 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.
Complex topographic landscapes
geeSEBAL frequently exhibits a bias in areas of high altitude and steep slopes. These topographic features affect the model’s endmember selection algorithm. A review of the surface temperature lapse rate, combined with the addition of terrain slope and aspect considerations to correct land surface temperature and to compute solar radiation will potentially improve results in such areas.
These corrections will also improve the hot and cold endmembers temperatures for internal calibration, improving ET estimates in the model.
Desert and arid areas
In desert areas and arid or semiarid climates, 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. The geeSEBAL team is currently working on improving the endmembers selection algorithm to provide more accurate ET estimations in desert and sparsely vegetated areas.
Large water bodies
The geeSEBAL team members are currently working to investigate and characterize model uncertainties and data gaps over large water bodies. One primary issue is related to masking geeSEBAL meteorological inputs (NLDAS and gridMET) over large water bodies (coastal areas and continental waters) for interpolation, to avoid high bias over land surfaces. Future improvements to geeSEBAL will include separate solutions for open water evaporation.
The following three known issues, or causes of SSEBop ET model error, have been identified for user interpretation and awareness. Future improvements will address high and low biases observed for different land cover types.
- Issue 1: Coastal areas along the west coast
The OpenET team has noticed a clear under-estimation bias in parts of California along the coast (up to 100-km inland), ranging from as far south as San Diego to just north of San Francisco. E.g., Salinas valley, San Joaquin Delta.
Our investigation so far points to a potential bias in the air temperature (lapse rate related) spatial pattern which is an input to key model parameters.
Potential solution: The team is improving the model parameterization scheme to correct for this. Satisfactory improvements have already been obtained for inclusion in the next update to the OpenET data.
- Issue 2: ETa artifacts over white sands and bright features
Desert white sands have a high albedo causing the Land Surface Temperature to appear cool, therefore resulting in an unrealistic ET fraction and ET estimate from SSEBop.
Potential Solution:These are small areas in the landscape and not vegetated surfaces; we can mask out these areas and the SSEBop solution should be ignored for these barren land cover types. It is possible some of these features may have moisture below the surface, but it is not clear how much of the temperature drop is from an ET process.
- Issue 3: SSEBop Estimating ET of “0” for Grapes and other crop types
We have noticed SSEBop tends to give a lower estimate for grapes and tends to produce ET estimates of “0” in the early part of the season.
Potential Solution: The team is currently updating the SSEBop code to reduce the “clamping” problem, where SSEBop frequently provides “0” for ET instead of a realistic of ET under conditions when ET is generally low. Modifications to SSEBop to improve estimates for grapes are ongoing and will be informed by comparisons against ground-based ET datasets collected in vineyards.