EO Data

Earth Observations Data

NDVI Anomaly

Variable: NDVI Anomaly (Normalized Difference Vegetation Index, unitless)

Organization in charge: NASA-UMD

Input data: Surface Reflectance Daily L3 Global 0.05Deg CMG (MOD09CMG)

Spatial Resolution: 0.05 degree

Temporal Resolution: Daily

Accuracy: Version-5 MODIS/Terra Surface Reflectance products are Validated Stage 2, meaning that accuracy has been assessed over a widely distributed set of locations and time periods via several ground-truth and validation efforts.

Short description of the variable:

The Normalized Difference Vegetation Index (NDVI) is computed as the normalized difference between band 2 (near infrared, 841–876 nm) and band 1 (red, 620–670 nm) of the MODIS Surface Reflectance Level 3 Climate Modeling Grid (CMG) product. MOD09CMG provides Bands 1–7 in a daily level-3 product gridded on a simple 0.05 degree (5,600-meter) Geographic projection.

The NDVI ranges between -1 and 1, where values close to 1 indicate higher vegetation content or healthier vegetation and values close to or below zero indicate lack of green vegetation. Therefore, the NDVI provides an estimation of the state and/or amount of vegetation. In Crop Monitor systems the daily NDVI anomaly is computed as the difference between the NDVI observation of a given time period and the median from the NDVI values observed at the same time period from 2000 to 2013.

Time interval of production: The NDVI anomaly is offered systematically on the 1st, 15th and 28th day of every month.

Use within GEOGLAM Crop Monitor activities:

The NDVI anomaly is a key variable within the Crop Monitor activities. This variable is used to monitor the vegetative state and health of the crops. A positive value indicates a more vigorous vegetative state of the crop compared to the averaged NDVI values. On the contrary, a negative value indicates a less vigorous state of the crop and it may suggest bad crop conditions or a slower growing rate of the crop due to environmental conditions.

Data Source: Publicly available documents

NDVI NDVI

Temperature Anomaly

Variable: Temperature Anomaly (°C)

Organization in charge: University of Maryland (UMD) and National Oceanic and Atmospheric Administration (NOAA)

Input data: NCEP-DOE Reanalysis 2 products distributed by NOAA Earth System Research Laboratory (ESRL)

Spatial Resolution: 0.5 degree

Temporal Resolution: Daily

Accuracy: Not available.

Short description of the variable:

The air temperature data are obtained from the NCEP-DOE Reanalysis 2 project which uses a state-of- the-art analysis/forecast system to perform data assimilation using past data from 1979 through the previous year. The NCEP/NCAR Reanalysis Project is a joint project between the National Centers for Environmental Prediction (NCEP, formerly "NMC") and the National Center for Atmospheric Research (NCAR). The goal of this joint effort is to produce new atmospheric analyses using historical data (1948 onwards), as well as to produce analyses of the current atmospheric state (Climate Data Assimilation System, CDAS).

In the Crop Monitor framework the monthly air temperature anomaly is obtained by computing the difference between the current month’s accumulation of the daily estimates from NCEP and the historical average air temperature in the same period of time. The historical average is derived as the median of the period 1990-2013.

Time interval of production: Monthly.

Use within GEOGLAM Crop Monitor activities:

The temperature anomaly map provides information about the deviation of the daily average temperature from the long term average temperature on that same date. In this case, the variation of temperatures is provided by the difference between the cumulated temperatures within a month and the historical median of cumulated temperatures in the same month. Cumulated temperature variations from -25 to +25 indicate minimum variations of temperature in comparison with the long term values. Negative cumulated temperatures indicate the temperature of the month has been lower than the long term average values. On the contrary, positive cumulated temperatures indicate temperatures higher than the long term average. Higher negative or positive values of cumulated temperature anomaly indicate that the deviation of temperatures was sustained over time. Therefore, they point to regions where crops may be susceptible to alterations in crop phenology cycle or adverse effects on crop health.

Data Source:

References:

  • Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S.-K., Hnilo, J. J., Fiorino, M., & Potter, G. L. (2002). NCEP–DOE AMIP-II Reanalysis (R-2). Bulletin of the American Meteorological Society, 83(11), 1631–1643. http://doi.org/10.1175/BAMS-83-11-1631
  • Kistler, R., Collins, W., Saha, S., White, G., Woollen, et al. (2001). The NCEP–NCAR 50–Year Reanalysis: Monthly Means CD–ROM and Documentation. Bulletin of the American Meteorological Society, 82(2), 247–267. http://journals.ametsoc.org/doi/abs/10.1175/

Cumulated Temperature Anomaly - JRC

Variable: Temperature Anomaly (°C)

Organization in charge: Joint Research Centre (JRC)

Input data: Radiances from the Advanced TIROS Operational Vertical Sounder (ATOVS) on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites are used at the European Centre for Medium-Range Weather Forecasts (ECMWF) to produce global atmospheric variables for weather forecasts (Matricardi et al., 2001).

Spatial Resolution: 0.25 degree

Temporal Resolution: Daily

Accuracy: Not specified.

Short description of the variable:

The temperature anomaly is calculated from the difference between the temperature value of a given date and the long term average value on the same date computed using data from 1979 to present. The daily air temperature estimation is obtained from the Deterministic forecast and Analysis model (OPE) forecast, produced at a spatial resolution of 0.25 degrees. The long term average is obtained using data from the European Reanalysis Interim (ERA-interim) archive which contains global atmospheric data from 1979 to present (Dee et al., 2011).

Time interval of production: The temperature anomaly product is generated at a monthly basis by the JRC.

Use within GEOGLAM Crop Monitor activities:

The temperature anomaly map provides information about the deviation of the daily average temperature from the long term average temperature on that same date. In this case, the variation of temperatures is provided monthly and represented as the sum of daily temperature anomalies within a month. Cumulated temperature variations from - 25 to +25 indicate minimum variations of temperature in comparison with the long term values. Negative cumulated temperatures indicate the temperature of the month has been lower than the long term average values. On the contrary, positive cumulated temperatures indicate temperatures higher than the long term average. Higher negative or positive values of cumulated temperature anomaly indicate that the deviation of temperatures was sustained over time. Therefore, they point to regions where crops may be susceptible to alterations in crop phenology or adverse effects on crop health.

Data Source: European Centre for Medium-range Weather Forecasts (ECMWF) via the Meteorological Archival and Retrieval System (MARS) which is the main repository of meteorological data at ECMWF.

References:

  • Berrisford, P., Dee, D., Poli, P., Brugge, R., Fielding, K. et al. (2011). The ERA Interim Archive version 2.0. Reading, United Kingdom. Retrieved from http://www.ecmwf.int/sites/default/files/elibrary/2011/8174-era-interim-archive-version-20.pdf
  • Dee, D. P.; Uppala, S. M.; Simmons, A. J.; Berrisford, P.; Poli, P. et al. (2011). The ERA- Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society. Vol. 37(656), p. 553-597.
  • Matricardi, M., Chevallier, F., & Tjemkes, S. (2001). An improved general fast radiative transfer model for the assimilation of radiance observations. Darmstadt, Germany. Retrieved from https://nwpsaf.eu/deliverables/rtm/papers/tm345.pdf
Temperature Temperature

Precipitation Anomaly

Variable: Precipitation Anomaly (mm of water)

Organization in charge: University of Maryland (UMD) and National Oceanic and Atmospheric Administration (NOAA)

Input data: NCEP-DOE Reanalysis 2 products distributed by NOAA Earth System Research Laboratory (ESRL).

Spatial Resolution: 0.5 degree

Temporal Resolution: Daily

Accuracy: Not available.

Short description of the variable:

The precipitation data are obtained from the NCEP-DOE Reanalysis 2 project which uses a state-of- the-art analysis/forecast system to perform data assimilation using past data from 1979 through the previous year. The NCEP/NCAR Reanalysis Project is a joint project between the National Centers for Environmental Prediction (NCEP, formerly "NMC") and the National Center for Atmospheric Research (NCAR). The goal of this joint effort is to produce new atmospheric analyses using historical data (1948 onwards), as well to produce analyses of the current atmospheric state (Climate Data Assimilation System, CDAS).

In the Crop Monitor framework the monthly precipitation anomaly is obtained by computing the difference between the current month’s accumulation of the daily estimates from NCEP and the historical average precipitation in the same period of time. The historical average is derived as the median of the period 1990-2013.

Time interval of production: Monthly.

Use within GEOGLAM Crop Monitor activities:

The precipitation anomaly map provides information about the deviation of the average precipitation from the long term average precipitation on that same period of time. In this case, the variation of precipitation is provided by the difference between the cumulated precipitation within a month and the historical median of cumulated precipitation in the same month. Cumulated precipitation variations from -50 to +50 indicate minimum variations of precipitation in comparison with the long term values. Negative cumulated precipitation indicate the precipitation of the month has been lower than the long term average values, suggesting drought conditions. On the contrary, positive cumulated precipitation indicate precipitation volume higher than the long term average, pointing to flooding events or an intense rain season. Both of these situations have a direct effect on crop health and phenology cycle. Therefore, the information offered by the cumulated precipitation anomaly maps help crop experts to monitor and predict crop conditions.

Data Source:

References:

  • Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S.-K., Hnilo, J. J., Fiorino, M., & Potter, G. L. (2002). NCEP–DOE AMIP-II Reanalysis (R-2). Bulletin of the American Meteorological Society, 83(11), 1631–1643. http://doi.org/10.1175/BAMS-83-11-1631
  • Kistler, R., Collins, W., Saha, S., White, G., Woollen, et al. (2001). The NCEP–NCAR 50–Year Reanalysis: Monthly Means CD–ROM and Documentation. Bulletin of the American Meteorological Society, 82(2), 247–267. http://journals.ametsoc.org/doi/abs/10.1175/

Cumulated Precipitation Anomaly - JRC

Variable: Precipitation Anomaly (mm of water)

Organization in charge: Joint Research Centre (JRC)

Input data: NCEP-DOE Reanalysis 2 products distributed by NOAA Earth System Research Laboratory (ESRL).

Spatial Resolution: 0.25 degree

Temporal Resolution: Daily

Accuracy: Not available.

Short description of the variable:

The precipitation anomaly is calculated as the difference between the precipitation volume and the long term average value on the same date computed using data from 1979 to present. The daily estimation of precipitation is obtained from the Deterministic forecast and Analysis model (OPE) forecast, produced at a spatial resolution of 0.25 degrees, and sum the given time period. The long term average is obtained using data from the European Reanalysis Interim (ERA-interim) archive which contains global atmospheric data from 1979 to present (Dee et al., 2011)..

Time interval of production: The precipitation anomaly product is generated at a monthly basis by the JRC.

Use within GEOGLAM Crop Monitor activities:

The precipitation anomaly map provides information about the deviation of the average precipitation from the long term average precipitation on that same period of time. In this case, the variation of precipitation is provided monthly and represented as the sum of daily precipitation anomalies within a month. Cumulated precipitation variations from -50 to +50 indicate minimum variations of precipitation in comparison with the long term values. Negative cumulated precipitation indicate the precipitation of the month has been lower than the long term average values, suggesting drought conditions. On the contrary, positive cumulated precipitation indicate precipitation volume higher than the long term average, pointing to flooding events or an intense rain season. Both of these situations have a direct effect on crop health and phenology cycle. Therefore, the information offered by the cumulated precipitation anomaly maps help crop experts to monitor and predict crop conditions.

Data Source: European Centre for Medium-range Weather Forecasts (ECMWF) via the Meteorological Archival and Retrieval System (MARS) which is the main repository of meteorological data at ECMWF.

References:

  • Berrisford, P., Dee, D., Poli, P., Brugge, R., Fielding, K. et al. (2011). The ERA Interim Archive version 2.0. Reading, United Kingdom. Retrieved from http://www.ecmwf.int/sites/default/files/elibrary/2011/8174-era- interim-archive- version-20.pdf
  • Dee, D. P.; Uppala, S. M.; Simmons, A. J.; Berrisford, P.; Poli, P. et al. (2011). The ERA- Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society. Vol. 37(656), p. 553-597.
  • Matricardi, M., Chevallier, F., & Tjemkes, S. (2001). An improved general fast radiative transfer model for the assimilation of radiance observations. Darmstadt, Germany. Retrieved from https://nwpsaf.eu/deliverables/rtm/papers/tm345.pdf
Rainfall Rainfall

Precipitation Anomaly CHIRPS

Variable: Precipitation Anomaly (mm of water)

Organization in charge: U.S. Geological Survey Earth Resources Observation and Science Center

Input data: Quasi-global (50°S–50°N, 180°E–180°W), 1981 to near-present gridded precipitation time series: the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data archive.

The main data sources used in the creation of CHIRPS were (Funk, et al. 2014):

  1. The monthly precipitation climatology, temporally disaggregated at each grid cell location into 72 pentadal (6-pentads per month) long-term average accumulation values, in millimeters;
  2. Quasi-global geostationary thermal infrared (TIR) satellite observations from two NOAA sources, the Climate Prediction Center (CPC) TIR (0.5 hour temporal resolution, 4 km spatial resolution, for 2000–present) and the National Climatic Data Center (NCDC) B1 TIR (3 hour temporal resolution, 8 km spatial resolution, for 1981–2008);
  3. The Tropical Rainfall Measuring Mission (TRMM) 3B42 product;
  4. Atmospheric model rainfall fields from the NOAA Climate Forecast System, version 2 (CFSv2); and
  5. In situ precipitation observations obtained from a variety of sources including national and regional meteorological services

Spatial Resolution: 0.05 degree

Temporal Resolution: 5 day

Accuracy: Detailed description of validation results can be found in Funk et al. (2015).

Short description of the variable:

The precipitation anomaly is computed as the difference between the sum of precipitation obtained for a given time period (5 days, 10 days or a month) and the long term average of the sum of precipitation values for the same period of time. The precipitation is obtained by the integration of thermal infrared satellite data, TRMM data, atmospheric models (to cover data gaps) and meteorological stations.

Time interval of production: Pentad, decadal and monthly

Use within GEOGLAM Crop Monitor activities:

The precipitation anomaly map provides information about the deviation of the average precipitation from the long term average precipitation on that same period of time. In this case, the variation of precipitation is provided monthly and represented as the sum of daily precipitation anomalies within a month. Cumulated precipitation variations from -50 to +50 indicate minimum variations of precipitation in comparison with the long term values. Negative cumulated precipitation indicate the precipitation of the month has been lower than the long term average values, suggesting drought conditions. On the contrary, positive cumulated precipitation indicate precipitation volume higher than the long term average, pointing to flooding events or an intense rain season. Both of these situations have a direct effect on crop health and phenology. Therefore, the cumulated precipitation anomaly maps help crop experts to monitor and predict crop conditions.

Data Source:

References:

CHIRPS Rainfall Anomaly CHIRPS Rainfall Anomaly

Soil Moisture Anomaly

Variable: Soil moisture anomaly

Organization in charge: NASA-USDA-FAS

Input data: SMOS Level 2 soil moisture products provided by ESA are gridded into daily composites by NOAA NESDIS Soil Moisture Operational Products System. The European Space Agency (ESA) Soil Moisture Ocean Salinity (SMOS) mission is a satellite-based, passive L-band radiometer instrument launched in November 2009. L- band microwave emission from the land surface can be inverted to provide an estimate of surface soil moisture conditions at a spatial resolution near 50-km.

Spatial Resolution: 50 km

Temporal Resolution: Daily

Accuracy:

Short description of the variable:

This L5 gridded surface anomaly soil moisture product is generated by the integration of the Soil Moisture Ocean Salinity (SMOS) surface soil moisture retrievals (Kerr et al., 2012) into the modified 2-Layer Palmer Water Balance Model, using a 1-dimensional, 30-member Ensemble Kalman filter (EnKF) approach which dynamically updates all model-based soil moisture predictions to reflect information contained in the SMOS imagery (Reichle et al., 2008). The 2-Layer Palmer Water Balance Model was forced with meteorological data obtained from the U.S. Air Force 557th Weather Wing (former U.S. Air Force Weather Agency). The assimilation of the SMOS surface soil moisture estimates allow to correct the Palmer model predictions for erroneous and anomalous rainfall data especially in ungauged areas and areas with limited rain-gauge coverage (Bolten, et al, 2010). Values represent standardized anomalies and are computed using a 31-days moving window.

Time interval of production: Monthly

Use within GEOGLAM Crop Monitor activities:

Soil moisture is an important variable in land-atmosphere feedbacks at weather and climate time scales because of its major effect on the partitioning of incoming radiation (available energy) into latent and sensible heat and on the allocation of precipitation into runoff, subsurface flow, and infiltration. Soil moisture is intimately involved in the feedback between climate and vegetation, since local climate and vegetation both influence soil moisture through evapotranspiration, while soil moisture and climate determine the type of vegetation in a region. Changes in soil moisture therefore have a serious impact on agricultural productivity, forestry and ecosystem health.

Data Source:

References:

  • Bolten, J. D., Crow, W. T., Zhan, X., Jackson, T. J., & Reynolds, C. A. (2010). Evaluating the utility of remotely sensed soil moisture retrievals for operational agricultural drought monitoring. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, 3(1), 57-66.
  • Kerr, Y. H., Waldteufel, P., Richaume, P., Wigneron, J. P., Ferrazzoli, P., et al. (2012). The SMOS soil moisture retrieval algorithm. Geoscience and Remote Sensing, IEEE Transactions on, 50(5), 1384-1403.
  • Reichle, Rolf H., Wade T. Crow, and Christian L. Keppenne (2008). An adaptive ensemble Kalman filter for soil moisture data assimilation. Water resources research, 44(3).
Soil Moisture Soil Moisture

Soil Water Index Anomaly

Variable: Soil water index anomaly

Organization in charge: EUMETSAT - Vienna University of technology (TU Wien)

Input data: Advanced Scatterometer (ASCAT) on-board the MetOp satellite, available from December 5, 2013.

Spatial Resolution: 0.01 degrees

Temporal Resolution: Daily

Accuracy: A comprehensive and extended validation of the SWI archive produced in the framework of the GMES project geoland2 using in situ data from the International Soil Moisture Network (ISMN) can be found in Paulik et al. (2014).

Short description of the variable:

The SWI estimations are based on Surface Soil Moisture estimates retrieved from the ASCAT scatterometer onboard the Metop-A satellite. The method was developed by the TU Wien (Wagner et al., 1999) and improved by Naeimi et al. (2009). This dataset is operationally provided in Near Real Time by EUMETSAT (Bartalis et al., 2008) and as a reprocessed archive by Vienna University of Technology.

Soil moisture, as measured by remote sensing techniques, represents only the first few centimeters of the soil. However, for scientists working in areas such as drought monitoring, and agricultural modeling a representation of root-zone soil moisture is more interesting and useful.

The Soil Water Index estimates global root-zone soil moisture conditions for a depth of roughly 50 - 100 centimeters (20 - 40 inch) by using an infiltration model describing the relation between surface soil moisture and profile soil moisture as a function of time. This makes it easy to use globally since it does not depend on other inputs like soil properties of which high quality data are not available on a global scale (Albergel et al., 2008).

The SWI anomaly is computed as the difference of the daily estimation and the historical average obtained from the historical archive from 1991 based on a multisensor data archive (Paulik et al., 2014).

Time interval of production: 5 days

Use within GEOGLAM Crop Monitor activities:

Soil moisture is an important variable in land-atmosphere feedbacks at weather and climate time scales because of its major effect on the partitioning of incoming radiation (available energy) into latent and sensible heat and on the allocation of precipitation into runoff, subsurface flow, and infiltration. Soil moisture is intimately involved in the feedback between climate and vegetation, since local climate and vegetation both influence soil moisture through evapotranspiration, while soil moisture and climate determine the type of vegetation in a region. Changes in soil moisture therefore have a serious impact on agricultural productivity, forestry and ecosystem health. Soil water index anomaly varies between -70 and 70% indicating the areas where root soil moisture is higher or lower than the historical average values. This variable is used as an indicator of water stress in crops.

Data Source:

References:

  • C. Albergel, C. Rüdiger, T. Pellarin, J.-C. Calvet, N. Fritz, F. Froissard, D. Suquia, a. Petitpa, B. Piguet, E. Martin (2008). From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations. Hydrology and Earth System Sciences, 12 , pp. 1323–1337.
  • Bartalis, Z., R. A. Kidd and K. Scipal (2006a). Development and implementation of a discrete global grid system for soil moisture retrieval using the MetOp ASCAT scatterometer. First EPS/MetOp RAO Workshop, 15-17 May 2006, Frascati, Italy, ESA Special Publication SP-618.
  • Wagner, W., Lemoine, G., and Rott, H. A (1999). Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data. Remote Sensing of Environment, vol.70, 191- 207.
Soil Moisture Soil Moisture

Evaporative Stress Index

Variable: Evaporative stress index

Organization in charge: USDA-NOAA

Input data: Hourly land-surface temperature and insolation from geostationary satellites such as GOES-East and West and Meteosat Second Generation (MSG), leaf area index and albedo from MODIS sensor, and surface meteorological and atmospheric temperature profile data from the North American Regional Reanalysis (NARR) dataset.

Spatial Resolution: 0.05 degrees

Temporal Resolution: 8 days

Accuracy: Detailed description of validation results can be found in Otkin et al. (2013)

Short description of the variable:

The Evaporative Stress Index (ESI) (Anderson et al., 2007b, 2011) represents anomalies in the ratio of actual-to- potential ET (fPET), generated with the thermal remote sensing based Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance model (Anderson et al. 1997; 2007a,b; 2011). ALEXI uses measurements of morning land-surface temperature rise retrieved from geostationary satellite thermal band imagery to solve the Two-Source Energy Balance (TSEB) algorithm (Norman et al., 1995) in time-differential model. Actual ET output from ALEXI is ratioed with estimates of the potential ET expected under non-moisture limiting conditions, yielding a non-dimensional ET variable, fPET, ranging 0 (dry) to approximately 1 (wet). The fPET time series are composited over periods of 1, 2 or 3 months, both to gap-fill cloudy pixels and to assess moisture variations at different timescales.

Given baseline conditions describing the mean and standard deviation in fPET for each composite interval, computed for a weekly moving window over the ALEXI period of record (currently 2000-present), the ESI is expressed as a standardized anomaly with values typically between -3σ (drier than normal) and +3σ (wetter than normal).

Time interval of production: Monthly

Use within GEOGLAM Crop Monitor activities:

The Evaporative Stress Index (ESI) describes temporal anomalies in evapotranspiration (ET), highlighting areas with anomalously high or low rates of water use across the land surface. Here, ET is retrieved via energy balance using remotely sensed land-surface temperature (LST) time-change signals. LST is a fast-response variable, providing proxy information regarding rapidly evolving surface soil moisture and crop stress conditions at relatively high spatial resolution. The ESI also demonstrates capability for capturing early signals of “flash drought”, brought on by extended periods of hot, dry and windy conditions leading to rapid soil moisture depletion. In the Crop Monitor activities the ESI serves as an indicator of the consequences of extreme weather events and can be used to detect crop stress.

Data Source:

References:

  • Anderson, M. C., J. M. Norman, G. R. Diak, W. P. Kustas, and J. R. Mecikalski (1997). A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sens. Environ., 60, 195-216.
  • Anderson, M. C., J. M. Norman, J. R. Mecikalski, J. P. Otkin, and W. P. Kustas (2007a). A climatological study of evapotranspiration and moisture stress across the continental U.S. based on thermal remote sensing: I. Model formulation. J. Geophys. Res., 112, D10117, doi:10110.11029/12006JD007506.
  • Anderson, M. C., J. M. Norman, J. R. Mecikalski, J. P. Otkin, and W. P. Kustas (2007b). A climatological study of evapotranspiration and moisture stress across the continental U.S. based on thermal remote sensing: II. Surface moisture climatology. J. Geophys. Res., 112, D11112, doi:11110.11029/12006JD007507
  • Anderson, M. C., C. R. Hain, B. Wardlow, J. R. Mecikalski, and W. P. Kustas (2011), Evaluation of a drought index based on thermal remote sensing of evapotranspiration over the continental U.S., J. Climate, 24, 2025-2044.
  • Norman, J. M., W. P. Kustas, and K. S. Humes, 1995: A two-source approach for estimating soil and vegetation energy fluxes from observations of directional radiometric surface temperature. Agric. For. Met., 77, 263-293.
  • Otkin, J. A., Anderson, M. C., Hain, C., Mladenova, I. E., Basara, J. B., & Svoboda, M. (2013). Examining Rapid Onset Drought Development Using the Thermal Infrared–Based Evaporative Stress Index. Journal of Hydrometeorology, 14(4), 1057–1074. http://doi.org/10.1175/JHM-D- 12-0144.1
ESI ESI

Actual Evapotranspiration Anomaly

Variable: Actual Evapotranspiration anomaly

Organization in charge: USGS

Input data: Multiple datasets were combined for the estimation of actual evapotranspiration anomalies (Senay et al. 2013):

  1. The clear-sky net radiation (R n ) was calculated using standard equations recommended by Allen et al. (1998).
  2. Air temperature data for 2000-2011 was obtained from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) (http://www.prism.oregonstate.edu/).
  3. 16 day normalized difference vegetation index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS).
  4. The topographic elevation data were obtained from Shuttle Radar Topographic Mission (SRTM) at 1 km spatial resolution (http://srtm.csi.cgiar.org/).
  5. The eight day land surface temperature (Ts) data from 1 km 8 day MODIS global LST and emissivity data (Terra MOD11A2.005) product.
  6. The daily short grass reference ET (ETo) was calculated from six hourly weather datasets from the National Oceanic and Atmospheric Administration's (NOAA) Global Data Assimilation System (GDAS) (Kanamitsu, 1989) using the standardized Penman-Monteith equation (Allen et al., 1998).
  7. Eddy covariance data (30-minute interval) from 45 AmeriFlux stations were aggregated to monthly time scale for 2005 (http://public.ornl.gov/ameriflux).

Spatial Resolution: 0.05 degrees

Temporal Resolution: 8 days

Accuracy: Detailed description of validation results can be found in (Senay et al. 2013).

Short description of the variable:

evaporation from soil. Actual ET (ETa) is produced using the operational Simplified Surface Energy Balance (SSEBop) model (Senay et al., 2013) for the period 2000 to present. The SSEBop combines ET fractions generated from remotely sensed MODIS thermal imagery, acquired every 8 days, with reference ET using a thermal index approach. The unique feature of the SSEBop parameterization is that it uses predefined, seasonally dynamic, boundary conditions that are unique to each pixel for the “hot/dry” and “cold/wet” reference points. The original formulation of SSEB is based on the hot and cold pixel principles of SEBAL (Bastiaanssen et al., 1998) and METRIC (Allen et al., 2007) models. The anomalies are the ratio of ETa and the corresponding median ETa, expressed as a percent value. The ETa anomaly products are available from 2001 to 2010.

Time interval of production: 8 days and Monthly

Use within GEOGLAM Crop Monitor activities:

The Actual Evapotranspiration (ETa) anomaly describes temporal anomalies in evapotranspiration (ET), highlighting areas with anomalously high or low rates of water use across the land surface. Here, ET is retrieved via energy balance using MODIS thermal imagery. In the Crop Monitor activities, the ETa anomaly serves as an indicator of the consequences of extreme weather events and can be used to detect crop stress situations, where the water requirements of the plant may be compromised.

Data Source:

References:

  • Allen, R.G., L.S. Pereira, D. Raes, and M. Smith (1998). Crop EvapoTranspiration: Guidelines for Computing Crop Water Requirements. In: United Nations FAO, Irrigation and Drainage Paper 56, FAO, Rome, Italy.
  • Allen, R.G., Tasumi, M., and Trezza, R. (2007). Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC) – Model. ASCE J. Irrigation and Drainage Engineering 133, 380-394.
  • Kanamitsu, M. (1989). Description of the NMC Global Data Assimilation and Forecast System. Weather and Forecasting, 4(3), 335–342.
  • Senay, G. B., Bohms, S., Singh, R. K., Gowda, P. H., Velpuri, N. M., Alemu, H., and Verdin, J. P. (2013). Operational Evapotranspiration Mapping Using Remote Sensing and Weather Datasets: A New Parameterization for the SSEB Approach. JAWRA Journal of the American Water Resources Association, 49(3), 577–591. http://doi.org/10.1111/jawr.12057
Evapotranspiration Evapotranspiration