Project -- MODIS Evapotranspiration (MOD16)
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MODIS Evapotranspiration Project (MOD16)

This project is a part of NASA/EOS project. Our goals are to estimate evapotranspiration from earth land surface by using satellite remote sensing and to make use of this technique for hydrological and ecological applications such as drought monitoring, watershed management, environmental assessment, etc.

Principal Investigators

Qiaozhen Mu (qiaozhen@ntsg.umt.edu)
Steve Running (swr@ntsg.umt.edu)

MOD16 Algorithm Description

This MODIS evapotranspiration (ET) algorithm is based on the Penman-Monteith (P-M) equation. We incorporated surface stomatal resistance and vegetation information derived from MODIS land products to estimate daily ET and potential ET (PET) which is then composited over an 8-day interval. ET/PET ratio represents not only the "dryness" of the land surface but also the characteristics of the land surface in terms of energy partitioning, which has a large influence on the local and regional climate and environment.

Published Articles

Development of a global evapotranspiration algorithm based on MODIS and global meteorology data
Mu, Q., F. A. Heinsch, M. Zhao, S. W. Running
Remote Sensing of Environment, Volume 111, page 519-536 - 2007 (doi: 10.1016/j.rse.2007.04.015)

Mu et al. (2007) developed a global remote sensing evapotranspiration (ET) algorithm based on Cleugh et al.’s (2007) Penman-Monteith based ET (RS-PM). Our algorithm considers both the surface energy partitioning process and environmental constraints on ET. We use ground-based meteorological observations and remote sensing data from the MODerate Resolution Imaging Spectroradiometer (MODIS) to estimate global ET by (1) adding vapor pressure deficit and minimum air temperature constraints on stomatal conductance; (2) using leaf area index as a scalar for estimating canopy conductance; (3) replacing the Normalized Difference Vegetation Index with the Enhanced Vegetation Index thereby also changing the equation for calculation of the vegetation cover fraction (F_C); and (4) adding a calculation of soil evaporation to the previously proposed RS-PM method.

We evaluate our algorithm using ET observations at 19 AmeriFlux eddy covariance flux towers. We calculated ET with both our Revised RS-PM algorithm and the RS-PM algorithm using Global Modeling and Assimilation Office (GMAO v. 4.0.0) meteorological data and compared the resulting ET estimates with observations. Results indicate that our Revised RS-PM algorithm substantially reduces the root mean square error (RMSE) of the 8-day latent heat flux (LE) averaged over the 19 towers from 64.6 W/m² (RS-PM algorithm) to 27.3 W/m² (Revised RS-PM) with tower meteorological data, and from 71.9 W/m² to 29.5 W/m² with GMAO meteorological data. The average LE bias of the tower-driven LE estimates to the LE observations changed from 39.9 W/m² to -5.8 W/m² and from 48.2 W/m² to -1.3 W/m² driven by GMAO data. The correlation coefficients increased slightly from 0.70 to 0.76 with the use of tower meteorological data. We then apply our Revised RS-PM algorithm to the globe using 0.05° MODIS remote sensing data and reanalysis meteorological data to obtain the annual global ET (MODIS ET) for 2001. As expected, the spatial pattern of the MODIS ET agrees well with that of the MODIS global terrestrial gross and net primary production (MOD17 GPP/NPP), with the highest ET over tropical forests and the lowest ET values in dry areas with short growing seasons. Our ET algorithm can capture the seasonal and interannual variability of water cycle. This MODIS ET product provides critical information on the regional and global water cycle and resulting environment changes.

Regional evaporation estimates from flux tower and MODIS satellite data
Cleugh, H. A., R. Leuning, Q. Mu and S. W. Running
Remote Sensing of Environment, Volume 106, page 285–304 - 2007 (doi: 10.1016/j.rse.2006.07.007)

Cleugh et al. (2007) developed a remote sensing ET algorithm based on Penman-Monteith equation (P-M). The model was tested using 3 years of evaporation and meteorological measurements from two contrasting Australian ecosystems, a cool temperate, evergreen Eucalyptus forest and a wet/dry, tropical savanna. The P-M model adequately estimated the magnitude and seasonal variation in evaporation in both ecosystems (RMSE=27W/m−², R²=0.74), demonstrating the validity of the proposed surface conductance algorithm. This, and the ability to constrain evaporation estimates via the energy balance, demonstrates the superiority of the P-M equation over the surface temperature-based model.

Evaluating water stress controls on primary production in biogeochemical and remote sensing based models
Mu, Q., M. Zhao, F. A. Heinsch, M. Liu, H. Tian and S. W. Running
Journal of Geophysical Research, Volume 112, Number G01012 - 2007 (doi: 10.1029/2006JG000179)

Water stress is one of the most important limiting factors controlling terrestrial primary production, and the performance of a primary production model is largely determined by its capacity to capture environmental water stress. The algorithm that generates the global near real-time MODIS GPP/NPP products (MOD17) uses VPD (Vapor Pressure Deficit) alone to estimate the environmental water stress. This paper compares the water stress calculation in the MOD17 algorithm with results simulated using a process-based biogeochemical model (Biome-BGC) to evaluate the performance of the water stress determined using the MOD17 algorithm. The investigation study areas include China and the conterminous U.S. because of the availability of daily meteorological observation data. Our study shows that VPD alone can capture interannual variability of the full water stress nearly over all the study areas. In wet regions, where annual precipitation is greater than 400 mm/yr, the VPD–based water stress estimate in MOD17 is adequate to explain the magnitude and variability of water stress determined from atmospheric VPD and soil water in Biome-BGC. In some dry regions, where soil water is severely limiting, MOD17 underestimates water stress, overestimates GPP, and fails to capture the intra-annual variability of water stress. The MOD17 algorithm should add soil water stress to its calculations in these dry regions, thereby improving GPP estimates. Interannual variability in water stress is simpler to capture than the seasonality, but it is more difficult to capture this interannual variability in GPP. The MOD17 algorithm captures inter- and intra-annual variability of both the Biome-BGC-calculated water stress and GPP better in the conterminous USA than in the strongly monsoon-controlled China.