The NASA Earth Observing System is a $7.3 billion program planning satellite-based earth monitoring for 15 years, and is the heart of global change science for the United States. The central sensor on board the Terra Satellite Platform is the Moderate Resolution Imaging Spectroradiometer (MODIS picture). Terra was successfully launched on December 16, 1999, and a second MODIS-based satellite, Aqua, was launched May 4, 2002.
The University of Montana is the only Forestry School in the country with a member on the EOS science team. Our responsibility is to provide computer programs to use this new satellite data to calculate global photosynthesis and evapotranspiration for all terrestrial biomes. We envision the EOS satellite to provide a dramatic improvement in our ability to accurately monitor global ecological conditions. Large scale climate shifts, deforestation, desertification, pollution damage, crop conditions, glacial retreats, flooding, wildfires and urbanization are examples of the types of earth system monitoring planned. Currently, as we work on these software products, we are using Montana as a testbed for this advanced satellite technology.
The University of Montana NTSG lab is under contract to NASA as a Science Compute Facility (SCF) on the Land Science team, tasked with developing two at-launch MODIS algorithms, and two post-launch algorithms. The post-launch algorithms under development include:
The goal of the MOD17 MODIS project is to provide continuous estimates of Gross/Net Primary Production (GPP/NPP) across Earth’s entire vegetated land surface. MOD17 GPP/NPP outputs are useful for natural resource and land management, global carbon cycle analysis, ecosystem status assessment, and environmental change monitoring. MOD17 is part of the NASA Earth Observation System (EOS) program and is the first satellite-driven dataset to monitor vegetation productivity on a global scale.
MOD17 Algorithm DescriptionThe MOD17 algorithm is based on the original radiation use efficiency logic of Monteith (1972), which suggests that productivity of annual crops under well-watered and fertilized conditions is linearly related to the amount of absorbed solar energy—specifically, the amount of absorbed Photosynthetically Active Radiation (APAR). The translation of APAR to an actual productivity estimate is conducted via a conversion efficiency parameter, ε, which varies by vegetation type and climate conditions. In consequence, MOD17 incorporates differences in maximum ε among vegetation types and also lowers ε under water-stressed and/or cold temperature conditions. To calculate NPP, MOD17 also estimates daily leaf and fine root maintenance respiration (R_lr), annual growth respiration (R_g), and annual maintenance respiration of live cells in woody tissue (R_m).
The main MOD17 data outputs include:
A more detailed overview of the MOD17 algorithm can be found in the MOD17 User’s Guide, Running et al. (2004), Zhao et al. (2005) and in the scientific publications listed under the publications tab above.
Main Data Inputs
To estimate GPP/NPP, the main data inputs to the MOD17 algorithm include:
Data Product Offerings
The MOD17 dataset (version 55) is provided on our FTP site from 2000 – 2012 at 8-day, monthly, and annual time steps. Updates are provided on an annual basis. Compared to the NASA/USGS LPDAAC 8-day and annual v5 product, NTSG MOD17 v55 contains the following main improvements:
Details of version 55 improvements can be found in Zhao et al. (2005), Zhao et al. (2006), and in the supplemental material of Zhao and Running (2010).
Figure 1. Potential limits to vegetation net primary production based on fundamental physiological limits by solar radiation, water balance, and temperature (from Churkina & Running, 1998; Nemani et al., 2003; Running et al., 2004).
Figure 2. The 8-day composite leaf area index (LAI) in Amazon region for the 8-day period 081 (March 21–28) in 2001 for (a) the original with no temporal interpolation of the LAI and (b) the temporally interpolated LAI. (Mu et al., 2007 Remote Sensing of Environment, in press; Zhao et al., 2005)
Figure 3. An example on how temporal filling unreliable 8-day Collection 4 FPAR/LAI, and therefore improved 8-day GPP and PsnNet for one MODIS 1-km pixel located in Amazon basin (lat = -5.0, lon = -65.0) (from Zhao et al., 2005)
Validation with GPP estimated at eddy flow towers
Interannual variability at the global scale and over the North American Carbon Program (NACP) domain
Response to Comments on "Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009" Zhao, M. and S. W. Running Science. 333: 1093, 2011
Samanta et al. and Medlyn challenge our report of reduced global terrestrial net primary production (NPP) from 2000 through 2009. Our new tests show that other vegetation indices had even stronger negative changes through the decade, and weakening temperature controls on water stress and respiration still did not produce a positive trend in NPP. These analyses strengthen the conclusion of drought-induced reduction in global NPP over the past decade.
Drought-induced reduction in global terrestrial net primary production from 2000 through 2009 Zhao, M. and S. W. Running Science. 329: 940-943. 2010
Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.
Improvements of the MODIS terrestrial gross and net primary production global data set Zhao, M., F. A. Heinsch, R. R. Nemani, and S. W. Running Remote Sensing of Environment. 95: 164–176. 2005MODIS primary production products (MOD17) are the first regular, near-real-time data sets for repeated monitoring of vegetation primary production on vegetated land at 1-km resolution at an 8-day interval. But both the inconsistent spatial resolution between the gridded meteorological data and MODIS pixels, and the cloud-contaminated MODIS FPAR/LAI (MOD15A2) retrievals can introduce considerable errors to Collection4 primary production (denoted as C4 MOD17) results. Here, we aim to rectify these problems through reprocessing key inputs to MODIS primary vegetation productivity algorithm, resulting in improved Collection5 MOD17 (here denoted as C5 MOD17) estimates. This was accomplished by spatial interpolation of the coarse resolution meteorological data input and with temporal filling of cloud-contaminated MOD15A2 data. Furthermore, we modified the Biome Parameter Look-Up Table (BPLUT) based on recent synthesized NPP data and some observed GPP derived from some flux tower measurements to keep up with the improvements in upstream inputs. Because MOD17 is one of the down-stream MODIS land products, the performance of the algorithm can be largely influenced by the uncertainties from upstream inputs, such as land cover, FPAR/LAI, the meteorological data, and algorithm itself. MODIS GPP fits well with GPP derived from 12 flux towers over North America. Globally, the 3-year MOD17 NPP is comparable to the Ecosystem Model–Data Intercomparison (EMDI) NPP data set, and global total MODIS GPP and NPP are inversely related to the observed atmospheric CO2 growth rates, and MEI index, indicating MOD17 are reliable products. From 2001 to 2003, mean global total GPP and NPP estimated by MODIS are 109.29 Pg C/year and 56.02 Pg C/year, respectively. Based on this research, the improved global MODIS primary production data set is now ready for monitoring ecological conditions, natural resources and environmental changes.
Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial primary production to the accuracy of meteorological reanalyses Zhao, M., S. W. Running, and R. R. Nemani Journal of Geophysical Research. 111, G01002, doi:10.1029/2004JG000004. 2006The Moderate Resolution Imaging Spectroradiometer (MODIS) on board NASA’s satellites, Terra and Aqua, dramatically improves our ability to accurately and continuously monitor the terrestrial biosphere. MODIS information is used to estimate global terrestrial primary production weekly and annually in near-real time at a 1-km resolution. MODIS terrestrial primary production requires daily gridded assimilation meteorological data as inputs, and the accuracy of the existing meteorological reanalysis data sets show marked differences both spatially and temporally. This study compares surface meteorological data sets from three well-documented global reanalyses, NASA Data Assimilation Office (DAO), European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA-40) and National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis 1, with observed weather station data and other gridded data interpolated from the observations, to evaluate the sensitivity of MODIS global terrestrial gross and net primary production (GPP and NPP) to the uncertainties of meteorological inputs both in the United States and the global vegetated areas. NCEP tends to overestimate surface solar radiation, and underestimate both temperature and vapor pressure deficit (VPD). ECMWF has the highest accuracy but its radiation is lower in tropical regions, and the accuracy of DAO lies between NCEP and ECMWF. Biases in temperature are mainly responsible for large VPD biases in reanalyses. MODIS NPP contains more uncertainties than GPP. Global total MODIS GPP and NPP driven by DAO, ECMWF, and NCEP show notable differences (>20 PgC/yr) with the highest estimates from NCEP and the lowest from ECMWF. Again, the DAO results lie somewhere between NCEP and ECMWF estimates. Spatially, the larger discrepancies among reanalyses and their derived MODIS GPP and NPP occur in the tropics. These results reveal that the biases in meteorological reanalyses can introduce substantial error into GPP and NPP estimations, and emphasize the need to minimize these biases to improve the quality of MODIS GPP and NPP products.
Evaluation of remote sensing based terrestrial productivity from MODIS using tower eddy flux network observations Heinsch, F. A., M. Zhao, S. W. Running, J. S. Kimball, R. R. Nemani, K. J. Davis, et al. IEEE Transactions on Geoscience and Remote Sensing, 44(7): 1908-1925. 2006The Moderate Resolution Spectroradiometer (MODIS) sensor has provided near real-time estimates of gross primary production (GPP) since March 2000. We compare four years (2000 to 2003) of satellite-based calculations of GPP with tower eddy CO2 flux-based estimates across diverse land cover types and climate regimes. We examine the potential error contributions from meteorology, leaf area index (LAI)/fPAR, and land cover. The error between annual GPP computed from NASA’s Data Assimilation Office’s (DAO) and tower-based meteorology is 28%, indicating that NASA’s DAO global meteorology plays an important role in the accuracy of the GPP algorithm. Approximately 62% of MOD15-based estimates of LAI were within the estimates based on field optical measurements, although remaining values overestimated site values. Land cover presented the fewest errors, with most errors within the forest classes, reducing potential error. Tower-based and MODIS estimates of annual GPP compare favorably for most biomes, although MODIS GPP overestimates tower-based calculations by 20%–30%. Seasonally, summer estimates of MODIS GPP are closest to tower data, and spring estimates are the worst, most likely the result of the relatively rapid onset of leaf-out. The results of this study indicate, however, that the current MODIS GPP algorithm shows reasonable spatial patterns and temporal variability across a diverse range of biomes and climate regimes. So, while continued efforts are needed to isolate particular problems in specific biomes, we are optimistic about the general quality of these data, and continuation of the MOD17 GPP product will likely provide a key component of global terrestrial ecosystem analysis, providing continuous weekly measurements of global vegetation production.
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, 112, G01012, doi: 10.1029/2006JG000179, 2007Water 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.
A continuous satellite-derived measure of global terrestrial primary production Running, S., R. R. Nemani, F. A. Heinsch, M. Zhao, M. Reeves, and H. Hashimoto. BioScience, 54(6): 547-560. 2004.Until recently, continuous monitoring of global vegetation productivity has not been possible because of technological limitations. This article introduces a new satellite-driven monitor of the global biosphere that regularly computes daily gross primary production (GPP) and annual net primary production (NPP) at 1-kilometer (km) resolution over 109,782,756 km2 of vegetated land surface. We summarize the history of global NPP science, as well as the derivation of this calculation, and current data production activity. The first data on NPP from the EOS (Earth Observing System) MODIS (Moderate Resolution Imaging Spectroradiometer) sensor are presented with different types of validation. We offer examples of how this new type of data set can serve ecological science, land management, and environmental policy. To enhance the use of these data by nonspecialists, we are now producing monthly anomaly maps for GPP and annual NPP that compare the current value with an 18-year average value for each pixel, clearly identifying regions where vegetation growth is higher or lower than normal.
Climate-driven increases in global terrestrial net primary production from 1982 to 1999 Nemani, R. R., C. D. Keeling, H. Hashimoto, W. M. Jolly, S. C. Piper, C. J. Tucker, R. B. Myneni, and S. W. Running. Science, 300: 1560-1563. 2003Recent climatic changes have enhanced plant growth in northern mid-latitudes and high latitudes. However, a comprehensive analysis of the impact of global climatic changes on vegetation productivity has not before been expressed in the context of variable limiting factors to plant growth. We present a global investigation of vegetation responses to climatic changes by analyzing 18 years (1982 to 1999) of both climatic data and satellite observations of vegetation activity. Our results indicate that global changes in climate have eased several critical climatic constraints to plant growth, such that net primary production increased 6% (3.4 petagrams of carbon over 18 years) globally. The largest increase was in tropical ecosystems. Amazon rain forests accounted for 42% of the global increase in net primary production, owing mainly to decreased cloud cover and the resulting increase in solar radiation.
MOD16 Global Terrestrial Evapotranspiration Data Set
This project is part of NASA/EOS project to estimate global terrestrial evapotranspiration from earth land surface by using satellite remote sensing data. MOD16 global evapotranspiration product can be used to calculate regional water and energy balance, soil water status; hence, it provides key information for water resource management. With long-term ET data, the effects of changes in climate, land use, and ecosystems disturbances (e.g. wildfires and insect outbreaks) on regional water resources and land surface energy change can be quantified.
The MOD16 global evapotranspiration (ET)/latent heat flux (LE)/potential ET (PET)/potential LE (PLE) datasets are regular 1-km2 land surface ET datasets for the 109.03 Million km2 global vegetated land areas at 8-day, monthly and annual intervals. The dataset covers the time period 2000 – 2010. Future years will be produced and posted periodically, but not in near-real time.
The MOD16 ET datasets are estimated using Mu et al.’s improved ET algorithm (2011) over previous Mu et al.’s paper (2007a). The ET algorithm is based on the Penman-Monteith equation (Monteith, 1965). Surface resistance is an effective resistance to evaporation from land surface and transpiration from the plant canopy.
Terrestrial ET includes evaporation from wet and moist soil, from rain water intercepted by the canopy before it reaches the ground, and the transpiration through stomata on plant leaves and stems. Evaporation of water intercepted by the canopy is a very important water flux for ecosystems with a high LAI. Canopy conductance for plant transpiration is calculated by using LAI to scale stomatal conductance up to canopy level. For many plant species during growing seasons, stomatal conductance is controlled by vapor pressure deficit (VPD) (Oren et al., 1999; Mu et al., 2007b; Running & Kimball, 2005) and daily minimum air temperature (Tmin). Tmin is used to control dormant and active growing seasons for evergreen biomes. High temperatures are often accompanied by high VPDs, leading to partial or complete closure of stomata. For a given biome type, two threshold values for Tmin and VPD are listed in the Biome-Property-Look-Up-Table (BPLUT) to control stomatal conductance (Mu et al., 2007a; 2009; 2011).
MOD16 products includes 8-day, monthly and annual ET, LE, PET, PLE and 8-day, annual quality control (ET_QC). The 8-day MOD16A2 QC field is inherited from MOD15A2 in the same period. However, the cloud-contaminated FPAR/LAI has been temporally filled with those having good QC. For annual QC of MOD16A3 products, we used the method proposed by Zhao et al. (2005) to define a more meaningful annual ET QC as
QC=100.0 X NUg/Totalg
where NUg is the number of days during growing season with unreliable or missing MODIS LAI inputs, and Totalg is total number of days in the growing season. The growing season is defined as all days with Tmin above the value where stomata close as in the BPLUT. The MOD16 ET algorithm has a good performance in generating global ET data products, providing critical information on global terrestrial water and energy cycles and environmental changes (Mu et al., 2007a, 2009, 2011).
Note: For some pixels in African rainforest, the MODIS albedo data from MCD43B2/MCD43B3 have no cloud free data throughout an entire year. As a result, corresponding fill values of ET/LE/PET/PLE are assigned for these pixels in that year.
Flowchart showing the logic behind MOD16 ET Algorithm for calculating daily MODIS ET (Mu et al., 2011)
Global Average ET over 2000-2006:
Seasonality of Gloabl ET:
Global annual ET anomalies (2001-2006) relative to the average over 2000-2006.
Global ET/PET ratio anaomalies (2001-2006) relative to the average ET/PET over 2000-2006:
North American ET over 2000-2006:
Seasonality of North American ET:
North American annual ET anomalies (2001-2006) relative to the average over 2000-2006:
North American ET/PET ratio anomalies (2001-1006) relative to the average ET/PET over 2000-2006:
Global GPP vs. ET:
The 8-day ET (0.1mm/8days or 0.1mm/5days) is the sum of ET during these 8-day time periods (5 days for 361 composite data in 2001, 2002, 2003, 2005, 2006, 2007, 2009, 2010, 6 days for 361 in 2000, 2004, 2008). The monthly ET (0.1mm/month) is the sum of monthly ET. For February, there are 29 days in a leap year and 28 days in normal years. The annual ET (0.1mm/yr) is the sum of the ET during each year. There are 366 days in 2000, 2004, 2008, and 365 days in 2001, 2002, 2003, 2005, 2006, 2007, 2009, 2010. The 8-day, monthly and annual LE/PLE (1.0e4 J/m2/day) is the average daily LE/PLE over the corresponding time period.
The users should multiply 0.1 to get the real ET/PET values in mm/8day or mm/month, or mm/yr, and 1.0e4 to get LE/PLE in J/m2/day.
For the 8-day and monthly ET/LE/PET/PLE, annual LE/PLE, the valid value range is -32767-32700.
Fill value, out of the earth 32767Water body 32766Barren or sparsely vegetated 32765Permanent snow and ice 32764Permanent wetland 32763Urban or Built-up 32762Unclassified 32761
For the annual ET/PET, the valid value range is 0- 65500.Fill value, out of the earth 65535Water body 65534Barren or sparsely vegetated 65533Permanent snow and ice 65532Permanent wetland 65531Urban or Built-up 65530Unclassified 65529
Daniel Siegel (firstname.lastname@example.org), Center for Research in Water Resources, University of Texas at Austin, developed a tool to process the MODIS data in sinusoidal equal area projection. We have never used it, but it might be a very useful tool for those who are not familiar with the MODIS data in sinusoidal equal area projection.
Algorithm Theoretical Basis Document: MODIS Global Terrestrial Evapotranspiration (ET) Product (NASA MOD16A2/A3) Collection 5. NASA Headquarters.
Qiaozhen Mu, Maosheng Zhao, Steven W. Running November 20, 2013
This Algorithm Theoretical Basis Document (ATBD) describes a level 4 MODIS land data product, MOD16, the global 8-day (MOD16A2) and annual (MOD16A3) terrestrial ecosystem Evapotranspiration (ET) dataset at 1-km spatial resolution over the 109.03 Million km2 global vegetated land areas. The MOD16 algorithm is based on the logic of the Penman-Monteith equation which uses daily meteorological reanalysis data and 8-day remotely sensed vegetation property dynamics from MODIS as inputs.
The MOD16 ET algorithm runs at daily basis and temporally, daily ET is the sum of ET from daytime and night. Vertically, ET is the sum of water vapor fluxes from soil evaporation, wet canopy evaporation and plant transpiration at dry canopy surface. MODIS 8-day FPAR is used as vegetation cover faction to quantify how much surface net radiation is allocated between soil and vegetation; MODIS 8-day albedo and daily surface downward solar radiation and air temperature from daily meteorological reanalysis data are used to calculate surface net radiation and soil heat flux; daily air temperature, vapor pressure deficit (VPD) and relative humidity data, and 8-day MODIS LAI are used to estimate surface stomatal conductance, aerodynamic resistance, wet canopy, soil heat flux and other key environmental variables. MODIS land cover is used to specify the biome type for each pixel, and the biome-dependent constant parameters for the algorithm are saved in a Biome-Property-Lookup-Table (BPLUT). Except for minimum daily air temperature and VPD, which are directly adopted from the existing algorithm of the MODIS global terrestrial gross and net primary production (MODIS GPP/NPP), the BPLUT is tuned largely based on a set of targeted annual ET for each biome derived from MODIS GPP and water use efficiency calculated from eddy flux towers.
The MOD16 ET has been validated with ET measured at eddy flux towers and ET estimated from 232 watersheds. Averaged over 2000-2010, the total global annual ET over the vegetated land surface is 63.4 103 km3, with an average of 569 ± 358 mm yr-1, comparable to the recent global estimates. Similar to other MODIS level 3 or level 4 MODIS land data products, 8-day and monthly MOD16A2 and annual MOD16A3 datasets are saved in 10-degree Sinusoidal HDFEOS tiles. Thanks to the powerful internal compression of HDFEOS, for each year, the size of the MOD16A2 and MOD16A3 together takes about 39GB. Since 2006, there have been 193 users from 30 countries requesting MODIS ET data from us and now MOD16 from 2000 to 2010 are ready and have been released to the public for free download at our ftp site, ftp://ftp.ntsg.umt.edu/pub/MODIS/Mirror/MOD16/ .
A Remotely Sensed Global Terrestrial Drought Severity Index Mu, Q., M. Zhao, J. S. Kimball, N. G. McDowell, S. W. Running (2013) Bulletin of the American Meteorological Society, 01/2013, Volume 94, Issue 1, Number 1, p.83.98, DOI:10.1175/BAMS-D-11-00213.1
Improvements to a MODIS Global Terrestrial Evapotranspiration Algorithm Mu, Q., M. Zhao, S. W. Running Remote Sensing of Environment, Volume 115, pages 1781-1800 (doi:10.1016/j.rse.2011.02.019)
MODIS global evapotranspiration (ET) products by Mu et al. [Mu, Q., F.A. Heinsch, M. Zhao, S.W. Running (2007) Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment 111, 519-536 (doi: 10.1016/j.rse.2007.04.015).] are the first regular 1-km2 land surface ET dataset for the 109.03 Million km2 global vegetated land areas at an 8-day interval. In this study, we have further improved the ET algorithm in Mu et al.’s paper (2007a, hereafter called old algorithm) by 1) simplifying the calculation of vegetation cover fraction; 2) calculating ET as the sum of daytime and nighttime components; 3) adding soil heat flux calculation; 4) improving estimates of stomatal conductance, aerodynamic resistance and boundary layer resistance; 5) separating dry canopy surface from the wet; and 6) dividing soil surface into saturated wet surface and moist surface. We compared the improved algorithm with the old one both globally and locally at 46 eddy flux towers. The global annual total ET over the vegetated land surface is 62.8×103 km3, agrees very well with other reported estimates of 65.5×103 km3 over the terrestrial land surface, which is much higher than 45.8×103 km3 estimated with the old algorithm. For ET evaluation at eddy flux towers, the improved algorithm reduces mean absolute bias (MAE) of daily ET from 0.39 mm day-1 to 0.33 mm day-1 driven by tower meteorological data, and from 0.40 mm day-1 to 0.31 mm day-1 driven by GMAO data, a global meteorological reanalysis dataset. MAE values by the improved ET algorithm are 24.6% and 24.1% of the ET measured from towers, within the range (10-30%) of the reported uncertainties in ET measurements, implying an enhanced accuracy of the improved algorithm. Compared to the old algorithm, the improved algorithm increases the skill score with tower-driven ET estimates from 0.50 to 0.55, and from 0.46 to 0.53 with GMAO-driven ET. Based on these results, the improved ET algorithm has a better performance in generating global ET data products, providing critical information on global terrestrial water and energy cycles and environmental changes.
Direct impacts on local climate of sugarcane expansion in Brazil Loarie, S. R., D. B. Lobell, G. P. Asner, Q. Mu, C. B. Field Nature Climate Change 1 (2): 105-109. (2011) | doi:10.1038/nclimate1067.
The increasing global demand for biofuels will require conversion of conventional agricultural or natural ecosystems. Expanding biofuel production into areas now used for agriculture reduces the need to clear natural ecosystems, leading to indirect climate benefits through reduced greenhouse-gas emissions and faster payback of carbon debts. Biofuel expansion may also cause direct, local climate changes by altering surface albedo and evapotranspiration, but these effects have been poorly documented. Here we quantify the direct climate effects of sugar-cane expansion in the Brazilian Cerrado, on the basis of maps of recent sugar-cane expansion and natural-vegetation clearance combined with remotely sensed temperature, albedo and evapotranspiration over a 1:9 million km2 area. On a regional basis for clear-sky daytime conditions, conversion of natural vegetation to a crop/pasture mosaic warms the cerrado by an average of 1:55 (1:45–1:65) °C, but subsequent conversion of that mosaic to sugar cane cools the region by an average of 0:93 (0:78–1:07) °C, resulting in a mean net increase of 0:6 °C. Our results indicate that expanding sugar cane into existing crop and pasture land has a direct local cooling effect that reinforces the indirect climate benefits of this land-use option.
Recent decline in the global land evapotranspiration trend due to limited moisture supply Jung, M., M. Reichstein, P. Ciais, S.I. Seneviratne, J. Sheffield, M. L. Goulden, G. B. Bonan, A. Cescatti, J. Chen, R. de Jeu, A. J. Dolman, W. Eugster, D. Gerten, D. Gianelle, N. Gobron, J. Heinke, J. S. Kimball, B. E. Law, L. Montagnani, Q. Mu, B. Mueller, K. W. Oleson, D. Papale, A. D. Richardson, O. Roupsard, S. W. Running, E. Tomelleri, N. Viovy, U. Weber, C. Williams, E. Wood, S. Zaehle and K. Zhang Nature, Volume 467, page 951--954 - October 2010 (doi: 10.1038/nature09396)
More than half of the solar energy absorbed by land surfaces is currently used to evaporate water. Climate changes is expected to intensify the hydrological cycle and to alter evapotranspiration, with implications for ecosystem services and feedback to regional and global climate. Evapotranspiration changes may already be underway, but direct observational constraints are lacking at the global scale. Until evidence is available, changes in the water cycle on land - a key diagnostic criterion of the effects of climate change and variability - remain uncertain. Here we provide a data-driven estimate of global land evapotranspiration from 1982 to 2008, compiled using a global monitoring network, meteorological and remote sensing observations and a machine learning algorithm. In Addition, we have assessed evapotranspiration variations over the same time period using an ensemble of process-based land-surface models. Our results suggest that global annual evapotranspiration increased on average by 7.1 ± 1.0 millimeters per year per decade from 1982 to 1997. After that, coincident with the last major El Niño event in 1998, the global evapotranspiration increase seems to have ceased until 2008. This change was driven primarily by moisture limitation in the Southern Hemisphere, particularly Africa and Australia. In these regions, microwave satellite observations indicate that soil moisture limitations on evapotranspiration largely explain the recent decline of the global land-evapotranspiration trend. Whether the changing behavior of evapotranspiration is representative of natural climate variability or reflects a more permanent reorganization of the land water cycle is a key question for earth system science.
Satellite assesment of land surface evapotranspiration for the pan-Arctic domain Mu, Q., L. A. Jones, J. S. Kimball, K. C. McDonald and S. W. Running Water Resources Research, Volume 45, Number W09420 - 2009 (doi: 10.1029/2008WR007189)
Regional evapotranspiration (ET), including water loss from plant transpiration and soil evaporation, is essential to understanding interactions between land-atmosphere surface energy and water balances. Vapor pressure deficit (VPD) and surface air temperature are key variables for stomatal conductance and ET estimation. Mu et al. (2007) developed an algorithm to estimate ET using the Penman-Monteith approach driven by MODIS (MODerate resolution Imaging Spectroradiometer) derived vegetation data and daily surface meteorological inputs including incoming solar radiation, air temperature and VPD. The model was applied using alternate daily meteorological inputs, including: 1) site level weather station observations, 2) VPD and air temperature derived from the Advanced Microwave Scanning Radiometer (AMSR-E) on the EOS Aqua satellite, and 3) Global Modeling and Assimilation Office (GMAO) reanalysis meteorology based surface air temperature, humidity and solar radiation data. Model performance was assessed across a North American latitudinal transect of six eddy covariance flux towers representing northern temperate grassland, boreal forest and tundra biomes. Model results derived from the three meteorology data sets agree well with observed tower fluxes (r>0.7; P<0.003; root mean square error of latent heat flux <30 W m-2) and capture spatial patterns and seasonal variability in ET. The MODIS-AMSR-E derived ET results also show similar accuracy to ET results derived from GMAO, while ET estimation error was generally more a function of algorithm parameterization than differences in meteorology drivers. Our results indicate significant potential for regional mapping and monitoring daily land surface ET using synergistic information from satellite optical-IR and microwave remote sensing.
The net carbon drawdown of small scale afforestation from satellite observations Montenegro, A., M. Eby, Q. Mu, M. Mulligan, A. J. Weaver, E. C. Wiebe, M. Zhao Global and Planetary Change 69 (2009), page 195-204 (doi:10.1016/j.gloplacha.2009.08.005)
Climate models indicate that warming due to increase in shortwave absorption from the lowering of albedo caused by afforestation reduces and can even overcome, particularly at high latitudes, the cooling caused by the carbon drawdown. Montenegro et al. used high resolution (0.05×0.05° to 1×1°) global satellite observations from an albedo and carbon density model, MOD16 ET model and cloud cover model to investigate the effects of afforestation. Results are markedly different from the coarser (~2.5×~2.5°) model-based studies. Between 40°S and 60°N afforestation always results in cooling. Many of the areas with the highest net carbon drawdown (drawdown after albedo effects) are at high latitudes. There is large zonal variability in drawdown and latitude is not a good indicator of afforestation efficiency. The overall efficiency of afforestation, defined as the net carbon drawdown divided by the total drawdown, is about 50%. By only considering the total drawdown and not considering albedo effects, the Kyoto Protocol carbon accounting rules grossly overestimate the cooling caused by afforestation drawdown.
Satellite based analysis of northern ET trends and associated changes in the regional water balance from 1983 to 2005 Zhang, K., J. S. Kimball, Q. Mu, L. A. Jones, S. Goetz and S. W. Running. Journal of Hydrology, Volume 379, page 92-110 - 2009 (doi: 10.1016/j.jhydrol.2009.09.047)
We developed an evapotranspiration (ET) algorithm driven by satellite remote sensing inputs, including AVHRR GIMMS NDVI, MODIS land cover and NASA/GEWEX solar radiation and albedo, and regionally corrected NCEP/NCAR Reanalysis daily surface meteorology. The algorithm was used to assess spatial patterns and temporal trends in ET over the pan-Arctic basin and Alaska from 1983 to 2005. We then analyzed associated changes in the regional water balance defined as precipitation (P) minus ET, where monthly P was defined from Global Precipitation Climatology Project (GPCP) and Global Precipitation Climatology Center (GPCC) sources. Monthly ET results derived from both in situ meteorological measurements and coarse resolution model reanalysis inputs agreed well (RMSE = 5.1–6.3 mm month-1; R2 = 0.91–0.92) with measurements from eight independent flux towers representing regionally dominant land cover types. ET showed generally positive trends over most of the pan-Arctic domain, though negative ET trends occurred over 32% of the region, primarily in boreal forests of southern and central Canada. Generally positive trends in ET, P and available long-term river discharge measurements imply that the pan-Arctic terrestrial water cycle is intensifying despite uncertainty in regional P and associated water balance estimates. Increasing water deficits in eastern Alaska, Canadian Yukon and western Prairie Provinces, and Northern Mongolia agree with regional drought records and recent satellite observations of vegetation browning and productivity decreases. Our results indicate that the pan-Arctic water balance is responding to a warming climate in complex ways with direct links to terrestrial carbon and energy cycles.
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 (FC); 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.
Our goals are to operationally detect all major terrestrial ecosystem disturbances by using satellite remote sensing. These disturbances can be induced by different causes, such as wildfires, hurricanes, insect outbreaks, heatwaves, wind, ice storms, and deforestation. Quantification of the time, extent and severity of disturbances and the following recovery is critical to expedite our understanding of how climate change and human activity affect the dynamics of ecosystems. It also provides vital information for the global carbon, water and energy studies, and assessments of ecosystem structure and biodiversity.
Following the occurrence of a major disturbance, there will be a reduction in Enhanced Vegetation Index (EVI) because of vegetation damage; in contrast, Land Surface Temperature (LST) will increase because more absorbed solar radiation will be converted into sensible heat as a result of the reduction in evapotranspiration from less vegetation density. MGDI takes advantage of the contrast changes in EVI and LST following disturbance to enhance the signal to effectively detect the location and intensity of disturbances.
A global comparison between station air temperatures and MODIS land surface temperatures reveals the cooling role of forests Mildrexler, D. J., M. Zhao and S. W. Running Journal of Geophysical Research, 116, G03025, doi:10.1029/2010JG001486, 2011.
Most global temperature analyses are based on station air temperatures. This study presents a global analysis of the relationship between remotely sensed annual maximum LST (LSTmax) from the Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and the corresponding site‐based maximum air temperature (Tamax) for every World Meteorological Organization station on Earth. The relationship is analyzed for different land cover types. We observed a strong positive correlation between LSTmax and Tamax. As temperature increases, LSTmax increases faster than Tamax and captures additional information on the concentration of thermal energy at the Earth’s surface, and biophysical controls on surface temperature, such as surface roughness and transpirational cooling. For hot conditions and in nonforested cover types, LST is more closely coupled to the radiative and thermodynamic characteristics of the Earth than the air temperature (Tair). Barren areas, shrublands, grasslands, savannas, and croplands have LSTmax values between 10°C and 20°C hotter than the corresponding Tamax at higher temperatures. Forest cover types are the exception with a near 1:1 relationship between LSTmax and Tamax across the temperature range and 38°C as the approximate upper limit of LSTmax with the exception of subtropical deciduous forest types where LSTmax occurs after canopy senescence. The study shows a complex interaction between land cover and surface energy balances. This global, semiautomated annual analysis could provide a new, unique, monitoring metric for integrating land cover change and energy balance changes.
Satellite finds highest land skin temperature on Earth Milldrexler D.J., M. Zhao and S.W. Running Bulletin of the American Meteorological Society. 92: 855–860. 2011
Finding the hottest spot on Earth based on scattered site-based air temperature measurements is a limited approach due to the lack of spatial coverage of the existing site-based weather stations. The Aqua/MODIS remote sensor is used to evaluate the continuous patterns of thermal maxima across the Earth's surface and identify the highest radiometric surface temperature annually for 7 years (2003 to 2009). The critical difference between radiometric surface temperature provided by the satellite and air temperature from WMO stations is described and contrasted with practical examples. A new histogram of the global maximum LSTs is presented that merges into a single metric important biophysical and biogeographical factors of the Earth system that are usually measured individually. These contributing factors can include 1) intensification of extreme maximum surface temperatures; 2) changes in land cover; 3) changes in albedo; 4) surface–atmosphere energy fluxes; 5) changes in ecosystem disturbance regimes; 6) air temperature; and 7) atmospheric aerosol concentrations. The annual maximum LST histogram could become a new type of integrative global change metric.
Testing a MODIS Global Disturbance Index across North America Mildrexler, D. J., M. Zhao and S. W. Running Remote Sensing of Environment. 113: 2103-2117. 2009
Large-scale ecosystem disturbances (LSEDs) have major impacts on the global carbon cycle as large pulses of CO2 and other trace gases from terrestrial biomass loss are emitted to the atmosphere during disturbance events. The high temporal and spatial variability of the atmospheric emissions combined with the lack of a proven methodology to monitor LSEDs at the global scale make the timing, location and extent of vegetation disturbance a significant uncertainty in understanding the global carbon cycle. The MODIS Global Disturbance Index (MGDI) algorithm is designed for large-scale, regular, disturbance mapping using Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and Aqua/MODIS Enhanced Vegetation Index (EVI) data. The MGDI uses annual maximum composite LST data to detect fundamental changes in land-surface energy partitioning, while avoiding the high natural variability associated with tracking LST at daily, weekly, or seasonal time frames. Here we apply the full Aqua/MODIS dataset through 2006 to the improved MGDI algorithm across the woody ecosystems of North America and test the algorithm by comparison with confirmed, historical wildfire events and the windfall areas of documented major hurricanes. The MGDI accurately detects the location and extent of wildfire throughout North America and detects high and moderate severity impacts in the windfall area of major hurricanes. We also find detections associated with clear-cut logging and land-clearing on the forest–agricultural interface. The MGDI indicates that 1.5% (195,580 km2) of the woody ecosystems within North America was disturbed in 2005 and 0.5% (67,451 km2) was disturbed in 2006. The interannual variability is supported by wildfire detections and official burned area statistics.
Ecosystem Disturbance, Carbon, and Climate Running, S. W. Science. 321: 652-653. 2008
Models of climate change effects should incorporate land-use changes and episodic disturbances such as fires and insect epidemics.
A New Satellite Based Methodology for Continental Scale Disturbance Detection Mildrexler, D. J., M. Zhao, F. A. Heinsch and S. W. Running Ecological Applications, 17: 235-250. 2007
The timing, location, and magnitude of major disturbance events are currently major uncertainties in the global carbon cycle. Accurate information on the location, spatial extent, and duration of disturbance at the continental scale is needed to evaluate the ecosystem impacts of land cover changes due to wildfire, insect epidemics, flooding, climate change, and human-triggered land use. This paper describes an algorithm developed to serve as an automated, economical, systematic disturbance detection index for global application using Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua Land Surface Temperature (LST) and Terra/MODIS Enhanced Vegetation Index (EVI) data from 2003 to 2004. The algorithm is based on the consistent radiometric relationship between LST and EVI computed on a pixel-by-pixel basis. We used annual maximum composite LST data to detect fundamental changes in land–surface energy partitioning, while avoiding the high natural variability associated with tracking LST at daily, weekly, or seasonal time frames. Verification of potential disturbance events from our algorithm was carried out by demonstration of close association with independently confirmed, well-documented historical wildfire events throughout the study domain. We also examined the response of the disturbance index to irrigation by comparing a heavily irrigated poplar tree farm to the adjacent semiarid vegetation. Anomalous disturbance results were further examined by association with precipitation variability across areas of the study domain known for large interannual vegetation variability. The results illustrate that our algorithm is capable of detecting the location and spatial extent of wildfire with precision, is sensitive to the incremental process of recovery of disturbed landscapes, and shows strong sensitivity to irrigation. Disturbance detection in areas with high interannual variability of precipitation will benefit from a multiyear data set to better separate natural variability from true disturbance.
As part of the MODIS global terrestrial evapotranspiration project, the goal of the Drought Severity Index (DSI) is to use satellite remote sensing data to monitor and detect drought on Earth’s land surface. The DSI enhances near-real-time drought monitoring capabilities that can assist decision makers in regional drought assessment and mitigation efforts, but without many of the constraints of more traditional drought monitoring methods.
The remotely-sensed DSI uses operational global land products derived from NASA satellite observations, namely the MODIS terrestrial evapotranspiration (ET) and vegetation greenness products. ET is a metric of ecosystem functional status and is directly related to the water, carbon and energy cycles of the land surface. The ratio of ET to potential ET (PET) is commonly used as an indicator of terrestrial water availability and associated wetness or drought. Satellite vegetation greenness indices (VIs), especially the Normalized Difference VI (NDVI), can potentially link climate changes (e.g. increasing frequency and severity of drought) and vegetation responses with land-atmosphere water, carbon and energy fluxes, and associated climate feedbacks (Atkinson et al., 2011). The DSI incorporates NDVI and the ratio of ET to PET into a single index theoretically ranging from unlimited negative values (drier than normal) to unlimited positive values (wetter than normal). More detailed information on the DSI can be found in Mu et al. (2013).
The DSI dataset is provided on our FTP site for Earth’s entire 109.03 million km2 global vegetated land surface from 2000 - 2011 at 8-day and annual time intervals, and 0.5 and 0.05 degree spatial resolution. Future years will be produced and posted periodically, but not in real time.
Annual global terrestrial Drought Severity Index (DSI) data over the 2000-2011 MODIS record. The DSI ranges theoretically from unlimited negative values to unlimited positive values for respective dry to wet climate deviations from prevailing conditions.
Annual global Palmer Drought Severity Index (PDSI) data over the 2000-2011 MODIS record. The PDSI ranges theoretically from unlimited negative values to unlimited positive values for respective dry to wet climate deviations from prevailing conditions.
Spatial correlation coefficient between 12-year annual global DSI and precipitation data (Chen et al., 2002) from 2000-2011.
Spatial patterns of annual DSI (a, b, e, f) and growing-season PDSI (c, d, g, h) for selected sub regions, including the Amazon (the region as in Lewis et al., 2011) in 2005 (a, c) and 2010 (b, d), western Europe (40°N ~ 66.5°N and -5°E ~ 15°E) in 2003 (e, g), and western Russia (40°N ~ 66.5°N and 30°E ~ 55°E) in 2010 (f, h).
Annual DSI, growing-season PDSI and MODIS (MOD17) NPP data for selected sub regions, including the Amazon (the region as in Lewis et al., 2011), western Europe (40°N ~ 66.5°N and -5°E ~ 15°E) and western Russia (40°N ~ 66.5°N and 30°E ~ 55°E) regions, and 2000-2011 period. Vertical grey bars denote years with documented droughts within each region.
Development of a global evapotranspiration algorithm based on MODIS and global meteorology data Mu, Q., F.A. Heinsch, M. Zhao, S.W. Running (2007) Remote Sensing of Environment, 111, 519-536 (doi: 10.1016/j.rse.2007.04.015)
Improvements to a MODIS Global Terrestrial Evapotranspiration Algorithm Mu, Q., M. Zhao, S. W. Running (2011) Remote Sensing of Environment, Volume 115, pages 1781-1800 (doi:10.1016/j.rse.2011.02.019)
Drought-induced reduction in global terrestrial net primary production from 2000 through 2009 Zhao, M., S. W. Running (2010) Science, 329: 940-943.
Improvements of the MODIS terrestrial gross and net primary production global data set Zhao, M., F. A. Heinsch, R. R. Nemani, and S. W. Running. (2005) Remote Sensing of Environment, 95: 164.176.
Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial primary production to the accuracy of meteorological reanalyses Zhao, M., S. W. Running, and R. R. Nemani. (2006) Journal of Geophysical Research, 111, G01002, doi:10.1029/2004JG000004.
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