MODIS Global Terrestrial Evapotranspiration (ET) Product (NASA MOD16A2/A3) Collection 5. NASA Headquarters

In the original EOS proposal competition in 1989, Dr. Steve Running proposed and was selected as MODIS Science team member responsible for Leaf area index, evapotranspiration and photosynthesis/net primary production, then designated as MOD 15, 16 and 17. At the ATBD review for at-launch products in 1995, NASA decided to give MOD 15 LA I/FPAR to Dr. Ranga Myneni to provide a more theoretically based algorithm, and Dr. Running was directed to focus on MOD 17 PSN/NPP for the Terra atlaunch data product. MOD 16 ET was not dropped, but was deprioritized. At the EOS recompete in 2003 NASA selected another investigator to build a MOD 16 ET product but this investigation was not renewed in 2007. In the interim Dr. Running and the NTSG group had changed from an energy balance - surface resistance concept to a Penman-Monteith concept, and had greater success building a globally applicable algorithm. Since much of the processing paralleled our MOD 17 product, NTSG tested, then generated initial global ET datasets. In the 2010 renewal competition for the MODIS Science Team, Dr. Running reproposed MOD 16, based on the new algorithm and global ET datasets now developed, and published in refereed journals. Now, with selection of our 2010 renewal proposal complete, we offer the ATBD. This document represents our formal ATBD for establishing this algorithm and dataset as the official MOD 16 Evapotranspiration product

Dynamics of MODIS evapotranspiration in South Africa

This paper describes the dynamics of evapotranspiration (ET) in South Africa using MOD16 ET satellite-derived data, and analyses the inter-dependency of variables used in the ET algorithm of Mu et al. (2011). Annual evapotranspiration is strongly dependent on rainfall and potential evapotranspiration (PET) in 4 climatically different regions of South Africa. Average ET in South Africa (2000–2012) was estimated to be 303 mm·a-1 or 481.4 x 109 m3·a1 (14% of PET and 67% of rainfall), mainly in the form of plant transpiration (T, 53%) and soil evaporation (Soil E, 39%). Evapotranspiration (ET) showed a slight tendency to decrease over the period 2000–2012 in all climatic regions, except in the south of the country (winter rainfall areas), although annual variations in ET resulted in the 13-year trends not being statistically significant. Evapotranspiration (ET) was spatially dependent on PET, T and vapour pressure deficit (VPD), in particular in winter rainfall and arid to semi-arid climatic regions. Assuming an average rainfall of 450 mm·a-1, and considering current best estimates of runoff (9% of rainfall), groundwater recharge (5%) and water withdrawal (2%), MOD16 ET estimates were about 15% short of the water balance closure in South Africa. The ET algorithm can be refined and tested for applications in restricted areas that are spatially heterogeneous and by accounting for soil water supply limiting conditions

Contribution of increasing CO2 and climate change to the carbon cycle in China\u27s ecosystems

Atmospheric CO2 and China\u27s climate have changed greatly during 1961–2000. The influence of increased CO2 and changing climate on the carbon cycle of the terrestrial ecosystems in China is still unclear. In this article we used a process-based ecosystem model, Biome-BGC, to assess the effects of changing climate and elevated atmospheric CO2 on terrestrial China\u27s carbon cycle during two time periods: (1) the present (1961–2000) and (2) a future with projected climate change under doubled CO2 (2071–2110). The effects of climate change alone were estimated by driving Biome-BGC with a fixed CO2 concentration and changing climate, while the CO2 fertilization effects were calculated as the difference between the results driven by both increasing CO2 and changing climate and those of variable climate alone. Model simulations indicate that during 1961–2000 at the national scale, changes in climate reduced carbon storage in China\u27s ecosystems, but increasing CO2 compensated for these adverse effects of climate change, resulting in an overall increase in the carbon storage of China\u27s ecosystems despite decreases in soil carbon. The interannual variability of the carbon cycle was associated with climate variations. Regional differences in climate change produced differing regional carbon uptake responses. Spatially, reductions in carbon in vegetation and soils and increases in litter carbon were primarily caused by climate change in most parts of east China, while carbon in vegetation, soils, and litter increased for much of west China. Under the future scenario (2071–2110), with a doubling CO2, China will experience higher precipitation and temperature as predicted by the Hadley Centre HadCM3 for the Intergovernmental Panel on Climate Change Fourth Assessment. The concomitant doubling of CO2 will continue to counteract the negative effects of climate change on carbon uptake in the future, leading to an increase in carbon storage relative to current levels. This study highlights the role of CO2 fertilization in the carbon budget of China\u27s ecosystems, although future studies should include other important processes such as land use change, human management (e.g., fertilization and irrigation), environmental pollution, etc

Local cooling and warming effects of forests based on satellite observations

The biophysical effects of forests on climate have been extensively studied with climate models. However, models cannot accurately reproduce local climate effects due to their coarse spatial resolution and uncertainties, and field observations are valuable but often insufficient due to their limited coverage. Here we present new evidence acquired from global satellite data to analyse the biophysical effects of forests on local climate. Results show that tropical forests have a strong cooling effect throughout the year; temperate forests show moderate cooling in summer and moderate warming in winter with net cooling annually; and boreal forests have strong warming in winter and moderate cooling in summer with net warming annually. The spatiotemporal cooling or warming effects are mainly driven by the two competing biophysical effects, evapotranspiration and albedo, which in turn are strongly influenced by rainfall and snow. Implications of our satellite-based study could be useful for informing local forestry policies

Evaluating water stress controls on primary production in biogeochemical and remote sensing based models

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 United States 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 intraannual 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 interannual and intraannual variability of both the Biome-BGC-calculated water stress and GPP better in the conterminous United States than in the strongly monsoon-controlled China

Satellite assessment of land surface evapotranspiration for the pan-Arctic domain

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. We developed an algorithm to estimate ET using the Penman-Monteith approach driven by Moderate Resolution Imaging Spectroradiometer (MODIS)-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 \u3e 0.7; P \u3c 0.003; root mean square error of latent heat flux \u3c30 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

Results from the Deep-Convective Clouds (DCC) Based Response Versus Scan-Angle (RVS) Characterization for the MODIS Reflective Solar Bands

The Terra and Aqua MODIS scan mirror reflectance is a function of the angle of incidence (AOI) and was characterized prior to launch by the instrument vendor. The relative change of the prelaunch response versus scan-angle (RVS) is tracked and linearly scaled on-orbit using observations at two AOIs of 11.2deg and 50.2deg corresponding to the moon view and solar diffuser, respectively. As the missions continue to operate well beyond their design life of 6 years, the assumption of linear scaling between the two AOIs is known to be inadequate in accurately characterizing the RVS, particularly at short wavelengths. Consequently, an enhanced approach of supplementing the on-board measurements with response trends from desert pseudo-invariant calibration sites (PICS) was formulated in MODIS Collection 6 (C6). An underlying assumption for the continued effectiveness of this approach is the long-term (multi-year) and short-term (month-to-month) stability of the PICS. Previous work has shown that the deep convective clouds (DCC) can also be used to monitor the on-orbit RVS performance with less trend uncertainties than desert sites. In this paper, the raw sensor response to the DCC is used to characterize the on-orbit RVS on a band and mirror side basis. These DCC-based RVS results are compared with the C6 PICS-based RVS, showing an agreement within 2% observed in most cases. The pros and cons of using a DCC-based RVS approach are also discussed in this paper. Although this reaffirms the efficacy of the C6 PICS-based RVS, the DCC-based RVS approach presents itself as an effective alternative for future considerations. Potential applications of this approach to other instruments such as SNPP and JPSS VIIRS are also discussed

Upscaling key ecosystem functions across the conterminous United States by a water-centric ecosystem model

We developed a water-centric monthly scale simulation model (WaSSI-C) by integrating empirical water and carbon flux measurements from the FLUXNET network and an existing water supply and demand accounting model (WaSSI). The WaSSI-C model was evaluated with basin-scale evapotranspiration (ET), gross ecosystem productivity (GEP), and net ecosystem exchange (NEE) estimates by multiple independent methods across 2103 eight-digit Hydrologic Unit Code watersheds in the conterminous United States from 2001 to 2006. Our results indicate that WaSSI-C captured the spatial and temporal variability and the effects of large droughts on key ecosystem fluxes. Our modeled mean (±standard deviation in space) ET (556 ± 228 mm yr−1) compared well to Moderate Resolution Imaging Spectroradiometer (MODIS) based (527 ± 251 mm yr−1) and watershed water balance based ET (571 ± 242 mm yr−1). Our mean annual GEP estimates (1362 ± 688 g C m−2 yr−1) compared well (R2 = 0.83) to estimates (1194 ± 649 g C m−2 yr−1) by eddy flux-based EC-MOD model, but both methods led significantly higher (25–30%) values than the standard MODIS product (904 ± 467 g C m−2 yr−1). Among the 18 water resource regions, the southeast ranked the highest in terms of its water yield and carbon sequestration capacity. When all ecosystems were considered, the mean NEE (−353 ± 298 g C m−2 yr−1) predicted by this study was 60% higher than EC-MOD\u27s estimate (−220 ± 225 g C m−2 yr−1) in absolute magnitude, suggesting overall high uncertainty in quantifying NEE at a large scale. Our water-centric model offers a new tool for examining the trade-offs between regional water and carbon resources under a changing environment

Comparing Evapotranspiration from Eddy Covariance Measurements, Water Budgets, Remote Sensing, and Land Surface Models over Canada

This study compares six evapotranspiration ET products for Canada’s landmass, namely, eddy covariance EC measurements; surface water budget ET; remote sensing ET from MODIS; and land surface model (LSM) ET from the Community Land Model (CLM), the Ecological Assimilation of Land and Climate Observations (EALCO) model, and the Variable Infiltration Capacity model (VIC). The ET climatology over the Canadian landmass is characterized and the advantages and limitations of the datasets are discussed. The EC measurements have limited spatial coverage, making it difficult for model validations at the national scale. Water budget ET has the largest uncertainty because of data quality issues with precipitation in mountainous regions and in the north. MODIS ET shows relatively large uncertainty in cold seasons and sparsely vegetated regions. The LSM products cover the entire landmass and exhibit small differences in ET among them. Annual ET from the LSMs ranges from small negative values to over 600 mm across the landmass, with a countrywide average of 256 ± 15 mm. Seasonally, the countrywide average monthly ET varies from a low of about 3 mm in four winter months (November–February) to 67 ± 7 mm in July. The ET uncertainty is scale dependent. Larger regions tend to have smaller uncertainties because of the offset of positive and negative biases within the region. More observation networks and better quality controls are critical to improving ET estimates. Future techniques should also consider a hybrid approach that integrates strengths of the various ET products to help reduce uncertainties in ET estimation