Flathead Lake, located in northwest Montana, is one of the 300 largest natural freshwater lakes in the world, covering an area of 480 km2 with a maximum depth of 113 m. The Lake is oligotrophic, yet experienced an increase in eutrophication from 1977 to 2001, and two lakewide blooms of macroalgae in 1984 and 1994 that represented anomalous declines in water quality likely due to increasing nutrient inputs from anthropogenic sources. Summer field surveys in 2004 and 2005 showed surface chlorophyll-a levels from 0.1 to 0.9 mg m-3, Secchi depths of 1.5 to 17.0 m, and surface temperatures from 8.3 to 22.6 C. Depth profiles from surface to lake bottom were also obtained using a flourometer and transmissometer. We examined the potential utility of MODIS medium resolution (250m and 500m) data (bands 1-4) and 1km ocean bands (8-14) to monitor spatial and temporal fluctuations in lake productivity indicators including chlorophyll content and turbidity. Several alternative approaches for retrieving water quality parameters from the MODIS data were evaluated, including atmospherically corrected reflectance products, and single scattering corrected radiance data. The zone of peak chlorophyll content and turbidity is found to occur immediately above the thermocline at water depths from 15-20m, but with statistically significant linkages to surface conditions. Initial results indicate that the single scattering corrected radiance data provide the best prediction of chlorophyll-a, Secchi depth, and turbidity of the first 5m depth (r2 = 0.46 - 0.75), but these parameters often co-vary at specific times throughout the season, creating difficulties in applying a consistent algorithm. Two complete daily time series from May 1 to Sept 30, 2004 were created from the 500m reflectance product and the single scattering corrected data to assess the sensor’s ability to track lake fluctuations in water quality indicators. Mean daily lake reflectance values from these time series are found to be sensitive to both atmospheric particulate deposition and river discharge inputs at weekly to monthly time scales. Our results show the potential of MODIS for water quality monitoring, but also highlight the need for improved algorithms and products specific to large inland water bodies.
Estimated Secchi Depth for Flathead Lake derived from MODIS 500m resolution multispectral image data for July 28, 2004; results are compared against field sampling data collected on July 31, 2004.
Secchi depth vs MODIS 500m reflectance ratio data over Flathead Lake.
Jones, M.O., 2006. Application of MODIS for monitoring water quality of a large oligotrophic lake. M.S. Thesis, University of Montana, 363.61, J782a, 62 pp.
Jones, M.O., J. Kimball, S.W. Running, B.K. Ellis, and A.E. Klene, 2005. Application of MODIS for monitoring water quality of a large oligotrophic lake. Eos Trans. AGU, 85(52), B41A-0160
The Western Arctic Linkage Experiment (WALE) was initiated to investigate the role of northern terrestrial ecosystems in the larger Arctic system response to global change through model and satellite remote sensing analyses of regional carbon, water and energy cycles (McGuire et al. overview paper below). The NTSG portion of this investigation focused on assessing annual variability and regional trends in vegetation productivity for the WALE domain of Alaska and NW Canada, and the primary mechanisms driving observed changes over the 19-year (1982 - 2000) study period. To accomplish these objectives, we applied a biome-specific production efficiency model (PEM) driven by daily surface meteorology and satellite remote sensing observations of photosynthetic leaf area. We also conducted prognostic regional simulations of terrestrial carbon budgets for the same period using two ecosystem process models, BIOME-BGC and TEM; these model simulations were used for independent assessment of satellite remote sensing derived results and to elucidate underlying mechanisms driving changes in vegetation productivity and the terrestrial carbon cycle. We find evidence of a small, but widespread, positive trend in vegetation gross and net primary production (GPP and NPP) for the region from 1982 to 2000 coinciding with summer warming of more than 1.8 °C and subsequent relaxation of cold temperature constraints to plant growth. Prognostic model simulation results were generally consistent with the remote sensing record and also indicated that an increase in soil decomposition and plant-available nitrogen with regional warming was partially responsible for the positive productivity response. Despite a positive trend in litter inputs to the soil organic carbon pool, the model results showed evidence of a decline in less labile soil organic carbon, which represents approximately 75% of total carbon storage for the region. These results indicate that the regional carbon cycle may accelerate under a warming climate by increasing the fraction of total carbon storage in vegetation biomass and more rapid turnover of the terrestrial carbon reservoir.
A second objective of our research was to clarify the role of spring thaw timing in determining annual vegetation productivity, and whether a recent advance in the northern seasonal thaw cycle is sufficient to account for the sign and magnitude of the estimated positive vegetation productivity trend for the western Arctic. To accomplish this objective we conducted a temporal change classification of daily terrestrial microwave emissions from the SSM/I time series to determine the spatial pattern and annual variability of the primary springtime thaw event for Alaska and Northwest Canada from 1988 to 2000. We compared these results with PEM and BIOME-BGC outputs across the domain to assess relations between thaw timing and spatial patterns and annual variability in vegetation structure and productivity for the region. The SSM/I derived timing of the primary springtime thaw event was well correlated with annual anomalies in maximum LAI in spring and summer (P ≤ 0.009; n = 13), and GPP and NPP (P ≤ 0.0002) for the region. Mean annual variability in springtime thaw was on the order of ±7 days, with corresponding impacts to annual productivity of approximately 1% per day. Years with relatively early seasonal thawing showed generally greater LAI and annual productivity, while years with delayed seasonal thawing showed corresponding reductions in canopy cover and productivity. The apparent sensitivity of LAI and vegetation productivity to springtime thaw indicates that a recent advance in the seasonal thaw cycle and associated lengthening of the potential period of photosynthesis in spring is sufficient to account for the sign and magnitude of an estimated positive vegetation productivity trend for the western Arctic from 1982-2000.
Euskirchen, E.S., A.D. McGuire, D.W. Kicklighter, Q. Zhuang, J.S. Clein, R.J. Dargaville, D.G. Dye, J.S. Kimball, K.C. McDonald, J.M. Melillo, V.E. Romanovsky, and N.V. Smith, 2006. Importance of recent shifts in soil thermal dynamics on growing season length, productivity, and carbon sequestration in terrestrial high-latitude ecosystems. Global Change Biology 12, 731-750.
Kimball, J.S., K.C. McDonald, and M. Zhao, 2006. Spring thaw and its effect on terrestrial vegetation productivity in the western Arctic observed from satellite microwave and optical remote sensing. Earth Interactions 10(21), 1-22.
Kimball, J.S., M. Zhao, A.D. McGuire, F.A. Heinsch, J. Clein, M. Calef, W.M. Jolly, S. Kang, S.E. Euskirchen, K.C. McDonald, and S.W. Running, 2006. Recent climate driven increases in vegetation productivity for the Western Arctic: Evidence of an acceleration of the northern terrestrial carbon cycle. Earth Interactions 11, 4, 1-23.
Kimball, J.S., M. Zhao, K.C. McDonald, and S.W. Running, 2006. Satellite remote sensing of terrestrial net primary production for the pan-Arctic basin and Alaska. Mitigation and Adaptation Strategies for Global Change DOI: 10.1007/s11027-005-9014-5.
McGuire, A.D., J.E. Walsh, and WALE project participants, 2007. The Western Arctic Linkage Experiment (WALE): Overview and Synthesis. Earth Interactions 10(21).
Sitch, S., A.D. McGuire, J.S. Kimball, N. Gedney, J. Gamon, R. Engstrom, A. Wolf, Q. Zhuang, and J. Clein, and K.C. McDonald, 2007. Assessing the carbon balance of circumpolar arctic tundra using remote sensing and process modeling. Ecological applications 17(1), 213-234.
This project is part of a larger NSF initiative called the Study of the Northern Alaska Coastal System (SNACS). Most regional observation networks indicate that dramatic changes have occurred across the Arctic in recent decades, but comparatively little work has been done to assess atmospheric and oceanic responses to the dramatic observed terrestrial changes. Both increases in surface air temperature and a shift in arctic air circulation patterns are likely to contribute to changes in ice distribution. Rising sea level, changes in coastal geography due to shoreline erosion, increased winds, storm surges, and flooding may be the direct results of the depletion of sea ice and the resulting increase in fetch. As the tightly linked land, ocean, and atmosphere systems of the Arctic respond to the effects of climate change, the challenges of modeling the arctic region need to addressed using high spatial resolution data, which current global climate models do not use due to computer resource limitations.
This project emphasizes linking the major arctic and human systems to understand current and likely future interactions through three scientific goals: 1) to estimate the historic and future impacts of variability within the ocean and atmospheric systems on terrestrial fluxes of gaseous (including CO2 and water vapor) and non-gaseous (particulate and dissolved organic matter, nutrients, and water) materials and energy between the land and the atmosphere and sea; 2) to evaluate the impacts of variation in radiation, climate, ocean circulation, ocean temperature, and sea ice position and extent on terrestrial processes, including those that have feedback on atmospheric and ocean processes; and 3) to provide high-resolution products (atmospheric, ice, ocean, and terrestrial) and related datasets, relevant to the patterns and controls of terrestrial and oceanic processes, for use in future analyses. More information is available at the SNACS project website.
Zhang, K., J.S. Kimball, E.H. Hogg, M. Zhao, W.C. Oechel, J. Cassano, and S.W. Running, 2007. Satellite remote sensing detection of a recent decline in northern high latitude terrestrial vegetation productivity with regional warming and drying. Global Change Biology (In-review).
This project is examining how biological and physical processes interact to control carbon uptake, storage and release in Arctic tundra ecosystems and how the self-organizing nature of these interactions varies across multiple spatial and temporal scales. Approximately 25% of the world’s soil organic carbon reservoir is stored at high northern latitudes in permafrost and seasonally-thawed soils in the Arctic, a region that is currently undergoing unprecedented warming and drying, as well as dramatic changes in human land use. Understanding how changes in annual and inter-annual ecosystem productivity interact and potentially offset the balance and stability of the Arctic soil carbon reservoir is of utmost importance to global climate change science. If there is a net loss of soil carbon to the atmosphere in the form of greenhouse gases (namely CO2 and CH4), greenhouse warming could be enhanced. This non-linear, potentially positive feedback response could very quickly cause Arctic terrestrial ecosystems to function in an unprecedented manner and with globally significant implications.
Our research benefits from a foundation and wealth of international and national carbon cycle research undertaken in northern Alaska and other Arctic regions over the past three decades. We have initiated a comprehensive study involving an integrated framework of multi-scale aircraft and satellite remote sensing, micrometeorological and CO2 and CH4 flux measurements and hydro-ecological process model simulations over a 350km North-South transect spanning the dominant Arctic topographic and land cover units of northern Alaska. The study region encompasses many long-term measurement sites that have been in place for 5 to 10 years. We are also conducting an extensive soil moisture manipulation involving a 60 hectare tundra flooding/draining experiment near Barrow Alaska on the Arctic Coastal Plain. The objective of this study is to quantify linkages between soil moisture and carbon uptake, storage and release over multiple spatial (microbial to landscape) and temporal (minutes to decades) scales. Only by increasing the spatial extent of our experimental manipulations and the duration of our observational time series can we better understand and predict the effect of scale on the complex coupling within Arctic ecosystems; namely, how small scale processes participate as components of higher scale phenomenon and how higher scale phenomenon constrain the former lower scale processes. This knowledge will improve our understanding of the current behavior and potential response of arctic tundra to global change, resulting in better predictions of feedbacks to climate and the global carbon cycle.
AIRSAR C-, L-, P-band radar backscatter image over the Barrow Alaska area Ecological Observatory (BEO) showing a complex spatial mosaic of shallow lakes and tundra wetlands. We are conducting a large scale water table manipulation experiment and investigating the use of airborne and microwave remote sensing techniques for mapping and scaling soil moisture and temperature controls on soil CO2 exchange. These techniques will lead to improved satellite based mapping of terrestrial carbon and water cycles for boreal and arctic biomes and a better understanding of the stability of terrestrial soil carbon stocks and potential feedbacks to global warming.
Jones, L.A., J.S. Kimball, K.C. McDonald, S.K. Chan, E.G. Njoku, and W.C. Oechel, 2006. Satellite microwave remote sensing of boreal and Arctic soil temperatures from AMSR-E. IEEE Transactions in Geoscience and Remote Sensing (In press).
Jones, L., J. Kimball, K. McDonald, E. Njoku, and W. Oechel, 2006. MODIS and AMSR-E synergistic modeling of Arctic and boreal terrestrial carbon dynamics. NASA Global Vegetation Workshop 2006: Long-term monitoring of vegetation variables using moderate resolution satellites. August 8-10, 2006, Missoula MT.
Zhang, K., J.S. Kimball, and M. Zhao, 2006. Sensitivity of pan-Arctic terrestrial net primary productivity simulations to daily surface meteorology from NCEP/NCAR and ERA-40 reanalyses. JGR Biogeosciences 112, G01011, 1-14, doi:10.1029/2006JG000249.
Zhang, K., J.S. Kimball, M. Zhao, W.C. Oechel, and S.W. Running, 2006. Analysis of pan-Arctic terrestrial primary productivity from 1982-2005 by combining AVHRR and MODIS products. NASA Global Vegetation Workshop 2006: Long-term global monitoring of vegetation variables using moderate resolution satellites. August 8-10, 2006, Missoula MT.
The geography and dynamics of water across this pan-Arctic region are important elements of the larger Earth System especially given growing evidence of the vulnerability of the Arctic climate and terrestrial biosphere to global change. The scope of this multidisciplinary project is develop online, near-real time capabilities for rapid assessment and monitoring pan- Arctic water budgets and river discharge to the Arctic Ocean. Major goals of the project are: 1) to develop, implement and maintain Arctic-RIMS (Rapid Integrated Monitoring System) for acquiring near-real time data and producing "quick-look" outputs that characterize terrestrial water and carbon budgets across the pan-Arctic drainage region; 2) To create hydrologically-based re-analysis products using Arctic-RIMS and to analyze these time series in our continuing work on spatial and temporal variability of the pan-Arctic land mass. Arctic-RIMS integrates a variety of surface station network, remote sensing, and modeling data sets and tools developed by the co-Investigators to produce time-varying, region-wide land surface water budgets including river inputs to the Arctic Ocean.
Map (upper left) of the SSM/I derived spring thaw trend (1988-2001) for the pan-Arctic basin and Alaska, excluding non-vegetated areas (in grey). The timing of spring thaw for the pan-Arctic is occurring 8 days (P<0.03) earlier, on average, over the 14 yr record, with respective 5 and 13 day advances for Eurasia and North America. The SSM/I thaw signal coincides with the seasonal relaxation of low temperature constraints to boreal-arctic NPP and the spring drawdown of atmospheric CO2 as reported by NOAA CMDL northern (>50°N) monitoring stations (above right); negative anomalies denote both earlier thaws and CO2 drawdown while positive values denote the opposite response. The advancing spring thaw trend may be a physical mechanism driving positive productivity trends and an advancing CO2 cycle for northern latitudes.
Euskirchen, E.S., A.D. McGuire, D.W. Kicklighter, Q. Zhuang, J.S. Clein, R.J. Dargaville, D.G. Dye, J.S. Kimball, K.C. McDonald, J.M. Melillo, V.E. Romanovsky, and N.V. Smith, 2005. Importance of recent shifts in soil thermal dynamics on growing season length, productivity, and carbon sequestration in terrestrial high-latitude ecosystems. Global Change Biology 12, 731-750.
Kimball, J.S., M. Zhao, K.C. McDonald, and S.W. Running, 2006. Satellite remote sensing of terrestrial net primary production for the pan-Arctic basin and Alaska. Mitigation and Adaptation Strategies for Global Change DOI: 10.1007/s11027-005-9014-5.
McDonald, K.C., J.S. Kimball, E. Njoku, R. Zimmermann, and M. Zhao, 2004. Variability in springtime thaw in the terrestrial high latitudes: Monitoring a major control on the biospheric asimilation of atmospheric CO2 with spaceborne microwave remote sensing. Earth Interactions 8(20), 1-23.
Rawlins, M.A., K.C. McDonald, S. Frolking, R.B. Lammers, M. Fahnestock, J.S. Kimball, and C.J. Vorosmarty, 2005. Remote sensing of snow at the pan-Arctic scale using the SeaWinds scatterometer. Journal of Hydrology, 312, 294-311.
Satellite remote sensing land atmosphere water and energy exchange.
The lack of available water constrains hydrologic and ecological processes for two-thirds of the Earth’s land surface. We are working with colleagues at the NASA Jet Propulsion Laboratory to develop new satellite microwave remote sensing algorithms for detecting and monitoring land-atmosphere water and energy exchange over North America. These data will provide continuous weekly-annual observations of surface evaporation and vegetation conditions from 1988 onward that can be used for a variety of applications including monitoring agricultural, rangeland and forest health, improving regional weather forecasts and water resource monitoring. Regional and global data sets developed from this project include satellite based evapotranspiration (ET), terrestrial freeze-thaw status and net primary productivity (NPP).
Frolking, S., T. Milliman, K. McDonald, J. Kimball, M. Zhao, and M. Fahnestock, 2006. Evaluation of the SeaWinds scatterometer for regional monitoring of vegetation phenology. Journal of Geophysical Research 111, D17302, doi:10.1029/2005JD006588.
Frolking, S., M. Fahnestock, T. Milliman, K. McDonald, and J.S. Kimball, 2005. Interannual variability in North American grassland biomass/productivity detected by SeaWinds scatterometer backscatter. Geophysical Research Letters, 32(21), L21409, 10.1029/2005GL024230.
Mu, Q., M. Zhao, F.A. Heinsch, M. Liu, H. Tian and S.W. Running. Evaluating water stress controls on primary production in biogeochemical and remote sensing based models. Journal of Geophysical Research, 112, G01012, doi:10.1029/2006JG000179.
Running, S.W., and J.S. Kimball, 2005. Satellite-based analysis of ecological controls for land-surface evaporation resistance. Encyclopedia of Hydrological Sciences. Vol. 5., M.G. Anderson and J.J. McDonnell (Eds.), John Wiley & Sons Ltd.
For decades scientists have sought to develop regionally applicable estimators of crop yield using models formulated from remote sensing data. With a few exceptions, most broad scale models, based on remote sensing, have used the Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) to derive retrospective, empirical relationships between NDVI and yield. However, while retrospective analyses provide insight into past performance, they do little to satisfy the need for near real time yield information. Application of these empirical NDVI models is limited to the regions and time frames for which the regression equations were formulated. This means that regression models must be carefully re-evaluated each season, limiting their practical utility. Unlike crops whose yield consists of above ground production, wheat yield is contained in storage organs, and is very sensitive to adverse meteorological conditions at critical growth stages, dictating that grain yield must be modeled and not inferred. To this end our study was designed to 1) assess the potential of MODIS GPP for estimating wheat yield in Montana and North Dakota and 2) define the practical limits within which wheat yield can be sufficiently estimated using these data. To achieve these objectives MODIS GPP data were integrated over different time periods within the 2001 and 2002 growing seasons and converted to wheat yield using a simple harvest index logic, across three spatial domains including counties, climate districts and states.
The study area consists of Montana and North Dakota (Figure 1). Most wheat in Montana (> 97 %) and North Dakota (> 99%) is grown under dryland conditions (i.e. without irrigation). In Montana, most agricultural lands are located in the eastern portion of the state while in North Dakota they are distributed throughout. For this study, analysis was confined to counties with greater than 12,000 ha of wheat planted in 2001 and 2002 (figure 2).
Gross primary productivity estimates from MODIS are given in kg C m2. These units are easily converted to biomass estimates because carbon comprises roughly 50% of vegetative biomass. At physiological maturity, approximately 90% of the accumulated biomass of wheat is above ground while the rest is allocated to roots. A broad review of past research indicated that, on average, across many cultivars and types of wheat (winter, spring, and durum), a harvest index of 38% can be used to estimate the amount of grain present in above ground biomass. We included this harvest index in our wheat yield formulation: EQUATION 1 where Yieldest is the yield estimate (kg ha1), a is an arbitrary growing season end point (DOY 208, 216, 225, or 233), GPPDOY is daily gross primary productivity (kg C m2), two is a conversion factor from carbon to biomass, 0.9 (90%) is the annual proportion of GPP allocated to above ground productivity, and HI is the harvest index of 38%.
Estimated wheat yields from MODIS GPP were compared with observed yield at the county, climate district, and state levels. Only state level wheat yield analysis was sufficiently accurate. Predicted wheat yields for both Montana and North Dakota were sufficiently accurate, and never deviated more than Â± 4.5% from actual yield for the duration of the study (Table 1). This research represents a preliminary attempt to fundamentally link M ODIS GPP to wheat yield in Montana and North Dakota while defining the practical limitations to this endeavor and provides the framework for a near real time wheat yield monitor.
Until recently, the Advanced Very High Resolution Radiometer (AVHRR) was the only broad scale, globally applicable satellite that provided direct spectral data suited for continual monitoring of vegetation. As such, many studies have successfully used AVHRR normalized difference vegetation index (NDVI) to infer photosynthetic monitor growing season phenology and estimate vegetation. On 18 December, 1999 the first Moderate Resolution Imaging Spectroradiometer (MODIS) was launched on the Terra platform of the Earth Observing System (EOS). The MODIS instrument provides new and improved capability for terrestrial remote sensing intended for global change research including a suite of standard products designed to remove the burden of most data processing requirements. To determine the practical usefulness of MODIS products, we sought to establish a relationship between MODIS leaf area index (LAI), enhanced vegetation index (EVI), and NDVI and the above-ground herbaceous green biomass in a semi-arid grassland ecosystem. This study focuses on relating the MODIS eight - day composite LAI, and 16 - day composite EVI and NDVI, to direct measures of above - ground biomass generated within the growing season during four time periods in two steps: 1) establish a methodology for converting plot level biomass measurements to a regional scale; and 2) characterize the relationship between selected MODIS land products and spatially scaled - field observations of grassland vegetation productivity.
Field data were collected in the Little Missouri National Grasslands (LMNG) of western North Dakota (Figure 1). This 809,380 ha area is managed primarily by the USDA Forest Service for cattle grazing, oil and gas leasing, wildlife habitat, and recreational uses (Jensen et al. 2001). Due to their large geographic expanse and the dominance of federal ownership, the LMNG provided an excellent opportunity for collecting field data and therefore relating MODIS - derived land products to grassland vegetation.
Biomass observations were made during the 2001 growing season at 2,200 plots (473 transects) across four time periods, each five days in length, in the LMNG. All herbaceous biomass within a 0.5 m2 quadrat was clipped at ground level every fifty meters along each transect and percentage of living vegetation was recorded. Herbaceous biomass was subsequently dried at 65 C° for at least 48 hours and weighed. Final estimates of above - ground net primary productivity within each clipped plot were determined by multiplying the percentage of living vegetation by the weight of dried biomass. For scaling, biomass measures were subsequently modeled within the spatial extent of each Thiesson polygon using a multiple regression formula combining ETM + NDVI, accumulated growing degree days (thermal time), and summation of precipitation, of the form: biomass = NDVI (65.0112)+ (pptsum(0.9) - gddsum^2(0.0013)) where biomass is the estimated biomass within each Thiesson polygon, NDVI is the average NDVI for a given polygon, pptsum is the summation of precipitation from 1 January 2001 to the date of ground sampling, and gddsum is the summation of thermal time (TAVGdaily - 0) from 1 January, 2001 to the date of ground sampling where TAVGdaily is the daily average temperature. Scaled biomass measurements were compared with MODIS LAI, NDVI and EVI.
MODIS LAI (Figure 2), EVI (Figure 3) and NDVI (Figure 3) were all closely related to observed biomass. The results of this study present a framework for linking small-scale field observations to MODIS LAI, EVI and NDVI while simultaneously providing much needed insight to the relationship between MODIS land products and vegetation productivity. The high correlation between MODIS land products and observed above - ground green biomass proved that MODIS land products are suitable for monitoring grassland vegetation dynamics in northern mixed grass prairie and appear to offer improved capabilities compared with the AVHRR NDVI relationships presented in previous work.
Using MODIS and eddy-flux estimates of Gross Primary Production (GPP) data from several climate regimes, we are analyzing the accuracy of the MODIS GPP (MOD17A2) algorithm. The standard MODIS Output uses coarse 1º X 1.25º resolution daily minimum air temperature and humidity from the NASA Data Assimilation Office (DAO) as a control on the photosynthetic assimilation of atmospheric CO2. We evaluate the sensitivity of MODIS outputs to input meteorology by using both DAO and site-based daily weather information to calculate GPP from the standard MODIS GPP Algorithm as part of an on-going validation effort for the MOD17 Algorithm (Figure 1).
The plan for validation combines satellite, model and tower outputs as in Figure 1. The model, Biome-BGC (NTSG - Ecosystem Modeling), is used to scale point-scale tower measurements to coarse-resolution satellite data, allowing for validation of satellite estimates of GPP and NPP. The MOD17A2 (EOS) algorithm is validated by using tower meteorology as inputs to the algorithm and comparing the results to tower estimates.
The MODIS GPP logic (Figure 2, 3) is based on the concept of Radiation Use Efficiency, developed by Monteith (1972, 1977). It requires a number of inputs from earlier in the MODIS product stream, including land cover classification, fraction of photosynthetically active radiation (fPAR), and leaf area index (LAI), as well as daily surface weather from the DAO including air temperature, vapor pressure deficit, and incident shortwave radiation. Additional parameters are found in the Biome Properties Lookup Table (BPLUT), which is divided into ten major biome types. Outputs from the MOD17 Algorithm include both 8-day total GPP (MOD17A2) and annual GPP/NPP (MOD17A3). An intermediate product, 8-day PSNnet is also created by the algorithm. More information on the MOD17 Algorithm and its application can be found in the MOD17 User\u2019s Guide and on the MODIS Project website.
One of the keys to the success of the MOD17 validation effort is our continued collaboration with member sites of FLUXNET, a global network of tower sites (Figure 4), which use eddy covariance techniques to measure the exchange of CO2 and water vapor between vegetation and the atmosphere. Current validation efforts at NTSG are focusing on towers within the AmeriFlux network, although validation efforts are being conducted globally. In addition, a typical tower (Figure 5) measures micrometeorological variables including temperature, precipitation, wind speed/direction and solar radiation. Flux towers have a relatively small footprint (radius < 1 km) in comparison with MODIS (pixel = 1 km2), making it necessary to use multiple measurement strategies to ensure complete validation (Figure 6). These strategies include both large-scale sampling (e.g., the BigFoot Project) and aircraft measurements of CO2 concentration.
Validation began with a frequency analysis of GPP by biome type (Figure 7). These results indicate that the MODIS GPP Algorithm is capturing the general trends of the biomes, with shrubs and grasslands being the least productive (<1 gC m-2 d-1) and forests being the most productive (ENF ~ 8 gC m-2 d-1; DBF ~ 9.5 gC m-2 d-1). Next, 5X5 km grids were established surrounding all of the active AmeriFlux sites for 2001. The MODIS GPP was calculated for each site (Figure 8). This analysis further supports our hypothesis that the algorithm is capturing general productivity trends. Given that land cover is quite heterogeneous, there is often a difference between the productivity of the 5X5 km grid and that of the tower pixel itself (Figure 9). Finally, a subset of tower sites were chosen for further analysis. The MODIS GPP outputs were compared directly with tower estimates of GPP (Figure 10). Current research indicates if DAO meteorology and tower meteorology are similar, MODIS GPP is comparable to tower GPP. But, if the coarse-resolution DAO data is not representative of the site, MODIS GPP can differ greatly from tower GPP. Current site data comparisons are weighted heavily towards forest biomes. Other sites need to be studied to determine if results are similar in other ecosystems.
In the process of urbanization, land formerly occupied by crops, grasslands or forest becomes permanently paved for buildings, parking lots and transportation. While urban areas have generally a lower photosynthetic capacity than the surrounding rural environments, intensively irrigated and fertilized lawns and trees often counterbalance the decline in net primary productivity (NPP) due to the replacement of vegetated surface with constructed materials.
This study attempts to quantify the NPP of urban areas from MODIS data. Since urban areas are masked out in the MODIS NPP (MOD17) product, the methodology uses MODIS NDVI data (MOD13) and the MOD15 (FPAR/LAI) and MOD17 backup algorithms.
Nighttime citylights from the DMSP/OLS for the years 1992/93 and 2000 and the 1km land cover map derived from the 1992 National Land Cover Data set provide the means to track the type of vegetation replaced by the urban expansion. The effect of urbanization on the regional photosynthetic capacity can be estimated by comparing the urban vegetation NPP with the average NPP of the pre-existing land cover.
Milesi, C., Elvidge, C. D., Nemani, R. R., & Running, S. W. Assessing the impact of urban land development on net primary productivity in the southeastern United States. Remote Sensing of Environment, In press.
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