This project is a part of NASA/EOS project. Our goals are to estimate GPP/NPP from earth land surface by using satellite remote sensing and to make use of this technique for natural resource and land management, global carbon cycle study, ecosystem status assessment, and environmental change monitoring.
The MOD17 MODIS GPP/NPP is the first continuous satellite-driven dataset monitoring global vegetation productivity. The algorithm is based on the original logic of Monteith, suggesting that NPP under non-stressed conditions is linearly related to the amount of absorbed Photosynthetically Active Radiation (PAR) during the growing season. In reality, vegetation growth is subject to a variety of stresses that tend to reduce the potential growth rate, especially stresses resulting from climate (temperature, radiation, and water), or the interaction of these primary abiotic controls, which impose complex and varying limitations on vegetation activity in different parts of the world. Figure 1 illustrates the range of three dominant climatic controls on global annual NPP, distributed from arctic tundra to tropical rainforests. MOD17 also combined these complex effects in the model.
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).
Unlike other MODIS 8-day or 16-day data products, MOD17 is the ACCUMULATED carbon fixed by vegetation through photosynthesis in a given period, and any unreliable MODIS input due to cloudiness or severe aerosol will introduce unreliable accumulated GPP/NPP. As shown in Figure 2, cloud-contaminated FPAR/LAI is a big issue for some regions like Amazon. Therefore, we need to fill those contaminated FPAR/LAI to enhance the quality of GPP and NPP estimates. Figure 2 and Figure 3 show how the filled FPAR/LAI enhance the quality of MOD17 estimates. NTSG provides the enhanced global 8-day and annual GPP/NPP for free at
Please get merged GeoTIFF Geographic MOD17A3 at the following URL:
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).
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.
MODIS 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. 2006
The 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. 2006
The 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, 2007
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.
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. 2003
Recent 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.
The daily meteorological input for the global 1-km MOD17 is a coarse spatial resolution reanalysis dataset from Global Modeling and Assimilation Office (GMAO/NASA). The coarse resolution (1 by 1.25 degree) and large uncertainties in the GMAO meteorological fields, especially surface solar radiation (please see Zhao et al., 2006 JGR paper in the above), introduce large uncertainties to MODIS GPP and NPP estimates. Therefore, high spatial resolution remotely sensed solar radiation or PAR will dramatically improve the accuracy of MOD17. We will use MODIS PAR dataset generated by Dr. Shunlin Liang at the University of Maryland to replace solar radiation by GMAO. For other meteorological variables (air temperature, vapor pressure deficit), we will use SOGS (Jolly et al., 2005, available at NTSG publication website) to generate high resolution dataset based on the point daily weather station data from networks of WMO and NCDC. The below example image is 250m MODIS GPP driven by 1-km SOGS meteorological data instead of GMAO for the period of 2-10 June 2003 (see Running et al., 2004).