EOS Moderate Resolution Imaging Spectroradiometer Research Articles

Satellite detection of soil moisture related water stress impacts on ecosystem productivity using the MODIS-based photochemical reflectance index

Abstract Satellite remote sensing provides continuous observations of vegetation properties that can be used to estimate global terrestrial ecosystem gross primary production (GPP). The Photochemical Reflectance Index (PRI) has been shown to be sensitive to vegetation photosynthetic light use efficiency (LUE), GPP and canopy water-stress. Here, we use the NASA EOS MODIS (Moderate Resolution Imaging Spectroradiometer) based PRI with eddy covariance CO2 flux measurements and meteorological observations from 20 tower sites representing major plant functional type (PFT) classes within the continental USA (CONUS) to assess GPP sensitivity to soil moisture related water stress. The sPRI (scaled PRI) metric derived using MODIS band 13 as a reference channel (sPRI13) shows generally higher correspondence with tower GPP estimates than other potential MODIS reference bands. The sPRI13 observations were used as a proxy for soil moisture related water supply constraints to LUE within a satellite data driven terrestrial carbon flux model to estimate GPP (GPPPRI). The GPPPRI calculations show generally favorable correspondence with tower GPP estimates (0.457 ≤ R2 ≤ 0.818), except for lower GPPPRI performance over evergreen needleleaf forest (ENF) sites. A regional model sensitivity analysis using the sPRI13 as a water supply proxy indicated that water restrictions limit GPP over more than 21% of the CONUS domain, particularly in drier climate areas where atmospheric moisture deficits (VPD) alone are insufficient to represent both atmosphere demand and water supply controls affecting productivity. Our results indicate strong potential of the MODIS sPRI13 to represent soil moisture related water supply controls influencing photosynthesis, with enhanced (1-km resolution) delineation of these processes closer to the scale of in situ tower observations. These observations may provide an effective tool for characterizing sub-grid spatial heterogeneity in soil moisture related controls that inform coarser scale observations and estimates determined from other satellite observations and earth system models.

Use of Landsat TM and EOS MODIS imaging technologies for estimation of winter wheat yield in the North China Plain

This study focuses on the methodologies of winter wheat yield prediction based on Land Satellite Thematic Map (TM) and Earth Observation System Moderate Resolution Imaging Spectroradiometer (MODIS) imaging technologies in the North China Plain. Routine field measurements were initiated during the periods when the Landsat satellite passed over the study region. Five Landsat TM images were acquired. Wheat yields of the experimental sites were recorded after harvest. Spectral vegetation indices were calculated from TM and MODIS images. The correlation analysis among wheat yield and spectral parameters revealed that TM renormalized difference water index (RDWI) and MODIS near-infrared reflectance had the highest correlation with yield at grain-filling stages. The models from the best-fitting method were used to estimate wheat yield based on TM and MODIS data. The average relative error of the root mean square error (RMSE) of the predicted yield was smaller from TM than from MODIS.

Numerical simulation of fluxes generated by inhomogeneities of the underlying surface over the Jinta Oasis in Northwestern China

Using land-use types derived from satellite remote sensing data collected by the EOS Moderate Resolution Imaging Spectroradiometer (EOS/MODIS), the mesoscale and turbulent fluxes generated by inhomogeneities of the underlying surface over the Jinta Oasis, northwestern China, were simulated using the Regional Atmospheric Modeling System (RAMS4.4). The results indicate that mesoscale circulation generated by land-surface inhomogeneities over the Jinta Oasis is more important than turbulence. Vertical heat fluxes and water vapor are transported to higher levels by mesoscale circulation. Mesoscale circulation also produces mesoscale synoptic systems and prevents water vapor over the oasis from running off. Mesoscale circulation transports moisture to higher atmospheric levels as the land-surface moisture over the oasis increases, favoring the formation of clouds, which sometimes leads to rainfall. Large-scale wind speed has a significant impact on mesoscale heat fluxes. During the active phase of mesoscale circulation, the stronger large-scale winds are associated with small mesoscale fluxes; however, background wind seems to intensify the turbulent sensible heat flux and turbulent latent heat flux. If the area of oasis is enlarged properly, mesoscale circulation will be able to transport moisture to higher levels, favoring the formation of rainfall in the oasis and protecting its “cold island” effect. The impact of irrigation on rainfall is important, and increasing irrigation across the oasis is necessary to protect the oasis.

Accuracies of Global Land Cover Maps Checked against Fluxnet Sites

Global land cover data products are key sources of information in understanding the complex interactions between human activities and global change. They play a critical role in improving performances of ecosystem, hydrological and atmospheric models. Three freely available global land cover products developed in the United States are popularly used by the scientific community. These include two global maps developed separately by the United States Geological Survey (USGS) and the University of Maryland (UMD) with NOAA Advanced Very High Resolution Radiometer (AVHRR) data, and one developed by Boston University with the EOS Moderate Resolution Imaging Spectroradiometer (MODIS) data. They are compared with known land cover types at 250 available Fluxnet sites around the world. The overall accuracies are 37%, 36% and 42%, respectively for the USGS, UMD and Boston global land cover maps. Some future global land cover mapping strategies are suggested.

Numerical simulations of the atmospheric and land conditions over the Jinta oasis in northwestern China with satellite‐derived land surface parameters

Mesoscale meteorological modeling is an important tool to help understand the energy budget of the oasis, while some of its initial field data are rough limited to describe the atmosphere and land conditions over a small oasis. Local factors including land surface parameters and their interaction with the atmosphere play a dominant role in the local circulation. Therefore, in this study, land surface parameters such as land‐use types, vegetation cover fraction, and surface layer soil moisture are derived by satellite remote sensing data from EOS Moderate Resolution Imaging Spectroradiometer (EOS/MODIS), and soil moisture at 10 cm and 200 cm depth are obtained by combining surface layer soil moisture with experiential statistics. Then the parameters are used to specify the respective options in the MM5 model. Comparison with the observations shows that the modeling including satellite values leads to improved meteorological simulations in the Jinta oasis, both for the oasis effect and the local wind circulation, especially for description of the inhomogeneous characteristics over the oasis. Replacing values in the initial field with data obtained from remote sensing removes the number of unknowns in the model and increases the accuracy of the energy budget. This work is a very valuable addition to current numerical research on local circumfluence over the oasis areas.

Fire radiative energy for quantitative study of biomass burning: derivation from the BIRD experimental satellite and comparison to MODIS fire products

A major focus in global change research is to quantify the amount of gaseous and particulate pollutants emitted from terrestrial vegetation fires. Determination of the emitted radiant energy released during biomass combustion episodes (the so-called fire radiative energy or FRE) has been suggested as a new tool for determining variations in biomass combustion rates and the rate of production of atmospheric pollutants. We review the physical principals behind the remote determination of FRE and present an alternative method for its derivation via analysis of ‘fire pixel’ radiances in the middle infrared spectral region. We compare our method to the existing FRE retrieval approach used in the EOS Moderate Resolution Imaging Spectro-radiometer (MODIS) fire products, and to retrievals of FRE based on derived fire temperature and area made via the so-called Bi-spectral method. We test each FRE retrieval method using both simulated data and imagery from a new experimental space mission, the Bi-spectral InfraRed Detection (BIRD) small satellite, which has sensors specifically designed for the study of active fires. We analyse near simultaneous MODIS and BIRD data of the fires that burned around Sydney, Australia in January 2002. Despite the markedly different pixel size and spectral coverage of these sensors, where the spatial extent of the fire pixel groups detected by MODIS and BIRD are similar, the derived values of FRE for these fires agree to within ±15 %. However, in certain fires, the lower spatial resolution of MODIS appears to prevent many of the less intensely radiating fire pixels being detected as such, meaning MODIS underestimates FRE for these fires by up to 46% in comparison to BIRD. Though the FRE release of each of these low intensity fire pixels is relatively low, their comparatively large number makes their overall FRE significant. Thus, total FRE release of the Sydney fires on 5 January 2002 is estimated to be 6.5×10 9 J s −1 via BIRD but 4.0×10 9 J s −1 via MODIS. The ability of BIRD to resolve individual fire fronts further allows the first accurate calculation of ‘radiative’ fireline intensity from spaceborne measurements, providing values of 15–75 kJ s −1 m −1 for fire fronts that are up to 9 km in length. Finally, we analyse the effectiveness of the satellite-based FRE retrieval methods in estimating the FRE from the active flaming and smouldering components only (FRE Active, believed to be proportional to the rate of biomass combustion), despite the sensor receiving additional radiance from the ‘cooling ground’. The MIR radiance method appears particularly strong in this regard, allowing FRE Active to be estimated to within ±30% in the range 100–100,000 J s −1 m −2. These results provide further confidence in the ability of spaceborne missions to derive physically meaningful values of FRE that could be used to support biomass burning emissions inventories. Future comparisons between FRE derived via MODIS and those from higher spatial resolution BIRD or airborne imagery may allow the MODIS-derived FRE values to be ‘calibrated’ for any systematic underestimation. We therefore expect FRE to become an important tool for enhancing global studies of terrestrial vegetation fires with infrared remote sensing, particularly as the majority of large fires are now imaged four times per day via the MODIS instruments on the Terra and Aqua spacecraft.

Areal estimates of fragmented land cover: Effects of pixel size and model-based corrections

The effects of coarse spatial resolution on the apparent extent of fragmented cover types on digital maps were assessed and a model-based method for correcting for underestimation due to omission of small fragments was tested. Burn scar maps based on Landsat Multispectral Scanner (MSS) imagery and a map of ponds in Arctic tundra based on ERS-1 synthetic aperture radar (SAR) imagery were spatially degraded to simulate EOS Moderate-Resolution Imaging Spectroradiometer (MODIS) and 100 m SAR maps. Omission of small fragments on the degraded imagery decreased the apparent extent by as much as 50%. The apparent extent on degraded maps, where various degrees of systematic tendency towards commission were simulated by identification of pixels with less than 50% burn scar or open water, was either close to that measured on the original map (for small classification bias) or overestimated (large classification bias). An area correction procedure based on fitting sizes to a lognormal distribution with censored data was tested. The area correction compensated for 25 to 70% of the underestimation relative to the original maps. Area correction led to inflated areal estimates with biased maps or with a Pareto rather than the lognormal distribution model, indicating that the areal correction procedure must be used judiciously if improved estimates are to be expected.

Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer

Daily distribution of the aerosol optical thickness and columnar mass concentration will be derived over the continents, from the EOS moderate resolution imaging spectroradiometer (MODIS) using dark land targets. Dark land covers are mainly vegetated areas and dark soils observed in the red and blue channels; therefore the method will be limited to the moist parts of the continents (excluding water and ice cover). After the launch of MODIS the distribution of elevated aerosol concentrations, for example, biomass burning in the tropics or urban industrial aerosol in the midlatitudes, will be continuously monitored. The algorithm takes advantage of the MODIS wide spectral range and high spatial resolution and the strong spectral dependence of the aerosol opacity for most aerosol types that result in low optical thickness in the mid‐IR (2.1 and 3.8 μm). The main steps of the algorithm are (1) identification of dark pixels in the mid‐IR; (2) estimation of their reflectance at 0.47 and 0.66 μm; and (3) derivation of the optical thickness and mass concentration of the accumulation mode from the detected radiance. To differentiate between dust and aerosol dominated by accumulation mode particles, for example, smoke or sulfates, ratios of the aerosol path radiance at 0.47 and 0.66 μm are used. New dynamic aerosol models for biomass burning aerosol, dust and aerosol from industrial/urban origin, are used to determine the aerosol optical properties used in the algorithm. The error in the retrieved aerosol optical thicknesses, τais expected to be Δτa= 0.05±0.2τa. Daily values are stored on a resolution of 10×10 pixels (1 km nadir resolution). Weighted and gridded 8‐day and monthly composites of the optical thickness, the aerosol mass concentration and spectral radiative forcing are generated for selected scattering angles to increase the accuracy. The daily aerosol information over land and oceans [Tanré et al., this issue], combined with continuous aerosol remote sensing from the ground, will be used to study aerosol climatology, to monitor the sources and sinks of specific aerosol types, and to study the interaction of aerosol with water vapor and clouds and their radiative forcing of climate. The aerosol information will also be used for atmospheric corrections of remotely sensed surface reflectance. In this paper, examples of applications and validations are provided.