Moderate Resolution Imaging Spectrometer Data Research Articles

Estimating the broadband longwave emissivity of global bare soil from the MODIS shortwave albedo product

AbstractA constant land surface longwave emissivity value, or very simple parameterization, has been adopted by current land surface models because of a current lack of reliable observations. Of all the various Earth surface types, bare soil has the highest variations in broadband emissivity (BBE). We propose here a new algorithm to estimate BBE in the 8–13.5 µm spectral range based on the Moderate Resolution Imaging Spectrometer (MODIS) albedo product for bare soil. This algorithm takes advantage of both Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) longwave emissivity and MODIS shortwave albedo products, as well as the established linear relationship between ASTER BBE and seven MODIS spectral albedos for bare soil. In order to mitigate step discontinuities in the global land surface BBE product, a transition zone was established and the BBE estimation method was also provided. Three linear formulae were derived for bare soil and transition zones, respectively. Given the accuracy of 0.01 for MODIS spectral albedo, the absolute accuracy of BBE retrieval is better than 0.017. The validation results obtained from the three field trials conducted in China and one field trial in western/southwestern U.S. indicated that the average difference between the estimated BBE and the measured BBE was 0.016. We have introduced a new strategy to generate global land surface BBE using MODIS data. This strategy was used to generate global 8 day 1 km land surface BBE products from 2000 through 2010.

Evaluation of wildfire propagation susceptibility in grasslands using burned areas and multivariate logistic regression

This research simulated wildfire propagation susceptibility based on multivariate logistic regression. Moderate Resolution Imaging Spectrometer (MODIS)-derived fuel indicators and topographic factors were the independent variables, and burnt areas served as the dependent variable. MODIS data were collected daily during the wildfire seasons of April to May and September to October from 2001 to 2007 to acquire information about live and dead fuel in the Mongolia–China grasslands. The inputs for the independent parameters for wildfire propagation susceptibility modelling were the normalized difference vegetation index (NDVI), optimized soil-adjusted vegetation index (OSAVI), moisture stress index (MSI), global vegetation moisture index (GVMI), dead fuel INDEX (DFI), elevation, slope, and aspect. Multivariate logistic regression ranking indicates that DFI, MSI, DEM, and OSAVI are the top four factors, with an overall accuracy of 80%. ‘Leave one out’ cross-validation demonstrated that the overall accuracy of the propagation susceptibility modelling ranged from 65% to 87%. Finally, the model was used to produce 10 day average wildfire propagation susceptibility maps during the wildfire seasons of 2001–2007 and to predict the location of burned areas. This research will be useful for understanding the propagation susceptibility of wildfires in grassland areas and for creating policies for preventing wildfire spread.

Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: a segmentation-based approach

FROM-GLC (Fine Resolution Observation and Monitoring of Global Land Cover) is the first 30 m resolution global land-cover map produced using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. Due to the lack of temporal features as inputs in producing FROM-GLC, considerable confusion exists among land-cover types (e.g. agriculture lands, grasslands, shrublands, and bareland). The Moderate Resolution Imaging Spectrometer (MODIS) provides high-temporal frequency information on surface cover. Other auxiliary bioclimatic, digital elevation model (DEM), and world maps on soil-water conditions are possible sources for improving the accuracy of FROM-GLC. In this article, a segmentation-based approach was applied to Landsat imagery to down-scale coarser-resolution MODIS data (250 m) and other 1 km resolution auxiliary data to the segment scale based on TM data. Two classifiers (support vector machine (SVM) and random forest (RF)) and two different strategies for use of training samples (global and regional samples based on a spatial temporal selection criterion) were performed. Results show that RF based on the global use of training samples achieves an overall classification accuracy of 67.08% when assessed by test samples collected independently. This is better than the 64.89% achieved by FROM-GLC based on the same set of test samples. Accuracies for vegetation cover types are most substantially improved.

Retrieval of the Haze Optical Thickness in North China Plain Using MODIS Data

China's industrialized regions have seen increasing occurrence of heavy haze caused by severe particle pollution. However, aerosol retrieval under these circumstances is often excluded from NASA's Moderate Resolution Imaging Spectrometer (MODIS) aerosol products due to cloud mask and suspected high surface reflectance. An algorithm to retrieve the haze aerosol optical thickness (HAOT) is developed using MODIS data to supplement the current MODIS retrieval algorithm. This method includes 1) haze identification, 2) the generation of a surface reflectance database using MODIS data in hazy conditions, and 3) the development of haze aerosol models with four aerosol components simulated by a global 3-D atmospheric chemical transport model (GEOS-Chem). This algorithm was used in combination with the MODIS dense dark vegetation algorithm to retrieve 1 km HAOT over North China Plain from March to September of 2008. The values of the retrieved HAOT values are mostly between 0.7–3, with a correlation coefficient of 0.82 with the Aerosol Robotic NETwork observations and a 19% mean relative difference. Retrieval uncertainties associated with the errors in haze detection, surface reflectance, and haze models were analyzed using ground measurements.

Extraction of snow cover information in sparse vegetation area based on spectral measurement and SRF by using MODIS data

In this paper,the linear spectral mixture model(LSMM)was used for the spectral unmixing analysis of the Moderate Resolution Imaging Spectrometer(MODIS)data of the study area in Gurbantunggut desert.Using the spectral response function(SRF)of MODIS1-7 bands,the authors transformed the end-member spectrum quasi-synchronously collected by the full-band spectrometer(ASD)to the pixel spectra,thus generating the discrete spectrum of MODIS1-7 bands.Compared with the MODIS end-member spectra obtained by minimum noise fraction(MNF)transform and pixel purity index(PPI),the end-member spectral values of the first band of MODIS were much larger than the transformed spectrum values,but the spectral values of the MODIS2-7 bands were close to the transformed values.Therefore,selecting the image end-member spectral values of the MODIS2-7 bands,the authors used LSMM to estimate the abundance of snow in the sparse vegetation area appropriately.Fitting the estimated snow component value to the normalized difference snow index(NDSI),the authors found that a significant correlation exists between them after excluding MODIS1 band.The correlation coefficients show that the snow component can be a typical index of the snow cover.

An improved cirrus detection algorithm MeCiDA2 for SEVIRI and its evaluation with MODIS

Abstract. In this study, a substantially improved version of the Meteosat cirrus detection algorithm (MeCiDA2) will be presented, which now allows application to the full earth disc visible by the Meteosat satellite. As cirrus clouds have an influence on the radiation budget of the earth, their optical properties and their global coverage has to be monitored at the global scale using instruments aboard geostationary satellites. Since MeCiDA was optimised for the area of Europe only, various changes were necessary to handle the variable conditions found over the full Meteosat disc. Required changes include the consideration of the viewing angle dependency and of the sensitivity of the 9.7 μm channel to the ozone column. To this end, a correction is implemented that minimises the influence of the variability of the stratospheric ozone. The evaluation of the proposed improvements is carried out by using MeCiDA applied to MODIS (moderate resolution imaging spectrometer) data to address viewing angle-dependent cirrus detection, and by additionally comparing it to the cloud optical properties MOD06 cirrus product. The new MeCiDA version detects less cirrus than the original one for latitudes larger than 40°, but almost the same amount elsewhere. MeCiDA's version for MODIS is more sensitive than that for SEVIRI (spinning enhanced visible and infrared imager) with cirrus occurrences higher by 10%, and the new MeCiDA provides almost the same cirrus coverage (±0.1) as given by the cloud phase optical properties from MODIS for latitudes smaller than 50°. Finally, the influence of sub-pixel clouds on the SEVIRI cirrus detection has been examined: more than 60% of the undetected SEVIRI cirrus pixels have a cirrus coverage smaller than 0.5.

Study of cloud microphysical properties over India during CAIPEEX using a mesoscale model with new cloud microphysical scheme—Part I

Incorporation of aerosol interaction in cloud microphysics is being the central issue now a day. In the present study, an improved and modified cloud microphysical scheme comprising bulk-formula two-moment mixed-phase cloud scheme has been incorporated with a regional model to study the cloud–aerosol interaction in the mixed-phase cloud system. The paper intends to sensitivity study as to what extent this new microphysical scheme can capture the observed cloud microphysical properties. The observed cloud microphysical hydrometeors are evaluated from MODIS (Moderate Resolution Imaging Spectrometer) and CAIPEEX (Cloud Aerosol Interaction and Precipitation Enhancement EXperiment) data set over the Indian peninsular region. Surface accumulated precipitation is collected from TRMM-3B42 data sets. One active convection (case study 1) and two suppressed convection events (case study 2 and case study 3) during Indian south west monsoon are simulated over three different locations of the Indian peninsular region with the help of a multi nested mesoscale model based on microphysical modification. The cloud microphysical properties such as cloud water path from satellite data retrieval and cloud drop number concentration, cloud droplet radius from CAIPEEX, are reproduced well by the modified microphysical scheme. The rainfall is also reasonably well simulated during active monsoon convection event (6th July, 2009) as well as suppressed monsoon convection events (15th June and 19th August, 2009). Moreover, another sensitivity experiment is done to include natural aerosols, like dust as a representation of heterogeneous ice nucleation. The inclusion of mineral dust effect as ice nuclei provides realistic cloud microphysical properties during present case studies successfully. So it is a worthy investigation of the role of different kinds of aerosols on the microphysics and precipitation processes.

Methodologies and Uncertainties in the Use of the Terrestrial Chlorophyll Index for the Sentinel-3 Mission

A methodology is described for the validation of Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) data over heterogeneous land surfaces in an agricultural region in Southern Italy. The approach involves the use inverse canopy reflectance modeling techniques to derive maps of canopy chlorophyll content (CCC) and leaf area index (LAI) at fine spatial resolution. Indirect field measurements are used for validation of the fine spatial resolution data. Subsequently, these maps are aggregated based on a regular grid at 1 km spatial resolution to validate MERIS Level 2 MTCI (300 m). RapidEye satellite sensor data with a pixel size of 6.5 m are used for this purpose. Based on a set of independent ground measurements, fine spatial resolution maps achieved an R2 = 0.78 and RMSE = 0.39 for CCC and R2 = 0.76 and RMSE = 0.64 for LAI. The relationship between MERIS L2 MTCI and CCC [g∙m−2] achieved a coefficient of determination of 0.74 and it resulted to be extremely statistically significant (p-value < 0.001). Additionally, a relative validation of two other satellite products at medium resolution spatial scale, namely MERIS leaf area index (LAI) and Moderate Resolution Imaging Spectrometer (MODIS) LAI was performed by comparison with the fine spatial resolution LAI map. Results indicated a better accuracy in LAI estimation of MERIS (RMSE = 0.33) compared to MODIS (RMSE = 0.81) data.

Biomass burning aerosol absorption measurements with MODIS using the critical reflectance method

[1] This research uses the critical reflectance technique, a space-based remote sensing method, to measure the spatial distribution of aerosol absorption properties over land. Choosing two regions dominated by biomass burning aerosols, a series of sensitivity studies were undertaken to analyze the potential limitations of this method for the type of aerosol to be encountered in the selected study areas, and to show that the retrieved results are relatively insensitive to uncertainties in the assumptions used in the retrieval of smoke aerosol. The critical reflectance technique is then applied to Moderate Resolution Imaging Spectrometer (MODIS) data to retrieve the spectral aerosol single-scattering albedo (SSA) in South African and South American biomass burning events. The retrieved results were validated with collocated Aerosol Robotic Network (AERONET) retrievals. Approximately 67% of the comparisons show a difference between MODIS and AERONET smaller than 0.03, the magnitude of the AERONET uncertainty. The overlap of the two retrievals increases to 88%, allowing for measurement variance in the MODIS retrievals, as well. The ensemble average of MODIS-derived SSA for the Amazon forest station is 0.92 at 670 nm, and 0.84–0.89 for the southern African savanna stations. The critical reflectance technique allows evaluation of the spatial variability of SSA and shows that SSA in South America exhibits higher spatial variation than in South Africa. The accuracy of the retrieved aerosol SSA from MODIS data indicates that this product can help to better understand how aerosols affect the regional and global climate.

Mapping fuel moisture codes using MODIS images and the Getis statistic over western Canada grasslands

In Canada, fire danger is predicted by the Canadian Forest Fire Danger Rating System (CFFDRS). One of its subsystems is the Canadian Fire Weather Index (FWI) system, which has two slow-drying fuel moisture codes: the drought code (DC) and the duff moisture code (DMC). Both codes are used in this study as a surrogate of dead fuel moisture. We evaluate the capability of Moderate Resolution Imaging Spectrometer (MODIS) observations to map DC and DMC. A comparison was made between 16-day composite MODIS images acquired in the 2000 to 2004 fire seasons over Grasslands National Park of Canada (GNPC), Saskatchewan, Canada and DC and DMC data interpolated from weather station records. Overall, spectral variables and DC and DMC exhibited clear seasonal trends, and were therefore influenced more by temporal factors than by land cover factors. Data were then grouped into temporal categories, by year and seasons, for stepwise multiple regression calculations. We note that the use of shortwave infrared (SWIR)-based variables slightly improved the performances of both DC and DMC models. Spectrally derived DC and DMC data showed improved spatial resolution of mapping drought over pre-burned areas compared to broadly interpolated weather station-based estimations. The spectral estimation was improved by applying the Getis statistic (Gi *) to the resulting maps, showing the potential of combining the Gi * statistic and MODIS data in predicting fuel moisture codes in relationship to fire occurrence.

Evaluating evapotranspiration and water-use efficiency of terrestrial ecosystems in the conterminous United States using MODIS and AmeriFlux data

In this study, we used the remotely-sensed data from the Moderate Resolution Imaging Spectrometer (MODIS), meteorological and eddy flux data and an artificial neural networks (ANNs) technique to develop a daily evapotranspiration (ET) product for the period of 2004–2005 for the conterminous U.S. We then estimated and analyzed the regional water-use efficiency (WUE) based on the developed ET and MODIS gross primary production (GPP) for the region. We first trained the ANNs to predict evapotranspiration fraction (EF) based on the data at 28 AmeriFlux sites between 2003 and 2005. Five remotely-sensed variables including land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), leaf area index (LAI) and photosynthetically active radiation (PAR) and ground-measured air temperature and wind velocity were used. The daily ET was calculated by multiplying net radiation flux derived from remote sensing products with EF. We then evaluated the model performance by comparing modeled ET with the data at 24 AmeriFlux sites in 2006. We found that the ANNs predicted daily ET well ( R 2 = 0.52–0.86). The ANNs were applied to predict the spatial and temporal distributions of daily ET for the conterminous U.S. in 2004 and 2005. The ecosystem WUE for the conterminous U.S. from 2004 to 2005 was calculated using MODIS GPP products (MOD17) and the estimated ET. We found that all ecosystems' WUE-drought relationships showed a two-stage pattern. Specifically, WUE increased when the intensity of drought was moderate; WUE tended to decrease under severe drought. These findings are consistent with the observations that WUE does not monotonously increase in response to water stress. Our study suggests a new water-use efficiency mechanism should be considered in ecosystem modeling. In addition, this study provides a high spatial and temporal resolution ET dataset, an important product for climate change and hydrological cycling studies for the MODIS era.

Estimation of water vapor content in near-infrared bands around 1 μm from MODIS data by using RM–NN

An algorithm based on the radiance transfer model (RM) and a dynamic learning neural network (NN) for estimating water vapor content from moderate resolution imaging spectrometer (MODIS) 1B data is developed in this paper. The MODTRAN4 is used to simulate the sun-surface-sensor process with different conditions. The dynamic learning neural network is used to estimate water vapor content. Analysis of the simulation data indicates that the mean and standard deviation of estimation error are under 0.06 gcm(-2 )and 0.08 gcm(-2). The comparison analysis indicates that the estimation result by RM-NN is comparable to that of a MODIS water vapor content product (MYD05_L2). Finally, validation with ground measurement data shows that RM-NN can be used to accurately estimate the water vapor content from MODIS 1B data, and the mean and standard deviation of the estimation error are about 0.12 gcm(-2 )and 0.18 gcm(-2).

Detection of sub-surface waterlogging using Terra-1 MODIS data

Waterlogging due to rising ground watertable, being a sub-surface phenomenon, is not amenable to detection by optical remote sensing. Microwave and thermal sensor data have, however, shown some promise in the detection of sub-surface waterlogging. The present study was taken up to evaluate the potential of near-IR, short-wave IR (SWIR) and thermal-IR data from Moderate Resolution Imaging Spectrometer (MODIS) aboard Terra-1 acquired during day-and-night time postmonsoon data for detection of sub-surface waterlogging. The approach involves retrieval of day-and-night land surface temperature (LST), generation of normalized difference of channel-2 and 6 (ND26); 2 and 7 (ND27); ground truth collection involving concurrent ground water table observations to satellite date of pass, thresholding of normalized differences (NDs) and correlating the NDs with depth of ground water table. Amongst various spectral indices, day and night-time LST difference (DLST) and night-time LST have been found to correlate well with the incidence of waterlogging (water table depth < 2m), followed by normalized difference of band-2 (841–876 nm) and band-7 (2105–2155 nm). The sensitivity of threshold limits for these indices was maximum for DLST followed by ND26 and ND27. Poor accuracy of detecting sub-surface waterlogging with thermal bands during day time is attributed to the non-corresponding of the time of Terra MODIS data acquisitions with thermal maxima of the terrain. Though the ND27 gave better accuracy to detect subsurface waterlogging, it is very sensitive to threshold limits.

Linking ecological information and radiative transfer models to estimate fuel moisture content in the Mediterranean region of Spain: Solving the ill-posed inverse problem

Live fuel moisture content (FMC) is a key factor required to evaluate fire risk and its operative and accurate estimation is essential for allocating pre-fire resources as a part of fire prevention. This paper presents an operative and accurate procedure to estimate FMC though MODIS (moderate resolution imaging spectrometer) data and simulation models. The new aspects of the method are its consideration of several ecological criteria to parameterize the models and consistently avoid simulating unrealistic spectra which might produce indetermination (ill-posed) problems when inverting the model. The methodology was operatively applicable to 12 shrubland plots located in different provinces of the Mediterranean region of Spain and tested with field data collected in those areas. The results showed that the proposed method efficiently tracks changes of FMC with average errors around 15%. However the model under-estimates FMC values higher than 135.68% since those situations were not included in the simulation scheme and the inversion precision is also dependent on an accurate estimation of LAI. These limitations will be overcome in future work mainly by including spectral signatures of vegetation with FMC values higher than 135.68% in the simulations, and by exploring new methods for LAI retrieval. Further efforts will also be devoted to extend this approach to other ecosystems.

The CM-SAF and FUB Cloud Detection Schemes for SEVIRI: Validation with Synoptic Data and Initial Comparison with MODIS and CALIPSO

Abstract The Satellite Application Facility on Climate Monitoring (CM-SAF) is aiming to retrieve satellite-derived geophysical parameters suitable for climate monitoring. CM-SAF started routine operations in early 2007 and provides a climatology of parameters describing the global energy and water cycle on a regional scale and partially on a global scale. Here, the authors focus on the performance of cloud detection methods applied to measurements of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on the first Meteosat Second Generation geostationary spacecraft. The retrieved cloud mask is the basis for calculating the cloud fractional coverage (CFC) but is also mandatory for retrieving other geophysical parameters. Therefore, the quality of the cloud detection directly influences climate monitoring of many other parameters derived from spaceborne sensors. CM-SAF products and results of an alternative cloud coverage retrieval provided by the Institut für Weltraumwissenschaften of the Freie Universität in Berlin, Germany (FUB), were validated against synoptic measurements. Furthermore, and on the basis of case studies, an initial comparison was performed of CM-SAF results with results derived from the Moderate Resolution Imaging Spectrometer (MODIS) and from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP). Results show that the CFC from CM-SAF and FUB agrees well with synoptic data and MODIS data over midlatitudes but is underestimated over the tropics and overestimated toward the edges of the visible Earth disk.

Service-Oriented Atmospheric Radiances (SOAR): Gridding and Analysis Services for Multisensor Aqua IR Radiance Data for Climate Studies

The Aqua spacecraft, launched on May 4, 2002, carries two well-calibrated independent infrared (IR) grating spectrometers Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectrometer (MODIS), which have been continuously returning upwelling IR spectral radiance measurements for over five years. Based on an Aqua Sr. Project Review, estimates of available flight fuel, power, and orbital projections assess the life span of the Aqua satellite, and these two instruments, to be reliable to 2013. Since launch, these instruments have generated petabytes of data, which are managed and made available by the Goddard Space Flight Center (GSFC) Earth Science Data and Information Services Center and GSFC MODAPS. Agencies such as NOAA, DOD, EPA, and USGS use the AIRS data mostly for weather-related applications, whereas MODIS data are used, in addition to some climate-related studies, for studies of weather, oceans, and land processes, aerosols, natural and man-made disasters, and earth ecology. The Science Investigator-led Processing Systems (SIPS) teams have made many of the desired products derived from these data sets available either as level 2 products and/or level 3 gridded product fields. However, no gridded level 3 data products of radiances, either averaged for a grid element, max, min, or as brightness temperatures (BTs), are provided directly by the SIPS. Thus, one impediment that the general community faces in accessing these MODIS produced petabytes of data is storing such large data sets, interpreting the multiformatted data, and transforming it into helpful data sets for climate-research needs. The Service-Oriented Atmospheric Radiance (SOAR) system has been designed to bridge these gaps and overcome the challenges of bringing this rich data source to the science community, by delivering applications that process these valuable radiance data into standard spatial-temporal grids as well as user-defined criteria on demand. SOAR can serve this community with aggregated, enriched, and thinned gridded data sets provided with access to the data on demand, with query and subsetting capabilities across many dimensions. In addition, SOAR provides online user-specified visualization and analysis requests, all accessible via a Web browser. The utility of SOAR is exposed via Web-service routines, using the Simple Object Access Protocol. The Web-service library and supporting technologies (Axis, PostgreSQL, and Tomcat) reside on a University of Maryland Baltimore Campus client server, which interfaces to and invokes algorithms on the process server, a high-performance computer cluster and storage system. These servers are connected to the sensor data stores at the GSFC via a high-speed fiber-optic network connection [10 Gb/s], providing reliable and fast on-demand access to a vast online library of AIRS and current monthly MODIS source data.

Estimation of net ecosystem carbon exchange for the conterminous United States by combining MODIS and AmeriFlux data

Eddy covariance flux towers provide continuous measurements of net ecosystem carbon exchange (NEE) for a wide range of climate and biome types. However, these measurements only represent the carbon fluxes at the scale of the tower footprint. To quantify the net exchange of carbon dioxide between the terrestrial biosphere and the atmosphere for regions or continents, flux tower measurements need to be extrapolated to these large areas. Here we used remotely sensed data from the Moderate Resolution Imaging Spectrometer (MODIS) instrument on board the National Aeronautics and Space Administration's (NASA) Terra satellite to scale up AmeriFlux NEE measurements to the continental scale. We first combined MODIS and AmeriFlux data for representative U.S. ecosystems to develop a predictive NEE model using a modified regression tree approach. The predictive model was trained and validated using eddy flux NEE data over the periods 2000–2004 and 2005–2006, respectively. We found that the model predicted NEE well ( r = 0.73, p < 0.001). We then applied the model to the continental scale and estimated NEE for each 1 km × 1 km cell across the conterminous U.S. for each 8-day interval in 2005 using spatially explicit MODIS data. The model generally captured the expected spatial and seasonal patterns of NEE as determined from measurements and the literature. Our study demonstrated that our empirical approach is effective for scaling up eddy flux NEE measurements to the continental scale and producing wall-to-wall NEE estimates across multiple biomes. Our estimates may provide an independent dataset from simulations with biogeochemical models and inverse modeling approaches for examining the spatiotemporal patterns of NEE and constraining terrestrial carbon budgets over large areas.

Using MODIS data to characterize seasonal inundation patterns in the Florida Everglades

Information regarding the spatial extent and timing of flooding in the world's major wetlands is important to a wide range of research questions including global methane models, water management, and biodiversity assessments. The Florida Everglades is one of the largest wetlands in the US, and is subject to substantial development and pressures that require intensive hydrological modeling and monitoring. The Moderate Resolution Imaging Spectrometer (MODIS) is a global sensor with high frequency repeat coverage and significant potential for mapping wetland extent and dynamics at moderate spatial resolutions. In this study, empirical models to predict surface inundation in the Everglades were estimated using MODIS data calibrated to water stage data from the South Florida Water Management District for the calendar year 2004. The results show that hydropatterns in the Florida Everglades are strongly correlated to a Tasseled Cap wetness index derived from MODIS Nadir Bidirectional Reflectance Function Adjusted Reflectance data. Several indices were tested, including the Normalized Difference Wetness Index and the diurnal land surface temperature difference, but the Tasseled Cap wetness index showed the strongest correlation to water stage data across a range of surface vegetation types. Other variables included in the analysis were elevation and percent tree cover present within a pixel. Using logistic regression and ensemble regression trees, maps of water depth and flooding likelihood were produced for each 16-day MODIS data period in 2004. The results suggest that MODIS is useful for dynamic monitoring of flooding, particularly in wetlands with sparse tree cover.

Scaling‐up technique for net ecosystem productivity of deciduous broadleaved forests in Japan using MODIS data

AbstractWe attempted to obtain carbon sequestration maps of deciduous forests in Japan using detectable parameters from the Moderate Resolution Imaging Spectrometer (MODIS) sensor and to determine how the spatial pattern of carbon sequestration differs within the same forest ecosystem type. For this investigation, we firstly parameterized the MODIS algorithm at one flux tower site, Takayama, for the years 2002–2003. The MODIS algorithm could link flux‐based net ecosystem productivity (NEP) with simple functions controlled by a thermal infrared band and a vegetation index. Second, the performance of the MODIS algorithm was validated through comparisons with the flux‐based NEP at another flux tower site, Hitsujigaoka. The MODIS‐based NEP at Hitsujigaoka was also within an accuracy of a flux‐based NEP with R2 of 0.879 and root mean square error of 1.64 gC m−2 day−1, regardless of canopy structure and age. The MODIS algorithm was noteworthy for its general applicability in different locations. Finally, we used the MODIS algorithm for the same forest ecosystem type in Japan for regional extrapolation of NEP. The MODIS‐based NEP of deciduous forests in Japan showed great variance with 347 ± 288 gC m−2 year−1 in 2002, according to the stand structure and climatic condition of the year. Studies for quantification of ecosystem carbon balance need to consider variance, frequency and spatial distributions of NEP. Satellite remote sensing demonstrated the potential for the large‐scale mapping of NEP.

Mapping incident photosynthetically active radiation from MODIS data over China

Photosynthetically active radiation (PAR) is a key input parameter for almost all terrestrial ecosystem models, but the spatial resolution of current PAR products is too coarse to satisfy regional application requirements. In this paper, we present an operational system for PAR retrieval from MODIS data that is based on an idea proposed by [Liang, S., Zheng, T., Liu, R., Fang, H., Tsay, S. -C., & Running, S. (2006). Estimation of incident photosynthetically active radiation from Moderate Resolution Imaging Spectrometer data. Journal of Geophysical Research, 111, D15208. doi:10.1029/2005JD006730]. However, the operational system for PAR retrieval described here contains several improvements. The algorithm utilizes MODIS 1B data combining MODIS land surface products and BRDF model parameters products to directly estimate diffuse PAR, direct PAR and total PAR. Times-series data interpolation removes the noise and cloud contamination of land surface reflectance. PAR is retrieved by searching look-up tables calculated using a radiative transfer model. The system can automatically process MODIS 1B data to generate instantaneous and daily PAR. The instantaneous PAR products are compared with observational data from seven ChinaFLUX stations, and daily total PAR estimates are compared with those estimates of global radiation from 98 meteorological stations over China. The results indicate that this approach can produce reasonable PAR estimates, although this method overestimates PAR for low values of PAR.