CiteULike: neteler's modis

Spatiotemporal variability of land surface moisture based on vegetation and temperature characteristics in Northern Shaanxi Loess Plateau, China

Zhengguo Li — 2008-05-19T09:39:38-00:00

Journal of Arid Environments, Vol. 72, No. 6. (June 2008), pp. 974-985.

Vegetation coverage and surface temperature are important parameters in describing the characteristics of land cover, which in combination can provide information on vegetation and soil moisture conditions at the surface. This paper aims to estimate spatial and temporal patterns of soil moisture in the Loess Plateau, China. Using Terra/MODIS images for each 10-day period in 2004 covering the semi-arid North Shaanxi Loess Plateau, a simplified land surface dryness index (Temperature-Vegetation Dryness Index, TVDI) developed by Sandholt [Sandholt, I., Rasmussena, K, Andersenb, J., 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment 79, 213-224.] was used to determine the relationship between surface temperature and vegetation index. From the analysis, it can be inferred that the trend in seasonal change of TVDI is high values in the dry season (spring or summer) and low values in the rainy season (autumn or winter). Moreover, the land surface moisture of each watershed had its seasonal characteristics. The relationship between TVDI and land cover types indicated that water-retention in forest and shrub areas was better than cropland and rangeland in relatively wet conditions, and rangeland was better than forest and shrub areas in dry conditions.

MODIS tasseled cap transformation and its utility

Xiaoyang Zhang — 2008-03-28T09:36:38-00:00

Geoscience and Remote Sensing Symposium, 2002. IGARSS '02. 2002 IEEE International, Vol. 2 (2002), pp. 1063-1065 vol.2.

A time series of globally distributed spectral MODIS NBARs (Nadir Bidirectional-reflectance-distribution-function Adjusted surface Reflectance) was used to determine initial tasseled cap coefficients. An assessment of an annual time series of tasseled cap features indicated their utility for detecting vegetation phenological cycles. The comparison analysis showed that the temporal pattern of NBAR greenness was closely correlated with the Enhanced Vegetation Index (EVI), while NBAR brightness matched MODIS global broadband albedos. Thresholded global NBAR wetnesses appear to relate to MODIS snow and ice presence as determined by the Normalized Difference Snow Index (NDSI).

Global Data for Ecology and Epidemiology: A Novel Algorithm for Temporal Fourier Processing MODIS Data.

JP Scharlemann — 2008-01-16T16:23:08-00:00

PLoS ONE, Vol. 3, No. 1. (2008)

BACKGROUND: Remotely-sensed environmental data from earth-orbiting satellites are increasingly used to model the distribution and abundance of both plant and animal species, especially those of economic or conservation importance. Time series of data from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensors on-board NASA's Terra and Aqua satellites offer the potential to capture environmental thermal and vegetation seasonality, through temporal Fourier analysis, more accurately than was previously possible using the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor data. MODIS data are composited over 8- or 16-day time intervals that pose unique problems for temporal Fourier analysis. Applying standard techniques to MODIS data can introduce errors of up to 30% in the estimation of the amplitudes and phases of the Fourier harmonics. METHODOLOGY/PRINCIPAL FINDINGS: We present a novel spline-based algorithm that overcomes the processing problems of composited MODIS data. The algorithm is tested on artificial data generated using randomly selected values of both amplitudes and phases, and provides an accurate estimate of the input variables under all conditions. The algorithm was then applied to produce layers that capture the seasonality in MODIS data for the period from 2001 to 2005. CONCLUSIONS/SIGNIFICANCE: Global temporal Fourier processed images of 1 km MODIS data for Middle Infrared Reflectance, day- and night-time Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) are presented for ecological and epidemiological applications. The finer spatial and temporal resolution, combined with the greater geolocational and spectral accuracy of the MODIS instruments, compared with previous multi-temporal data sets, mean that these data may be used with greater confidence in species' distribution modelling.

An overview of MODIS Land data processing and product status

CO Justice — 2007-07-15T16:47:49-00:00

Remote Sensing of Environment, Vol. 83, No. 1-2. (November 2002), pp. 3-15.

Data from the first Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the NASA Terra Platform are being used to provide a new generation of land data products in support of the National Aeronautics and Space Administration (NASA)'s Earth Science Enterprise, global change research and natural resource management. The MODIS products include global data sets heretofore unavailable, derived from new moderate resolution spectral bands with spatial resolutions of 250 m to 1 km. A partnership between Science Team members and the MODIS Science Data Support Team is producing data sets of unprecedented volume and number for the land research and applications. This overview paper provides a summary of the instrument performance and status, the data production system, the products, their status and availability for land studies.

The MODIS Land product quality assessment approach

David Roy — 2007-05-12T20:30:22-00:00

Remote Sensing of Environment, Vol. 83, No. 1-2. (November 2002), pp. 62-76.

The correct interpretation of scientific information from global, long-term series of remote sensing products requires the ability to discriminate between product artifacts and changes in the Earth processes being monitored. A suite of global land surface products is made from Moderate Resolution Imaging Spectroradiometer (MODIS) instrument data. Quality assessment (QA) is an integral part of this production chain and focuses on evaluating and documenting the scientific quality of the products with respect to their intended performance. This paper describes the QA approach adopted by the MODIS Land (MODLAND) Science Team and coordinated by the MODIS Land Data Operational Product Evaluation (LDOPE) facility. The described methodology represents a new approach for assessing and ensuring the performance of land remote sensing products that are generated on a systematic basis.

MODIS Snow Products User Guide for Collection 4 Data Products

GA Riggs — 2007-05-10T08:32:18-00:00

(2006)

Algorithm Theoretical Basis Document (ATBD) for the MODIS Snow- and Sea Ice-Mapping Algorithms

DK Hall — 2007-05-10T08:32:18-00:00

(2001)

Early detection of TBEv spatial distribution and activity in the Province of Trento assessed using serological and remotely-sensed climatic data

A Rizzoli — 2007-05-05T20:13:14-00:00

Geospatial Health, Vol. 1, No. 2. (May 2007), pp. 169-176.

New human cases of tick borne encephalitis (TBE) have recently been recorded outside the recognized foci of this disease, i.e. in the province of Trento in northern Italy. In order to predict the highest risk areas for increased tick-borne encephalitis virus activity, we have combined cross-sectional serological data, obtained from 459 domestic goats, with analysis of the autumnal cooling rate based on Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) data. A significant relationship between finding antibodies against the virus in serum (seroprevalence) in goats and the autumnal cooling rate was detected, indicating that the transmission intensity of the virus does not only vary spatially, but also in relation to climatic factors. Virus seroprevalence in goats was correlated with the occurrence of tick-borne encephalitis in humans and also with the average number of forestry workers' tick bites, demonstrating that serological screening of domestic animals, combined with an analysis of the autumnal cooling rate, can be used as early-warning predictors of tick-borne encephalitis risk in humans.

Atmospheric correction algorithm: spectral reflectances (MOD09). Algorithm Technical Background Document (ATBD)

EF Vermote — 2007-05-05T19:50:47-00:00

(1999)

MODIS Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation Absorbed by Vegetation (FPAR) Product (MOD15) Algorithm Theoretical Basis Document

Y Knyazikhin — 2007-05-05T19:41:51-00:00

(1999)

Satellite-based modeling of gross primary production in an evergreen needleleaf forest

Xiangming Xiao — 2007-02-27T12:44:33-00:00

Remote Sensing of Environment, Vol. 89, No. 4. (29 February 2004), pp. 519-534.

The eddy covariance technique provides valuable information on net ecosystem exchange (NEE) of CO2, between the atmosphere and terrestrial ecosystems, ecosystem respiration, and gross primary production (GPP) at a variety of CO2 eddy flux tower sites. In this paper, we develop a new, satellite-based Vegetation Photosynthesis Model (VPM) to estimate the seasonal dynamics and interannual variation of GPP of evergreen needleleaf forests. The VPM model uses two improved vegetation indices (Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI)). We used multi-year (1998-2001) images from the VEGETATION sensor onboard the SPOT-4 satellite and CO2 flux data from a CO2 eddy flux tower site in Howland, Maine, USA. The seasonal dynamics of GPP predicted by the VPM model agreed well with observed GPP in 1998-2001 at the Howland Forest. These results demonstrate the potential of the satellite-driven VPM model for scaling-up GPP of forests at the CO2 flux tower sites, a key component for the study of the carbon cycle at regional and global scales.

Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data

Xiangming Xiao — 2007-02-24T06:04:41-00:00

Remote Sensing of Environment, Vol. 91, No. 2. (30 May 2004), pp. 256-270.

Net ecosystem exchange (NEE) of CO2 between the atmosphere and forest ecosystems is determined by gross primary production (GPP) of vegetation and ecosystem respiration. CO2 flux measurements at individual CO2 eddy flux sites provide valuable information on the seasonal dynamics of GPP. In this paper, we developed and validated the satellite-based Vegetation Photosynthesis Model (VPM), using site-specific CO2 flux and climate data from a temperate deciduous broadleaf forest at Harvard Forest, Massachusetts, USA. The VPM model is built upon the conceptual partitioning of photosynthetically active vegetation and non-photosynthetic vegetation (NPV) within the leaf and canopy. It estimates GPP, using satellite-derived Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), air temperature and photosynthetically active radiation (PAR). Multi-year (1998-2001) data analyses have shown that EVI had a stronger linear relationship with GPP than did the Normalized Difference Vegetation Index (NDVI). Two simulations of the VPM model were conducted, using vegetation indices from the VEGETATION (VGT) sensor onboard the SPOT-4 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra satellite. The predicted GPP values agreed reasonably well with observed GPP of the deciduous broadleaf forest at Harvard Forest, Massachusetts. This study highlighted the biophysical performance of improved vegetation indices in relation to GPP and demonstrated the potential of the VPM model for scaling-up of GPP of deciduous broadleaf forests.

NDWI -- A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water From Space

Bo-Cai Gao — 2007-02-24T05:25:58-00:00

Remote Sensing of Environment, Vol. 58, No. 3. (December 1996), pp. 257-266.

The normalized difference vegetation index (NDVI) has been widely used for remote sensing of vegetation for many years. This index uses radiances or reflectances from a red channel around 0.66 m and a near-IR channel around 0.86 m. The red channel is located in the strong chlorophyll absorption region, while the near-IR channel is located in the high reflectance plateau of vegetation canopies. The two channels sense very different depths through vegetation canopies. In this article, another index, namely, the normalized difference water index (NDWI), is proposed for remote sensing of vegetation liquid water from space. NDWI is defined as ((0.86 m) - (1.24 m))/((0.86 m) + (1.24 m)), where represents the radiance in reflectance units. Both the 0.86-m and the 1.24-m channels are located in the high reflectance plateau of vegetation canopies. They sense similar depths through vegetation canopies. Absorption by vegetation liquid water near 0.86 m is negligible. Weak liquid absorption at 1.24 m is present. Canopy scattering enhances the water absorption. As a result, NDWI is sensitive to changes in liquid water content of vegetation canopies. Atmospheric aerosol scattering effects in the 0.86-1.24 m region are weak. NDWI is less sensitive to atmospheric effects than NDVI. NDWI does not remove completely the background soil reflectance effects, similar to NDVI. Because the information about vegetation canopies contained in the 1.24-m channel is very different from that contained in the red channel near 0.66 m, NDWI should be considered as an independent vegetation index. It is complementary to, not a substitute for NDVI. Laboratory-measured reflectance spectra of stacked green leaves, and spectral imaging data acquired with Airborne Visible Infrared Imaging Spectrometer (AVIRIS) over Jasper Ridge in California and the High Plains in northern Colorado, are used to demonstrate the usefulness of NDWI. Comparisons between NDWI and NDVI images are also given.

Study on NDVI-Ts space by combining LAI and evapotranspiration

Lijuan Han — 2007-02-06T17:16:31-00:00

Science in China Series D: Earth Sciences, Vol. V49, No. 7. (6 July 2006), pp. 747-754.

Improved estimates of net primary productivity from modis satellite data at regional and local scales.

Y Pan — 2006-07-30T19:50:01-00:00

Ecol Appl, Vol. 16, No. 1. (February 2006), pp. 125-132.

We compared estimates of net primary production (NPP) from the MODIS satellite with estimates from a forest ecosystem process model (PnET-CN) and forest inventory and analysis (FIA) data for forest types of the mid-Atlantic region of the United States. The regional means were similar for the three methods and for the dominant oak-hickory forests in the region. However, MODIS underestimated NPP for less-dominant northern hardwood forests and overestimated NPP for coniferous forests. Causes of inaccurate estimates of NPP by MODIS were (1) an aggregated classification and parameterization of diverse deciduous forests in different climatic environments into a single class that averages different radiation conversion efficiencies; and (2) lack of soil water constraints on NPP for forests or areas that occur on thin or sandy, coarse-grained soil. We developed the "available soil water index" for adjusting the MODIS NPP estimates, which significantly improved NPP estimates for coniferous forests. The MODIS NPP estimates have many advantages such as globally continuous monitoring and remarkable accuracy for large scales. However, at regional or local scales, our study indicates that it is necessary to adjust estimates to specific vegetation types and soil water conditions.

Assessment of MODIS-EVI, MODIS-NDVI and VEGETATION-NDVI Composite Data Using Agricultural Measurements: An Example at Corn Fields in Western Mexico.

Pei-Yu Chen — 2006-07-30T19:44:12-00:00

Environ Monit Assess (17 December 2005), pp. 1-14.

Although several types of satellite data provide temporal information of the land use at no cost, digital satellite data applications for agricultural studies are limited compared to applications for forest management. This study assessed the suitability of vegetation indices derived from the TERRA-Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and SPOT-VEGETATION (VGT) sensor for identifying corn growth in western Mexico. Overall, the Normalized Difference Vegetation Index (NDVI) composites from the VGT sensor based on bi-directional compositing method produced vegetation information most closely resembling actual crop conditions. The NDVI composites from the MODIS sensor exhibited saturated signals starting 30 days after planting, but corresponded to green leaf senescence in April. The temporal NDVI composites from the VGT sensor based on the maximum value method had a maximum plateau for 80 days, which masked the important crop transformation from vegetative stage to reproductive stage. The Enhanced Vegetation Index (EVI) composites from the MODIS sensor reached a maximum plateau 40 days earlier than the occurrence of maximum leaf area index (LAI) and maximum intercepted fraction of photosynthetic active radiation (fPAR) derived from in-situ measurements. The results of this study showed that the 250-m resolution MODIS data did not provide more accurate vegetation information for corn growth description than the 500-m and 1000-m resolution MODIS data.

MODIS Land-Surface Temperature products users' guide

Z Wan — 2006-07-28T09:34:37-00:00

(2003)

MODIS Land-Surface Temperature. Algorithm theoretical basis document (LST ATBD): LST calculations

Z Wan — 2006-07-28T09:33:59-00:00

(1999)

MODIS vegetation index (MOD 13). Algorithm theoretical basis document ATBD13

A Huete — 2006-07-28T09:33:12-00:00

(1999)

Satellite-based mapping of the growing season and bioclimatic zones in Fennoscandia

Stein Karlsen — 2006-07-03T15:23:13-00:00

Global Ecology & Biogeography, Vol. 15, No. 4. (July 2006), pp. 416-430.

Aim To test whether satellite-derived NDVI values obtained during the growing season as delimited by the onset of phenological phases can be used to map bioclimatically a large region such as Fennoscandia. Location Fennoscandia north of about 58° N and neighbouring parts of NW Russia. Methods Phenology data on birch from 15 research stations and the half-monthly GIMMS-NDVI data set with 8 × 8 km2 resolution from the period 1982–2002 were used to characterize the growing season. To link surface phenology with NDVI data, new algorithms on a pixel-by-pixel basis that show high correlation with phenophases on birch were developed. Then, time-integrated values (TI NDVI) during the phenologically defined growing season were computed to produce a bioclimatological map of Fennoscandia, which was tested and correlated with growing degree days (GDD) obtained from 20 meteorological stations. The map was also compared vs. traditional bioclimatic maps, and analysed for error factors distorting NDVI values. Results The correlation between GDD and TI NDVI data during the phenologically defined growing season was very high. Therefore, the TI NDVI map could be presented as a bioclimatic map reflecting GDD. However, several major areas have interfering factors distorting NDVI values, such as the pixel heterogeneity caused by the altitudinal mosaic in western Norway, the mosaic of lakes in southeastern Finland, and the agriculture-dominated areas in southern Fennoscandia. Main conclusions TI NDVI data from the phenologically defined growing season during 1982–2002 in Fennoscandia can be processed as a bioclimatic map reflecting GDD, except for the areas distorting NDVI values by their strong ground-cover heterogeneity.

Time Series Generator - Ein flexibles Softwaremodul zur Generierung und Bewertung von Zeitserien aus NASA MODIS Datenprodukten

C Conrad — 2006-06-22T13:46:56-00:00

(June 2005), pp. 100-105.

Das Potenzial von Zeitreihen optischer Satellitendaten wird schon länger vielfältig genutzt. Zur Klassifikation von Landbedeckung und -nutzung, sowie für Trendanalysen und Monitoring von Vegetation existieren diverse Verfahren (COPPIN et al. 2001). Die Zeitreihen dienen darüber hinaus der Gewinnung von Parametern für Modellierungen, beispielsweise in Hydrologie oder Klimatologie. Archivmaterial zeitlich hoch auflösender Sensoren wie der Advanced Very High Resolution Radiometer AVHRR auf den NOAA-Satelliten bieten dazu über 30 Jahre Datenmaterial.

Generation and assessment of MODIS time series using quality information

RR Colditz — 2006-06-22T13:46:55-00:00

(August 2006)

Monitoring and modeling extensive Earth surface processes for regional to global applications such as carbon budgeting or biomass estimation requires time series derived from remotely sensed imagery. Time series are also needed for discrimination of long-term land cover change from short-term variations, mapping of vegetation dynamics and improved land cover mapping and update. The results of these applications, however, clearly depend on the quality of the time series. Cloud coverage, high aerosol content, adverse view and illumination angles, or sensor defects affect and corrupt the data and may lead to false conclusions. Value-added MODIS data contain detailed pixel level quality information. This source of meta-data highly suits for data analysis or generation of time series. A software package, called Time Series Generator (TiSeG), has been developed to analyze data quality and estimate the quality of time series to be generated. TiSeG meets the challenge to weight the data quality against the quantity of available data for meaningful time series construction.

Ableitung von phänologischen Verlaufsmustern aus MODIS-Zeitserien und Möglichkeiten der Anwendung

RR Colditz — 2006-06-22T13:46:55-00:00

(2005), pp. 94-99.

Zeitlich hochaufgelöste operationelle Satellitendaten wie NOAA AVHRR und MODIS eignen sich besonders zur Extraktion und Analyse zeitlicher Verlaufsmuster. Insbesondere die MODIS-Daten stellen eine geeignete Ressource aktueller Daten dar und ermöglichen durch die detaillierten Qualitätsinformationen eine verbesserte Erstellung von Zeitserien. Diese Studie extrahiert zeitliche Signaturen aus MODIS NDVI- und LAI-Daten mittels zweier Landbedeckungsklassifikationen aus dem Jahr 2002 für die Beispielregionen Usbekistan und westliches Afrika. Die abgeleiteten Verlaufsmuster werden zeitlich analysiert und zur Landbedeckungsklassifikation mittels eines Nearest Neighbour Ansatzes auch für andere Jahre angewendet. Somit ist unter der Berücksichtigung phänologischer Variationen ein jährliches Klassifikationsupdate möglich.

Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI

Pieter Beck — 2006-01-29T15:50:05-00:00

Remote Sensing of Environment, Vol. 100, No. 3. (15 February 2006), pp. 321-334.

Current models of vegetation dynamics using the normalized vegetation index (NDVI) time series perform poorly for high-latitude environments. This is due partly to specific attributes of these environments, such as short growing season, long periods of darkness in winter, persistence of snow cover, and dominance of evergreen species, but also to the design of the models. We present a new method for monitoring vegetation activity at high latitudes, using Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI. It estimates the NDVI of the vegetation during winter and applies a double logistic function, which is uniquely defined by six parameters that describe the yearly NDVI time series. Using NDVI data from 2000 to 2004, we illustrate the performance of this method for an area in northern Scandinavia (35 x 162 km2, 68[deg] N 23[deg] E) and compare it to existing methods based on Fourier series and asymmetric Gaussian functions. The double logistic functions describe the NDVI data better than both the Fourier series and the asymmetric Gaussian functions, as quantified by the root mean square errors. Compared with the method based on Fourier series, the new method does not overestimate the duration of the growing season. In addition, it handles outliers effectively and estimates parameters that are related to phenological events, such as the timing of spring and autumn. This makes the method most suitable for both estimating biophysical parameters and monitoring vegetation phenology.

On the relationship of NDVI with leaf area index in a deciduous forest site

Quan Wang — 2005-08-05T12:25:05-00:00

Remote Sensing of Environment, Vol. 94, No. 2. (30 January 2005), pp. 244-255.

Numerous studies have reported on the relationship between the normalized difference vegetation index (NDVI) and leaf area index (LAI), but the seasonal and annual variability of this relationship has been less explored. This paper reports a study of the NDVI-LAI relationship through the years from 1996 to 2001 at a deciduous forest site. Six years of LAI patterns from the forest were estimated using a radiative transfer model with input of above and below canopy measurements of global radiation, while NDVI data sets were retrieved from composite NDVI time series of various remote sensing sources, namely NOAA Advanced Very High Resolution Radiometer (AVHRR; 1996, 1997, 1998 and 2000), SPOT VEGETATION (1998-2001), and Terra MODIS (2001). Composite NDVI was first used to remove the residual noise based on an adjusted Fourier transform and to obtain the NDVI time-series for each day during each year.The results suggest that the NDVI-LAI relationship can vary both seasonally and inter-annually in tune with the variations in phenological development of the trees and in response to temporal variations of environmental conditions. Strong linear relationships are obtained during the leaf production and leaf senescence periods for all years, but the relationship is poor during periods of maximum LAI, apparently due to the saturation of NDVI at high values of LAI. The NDVI-LAI relationship was found to be poor (R2 varied from 0.39 to 0.46 for different sources of NDVI) when all the data were pooled across the years, apparently due to different leaf area development patterns in the different years. The relationship is also affected by background NDVI, but this could be minimized by applying relative NDVI.Comparisons between AVHRR and VEGETATION NDVI revealed that these two had good linear relationships (R2=0.74 for 1998 and 0.63 for 2000). However, VEGETATION NDVI data series had some unreasonably high values during beginning and end of each year period, which must be discarded before adjusted Fourier transform processing. MODIS NDVI had values greater than 0.62 through the entire year in 2001, however, MODIS NDVI still showed an "M-shaped" pattern as observed for VEGETATION NDVI in 2001. MODIS enhanced vegetation index (EVI) was the only index that exhibited a poor linear relationship with LAI during the leaf senescence period in year 2001. The results suggest that a relationship established between the LAI and NDVI in a particular year may not be applicable in other years, so attention must be paid to the temporal scale when applying NDVI-LAI relationships.

Comparison of non-linear mixture models: sub-pixel classification

Weiguo Liu — 2005-08-05T12:23:31-00:00

Remote Sensing of Environment, Vol. 94, No. 2. (30 January 2005), pp. 145-154.

Sub-pixel level classification is essential for the successful description of many land cover patterns with spatial resolution of less than 1 km and has been widely used in global or continental scale land cover mapping with remote sensing data. This paper presents a general comparison of four non-linear models for sub-pixel classification: ARTMAP, ART-MMAP, Regression Tree (RT) and Multilayer Perceptron (MLP) with Back-Propagation (BP) algorithm. The comparison is based on four factors: accuracy, model complexity, interpolation ability and error distribution. Two data sets, one simulated and one real world MODIS satellite image, were used to demonstrate the characteristics of each model. Experimental results show the superior performance of MLP with the simulated data set and better performance of ART-MMAP with the MODIS data set.

Overview of the radiometric and biophysical performance of the MODIS vegetation indices

A Huete — 2005-08-05T10:25:00-00:00

Remote Sensing of Environment, Vol. 83, No. 1-2. (November 2002), pp. 195-213.

We evaluated the initial 12 months of vegetation index product availability from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Earth Observing System-Terra platform. Two MODIS vegetation indices (VI), the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), are produced at 1-km and 500-m resolutions and 16-day compositing periods. This paper presents an initial analysis of the MODIS NDVI and EVI performance from both radiometric and biophysical perspectives. We utilize a combination of site-intensive and regionally extensive approaches to demonstrate the performance and validity of the two indices. Our results showed a good correspondence between airborne-measured, top-of-canopy reflectances and VI values with those from the MODIS sensor at four intensively measured test sites representing semi-arid grass/shrub, savanna, and tropical forest biomes. Simultaneously derived field biophysical measures also demonstrated the scientific utility of the MODIS VI. Multitemporal profiles of the MODIS VIs over numerous biome types in North and South America well represented their seasonal phenologies. Comparisons of the MODIS-NDVI with the NOAA-14, 1-km AVHRR-NDVI temporal profiles showed that the MODIS-based index performed with higher fidelity. The dynamic range of the MODIS VIs are presented and their sensitivities in discriminating vegetation differences are evaluated in sparse and dense vegetation areas. We found the NDVI to asymptotically saturate in high biomass regions such as in the Amazon while the EVI remained sensitive to canopy variations.

Using MODIS snow cover maps in modeling snowmelt runoff process in the eastern part of Turkey

AE Tekeli — 2005-07-29T16:34:59-00:00

Remote Sensing of Environment, Vol. 97, No. 2. (30 July 2005), pp. 216-230.

Water perhaps is the most valuable natural asset in the Middle East as it was a historical key for settlement and survival in Mesopotamia, "the land between two rivers". At present, the Euphrates and Tigris are the two largest trans-boundary rivers in Western Asia where Turkey, Syria, Iran, Iraq and Saudi Arabia are the riparian countries. The Euphrates and Tigris basins are largely fed from snow precipitation whereby nearly two-thirds occur in winter and may remain in the form of snow for half of the year. The concentration of discharge mainly from snowmelt during spring and early summer months causes not only extensive flooding, inundating large areas, but also the loss of much needed water required for irrigation and power generation purposes during the summer season. Accordingly, modeling of snow-covered area in the mountainous regions of Eastern Turkey, as being one of the major headwaters of Euphrates-Tigris basin, has significant importance in order to forecast snowmelt discharge especially for energy production, flood control, irrigation and reservoir operation optimization.A pilot basin, located on the upper Euphrates River, is selected where five automated meteorological and snow stations are installed for real time operations. The daily snow cover maps obtained from Moderate Resolution Imaging Spectroradiometer MODIS at 500 m resolution are compared with ground information for the winter of 2002-2003 both during accumulation and ablation and at accumulation stage for the winter of 2003-2004. The snow presence on the ground is determined from the snow courses performed. Such measurements were made at 19 points in and around the upper Euphrates River in Turkey and at 20 points in the upper portion of the pilot basin for the winters of 2002-2003 and 2003-2004, respectively. Comparison of MODIS snow maps with in situ measurements over the snow season show good agreement with overall accuracies ranging between 62% and 82% considering the shift in the days of comparison. The main reasons to have disagreement between MODIS and in situ data are the high cloud cover frequency in the area and the current version of the MODIS cloud-mask that appears to frequently map edges of snow-covered areas and land surfaces. The effect of elevation and land cover types on validation of MODIS snow cover maps is also analyzed. In order to minimize the cloud cover and maximize the snow cover, MODIS-8 daily snow cover products are used in deriving the snow depletion curve, which is one of the input parameters of the snowmelt runoff model (SRM). The initial results of modeling process show that MODIS snow-covered area product can be used for simulation and also for forecasting of snowmelt runoff in basins of Turkey.

Terra and Aqua: new data for epidemiology and public health

Andrew Tatem — 2005-07-22T15:25:28-00:00

International Journal of Applied Earth Observation and Geoinformation, Vol. 6, No. 1. (November 2004), pp. 33-46.

Earth-observing satellites have only recently been exploited for the measurement of environmental variables of relevance to epidemiology and public health. Such work has relied on sensors with spatial, spectral and geometric constraints that have allowed large-area questions associated with the epidemiology of vector-borne diseases to be addressed. Moving from pretty maps to pragmatic control tools requires a suite of satellite-derived environmental data of higher fidelity, spatial resolution, spectral depth and at similar temporal resolutions to existing meteorological satellites. Information derived from sensors onboard the next generation of moderate-resolution Earth-observing sensors may provide the key. The MODIS and ASTER sensors onboard the Terra and Aqua platforms provide substantial improvements in spatial resolution, number of spectral channels, choices of bandwidths, radiometric calibration and a much-enhanced set of pre-processed and freely available products. These sensors provide an important advance in moderate-resolution remote sensing and the data available to those concerned with improving public health.

Detection of Snow Cover Using Millimeter-Wave Imaging Radiometer (MIR) Data - an update

A Tait — 2005-07-15T16:01:26-00:00

Remote Sensing of Environment, Vol. 68, No. 1. (April 1999), pp. 53-60.

Millimeter-wave Imaging Radiometer (MIR) data, collected from a NASA ER-2 flight over the Great Lakes region and New England in February 1997, are used to identify snow cover. Data at 89 GHz, 150 GHz, and 220 GHz are compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product. Additional data sets used in this analysis are the International Geosphere Biosphere Programme (IGBP) land cover classification and the MODIS cloud cover mask. Results show that for cloud-free scenes, the MIR data are well related to the snow and vegetation cover. Under cloudy conditions the MODIS snow mapping algorithm, which uses visible and near-infrared data, is unable to detect snow cover. However, the surface cover is still clearly discernible in the MIR images. It is suggested that high frequency microwave data may be used to supplement the MODIS, or other visible and near-infrared derived snow cover products, during cloudy conditions. The development of snow cover algorithms combining future Earth Observing System (EOS) microwave, visible, and infrared data are discussed.

MODIS snow-cover products

DK Hall — 2005-07-15T15:49:21-00:00

Remote Sensing of Environment, Vol. 83, No. 1. (November 2002), pp. 181-194.

On December 18, 1999, the Terra satellite was launched with a complement of five instruments including the Moderate Resolution Imaging Spectroradiometer (MODIS). Many geophysical products are derived from MODIS data including global snow-cover products. MODIS snow and ice products have been available through the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) since September 13, 2000. MODIS snow-cover products represent potential improvement to or enhancement of the currently available operational products mainly because the MODIS products are global and 500-m resolution, and have the capability to separate most snow and clouds. The MODIS snow-mapping algorithms are automated, which means that a consistent data set may be generated for long-term climate studies that require snow-cover information. Extensive quality assurance (QA) information is stored with the products. The MODIS snow product suite begins with a 500-m resolution, 2330-km swath snow-cover map, which is then gridded to an integerized sinusoidal grid to produce daily and 8-day composite tile products. The sequence proceeds to a climate-modeling grid (CMG) product at 0.05o resolution, with both daily and 8-day composite products. Each pixel of the daily CMG contains fraction of snow cover from 40% to 100%. Measured errors of commission in the CMG are low, for example, on the continent of Australia in the spring, they vary from 0.02% to 0.10%. Near-term enhancements include daily snow albedo and fractional snow cover. A case study from March 6, 2000, involving MODIS data and field and aircraft measurements, is presented to show some early validation work.

Development of Methods for Mapping Global Snow Cover Using Moderate Resolution Imaging Spectroradiometer Data

DK Hall — 2005-07-15T15:49:19-00:00

Remote Sensing of Environment, Vol. 54, No. 2. (November 1995), pp. 127-140.

An algorithm is being developed to map global snow cover using Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) data beginning at launch in 1998. As currently planned, digital maps will be produced that will provide daily, and perhaps maximum weekly, global snow cover at 500-m spatial resolution. Statistics will be generated on the extent and persistence of snow cover in each pixel for each weekly map, cloud cover permitting. It will also be possible to generate snow-cover maps at 250-m spatial resolution using MODIS data, and to study snow-cover characteristics. Preliminary validation activities of the prototype version of our snow-mapping algorithm, SNOMAP, have been undertaken. SNOMAP will use criteria tests and a decision rule to identify snow in each 500-m MODIS pixel. Use of SNOMAP on a previously mapped Landsat Thematic Mapper (TM) scene of the Sierra Nevadas has shown that SNOMAP is 98% accurate in identifying snow in pixels that are snow covered by 60% or more. Results of a comparison of a SNOMAP classification with a supervised-classification technique on six other TM scenes show that SNOMAP and supervised-classification techniques agree to within about 11% or less for nearly cloud-free scenes and that SNOMAP provided more consistent results. About 10% of the snow cover, known to be present on the 14 March 1991 TM scene covering Glacier National Park in northern Montana, is obscured by dense forest cover. Mapping snow cover in areas of dense forests is a limitation in the use of this procedure for global snow-cover mapping. This limitation, and sources of error will be assessed globally as SNOMAP is refined and tested before and following the launch of MODIS.

Comparison of methods of snow cover mapping by analysing the solar spectrum of satellite remote sensing data in China

J Wang — 2005-07-15T15:49:14-00:00

International Journal of Remote Sensing, Vol. 24, No. 21. (2003), pp. 4129-4136.

Three methods, supervised classification (SC), digital number (DN) statistics and Normalized Difference Snow Index (NDSI), are used to map snow cover and then calculate snow cover area. Data sets from Landsat TM, Moderate Resolution Imaging Spectroradiometer (MODIS) and NOAA/AVHRR are selected because these sensors of different spatial resolution provide the most up to date remote sensing data for China. The results show that the best method for obtaining the snow index is different for each of these sensor products because of their different spatial and temporal resolutions and objectives of application. Reflectivity threshold statistics (RTs) should be used if the data series is incomplete; whereas SC needs a relatively accurate signature file for classification. A valid and rational method has been certified which selects NDSI for extracting snow pixels. Meanwhile, we introduce the brightness compensation method for decreasing the impact of topographic shading on distinguishing of snow pixels.

Validation of daily MODIS snow cover maps of the Upper Rio Grande River Basin for the 2000-2001 snow year

Andrew Klein — 2005-07-15T15:34:28-00:00

Remote Sensing of Environment, Vol. 86, No. 2. (30 July 2003), pp. 162-176.

Snow cover represents an important water resource for the Upper Rio Grande River Basin of Colorado and New Mexico. Accuracy assessment of MODIS snow products was accomplished using Geographic Information System (GIS) techniques. Daily snow cover maps produced from Moderate Resolution Imaging Spectroradiometer (MODIS) data were compared with operational snow cover maps produced by the National Operational Hydrologic Remote Sensing Center (NOHRSC) and against in situ Snowpack Telemetry (SNOTEL) measurements for the 2000-2001 snow season. Over the snow season, agreement between the MODIS and NOHRSC snow maps was high with an overall agreement of 86%. However, MODIS snow maps typically indicate a higher proportion of the basin as being snow-covered than do the NOHRSC snow maps. In particular, large tracts of evergreen forest on the western slopes of the San de Cristo Range, which comprise a large portion of the eastern margin of the basin, are more consistently mapped as snow-covered in the MODIS snow products than in the NOHRSC snow products. NOHRSC snow maps, however, typically indicate a greater proportion of the central portion of the basin, predominately in cultivated areas, as snow. Comparisons of both snow maps with in situ SNOTEL measurements over the snow season show good overall agreement with overall accuracies of 94% and 76% for MODIS and NOHRSC, respectively. A lengthened comparison of MODIS against SNOTEL sites, which increases the number of comparisons of snow-free conditions, indicates a slightly lower overall classification accuracy of 88%. Errors in mapping extra snow and missing snow by MODIS are comparable, with MODIS missing snow in approximately 12% of the cases and mapping too much snow in 15% of the cases. The majority of the days when MODIS fails to map snow occurs at snow depths of less than 4 cm.

Snow-cover mapping in forests by constrained linear spectral unmixing of MODIS data

Dagrun Vikhamar — 2005-07-15T15:34:26-00:00

Remote Sensing of Environment, Vol. 88, No. 3. (15 December 2003), pp. 309-323.

A snow-cover mapping method accounting for forests (SnowFrac) is presented. SnowFrac uses spectral unmixing and endmember constraints to estimate the snow-cover fraction of a pixel. The unmixing is based on a linear spectral mixture model, which includes endmembers for snow, conifer, branches of leafless deciduous trees and snow-free ground. Model input consists of a land-cover fraction map and endmember spectra. The land-cover fraction map is applied in the unmixing procedure to identify the number and types of endmembers for every pixel, but also to set constraints on the area fractions of the forest endmembers. SnowFrac was applied on two Terra Moderate Resolution Imaging Spectroradiometer (MODIS) images with different snow conditions covering a forested area in southern Norway. Six experiments were carried out, each with different endmember constraints. Estimated snow-cover fractions were compared with snow-cover fraction reference maps derived from two Landsat Enhanced Thematic Mapper Plus (ETM+) images acquired the same days as the MODIS images. Results are presented for non-forested areas, deciduous forests, coniferous forests and mixed deciduous/coniferous forests. The snow-cover fraction estimates are enhanced by increasing constraints introduced to the unmixing procedure. The classification accuracy shows that 96% of the pixels are classified with less than 20% error (absolute units) on 7 May 2001 when all forested and non-forested areas are included. The corresponding figure for 4 May 2000 is 88%.

A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter

Jin Chen — 2005-07-15T15:34:21-00:00

Remote Sensing of Environment, Vol. 91, No. 3-4. (30 June 2004), pp. 332-344.

Although the Normalized Difference Vegetation Index (NDVI) time-series data, derived from NOAA/AVHRR, SPOT/VEGETATION, TERRA or AQUA/MODIS, has been successfully used in research regarding global environmental change, residual noise in the NDVI time-series data, even after applying strict pre-processing, impedes further analysis and risks generating erroneous results. Based on the assumptions that NDVI time-series follow annual cycles of growth and decline of vegetation, and that clouds or poor atmospheric conditions usually depress NDVI values, we have developed in the present study a simple but robust method based on the Savitzky-Golay filter to smooth out noise in NDVI time-series, specifically that caused primarily by cloud contamination and atmospheric variability. Our method was developed to make data approach the upper NDVI envelope and to reflect the changes in NDVI patterns via an iteration process. From the results obtained by applying the newly developed method to a 10-day MVC SPOT VGT-S product, we provide optimized parameters for the new method and compare this technique with the BISE algorithm and Fourier-based fitting method. Our results indicate that the new method is more effective in obtaining high-quality NDVI time-series.

Estimating fractional snow cover from MODIS using the normalized difference snow index

VV Salomonson — 2005-07-15T15:34:21-00:00

Remote Sensing of Environment, Vol. 89, No. 3. (15 February 2004), pp. 351-360.

Snow-cover information is important for a wide variety of scientific studies, water supply and management applications. The NASA Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) provides improved capabilities to observe snow cover from space and has been successfully using a normalized difference snow index (NDSI), along with threshold tests, to provide global, automated binary maps of snow cover. The NDSI is a spectral band ratio that takes advantage of the spectral differences of snow in short-wave infrared and visible MODIS spectral bands to identify snow versus other features in a scene. This study has evaluated whether there is a "signal" in the NDSI that could be used to estimate the fraction of snow within a 500 m MODIS pixel and thereby enhance the use of the NDSI approach in monitoring snow cover. Using Landsat 30-m observations as "ground truth," the percentage of snow cover was calculated for 500-m cells. Then a regression relationship between 500-m NDSI observations and fractional snow cover was developed over three different snow-covered regions and tested over other areas. The overall results indicate that the relationship between fractional snow cover and NDSI is reasonably robust when applied locally and over large areas like North America. The relationship offers advantages relative to other published fractional snow cover algorithms developed for global-scale use with MODIS. This study indicates that the fraction of snow cover within a MODIS pixel using this approach can be provided with a mean absolute error less than 0.1 over the range from 0.0 to 1.0 in fractional snow cover.

A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets

Chandra Giri — 2005-05-05T21:14:35-00:00

Remote Sensing of Environment, Vol. 94, No. 1. (15 January 2005), pp. 123-132.

Accurate and up-to-date global land cover data sets are necessary for various global change research studies including climate change, biodiversity conservation, ecosystem assessment, and environmental modeling. In recent years, substantial advancement has been achieved in generating such data products. Yet, we are far from producing geospatially consistent high-quality data at an operational level. We compared the recently available Global Land Cover 2000 (GLC-2000) and MODerate resolution Imaging Spectrometer (MODIS) global land cover data to evaluate the similarities and differences in methodologies and results, and to identify areas of spatial agreement and disagreement. These two global land cover data sets were prepared using different data sources, classification systems, and methodologies, but using the same spatial resolution (i.e., 1 km) satellite data. Our analysis shows a general agreement at the class aggregate level except for savannas/shrublands, and wetlands. The disagreement, however, increases when comparing detailed land cover classes. Similarly, percent agreement between the two data sets was found to be highly variable among biomes. The identified areas of spatial agreement and disagreement will be useful for both data producers and users. Data producers may use the areas of spatial agreement for training area selection and pay special attention to areas of disagreement for further improvement in future land cover characterization and mapping. Users can conveniently use the findings in the areas of agreement, whereas users might need to verify the informaiton in the areas of disagreement with the help of secondary information. Learning from past experience and building on the existing infrastructure (e.g., regional networks), further research is necessary to (1) reduce ambiguity in land cover definitions, (2) increase availability of improved spatial, spectral, radiometric, and geometric resolution satellite data, and (3) develop advanced classification algorithms.

Accuracy assessment of the MODIS 16-day albedo product for snow: comparisons with Greenland in situ measurements

Julienne Stroeve — 2005-07-15T14:58:11-00:00

Remote Sensing of Environment, Vol. 94, No. 1. (15 January 2005), pp. 46-60.

The accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day albedo product (MOD43) is assessed using ground-based albedo observations from automatic weather stations (AWS) over spatially homogeneous snow and semihomogeneous ice-covered surfaces on the Greenland ice sheet. Data from 16 AWS locations, spanning the years 2000-2003, were used for this assessment. In situ reflected shortwave data were corrected for a systematic positive spectral sensitivity bias of between 0.01 and 0.09 on a site-by-site basis using precise optical black radiometer data. Results indicate that the MOD43 albedo product retrieves snow albedo with an average root mean square error (RMSE) of [plus-or-minus sign]0.07 as compared to the station measurements, which have [plus-or-minus sign]0.035 RMSE uncertainty. If we eliminate all satellite retrievals that rely on the backup algorithm and consider only the highest quality results from the primary bidirectional reflectance distribution function (BRDF) algorithm, the MODIS albedo RMSE is [plus-or-minus sign]0.04, slightly larger than the in situ measurement uncertainty. There is general agreement between MODIS and in situ observations for albedo <0.7, while near the upper limit, a -0.05 MODIS albedo bias is evident from the scatter of the 16-site composite.

Time series processing of MODIS satellite data for landscape epidemiological applications

M Neteler — 2005-04-27T20:04:32-00:00

International Journal of Geoinformatics. Special Issue on FOSS/GRASS 2004 & GIS-IDEAS 2004, Vol. 1, No. 1. (March 2005), pp. 133-138.

This paper reports on the processing of time series of MODIS NDVI/EVI and LST satellite data in a Geographical Information System (GIS). The required data preparations for the integration of MODIS data in GIS is described with focus on the reprojection from MODIS/Sinusoidal projection to national coordinate systems. To remove low quality pixels, the MODIS quality maps are utilised. We explain subsequent filtering of Land Surface Temperature maps with an outlier detector to eliminate originally undetected cloud pixels. Further analysis of time series is briefly discussed as well as related landscape epidemiological applications in the field of tick-borne diseases.