Swarvanu Dasgupta

Soil moisture estimation using MODIS and ground measurements in eastern China

Recent technological advances in remote sensing have shown that soil moisture can be measured by microwave remote sensing under some topographic and vegetation cover conditions. However, current microwave technology limits the spatial resolution of soil moisture data. It has been found that the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) are related to surface soil moisture; therefore, a relationship between ground observed soil moisture and satellite NDVI and LST products can be developed. Three years of 1 km NDVI and LST products from the Moderate Resolution Imaging Spectroradiometer (MODIS) have been combined with ground measured soil moisture to determine regression relationships at a 1 km scale. Results show that MODIS NDVI and LST are strongly correlated with the ground measured soil moisture, and regression relationships are land cover and soil type dependent. These regression relationships can be used to generate soil moisture estimates at moderate resolution for study area.

Design of a susceptibility index for fire risk monitoring

In this letter, we present a new remote sensing fire susceptibility index (FSI) based on the physical concept of heat energy of preignition. This physical basis allows computations of ignition probabilities and comparisons of fire risk across ecoregions. The index has the flexibility to be localized to a vegetation type or ecoregion for improved performance. The computation of the index requires inputs of fuel temperature and fuel moisture content, both of which can be estimated using remote sensing techniques. While Moderate Resolution Imaging Spectrometer data for surface temperature are used as a proxy for fuel temperature, live fuel moisture is estimated by a linear regression technique utilizing the correlation between model-based live fuel moisture measurements at automated ground stations and the ratio of normalized difference vegetation index and surface temperature. FSIs are computed for the Georgia region during the spring and summer months of 2004 and validated with the well-tested fire potential index (FPI). Results show a good agreement between FSI and FPI. It suggests that FSI can be a good estimator of fire risk.

Multi-sensor study of aerosols from 2007 Okefenokee forest fire.

This paper uses multi-sensor remote sensing data to study the type and spatio-temporal variability of aerosols emitted from forest fires. The study is based on the Okefenokee Swamp fire that ravaged parts of Georgia and Florida between May and June of 2007. Moderate Resolution Imaging Spectroradiometer (MODIS) data is used to study the aerosol type and its spatial distribution. Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data is used to study the vertical distribution of aerosols. The results show that there is a high concentration of fine mode aerosols during the fire episode. It is also observed that the 24 hour averaged PM2.5 concentration was above unhealthy levels on several occasions, in some instances reaching values over 50µg/m 3. The PM10 concentration on the other hand was below unhealthy levels although there were numerous instances of episodic non attainment of the PM10 air quality standard. The study shows that the vertical reach of the aerosol plume over the land ranged from 2 to 3 kilometers.

Constrained radiative transfer inversions for vegetation moisture retrievals in grasslands

The retrieval of Live Fuel Moisture Content (LFMC) over fire prone grasslands is important for fire risk and drought assessment. Radiative transfer (RT) model based inversion of measured reflectances for retrievals of LFMC offers a promising method for estimating LFMC. This paper evaluates the extent to which inverse RT model based LFMC retrievals over grasslands can be improved by the use of prior information on soil moisture and LAI. However due to the uncertainty in the procedures used in obtaining the pre-retrieval information about LAI and soil moisture, the prior information is more likely to be in terms of an expected range for LAI and soil moisture rather than exact values. This study uses simulations from coupled soil-leaf-canopy radiative transfer models to investigate the extent to which such categorical prior information may reduce the uncertainty in LFMC retrievals. Results show that under the experimental conditions used in this study, prior information on LAI and soil moisture improves LFMC estimation on the average by about 2.3 to 3.4% (absolute LFMC) depending on the quality and accuracy of the prior information. This can be equivalent to a relative improvement of about 18-27%. This can be significant, since at the dry conditions represented by this study, when fire spread is highly sensitive to LFMC, such improvements in LFMC could considerably improve fire spread predictions and aid fire management decision making. Uncertainty analysis in terms of prediction intervals and standard deviation of errors also show that improvements are significant.

Development and enhancement of calibration/validation toolkit for supporting NPOESS/NPP missions

Calibration and validation (Cal/Val) toolkits are very critical for both satellite pre-launch end-to-end testing and post-launch real data quality check-out. In this paper, we present a AIRS-MODIS-VIIRS band mapping toolkit, which is designed for cross comparison and cross calibration of AIRS-MODIS-VIIRS thermal infrared bands based on the band-mapping approach we proposed. The main functionalities of this toolkit include spatial match-up between AIRS and MODIS foot prints, spectral simulation of VIIRS and MODIS thermal infrared bands with AIRS measurements, visual and statistical analyses among VIIRS, MODIS and AIRS thermal infrared bands for stratified scene characteristics, channels and scan angles. It also provides basic data manipulation and file format conversion capabilities. This toolkit can provide supports for VIIRS calibration and can be further enhanced into a NPOESS/NPP post-launch toolkit for real-time cross-instrument calibration and data quality check.

Use of hyperspectral remote sensing for detection and monitoring of chemical and biological agents: a survey

This paper surveys the potential use of hyperspectral imaging technology for standoff detection of chemical and biological agents in terrorism defense applications. In particular it focuses on the uses of hyperspectral imaging technology to detect and monitor chemical and biological attacks. In so doing it examines current technologies, their advantages and disadvantages, and investigates the possible role of hyperspectral imaging for homeland security applications. The study also addresses and provides applicable solutions for several of the potential challenges that currently create barriers to the full use of hyperspectral technology in the standoff detection of likely available chemical and biological agents.

Soil adjusted vegetation water content retrievals in grasslands

Soil contamination of canopy reflectance over grasslands can cause errors in empirical vegetation water content (VWC) retrievals using the NDII (Normalized Difference Infrared Index, [ρ0.86−ρ1.64]/[ρ0.86+ρ1.64]). Minimization of soil contamination by NDII relies on the existence of a quasi straight soil line and quasi straight VWC isolines (lines of equal VWC) in the 1.64–0.86 µm reflectance space. Further the VWC isolines are expected to meet at the origin of the 1.64–0.86 µm reflectance space. Considering soil moisture as the primary determinant of soil reflectance variation at a given location, this study investigates the effect of soil moisture on the nature of soil lines and VWC isolines under grassland conditions. Reflectance simulations from coupled soil‐leaf‐canopy reflectance models under grassland conditions show that soil lines and VWC isolines are expected to be curved and may not converge at the origin. This behaviour is attributed to disproportionate soil moisture related absorption processes operating at 1.64 µm and 0.86 µm. A new technique that accounts for these inconsistencies in NDII assumptions is proposed for VWC retrievals. The technique consists of using separate regression relationships between VWC and a Soil Adjusted NDII (SANDII) based on the volumetric soil moisture category of the background. SANDII, based on the idea borrowed from the Soil Adjusted Vegetation Index (SAVI) is an origin shifted transformation of NDII. The optimum origin that reduces VWC retrieval errors is shown to be soil moisture category specific. The proposed technique requires categorical soil moisture information in order to decide which regression relationship to apply for VWC retrievals. Climatology, meteorological models or microwave observations are expected to be reliable resources for such categorical soil moisture information. Evaluations of the proposed technique using simulated reflectances showed that absolute errors in VWC retrievals were reduced by an average 20% as compared to the traditional NDII regression method. Such improvements are expected to be significant for fire‐risk applications. Finally supporting evidence for the need of an origin translated NDII is provided using data collected over pastures during the Soil Moisture Experiment 2003 (SMEX03) field campaign.

Global MODIS Remote Sensing Data for Local Usage: Vaccess/MAGIC

The Moderate Resolution Imaging Spectroradiometer (MODIS), as a key research instrument, was successfully launched on Dec. 18, 1999 onboard the Terra (previously known as EOS AM-1) satellite. The second MODIS was launched onboard the Aqua (EOS PM-1) satellite in May 2002 (http://modis.gsfc.nasa.gov). Because the MODIS senses all the Earth’s surface in 36 spectral bands spanning the visible (0.415 µm) to infrared (14.235 µm) spectrum at nadir spatial resolution of 1 km, 500 m and 250 m (Justice et al., 1998), the associated remote sensing applications are of interest not only to land, ocean, and atmosphere discipline researchers but also to application, interdisciplinary and environmental scientists (Salamonson et al., 1989, 2005).

Combining MODIS and AMSR-E-based vegetation moisture retrievals for improved fire risk monitoring

Research has shown that remote sensing in both the optical and microwave domain has the capability of estimating vegetation water content (VWC). Though lower in spatial resolution than MODIS optical bands, AMSR-E microwave measurements are typically less affected by clouds, water vapor, aerosol or solar illumination, making them complementary to MODIS real time measurements over regions of clouds and haze. In this study we explored a wavelet based approach for combining vegetation water content observations derived from higher spatial resolution MODIS and lower spatial resolution AMSR-E microwave measurements. Regression analysis between AMSR-E VWC and spatially aggregated MODIS NDII (Normalized Difference Infrared Index) was first used to scale MODIS NDII to MODIS VWC products. Our approach for combining information from the two sensors resorts to multiresolution wavelet decomposition of MODIS VWC into a set of detail images and a single approximation image at AMSR-E resolution. The substitution method of image fusion is then undertaken, in which the approximation image is replaced by AMSR-E VWC image, prior to using inverse wavelet transform to construct a merged VWC product. The merged VWC product thus has information from both MODIS and AMSR-E measurements. The technique is applied over low vegetation regions in Texas grasslands to obtain merged VWC products at intermediate resolutions of ~1.5km. Apart from offering a way to calibrate MODIS VWC content products to AMSR-E observations, the technique has the potential for downscaling AMSR-E VWC to higher spatial resolution over moderately cloudy or hazy regions where MODIS reflective bands become contaminated by the atmosphere. During such situations when contaminated MODIS signals cannot be used to obtain the wavelet detail images, MODIS detail images from a preceding time step is used to downscale the current AMSR-E VWC to higher resolutions. This approach of using detail images from the recent past would be justified if the detail images containing the high frequency components of the image change slowly. Correlation analysis of detail images from consecutive time steps shows that this is approximately true, at-least for the low spatial resolution detail images. Our approach yields accuracy of around 77% on the average over the selected study region and temporal period. This technique thus has the potential for ensuring the data continuity of high spatial resolution VWC products, a requirement essential for fire risk monitoring.