John J. Qu

Oil spill detection with fully polarimetric UAVSAR data.

In this study, two ocean oil spill detection approaches based on four scattering matrices measured by fully polarimetric synthetic aperture radar (SAR) are presented and compared. The first algorithm is based on the co-polar correlation coefficient, ρ, and the scattering matrix decomposition parameters, Cloud entropy (H), mean scattering angle (α) and anisotropy (A). While each of these parameters has oil spill signature in it, we find that combining these parameters into a new parameter, F, is a more effective way for oil slick detection. The second algorithm uses the total power of four polarimetric channels image (SPAN) to find the optimal representation of the oil spill signature. Otsu image segmentation method can then be applied to the F and SPAN images to extract the oil slick features. Using the L-band fully polarimetric Uninhabited Aerial Vehicle - synthetic aperture radar (UAVSAR) data acquired during the 2010 Deepwater Horizon oil spill disaster event in the Gulf of Mexico, we are able to successfully extract the oil slick information in the contaminated ocean area. Our result shows that both algorithms perform well in identifying oil slicks in this case.

Simulating the formation of Hurricane Isabel (2003) with AIRS data

[1] Using the AIRS retrieved temperature and humidity profiles, the Saharan Air Layer (SAL) influence on the formation of Hurricane Isabel (2003) is simulated numerically with the MM5 model. The warmth and dryness of the SAL (the thermodynamic effect) is assimilated by use of the nudging technique, which enables the model thermodynamic state to be relaxed to the profiles of the AIRS retrieved data for the regions without cloud contamination. By incorporating the AIRS data, MM5 better simulates the large-scale flow patterns and the timing and location of the formation of Hurricane Isabel and its subsequent track. By comparing with an experiment without nudging of the AIRS data, it is shown that the SAL may have delayed the formation of Hurricane Isabel and inhibited the development of another tropical disturbance to the east. This case study confirms the argument by Dunion and Velden (2004) that the SAL can suppress Atlantic tropical cyclone activity by increasing the vertical wind shear, reducing the mean relative humidity, and stabilizing the environment at lower levels. Citation: Wu, L., S. A. Braun, J. J. Qu, and X. Hao (2006), Simulating the formation of Hurricane Isabel (2003) with AIRS data, Geophys. Res. Lett., 33, L04804, doi:10.1029/2005GL024665.

A new method for retrieving band 6 of aqua MODIS

The Moderate Resolution Imaging Spectroradiometer (MODIS) is a key research instrument for the NASA Earth Observing System (EOS) mission. It was successfully launched onboard the Terra satellite in December 1999 and Aqua satellite in May 2002. Both MODIS instruments have been working well except that 15 of the 20 detectors in Aqua MODIS band 6 (1.628-1.652 /spl mu/m) are either nonfunctional or noisy. The striping in Aqua MODIS band 6 caused by its nonfunctional or noisy detectors has been a serious problem for MODIS snow products, which use band 6 primarily for snow detection. MODIS scientists have been using Aqua MODIS band 7 (2.105-2.155 /spl mu/m) instead of band 6 for computing the normalized difference snow index to continue detecting global snow coverage. The main objective of this letter is to retrieve Aqua MODIS band 6 using other bands based on their relationships in Terra MODIS. The band retrieval approach proposed in this letter is also very useful for the next generation of MODIS sensor-the Visible/Infrared Imager/Radiometer Suite (VIIRS) band M10 proxy data generation. Such proxy data can support the VIIRS prelaunch end-to-end testing, postlaunch calibration/validation, and data quality checking.

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.

Saharan dust storm detection using moderate resolution imaging spectroradiometer thermal infrared bands

This paper investigates the approaches of Saharan dust storm detection with the Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared bands and presents an index, thermal-infrared dust index, through quantitative analysis of MODIS data for major dust events over the Atlantic Ocean during year 2004-2006. It is found that aerosol optical thickness (AOT) at 550nm has close relationships with the brightness temperature of MODIS bands 20, 30, 31, and 32. The proposed thermal-infrared dust index matches MODIS AOT very well with a squared correlation coefficient of 0.7646. Case study and statistical analysis suggest the potential to use 1km thermal-infrared dust index data for detecting dust storms and evaluating the potential impacts of regional weather, such as Atlantic hurricanes, and climate in the near future.

Smoke incursions into urban areas: simulation of a Georgia prescribed burn

This study investigates smoke incursion into urban areas by examining a prescribed burn in central Georgia, USA, on 28 February 2007. Simulations were conducted with a regional modeling framework to understand transport, dis- persion, and structure of smoke plumes, the air quality effects, sensitivity to emissions, and the roles of burn management strategy in mitigating the effects.The results indicate that smoke plumes first went west, but turned north-west at noon owing to a shift in wind direction. The smoke then invaded metropolitan Atlanta during the evening rush hour. The plumes caused severe air quality problems in Atlanta. Some hourly ground PM2.5 (particulate matter not greater than 2.5 µm in diameter) concentrations at three metropolitan Atlanta locations were three to four times as high as the daily (24-h) US National Ambi- ent Air Quality Standard. The simulated shift in the smoke transport direction and the resultant effects on air quality are sup- ported by the satellite and ambient air measurements. Two sensitivity simulations indicate a nearly linear relation between the emission intensities and PM2.5 concentrations. Two other simulations indicate that the impacts on air quality for the residents of Atlanta during the evening commute could have been reduced if the starting time of the burn had been altered.

Estimating dry matter content from spectral reflectance for green leaves of different species

Efficient and accurate detection of the temporal dynamics and spatial variations of leaf dry matter content would help monitor key properties and processes in vegetation and the wider ecosystem. However, leaf water content strongly absorbs at shortwave infrared wavelengths, reducing the signal from dry matter. The major objective of this study was to examine relationship between spectral reflectance of fresh leaves and the ratio of leaf dry mass to leaf area, across a wide range of species at the leaf scale. A narrow-band, normalized index combining two distinct wavebands centred at 1649 and 1722 nm achieved the highest overall performance and discriminatory power compared to either single band or first derivatives. The normalized index was evaluated using the PROSPECT (leaf optical properties spectra) simulated reflectance spectra and field measurements from the Leaf Optical Properties Experiment (LOPEX) data set. Both evaluations show that leaf dry matter contents were retrievable with R 2 of 0.845 and 0.681 and regression slopes of 0.903 and 0.886. This study suggests that spectral reflectance measurements hold promise for the assessment of dry matter content for green leaves. Further investigation needs to be conducted to evaluate the effectiveness of this normalized index at canopy scales.

Status of Aqua MODIS spatial characterization and performance

NASAs EOS Aqua spacecraft was launched on May 04, 2002. The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of the six Earth-observing sensors aboard the EOS Aqua spacecraft. MODIS is the highest spatial resolution instrument on the Aqua platform with data products generated in 250m, 500m, and 1000m resolutions (nadir). It has 36 spectral bands, a total of 490 detectors, located on four focal plane assemblies (FPAs) with two of them controlled during operation at 83K by a passive radiative cooler. In addition to radiometric calibration and spectral characterization, MODIS spatial performance was extensively characterized pre-launch, including measurements of band-to-band registration (BBR), FPA to FPA registration (FFR), line spread function (LSF), modulation transfer function (MTF), and instantaneous field-of-view (IFOV). The sensors spatial characterization is monitored by an on-board calibrator, the spectro-radiometric calibration assembly (SRCA). In this paper, we will briefly describe MODIS SRCA spatial characterization methodologies and operational activities. We will focus on the sensors spatial performance using four years of on-orbit observations and, consequently, evaluate the SRCAs performance. On-orbit results of key spatial characterization parameters (BBR, FFR, and MTF) will be examined and compared to pre-launch measurements and design requirements.

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.

Satellite-based applications on climate change

From the Contents: Introduction to Satellite Remote Sensing of Climate.- Overview of Satellite-based Measurements and Applications.- Calibrating A System of Satellites.- MODIS Ten Year Performance to Support Climate Change Study.- Introduction to NOAA Climate Data Record Program.- Comparison of Trends and Solar Forcing in MSU, ERA-40 and NCEP/NCAR Analysis of Stratospheric and Tropospheric Temperature in the period of 1979-2002.- Development of the Global Mutispectral Imager Thermal Emissive FCDRs.- Atmospheric and climate applications.- Global Precipitation Monitoring.- Developing a Historical Precipitation Record.- Atmospheric Temperature Climate Data Records from Satellite Microwave Sounders.