Xianjun Hao

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.

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.

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.

Sand and dust storm detection over desert regions in China with MODIS measurements

A new approach is developed for quick detection of sand and dust storms (SDSs) over arid and semi-arid regions of the northwestern part of China, where the bright-reflecting source areas of Asian dust outbreaks are located. The Asian dust particles, once with proper conditions, can even transport across the Pacific Ocean and reach the USA and Canada. Remote-sensing data products of mineral dust near its source are deficient because of the radiance contributions of the bright surface. In this article, based on Moderate Resolution Imaging Spectroradiometer (MODIS) measurements, consecutive separation of dust cloud from bright underlying surface and water/ice cloud is completed by utilizing a refined cloud mask algorithm and the normalized difference dust index (NDDI). Thresholds are determined through statistical analysis of MODIS measurements over the Taklimakan and Gobi deserts. Validations with ground observations over the sites in Inner Mongolia and Xinjiang in China demonstrated good performance of the proposed method in separating SDS from bright surface and cloud.

Smoke plume detection in the eastern United States using MODIS

In the eastern United States large amounts of smoke emitted from both wildfires and prescribed fires affect the regional air quality and long‐term climate and may have an impact on public health. Satellite remote sensing is an effective approach for detecting and monitoring the smoke plume. The spectral characteristics of smoke plume are measurably different from those of other cover types, such as vegetation, cloud, snow, and so on. A multi‐threshold method has been developed for detecting smoke plumes with eight MODIS spectral bands based on the analysis of spectral characteristics of different cover types. A series of tests are applied to all pixels in one granule (5‐min measurements) to filter out non‐smoke pixels step by step with water masking. At each step, specific thresholds are utilized. The results have been validated with true color images for a number of cases from different areas and time, showing that the algorithm works well except for a few missing or incorrect identified smoke pixels.

Detecting vegetation change with satellite remote sensing over 2007 Georgia wildfire regions

The wildfires which occurred in April 2007 in southern Georgia lasted for almost two months. Approximately 386,722 acres were burned. In this paper, we explored the strategy to use MODIS products to study fire impacts on vegetation. Vegetation variations caused by the fires are studied using these MODIS products: 8-day composite fire products, 16-day composite vegetation indices, and land cover types. Several tiles of MODIS products from dates immediately before and after the fire were employed to monitor vegetation changes. Unburned control plots were selected to establish new variables QNDVI and QEVI which are used to evaluate the vegetation recovery status. The results show that vegetation indices: NDVI and EVI decreased dramatically after the fires. The variations of QNDVI and QEVI indicate that vegetation status still has a disparity between fire spots and surrounding undisturbed area, even though commonly used vegetation indices of fire spots have attained pre-fire levels. The appropriate distance outside the fire spots for selecting control plots is related to the size of the burned area. The larger the burned area is, the bigger distance we should choose for control plots. EVI and QEVI are better indicators to show vegetation changes due to fires. The method presented in this paper can be employed to monitor vegetation change to fires as well as to indicate different recovery rate. It also can be useful to identify the fire severity and assess the ecological consequence of fires.

Impact assessment of Aqua MODIS band-to-band misregistration on snow index

The MODerate Resolution Imaging Spectroradiometer (MODIS) is a key 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. MODIS senses the Earths surface in thirty-six spectral bands which are distributed on four Focal Plane Assemblies (FPAs): Visible (VIS), Near-Infrared (NIR), Short-and Middle-wavelength IR (SMIR), and Long-wavelength IR (LWIR). It was found from sensor pre-launch measurements that Aqua MODIS SMIR/LWIR FPAs had a large misalignment or misregistration relative to the VIS/NIR FPAs in both along-scan and along-track directions. The misregistration of the two FPA groups has remained nearly the same during its on-orbit operation. Consequently this has been a major concern for Aqua MODIS performance since it could affect the quality of MODIS products which utilize bands from both the VIS/NIR and SMIR/LWIR FPAs, for example, the snow index. This paper focuses on investigating the impact of Aqua MODIS FPA-to-FPA or band-to-band misregistration on its snow index (NDSI) derived from measurements made by VIS band 4 and SWIR band 7. Preliminary results show that shifting one pixel (500 m) forward in the along-track direction of band 7 can improve the band-to-band registration between bands 4 and 7 and, therefore, the quality of Aqua MODIS snow mapping. This study will help MODIS data users to understand the potential impact of band-to-band misregistration on MODIS science products, and also be useful for the future sensor design.