A Comparison of AVIRIS and Landsat for Land Use Classification at the Urban Fringe

In this study we tested whether AVIRIS data allowed for improved land use classification over synthetic Landsat ETM+ data for a location on the urban-rural fringe of Colorado. After processing the AVIRIS image and creating a synthetic Landsat image, we used standard classification and post-classification procedures to compare the data sources for land use mapping. We found that, for this location, AVIRIS holds modest, but real, advantages over Landsat for the classification of heterogeneous and vegetated land uses. Furthermore, this advantage comes almost entirely from the large number of sensor spectral bands rather than the high Signal-to-Noise Ratio (SNR)

A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land

Cloud and Aerosol Imager (CAI) onboard the Greenhouse Gases Observing Satellite (GOSAT) is a multi-band sensor designed to observe and acquire information on clouds and aerosols. In order to retrieve aerosol optical depth (AOD) over land from the CAI sensor, a Dark Target (DT) algorithm for GOSAT CAI was developed based on the strategy of the Moderate Resolution Imaging Spectroradiometer (MODIS) DT algorithm. When retrieving AOD from satellite platforms, determining surface contributions is a major challenge. In the MODIS DT algorithm, surface signals in the visible wavelengths are estimated based on the relationships between visible channels and shortwave infrared (SWIR) near the 2.1 µm channel. However, the CAI only has a 1.6 µm band to cover the SWIR wavelengths. To resolve the difficulties in determining surface reflectance caused by the lack of 2.1 μm band data, we attempted to analyze the relationship between reflectance at 1.6 µm and at 2.1 µm. We did this using the MODIS surface reflectance product and then connecting the reflectances at 1.6 µm and the visible bands based on the empirical relationship between reflectances at 2.1 µm and the visible bands. We found that the reflectance relationship between 1.6 µm and 2.1 µm is typically dependent on the vegetation conditions, and that reflectances at 2.1 µm can be parameterized as a function of 1.6 µm reflectance and the Vegetation Index (VI). Based on our experimental results, an Aerosol Free Vegetation Index (AFRI2.1)-based regression function connecting the 1.6 µm and 2.1 µm bands was summarized. Under light aerosol loading (AOD at 0.55 µm < 0.1), the 2.1 µm reflectance derived by our method has an extremely high correlation with the true 2.1 µm reflectance (r-value = 0.928). Similar to the MODIS DT algorithms (Collection 5 and Collection 6), a CAI-applicable approach that uses AFRI2.1 and the scattering angle to account for the visible surface signals was proposed. It was then applied to the CAI sensor for AOD retrieval; the retrievals were validated by comparisons with ground-level measurements from Aerosol Robotic Network (AERONET) sites. Validations show that retrievals from the CAI have high agreement with the AERONET measurements, with an r-value of 0.922, and 69.2% of the AOD retrieved data falling within the expected error envelope of ± (0.1 + 15% AODAERONET)

Satellite estimates of wide-range suspended sediment concentrations in Changjiang (Yangtze) estuary using MERIS data

The Changjiang (Yangtze) estuarine and coastal waters are characterized by suspended sediments over a wide range of concentrations from 20 to 2,500 mg l-1. Suspended sediment plays important roles in the estuarine and coastal system and environment. Previous algorithms for satellite estimates of suspended sediment concentration (SSC) showed a great limitation in that only low to moderate concentrations (up to 50 mg l-1) could be reliably estimated. In this study, we developed a semi-empirical radiative transfer (SERT) model with physically based empirical coefficients to estimate SSC from MERIS data over turbid waters with a much wider range of SSC. The model was based on the Kubelka–Munk two-stream approximation of radiative transfer theory and calibrated using datasets from in situ measurements and outdoor controlled tank experiments. The results show that the sensitivity and saturation level of remote-sensing reflectance to SSC are dependent on wavelengths and SSC levels. Therefore, the SERT model, coupled with a multi-conditional algorithm scheme adapted to satellite retrieval of wide-range SSC, was proposed. Results suggest that this method is more effective and accurate in the estimation of SSC over turbid water

Downscaling landsat land surface temperature over the urban area of Florence

A new downscaling algorithm for land surface temperature (LST) images retrieved from Landsat Thematic Mapper (TM) was developed over the city of Florence and the results assessed against a high-resolution aerial image. The Landsat TM thermal band has a spatial resolution of 120 m, resampled at 30 m by the US Geological Survey (USGS) agency, whilst the airborne ground spatial resolution was 1 m. Substantial differences between Landsat USGS and airborne thermal data were observed on a 30 m grid: therefore a new statistical downscaling method at 30 m was developed. The overall root mean square error with respect to aircraft data improved from 3.3 °C (USGS) to 3.0 °C with the new method, that also showed better results with respect to other regressive downscaling techniques frequently used in literature. Such improvements can be ascribed to the selection of independent variables capable of representing the heterogeneous urban landscape

Numerical study on signatures of atmospheric convective cells in radar images of the ocean

Current and wind variations at the ocean surface can give rise to a modulation of the sea surface roughness and thus become visible in radar images. The discrimination between radar signatures of oceanic and atmospheric phenomena can be quite difficult, since signatures of different origin can have very similar shapes and magnitudes and are often superimposed upon each other. In this work we employ a numerical radar imaging model for an investigation of typical properties of radar signatures of atmospheric convective cells and of theoretical differences between such atmospherically induced radar signatures and those of oceanic phenomena. We show that main characteristics of observed multifrequency/multipolarization radar signatures of atmospheric convective cells over the Gulf Stream are reproduced quite well by the proposed model. This encourages us to vary wind and radar parameters systematically in order to get a general overview of the dependency of atmospherically induced radar signatures on these parameters. Finally, we compare typical characteristics of radar signatures of atmospheric and oceanic phenomena, and we present simulated radar images of a scenario of superimposed atmospheric convective cells and oceanic internal waves. We show that the proposed model supports the experimental finding that radar signatures of oceanic phenomena are stronger at horizontal (HH) than at vertical (VV) polarization, while atmospherically induced radar signatures are better visible at VV polarization

Potential spin-offs of the carbon dioxide observational platform system (CO-OPS) for remote sensing opportunities

Alternate remote sensing techniques that could utilize the slight losses of energy from the microwave beam which powers the NASA/MSFC Carbon Dioxide Observational Platform System (CO-OPS) to achieve the objectives of the U.S. Department of Energy (DOE) Carbon Dioxide Research Program's regional observational data requirements, ODRs, are addressed heuristically. The opportunity for regional remote sensing of the carbon dioxide and water vapor constituents in the atmosphere are discussed as a potential spin off of the CO-OPS. The CO-OPS is envisioned as a high altitude (approx. 25 km) observational platform system powered by microwave energy for regional observational use by the DOE in their Carbon Dioxide Research Program

Summary of the Active Microwave Workshop, chapter 1

An overview is given of the utility, feasibility, and advantages of active microwave sensors for a broad range of applications, including aerospace. In many instances, the material provides an in-depth examination of the applicability and/or the technology of microwave remote sensing, and considerable documentation is presented in support of these techniques. An assessment of the relative strengths and weaknesses of active microwave sensor data indicates that satisfactory data are obtainable for several significant applications