Remote sensing techniques are a powerful tool for monitoring littoral zones. Optical sensors can be used to quantify water quality parameters such as suspended sediments. It is possible to estimate the Total Suspended Matter (TSM) concentration using multi-spectral satellite images. In order to extract meaningful information, the satellite data needs to be validated with in situ measurements. The main objective of this work was to quantify the TSM in sea breaking zone, using multi-spectral satellite images. A part of the northwest coast of Portugal, centered around Aveiro, was chosen as a test area. Several methodologies have been used to establish a relationship between the above sea water reflectance and the TSM concentration. Various field trips were done in order to simultaneously obtain water samples and reflectance measurements. A relationship between TSM concentration and reflectance was established for the range 400 - 900 nm. Data from Landsat TM, SPOT HRVIR and ASTER were calibrated and geometric corrected. The reflectance values were used to estimate the TSM concentration using the relationships established using the field measurements. The model coefficients and correlation factors, for identical bands on different sensors, presented a high similarity. The results have been incorporated in a Geographical Information System (GIS).
Polar orbiting satellites with low spatial resolution sensors, such as the AVHRR, provide repeated global coverage of the Earth. The data is directly transmitted to ground stations, and in some cases distributed immediately after the data acquisition. Near real time applications can be implemented if the adequate processing tools are available. This paper presents a near real time processing system, developed for NOAA/AVHRR data acquired from the Dundee satellite station. The system performs image calibration, geometric corrections and atmospheric corrections with minimum operator intervention. The geometric corrections consist of an orbital-based correction refined by the automatic identification of Ground Control Points (GCPs) by image matching. The atmospheric correction is based on simulations performed on the 6S radiative transfer code using a set of typical and expected values for the most significant parameters. An attempt to evaluate the error associated with the simplified atmospheric correction method was carried out. As an illustration, 3 AVHRR images from NOAA 16 were processed. The ranges of values encountered for the most relevant parameters were analyzed. The range and average values for the reflectance channels 1 and 2 with and without the atmospheric correction are compared. These were used to produce standard Normalized Difference Vegetation Index (NDVI) images and atmospheric corrected NDVI images.
Polar orbital satellites wiht low spatial resolution sensors, such as the AVHRR, provide global coverage with a short repetition period. The data is direclty transmitted to ground stations, and can be distributed immediately after data acquistion. Near real time applications can be implemented if the adequate processing tools are available. One task usually needed is the geometric correction of image data. Automatic methods, based on satellite orbital parameters, can in some cases provide satisfactory results. However, the identification of Ground Control Points (GCPs) is generally required in order to achieve registration errors below the pixel size. A fully automatic method for the geometric registration of AVHRR data is proposed here. The method comprises four stages: (i) an initial iamge transformation based on orbital parameters, (ii) image segmentation of this image into 3 main classes and 9 additional classes of mixed water and land at various levels, (iii) automatic GCP collection by image matching, (iv) final image production combining both orbital and GCP information. The method was tested on ten images of the Iberian Peninsula, and proved effective in accurately geo-referencing sub-sections of an AVHRR scene of medium dimension in a few minutes.