Markus Reinhold's List: bibliography

Optimal estimation (OE) improves sea surface temperature (SST) estimated from satellite infrared imagery in the “split-window”, in comparison to SST retrieved using the usual multi-channel (MCSST) or non-linear (NLSST) estimators. This is demonstrated using three months of observations of the Advanced Very High Resolution Radiometer (AVHRR) on the first Meteorological Operational satellite (Metop-A), matched in time and space to drifter SSTs collected on the global telecommunications system. There are 32,175 matches. The prior for the OE is forecast atmospheric fields from the Météo-France global numerical weather prediction system (ARPEGE), the forward model is RTTOV8.7, and a reduced state vector comprising SST and total column water vapour (TCWV) is used. Operational NLSST coefficients give mean and standard deviation (SD) of the difference between satellite and drifter SSTs of 0.00 and 0.72 K. The “best possible” NLSST and MCSST coefficients, empirically regressed on the data themselves, give zero mean difference and SDs of 0.66 K and 0.73 K respectively. Significant contributions to the global SD arise from regional systematic errors (biases) of several tenths of kelvin in the NLSST. With no bias corrections to either prior fields or forward model, the SSTs retrieved by OE minus drifter SSTs have mean and SD of − 0.16 and 0.49 K respectively. The reduction in SD below the “best possible” regression results shows that OE deals with structural limitations of the NLSST and MCSST algorithms. Using simple empirical bias corrections to improve the OE, retrieved minus drifter SSTs are obtained with mean and SD of − 0.06 and 0.44 K respectively. Regional biases are greatly reduced, such that the absolute bias is less than 0.1 K in 61% of 10°-latitude by 30°-longitude cells. OE also allows a statistic of the agreement between modelled and measured brightness temperatures to be calculated. We show that this measure is more efficient than the current system of confidence levels at identifying reliable retrievals, and t

A global poverty map has been produced at 30 arcsec resolution using a poverty index calculated by dividing population count (LandScan 2004) by the brightness of satellite observed lighting (DMSP nighttime lights). Inputs to the LandScan product include satellite-derived land cover and topography, plus human settlement outlines derived from high-resolution imagery. The poverty estimates have been calibrated using national level poverty data from the World Development Indicators (WDI) 2006 edition. The total estimate of the numbers of individuals living in poverty is 2.2 billion, slightly under the WDI estimate of 2.6 billion. We have demonstrated a new class of poverty map that should improve over time through the inclusion of new reference data for calibration of poverty estimates and as improvements are made in the satellite observation of human activities related to economic activity and technology access.

A general framework for testing the quality of the segmentation of a multi-spectral satellite image is proposed. The method is based on the production of synthetic images with the spectral characteristics of the image pixels extracted from a signature multi-spectral image. The knowledge of the location of objects in the synthetic image provides a reference segmentation, which allows for a quantitative evaluation of the quality provided by a segmentation algorithm. The Hammoude metric and three external similarity indices (Rand, Corrected Rand, and Jaccard) were chosen to perform this evaluation, but other metrics can also be used. The proposed methodology can be used for any type of satellite image (or multi-spectral image), set of land cover types, and segmentation algorithms.

A practical application was carried out to illustrate the value of the proposed method. A SPOT satellite image was used to extract the spectral signature of 8 land cover types. Three test images were produced using the 8 land cover classes and two different 5 class sub-sets. The segmentation results provided by a standard algorithm were compared with the reference or expected segmentation. The results clearly indicate that the quality of a segmentation obtained from a multi-spectral image not only depends on the geometric properties of the objects present in the image, but also on their spectral characteristics. The results suggest that a specific evaluation should be carried out for each particular experiment, as the segmentation results are very dependent on the choice of land cover types.

This paper presents a linear feature extraction method. Least squares template matching (LSTM) is adopted as the computational tool to fit the linear ...

Landscapes are complex systems composed of a large number of heterogeneous components as well as explicit homogeneous regions that have similar spectr...

A Global Irrigated Area Map (GIAM) has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth and g...

The extreme learning machine (ELM), a single hidden layer neural network based supervised classifier is used for remote sensing classifications. In co...