Understanding ‘saturation’ of radar signals over forests

There is an urgent need to quantify anthropogenic influence on forest carbon stocks. Using satellite-based radar imagery for such purposes has been challenged by the apparent loss of signal sensitivity to changes in forest aboveground volume (AGV) above a certain ‘saturation’ point. The causes of saturation are debated and often inadequately addressed, posing a major limitation to mapping AGV with the latest radar satellites. Using ground- and lidar-measurements across La Rioja province (Spain) and Denmark, we investigate how various properties of forest structure (average stem height, size and number density; proportion of canopy and understory cover) simultaneously influence radar backscatter. It is found that increases in backscatter due to changes in some properties (e.g. increasing stem sizes) are often compensated by equal magnitude decreases caused by other properties (e.g. decreasing stem numbers and increasing heights), contributing to the apparent saturation of the AGV-backscatter trend. Thus, knowledge of the impact of management practices and disturbances on forest structure may allow the use of radar imagery for forest biomass estimates beyond commonly reported saturation points

Carbon losses from deforestation and widespread degradation offset by extensive growth in African woodlands

Degradation—the loss of carbon stored in intact woodland—is very difficult to measure over large areas. Here, the authors show that carbon emissions from degradation in African woodlands greatly exceed those from deforestation, but are happening alongside widespread increases in biomass in remote areas

Global Soil Moisture Patterns Observed by Space Borne Microwave Radiometers and Scatterometers

Within the scope of the upcoming launch of a new water related satellite mission (SMOS) a global evaluation study was performed on two available global soil moisture products. ERS scatterometer surface wetness data was compared to AMSR-E soil moisture data. This study pointed out a strong similarity between both products in sparse to moderate vegetated regions with an average correlation coefficient of 0.83. Low correlations were found in densely vegetated areas and deserts. The low values in the vegetated regions can be explained by the limited soil moisture retrieval capabilities over dense vegetation covers. Soil emission is attenuated by the canopy and tends to saturate the microwave signal with increasing vegetation density, resulting in a decreased sensor sensitivity to soil moisture variations. It is expected that the new low frequency satellite mission (SMOS) will obtain soil moisture products with a higher quality in these regions. The low correlations in the desert regions are likely due to volume scattering or to the dielectric dynamics within the soil. The volume scattering in dry soils causes a higher backscatter under very dry conditions than under conditions when the sub-surface soil layers are somewhat wet. In addition, at low moisture levels the dielectric constant has a reduced sensitivity in response to changes in the soil moisture content. At a global scale the spatial correspondence of both products is high and both products clearly distinguish similar regions with high seasonal and inter annual variations. Based on the global analyses we concluded that the quality of both products was comparable and in the sparse to moderate vegetated regions both products may be beneficial for large scale validation of SMOS soil moisture. Some limitations of the studied products are different, pointing to significant potential for combining both products into one superior soil moisture data set. © The Author(s) 2008

Flood mapping in vegetated and urban areas and other challenges: Models and methods

Floods are the most frequent weather disasters in the world and the most costly in terms of economic losses. Mapping flood extension is fundamental to ascertain the damage and for relief organization. Spaceborne synthetic aperture radar (SAR) systems represent a powerful tool to monitor floods because of their all-weather capability, the very high spatial resolution of the new generation of instruments, and the short revisit time of the present and future satellite constellations. However, mapping flooded vegetated and urban areas still represents a challenging problem. Modeling different targets both in the presence and in the absence of flood water is a very complex task. In the first part of the chapter we review these challenging conditions, showing their potential effects on radar data and in particular on COSMO-SkyMed images. In some cases the potential of electromagnetic models to predict the radar response is shown. A second part of the chapter illustrates a number of strategies one can exploit, with examples showing the achievable performances with respect to a simple mapping of dark areas in the SAR image