Quantitative Estimation of Surface Soil Moisture in Agricultural Landscapes using Spaceborne Synthetic Aperture Radar Imaging at Different Frequencies and Polarizations

Soil moisture and its distribution in space and time plays an important role in the surface energy balance at the soil-atmosphere interface. It is a key variable influencing the partitioning of solar energy into latent and sensible heat flux as well as the partitioning of precipitation into runoff and percolation. Due to their large spatial variability, estimation of spatial patterns of soil moisture from field measurements is difficult and not feasible for large scale analyses. In the past decades, Synthetic Aperture Radar (SAR) remote sensing has proven its potential to quantitatively estimate near surface soil moisture at high spatial resolutions. Since the knowledge of the basic SAR concepts is important to understand the impact of different natural terrain features on the quantitative estimation of soil moisture and other surface parameters, the fundamental principles of synthetic aperture radar imaging are discussed. Also the two spaceborne SAR missions whose data was used in this study, the ENVISAT of the European Space Agency (ESA) and the ALOS of the Japanese Aerospace Exploration Agency (JAXA), are introduced. Subsequently, the two essential surface properties in the field of radar remote sensing, surface soil moisture and surface roughness are defined, and the established methods of their measurement are described. The in situ data used in this study, as well as the research area, the River Rur catchment, with the individual test sites where the data was collected between 2007 and 2010, are specified. On this basis, the important scattering theories in radar polarimetry are discussed and their application is demonstrated using novel polarimetric ALOS/PALSAR data. A critical review of different classical approaches to invert soil moisture from SAR imaging is provided. Five prevalent models have been chosen with the aim to provide an overview of the evolution of ideas and techniques in the field of soil moisture estimation from active microwave data. As the core of this work, a new semi-empirical model for the inversion of surface soil moisture from dual polarimetric L-band SAR data is introduced. This novel approach utilizes advanced polarimetric decomposition techniques to correct for the disturbing effects from surface roughness and vegetation on the soil moisture retrieval without the use of a priori knowledge. The land use specific algorithms for bare soil, grassland, sugar beet, and winter wheat allow quantitative estimations with accuracies in the order of 4 Vol.-%. Application of remotely sensed soil moisture patterns is demonstrated on the basis of mesoscale SAR data by investigating the variability of soil moisture patterns at different spatial scales ranging from field scale to catchment scale. The results show that the variability of surface soil moisture decreases with increasing wetness states at all scales. Finally, the conclusions from this dissertational research are summarized and future perspectives on how to extend the proposed model by means of improved ground based measurements and upcoming advances in sensor technology are discussed. The results obtained in this thesis lead to the conclusion that state-of-the-art spaceborne dual polarimetric L-band SAR systems are not only suitable to accurately retrieve surface soil moisture contents of bare as well as of vegetated agricultural fields and grassland, but for the first time also allow investigating within-field spatial heterogeneities from space

NASA earth science and applications division: The program and plans for FY 1988-1989-1990

Described here are the Division's research goals, priorities and emphases for the next several years and an outline of longer term plans. Included are highlights of recent accomplishments, current activities in FY 1988, research emphases in FY 1989, and longer term future plans. Data and information systems, the Geodynamics Program, the Land Processes Program, the Oceanic Processes Program, the Atmospheric Dynamics and Radiation Program, the Atmospheric Chemistry Program, and space flight programs are among the topic covered

Laboratory for Oceans

A review is made of the activities of the Laboratory for Oceans. The staff and the research activities are nearly evenly divided between engineering and scientific endeavors. The Laboratory contributes engineering design skills to aircraft and ground based experiments in terrestrial and atmospheric sciences in cooperation with scientists from labs in Earth sciences

Spatio-temporal and structural analysis of vegetation dynamics of Lowveld Savanna in South Africa

Savanna vegetation structure parameters are important for assessing the biomes status under various disturbance scenarios. Despite free availability remote sensing data, the use of optical remote sensing data for savanna vegetation structure mapping is limited by sparse and heterogeneous distribution of vegetation canopy. Cloud and aerosol contamination lead to inconsistency in the availability of time series data necessary for continuous vegetation monitoring, especially in the tropics. Long- and medium wavelength microwave data such as synthetic aperture radar (SAR), with their low sensitivity to clouds and atmospheric aerosols, and high temporal and spatial resolution solves these problems. Studies utilising remote sensing data for vegetation monitoring on the other hand, lack quality reference data. This study explores the potential of high-resolution TLS-derived vegetation structure variables as reference to multi-temporal SAR datasets in savanna vegetation monitoring. The overall objectives of this study are: (i) to evaluate the potential of high-resolution TLS-data in extraction of savanna vegetation structure variables; (ii) to estimate landscape-wide aboveground biomass (AGB) and assess changes over four years using multi-temporal L-band SAR within a Lowveld savanna in Kruger National Park; and (iii) to assess interactions between C-band SAR with various savanna vegetation structure variables. Field inventories and TLS campaign were carried out in the wet and dry seasons of 2015 respectively, and provided reference data upon which AGB, CC and cover classes were modelled. L-band SAR modelled AGB was used for change analysis over 4 years, while multitemporal C-band SAR data was used to assess backscatter response to seasonal changes in CC and AGB abundant classes and cover classes. From the AGB change analysis, on average 36 ha of the study area (91 ha) experienced a loss in AGB above 5 t/ha over 4 years. A high backscatter intensity is observed on high abundance AGB, CC classes and large trees as opposed to low CC and AGB abundance classes and small trees. There is high response to all structure variables, with C-band VV showing best polarization in savanna vegetation mapping. Moisture availability in the wet season increases backscatter response from both canopy and background classes

Polarimetric data for tropical forest monitoring : studies at the Colombian Amazon

An urgent need exists for accurate data on the actual tropical forest extent, deforestation, forest structure, regeneration and diversity. The availability of accurate land cover maps and tropical forest type maps, and the possibility to update these maps frequently, is of great importance for the development and success of monitoring systems. For areas like the Amazon the use of optical remote sensing systems as the source of information, is impeded by the permanent presence of clouds that affect the interpretation and the accuracy of the algorithms for classification and map production. The capabilities of radar systems to acquire cloud free images and the penetration of the radar waves into the forest canopy make radar systems suitable for monitoring activities and provide additional and complementary data to optical remote sensing systems. Information regarding forest structure, forest biomass, and vegetation cover and flooding can be associated with radar images because of the typical wave-object interaction properties of the radar systems.In this thesis new algorithms for the classification of radar images and the production of accurate maps are presented. The production of specific maps is studied by applying the developed algorithms to two different study areas in the Colombian Amazon. The first site, San José del Guaviare, is a colonisation area with active deforestation activities and dynamic land cover change. The second area is a pristine natural forest with high diversity of landscapes.A detailed statistical description of the polarimetric AirSAR data is made in terms of backscatter (gamma values), polarimetric phase difference and polarimetric correlation. This approach allows a better interpretation of physical backscatter mechanisms important for interpretation of images in relation to ground parameters. Theoretical cumulative probability density distributions (pdf) are used to describe the mean field values and the standard deviation for a class. A Gausian distribution is used to describe the field average gamma values; a circular Gausian distribution is used to describe the field average HH-VV phase difference and a Beta distribution is used to described the field average HH-VV phase correlation. The accuracy of the estimation of the field-averaged values depends on the level of speckle, i.e. number of independent looks. This effect is included in the calculation of the pdf's and therefore can be simulated.For the Guaviare site the classification algorithm is used to assess the AirSAR data in the production of a land cover type map. Classification accuracies are calculated for different combinations of bands and level of speckle. An accuracy of 98.7% was calculated for a map when combining L-HV and P-RR polarisations. Confusion between classes are studied to evaluate the use of radar bands for monitoring activities, e.g. loss of forest or detection of new deforested areas. In addition a biomass map is created by using the empirical relationship between the combination of the same radar bands and the biomass estimations from 28 plots as measured in the field. The agreement of the biomass map with the land cover map is used to evaluate the biomass classification.For the Araracuara site the classification algorithm is used to assess the use of polarimetric data for forest structural type mapping and indirect forest biophysical characterisation. 23 field-measured plots used for forest structural characterisation are used to assess the accuracy of the classification. A new SAR derived legend is more suitable for the SAR map allowing better physical interpretation of results. A method based on iterated conditional modes is introduced to create maps from the classified radar images, increasing in most of the cases the accuracy of the classification. The structural type map with 15 classes can be classified with accuracies ranging from 68% to 94% depending on the classification and the mapping approach. The relationship between forest structure and polarimetric signal properties is studied in detail by using a new decomposition of polarimetric coherence, based on a simple physical description of the wave-object interactions. The accuracy of the complex coherence is described using the complex Wishart distribution. In addition for the same area, a biomass map is created using the previous structural type characterisation as the basis for the classification, overcoming problems as the well know radar signal saturation.The possibilities and restrictions of creating biomass maps with AirSAR polarimetric images are deeply investigated. Two different approaches are proposed depending on the terrain conditions. A theoretical exploration on the physical limits for radar biomass inversion is made by using a new interface model, called LIFEFORM that describes the layered tropical forest in terms of scatterers. The UTARTCAN scattering model is used to analyse the effect of flooding, forest structure and terrain roughness in the biomass inversion