A soil moisture algorithm using tilted Bragg approximation
In this paper, we present a soil moisture algorithm theoretically derived using the tilted Bragg proximation.
soil moisture polarimetric SAR data analysis↗
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Vegetation Effects on Soil Moisture Estimation
Several successful algorithms have been developed to estimate soil moisture of bare surfaces. We previously reported a new algorithm using the tilted Bragg approximation. However, these algorithms are only applicable to bare surfaces. When vegetation is present, soil moisture is typically underestimated by bare surface algorithms. In order to derive soil moisture under vegetation, we have to understand the complex scattering process due to vegetation. Our main interest is to retrieve the global soil moisture information using Hydros L-band polarimetric radar data. The Hydros mission will provide the first global view of land soil moisture using L-band radar and radiometer. The unique characteristics of the Hydros data are the availability of the low resolution soil moisture information from radiometer data and the continuous time series radar data collected at the same incidence angle. In this paper, we will examine a potential inversion algorithm to retrieve soil moisture under vegetation canopies using Hydros L-band polarimetric radar data.
soil moisture↗
Monsoon 1990: Preliminary SAR results
Multifrequency polarimetric synthetic aperture radar (SAR) images of the Walnut Gulch watershed near Tombstone, Arizona were acquired on 28 Mar. 1990 and on 1 Aug. 1990. Trihedral corner reflectors were deployed prior to both overflights to allow calibration of the two SAR data sets. During both overflights, gravimetric soil moisture and dielectric constant measurements were made. Detailed vegetation height, density, and water content measurements were made as part of the Monsoon 1990 Experiment. Preliminary results based on analysis of the multitemporal polarimetric SAR data are presented. Only the C-band data (5.7-cm wavelength) radar images show significant difference between Mar. and Aug., with the strongest difference observed in the HV images. Based on the radar data analysis and the in situ measurements, we conclude that these differences are mainly due to changes in the vegetation and not due to the soil moisture changes.
Vanzyl, Jakob J.↗
Monsoon '90 - Preliminary SAR results
Multifrequency polarimetric synthetic aperture radar (SAR) images of the Walnut Gulch watershed near Tombstone, Arizona were acquired on 28 Mar. 1990 and on 1 Aug. 1990. Trihedral corner reflectors were deployed prior to both overflights to allow calibration of the two SAR data sets. During both overflights, gravimetric soil moisture and dielectric constant measurements were made. Detailed vegetation height, density, and water content measurements were made as part of the Monsoon 1990 Experiment. Preliminary results based on analysis of the multitemporal polarimetric SAR data are presented. Only the C-band data (5.7-cm wavelength) radar images show significant difference between Mar. and Aug., with the strongest difference observed in the HV images. Based on the radar data analysis and the in situ measurements, we conclude that these differences are mainly due to changes in the vegetation and not due to the soil moisture changes.
Dubois, Pascale C.↗
A Parameterized Inversion Model for Soil Moisture and Biomass from Polarimetric Backscattering Coefficients
A semi-empirical algorithm for the retrieval of soil moisture, root mean square (RMS) height and biomass from polarimetric SAR data is explained and analyzed in this paper. The algorithm is a simplification of the distorted Born model. It takes into account the physical scattering phenomenon and has three major components: volume, double-bounce and surface. This simplified model uses the three backscattering coefficients ( sigma HH, sigma HV and sigma vv) at low-frequency (P-band). The inversion process uses the Levenberg-Marquardt non-linear least-squares method to estimate the structural parameters. The estimation process is entirely explained in this paper, from initialization of the unknowns to retrievals. A sensitivity analysis is also done where the initial values in the inversion process are varying randomly. The results show that the inversion process is not really sensitive to initial values and a major part of the retrievals has a root-mean-square error lower than 5% for soil moisture, 24 Mg/ha for biomass and 0.49 cm for roughness, considering a soil moisture of 40%, roughness equal to 3cm and biomass varying from 0 to 500 Mg/ha with a mean of 161 Mg/ha
soil moisture↗
Modeling and Prediction of Wildfire Hazard in Southern California, Integration of Models with Imaging Spectrometry
Large urban wildfires throughout southern California have caused billions of dollars of damage and significant loss of life over the last few decades. Rapid urban growth along the wildland interface, high fuel loads and a potential increase in the frequency of large fires due to climatic change suggest that the problem will worsen in the future. Improved fire spread prediction and reduced uncertainty in assessing fire hazard would be significant, both economically and socially. Current problems in the modeling of fire spread include the role of plant community differences, spatial heterogeneity in fuels and spatio-temporal changes in fuels. In this research, we evaluated the potential of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR) data for providing improved maps of wildfire fuel properties. Analysis concentrated in two areas of Southern California, the Santa Monica Mountains and Santa Barbara Front Range. Wildfire fuel information can be divided into four basic categories: fuel type, fuel load (live green and woody biomass), fuel moisture and fuel condition (live vs senesced fuels). To map fuel type, AVIRIS data were used to map vegetation species using Multiple Endmember Spectral Mixture Analysis (MESMA) and Binary Decision Trees. Green live biomass and canopy moisture were mapped using AVIRIS through analysis of the 980 nm liquid water absorption feature and compared to alternate measures of moisture and field measurements. Woody biomass was mapped using L and P band cross polarimetric data acquired in 1998 and 1999. Fuel condition was mapped using spectral mixture analysis to map green vegetation (green leaves), nonphotosynthetic vegetation (NPV; stems, wood and litter), shade and soil. Summaries describing the potential of hyperspectral and SAR data for fuel mapping are provided by Roberts et al. and Dennison et al. To utilize remotely sensed data to assess fire hazard, fuel-type maps were translated into standard fuel models accessible to the FARSITE fire spread simulator. The FARSITE model and BEHAVE are considered industry standards for fire behavior analysis. Anderson level fuels map, generated using a binary decision tree classifier are available for multiple dates in the Santa Monica Mountains and at least one date for Santa Barbara. Fuel maps that will fill in the areas between Santa Barbara and the Santa Monica Mountains study sites are in progress, as part of a NASA Regional Earth Science Application Center, the Southern California Wildfire Hazard Center. Species-level maps, were supplied to fire managing agencies (Los Angeles County Fire, California Department of Forestry). Research results were published extensively in the refereed and non-refereed literature. Educational outreach included funding of several graduate students, undergraduate intern training and an article featured in the California Alliance for Minorities Program (CAMP) Quarterly Journal.
Roberts, Dar A.↗
Ground truth measurement for the analysis of airborne SAR data recorded over Oberpfaffenhofen, FRG, 1989
As a preliminary investigation to the joint multiparameter SIR-C/X-SAR shuttle experiment of NASA/JPL (USA), DLR (FRG), and PSN (Italy) which is scheduled for the year 1992 an airborne SAR campaign was conducted over Oberpfaffenhofen, FRG, in August 1989. Primarily this campaign was planned to test and verify equipment and algorithms developed at the DLR to calibrate multifrequency polarimetric SAR data. Oberpfaffenhofen is designated as one of the super test sites for the SIR-C/X-SAR experiment which will be imaged under all circumstances except severe mission errors. A super test site drives radar parameters and look directions and the recorded SAR data will be calibrated. In addition ancillary data will be available for the site. During the airborne STAR campaign conducted in the week of August 14th 1989 various sensor types were used to record remote sensing data over the calibration test site and its vicinity: the polarimetric DC-8 JPL-SAR (P-, L-, C-band), the DLR airborne SAR (C-, X-band), color infrared aerial photography (DLR), and the truck-mounted scatterometer (C- and X-band) of the Institute for Navigation, University of Stuttgart (INS). Because of this variety of different sensor types used and out of the fact that sufficiently large forested and agriculturally used areas were planned to be covered by these sensors, the interest of several German research groups involved in investigations concerning SAR land applications arose. The following groups carried out different ground-truth measurements: University of Bonn, Institute for plant cultivation (plant morphology and moisture content); University of Braunschweig, Institute for Geography (soil moisture and surface roughness); University of Freiburg, Institute for Geography (dielectric soil properties, landuse); and University of Munich, Institute for Geography (landuse inventory, plant, surface, and soil parameters). This paper presents the joint ground truth activities of the Institute for Geography, University of Munich, and the German Remote Sensing Data Center of the DLR.
Bayer, T.↗