Ujjwal Narayan

High-resolution change estimation of soil moisture using L-band radiometer and Radar observations made during the SMEX02 experiments

The soil moisture experiments held during June-July 2002 (SMEX02) at Iowa demonstrated the potential of the L-band radiometer (PALS) in estimation of near surface soil moisture under dense vegetation canopy conditions. The L-band radar was also shown to be sensitive to near surface soil moisture. However, the spatial resolution of a typical satellite L-band radiometer is of the order of tens of kilometers, which is not sufficient to serve the full range of science needs for land surface hydrology and weather modeling applications. Disaggregation schemes for deriving subpixel estimates of soil moisture from radiometer data using higher resolution radar observations may provide the means for making available global soil moisture observations at a much finer scale. This paper presents a simple approach for estimation of change in soil moisture at a higher (radar) spatial resolution by combining L-band copolarized radar backscattering coefficients and L-band radiometric brightness temperatures. Sensitivity of AIRSAR L-band copolarized channels has been demonstrated by comparison with in situ soil moisture measurements as well as PALS brightness temperatures. The change estimation algorithm has been applied to coincident PALS and AIRSAR datasets acquired during the SMEX02 campaign. Using AIRSAR data aggregated to a 100-m resolution, PALS radiometer estimates of soil moisture change at a 400-m resolution have been disaggregated to 100-m resolution. The effect of surface roughness variability on the change estimation algorithm has been explained using integral equation model (IEM) simulations. A simulation experiment using synthetic data has been performed to analyze the performance of the algorithm over a region undergoing gradual wetting and dry down.

Web service based hydrologic data distribution system

Hydrological modeling has been undergoing an exciting phase of transformation driven by rapid advances in remote sensing technology and computing power. In particular, NASAs Earth Observation System (EOS) suite of satellite platforms and sensors has made available a variety of biophysical measurements that have the potential to immensely advance the science and application of hydrology. These sensors use a variety of remote sensing technologies to make measurements of hydrological variables at several scales of spatial and temporal resolution. These rich datasets are available in a variety of data formats with different data access techniques, data quality issues and temporal and spatial extents that may be intimidating to the end user. As more and more watersheds become gauged and satellite instruments get deployed, it becomes important to make data availability and usage as streamlined as possible for potential users. We propose a Web Services based data distribution system to deliver remote sensing data to the hydrologic community. The use of Web Services for data delivery will permit hydrologists to directly integrate data sources into their hydrological models with minimal effort.

A simple method for spatial disaggregation of radiometer derived soil moisture using higher resolution radar observations

The SMEX02 experiments held in June-July 2002, at Iowa demonstrated the potential of the L band radiometer (PALS) in estimation of near surface soil moisture under dense vegetation canopy conditions. The L band radar was also shown to be sensitive to near surface soil moisture. However, the spatial resolution of a typical satellite L band radiometer is of the order of tens of kilometers, which is not sufficient to serve the full range of science needs for land surface hydrology and weather modeling applications. Disaggregation schemes for deriving sub pixel estimates of soil moisture from radiometer data using higher resolution radar observations may provide the means for making available global soil moisture observations at much finer scale. This paper presents a simple approach for disaggregation of coarser resolution radiometer estimates of soil moisture using higher resolution radar backscatter and vegetation water content measurements. The algorithm has been applied to coincident PALS radiometer and Airsar datasets of 400 m and 30 m spatial resolutions respectively acquired during the SMEX02 campaign. PALS radiometer estimates of soil moisture at a 400 m resolution have been disaggregated to 100 m resolution.

Microwave remote sensing: a perspective from the last few field experiments

There have been numerous field experiments which have tested the effectiveness of microwave remote sensing, both active and passive under varied land surface conditions. The Southern Great Plains Experiment 1999 (SGP99) was held in Chickasha Oklahoma where winter wheat and rangeland was the predominant land surface type whereas at the Soil Moisture Experiment 2002 (SMEX02) in Walnut River watershed in Ames Iowa it was a mixture of corn, and soyabeans. In the SMEX03 (Soil Moisture Experiment in 2003) in Little River Watershed, the land surface was a mixture of peanuts, vegetables, cotton and pasture and for SMEX04 (Soil Moisture Experiment in 2004) in Walnut Gulch, Arizona, the land surface cover is primarily brush and grass covered rangeland vegetation. Given that microwaves have low sensitivity to soil moisture in the presence of vegetation, these field experiments offer an opportunity to examine observations of sensitivity in the presence of varied (and varying with time) vegetation densities. In addition, in each of these experiments, there were different instruments on aircrafts and satellite sensors that were deployed. In SGP99, we had observations from the PALS (Passive Active L and S band Radar and Radiometer), PSR (Polarimetric Scanning Radiometer) from the C130 aircraft and the TMI (TRMM Microwave Imager) and SSM/I (Special Sensor Microwave Imager) from space. In SMEX02, we had PALS, PSR, AIRSAR (Airborne Synthetic Aperture Radar) from the aircraft platforms and in SMEX03 PSR only. Satellite sensors in SMEX02 and SMEX03 included AMSR (Advanced Microwave Scanning Radiometer), TMI, and SSM/I. In the recently concluded SMEX04, PSR was used from the aircraft and AMSR, TMI and SSM/I satellite observations were available. We will use these sensors and observations in the microwave channel in conjunction with ground observations of vegetation characteristics and soil moisture to study the sensitivity of microwaves to soil moisture under varied land surface conditions.

A simple algorithm for spatial disaggregation of radiometer derived soil moisture using higher resolution radar observations

The SMEX02 experiments held in June-July 2002, at Iowa demonstrated the potential of an L band radiometer (PALS) in estimation of near surface soil moisture under dense vegetation canopy conditions. The L band radar was also shown to be sufficiently sensitive to near surface soil moisture. However, the spatial resolution of a typical satellite mounted L band radiometer is of the order of 10s of kilometers which is not sufficient to serve the science needs of land surface hydrology and weather modeling applications. Disaggregation schemes for deriving sub pixel estimates of soil moisture from radiometer data using higher resolution radar observations hold the promise of making global soil moisture observations at much finer scale available. The HYDROS instrument is proposed to have an L band radiometer and L band radar onboard. The passive instrument has spatial resolution of the order of tens of kilometers and operates along with the active instrument that takes observations at a resolution of tens of meters. This paper presents a simple approach for disaggregation of coarser resolution radiometer estimates of soil moisture using higher resolution radar backscatter measurements. The algorithm has been applied to a coincident PALS radar/radiometer and AIRSAR dataset acquired during the SMEX02 campaign

Estimation of soil moisture using data from advanced microwave scanning radiometer

Soil moisture is an important variable controlling biogeochemical cycles, heat exchange and infiltration rates at land/atmosphere boundary. The microwave portion of the electromagnetic spectrum have been used to monitor the moisture content of soils due to the sensitivity of microwave brightness temperatures to land surface variables. In this paper, the simulation of the C-band brightness temperatures are carried out for the SMEX02 (Soil Moisture Experiments 2002) regions in Ames, Iowa for the time period between June 25 to July 31, 2002. The simulated brightness temperatures have been compared with the corresponding observations using the Advanced Microwave Scanning Radiometer (AMSR).

Spatial downscaling of coarse passive radiometer soil moisture using radar, vegetation index and surface temperature

Soil moisture derived from passive microwave radiometers have low spatial resolution due to limitation of antenna size. In the case of many geophysical applications high spatial resolution is desired, examples of these include weather, catchment hydrology and agriculture. In this paper we present two innovative methods to downscale passive soil moisture retrievals using (a) radar backscatter and (b) vegetation index and surface temperature.