Amir E. Azar

Effect of Land Cover Heterogeneity on Soil Moisture Retrieval Using Active Microwave Remote Sensing Data

This study addresses the issue of the variability and heterogeneity problems that are expected from a sensor with a larger footprint having homogenous and heterogeneous sub-pixels. Improved understanding of spatial variability of soil surface characteristics such as land cover and vegetation in larger footprint are critical in remote sensing based soil moisture retrieval. This study analyzes the sub-pixel variability (standard deviation of sub- grid pixels) of Normalized Difference Vegetation Index and SAR backscatter. Back- propagation neural network was used for soil moisture retrieval from active microwave remote sensing data from Southern Great Plains of Oklahoma. The effect of land cover heterogeneity (number of different vegetation species within pixels) on soil moisture retrieval using active microwave remote sensing data was investigated. The presence of heterogeneous vegetation cover reduced the accuracy of the derived soil moisture using microwave remote sensing data. The results from this study can be used to characterize the uncertainty in soil moisture retrieval in the context of Soil Moisture Active and Passive (SMAP) mission which will have larger footprint.

The Effect of Vegetation Cover on Snow Cover Mapping from Passive Microwave Data

Snow-cover parameters are being increasingly used as inputs to hydrological models. Having an accurate estimation of the snow cover characteristics during the snowmelt season is indispensable for an efficient hydrological modeling and for an improved snowmelt runoff forecasts. In this paper, we used an adaptive neural network system to generate the spatial distribution of snow accumulation from multi-channel SSM/I data in the Northern Midwest of the United States. Five SSM/I channels were used in this experiment (19H, 19V, 22V, 37V, and 85V). Three snow days with high snow accumulation and no precipitation have been selected during the 2001/2002 winter season to train and test the neural network system. Snow depth measurements have been collected from the National Climatic Data Center (NCDC) through the Cooperative Observer Network for the U.S. snow Monitoring. The snow depths have been compiled and gridded into 25 km times 25 km grid to match the final SSM/I resolution. Different vegetation-related parameters (NDVI, optical depth, homogeneity) have been collected and gridded over the study area. The current results have shown a significant effect of vegetation cover properties on the mapping accuracy. Furthermore, the addition of vegetation related information to the mapping process has shown to have a positive impact on mapping performance, especially for areas with shallow snow cover (less than 5 cm)

Analysis of Snowpack Properties and Estimation of Snow Grain Size using CLPX Data

In this study, we focus on analysis of snow grain size behavior with respect to other snow parameters such as snow depth, density, and temperature. We derive a pattern which can be used to approximate the range of grain size variations. NASA Cold-Land Process Experiment data includes measurements of the grain size, density, and temperature in different layers of snowpack profile. The analysis showed that the snow density and temperature usually increase towards the snow-ground interface. The grain size profile shows an increase as well towards the bottom. Overall, the snow grain size variation is highly correlated with both snow density and temperature. The correlation is generally higher between snow grain size and temperature as compared with grain size and density. Thus, snowpack temperature profile might be estimated by a linear function having the top and bottom temperature. Using snowpack temperature, the grain size evolution can be approximated.

Improvement in Estimating Snowpack Properties with SSM/I Data and Land Cover Using Artificial Neural Networks

The goal of this study is to develop an algorithm to estimate snow water equivalent (SWE) in Great Lakes area based on a three year time series of SSM/I data along with corresponding ground truth data. An assortment of SSM/I EASE-GRID pixels was selected for time series analysis. The pixels were selected based on the amount of snow, latitude, and land cover. Two types of ground truth data were used: 1 - point-based snow depth observations from NCDC; 2 - grid based SNODAS-SWE dataset, produced by NOHRSC. To account for land cover variation in a quantitative way a NDVI was used. To do the time series analysis, three scattering signatures of GTVN (19V-37V), GTH (19H-37H), and SSI (22V-85V) were derived. The analysis shows at lower latitudes of the study area there is no correlation between GTH and GTVN vs snow depth. On the other hand SSI shows an average correlation of 75 percent with snow depth in lower latitudes makes it suitable for shallow snow. In model development a multi-linear algorithm was defined to estimate SWE using NDVI values along with the location of the pixels as classification criteria. The results show up to 60 percent correlation between estimated and ground truth SWE

EFFECT OF SUB-PIXEL VARIABILITY AND LAND-COVER ON SOIL MOISTURE RETRIEVAL FROM RADARSAT-1 DATA

The dynamic of soil moisture is generally affected by the spatial variation in soil surface characteristics such as land cover, vegetation density, soil texture, and soil material. The main purpose of this project is to develop neural network algorithm for soil moisture retrieval from active microwave data. A back-propagation neural network has been used to estimate the soil moisture from Synthetic Aperture Radar data. Soil moisture data with a spatial resolution of 800 m acquired during the SGP97 campaign, were used as truth data in the training and the validation processes. In addition to backscatter values retrieved from RADARSAT-1 image, normalized difference vegetation index (NDVI), land cover and soil texture have been added as an input to neural network algorithm. The effects of sub-pixels variability of the NDVI and land cover type on the retrieval of soil moisture have been investigated by comparing the measured and the predicted soil moisture. Further, all training and validation pixels (800 m resolution) have been labeled as either homogeneous or heterogeneous based on the occurrence of the same land cover type. The results showed that, homogeneous pixels are more likely to have better accuracy than heterogeneous pixels in soil moisture classification. A better correlation between soil moisture and SAR backscattering was found in areas with high soil moisture content, where the surface wetness dominated the vegetation contribution to the radar backscatter

Analysis of the Effects of Snowpack Properties on Satellite Microwave Brightness Temperature and Emissivity Data

Spatial variations of snowpack properties are an essential component in flood predictions and water resource management. Satellite microwave remote sensing has shown great potential in retrieving snowpack properties such as: snow depth, snow grain size, and snow density. In this research, we investigate the potential of microwave emissivity which is highly influenced by snowpack properties. Brightness temperature and emissivity data generated from HUT (Helsinki University of Technology) microwave emission of snow model were evaluated with satellite microwave measurements. The comparison of the real measurements (in-situ and satellite) with the modeled results shows that the scattering signature (19GHz-37GHz and 19GHz-85GHz) shows better results in emissivities rather than brightness temperature data. Furthermore, the over the deep snow (>30cm), the emissivities scattering signature of (19GHz- 37GHz) has best performance while over shallow snow (<30cm) the emissivities scattering signature of (19GHz- 85GHz) performs superior. The results indicate the validity of grain growth assumption to some extent but it fails to address it quantitatively as a function of time.

A Comparison of Snowpack Properties Derived from SSM/I Emissivity Data with Snowpack Properties Derived from SSM/I Brightness Temperature Data

Snowpack properties highly influence the microwave scattering. Brightness temperature from SSM/I has the effect of atmosphere especially in high frequency channels. Microwave emissivities, which have been derived by removing the contribution of the atmosphere, clouds, and rain, can also be used to estimate snowpack properties. In order to improve microwave based Snow Water Equivalent (SWE) retrievals, snow pack properties variations need to be taken to account. The sensitivity of passive microwave satellite observations to snow characteristics is evaluated, between 19 and 85 GHz, for the Great Plains area in USA and Canada. A microwave snow emission model was applied to the data base to retrieve snowpack properties. The results indicate emissivities at high frequency (85 GHz) are very sensitive to the presence of snow on the ground, even for very low snow depth, they also show a better performance in estimation of snowpack properties.

Development of an Algorithm to Estimate SWE in Great Lakes Area Using Microwave Data

The goal of this study is to develop an algorithm to estimate Snow Water Equivalent (SWE) in Great Lakes area, using a three year time series of SSM/I data along with corresponding ground truth data. The study area is located between latitudes 41N-49N and longitudes 87W-98W. The northern part of the study area is covered by snow for the whole winter season however for the southern part there is a pattern of snow-fall and snow melt within the season. In addition to snow pattern, the land cover type varies a wide range including, Evergreen Needle leaf forest, Deciduous Broadleaf forest, cropland, woodland and dry land. Seven SSM/I channels) formatted in EASE-Grid 25km by 25km have been used in this study. Two types of ground truth data were used: 1- Point-based snow depth observations from National Climate Data Center (NCDC) snow monitoring section; 2- Grid based SNODAS-SWE dataset, produced at 1km spatial resolution by National Operational Hydrological Remote Sensing Center (NOHRSC). To do the time series analysis, three scattering signatures of GTVN (19V-37V), GTH (19H-37H), and SSI (22V-85V) were derived. The analysis shows at lower latitudes of the study area there is no correlation between GTH and GTVN versus snow depth. On the other hand SSI shows an average correlation of 75 percent with snow depth in lower latitudes. This is due to the saturation of channel 85GHz which makes SSI only suitable for estimating shallow snow cover. As the first step in model development a multi-linear algorithm was defined to estimate SWE using microwave along with NDVI. This algorithm divides the study area into sub-areas based on their NDVI value and the geographical coordinates. For each of those sub-areas a separate linear algorithm is defined to estimate SWE using GTVN and SSI indexes. The results show up to 60 percent correlation between estimated and ground truth SWE. Also, contradictory to Chang and Goodison-Walker algorithms that highly underestimate the total SWE, multi-linear algorithm tends to follow the ground truth pattern of total SWE in the study area. Second, an Artificial Neural Network (ANN) model was developed. The ANN model with seven SSM/I channels and NDVI as its input showed better correlations compare to using only SSM/I as input. This indicates the potential of using ANNs to use combination of various data types.

Evaluation of SSM/I filtering algorithm for snow cover identification in Northern New York State

Snow-cover parameters are being increasingly used as input to hydrological models. An accurate knowledge of the onset of snow melts and snow water equivalent values are important variables in different hydrological applications such as Hooding prediction, reservoir management and agricultural activities. However, the traditional field sampling methods and the ground-based data collection are often very sparse, time consuming, and expensive compared to the coverage provided by remote sensing techniques. Microwave remote sensing techniques have been investigated by numerous researchers using various sensors and have been demonstrated to be effective for monitoring snow pack parameters such as spatial and temporal distribution, snow water equivalent, depth, and snow condition (wet/dry state). Those researches have resulted that the microwave brightness temperature and the microwave backscattering are related to the snow cover structure with different correlation degrees. The primary objective of this research is to produce a spatial estimation of snow water equivalent in a timely fashion with sufficient spatial and temporal resolution using multi-source microwave and optical data. The final product of this project will be an additional tool for flood warning and water resource forecasts, which can be an additional input to the actual hydrological models. The contribution of remote sensing snow related information into the advanced hydrologic prediction system (AHPS) operated by NWS/NOAA (with 4 km grid resolution) will be also evaluated. This paper presents the first step of this project: data collection and evaluation