Reza Khanbilvardi

Description of flow through a natural wetland using dye tracer tests

Abstract A series of dye tracer tests was performed to determine average flow velocities through various segments of a natural wetland located in Westchester, NY. The wetland was divided into segments using the US Fish and Wildlife Services National Wetlands Inventory (NWI) classification scheme and mapped using a Global Positioning System (GPS). During different levels of flow, dye was pumped into the headwaters of the wetland. The dye was collected with auto samplers at several sampling stations located at the transition zones between wetland segments. Results indicate a significant difference in flow characteristics between two classification types, scrub–shrub and emergent. High data variability was found for samples collected furthest from the dye injection point. Velocities observed for the scrub–shrub-classified segment ranged from 1.11 to 23.08 m/min; for the emergent-classified segment, velocities ranged from 1.54 to 7.68 m/min. Differences in the velocities between the two wetland types can be attributed to sinuosity of the stream channels and vegetation in the flood plain associated with each type of wetland. Results have been used to develop a model to simulate flow through a naturally occurring wetland and could be used to improve design parameters for natural wetland restoration.

Development of a green roof environmental monitoring and meteorological network in New York City.

Green roofs (with plant cover) are gaining attention in the United States as a versatile new environmental mitigation technology. Interest in data on the environmental performance of these systems is growing, particularly with respect to urban heat island mitigation and stormwater runoff control. We are deploying research stations on a diverse array of green roofs within the New York City area, affording a new opportunity to monitor urban environmental conditions at small scales. We show some green roof systems being monitored, describe the sensor selection employed to study energy balance, and show samples of selected data. These roofs should be superior to other urban rooftops as sites for meteorological stations.

Analysis of Large Scale Spatial Variability of Soil Moisture Using a Geostatistical Method

Spatial and temporal soil moisture dynamics are critically needed to improve the parameterization for hydrological and meteorological modeling processes. This study evaluates the statistical spatial structure of large-scale observed and simulated estimates of soil moisture under pre- and post-precipitation event conditions. This large scale variability is a crucial in calibration and validation of large-scale satellite based data assimilation systems. Spatial analysis using geostatistical approaches was used to validate modeled soil moisture by the Agriculture Meteorological (AGRMET) model using in situ measurements of soil moisture from a state-wide environmental monitoring network (Oklahoma Mesonet). The results show that AGRMET data produces larger spatial decorrelation compared to in situ based soil moisture data. The precipitation storms drive the soil moisture spatial structures at large scale, found smaller decorrelation length after precipitation. This study also evaluates the geostatistical approach for mitigation for quality control issues within in situ soil moisture network to estimates at soil moisture at unsampled stations.

Validation of NOAA-Interactive Multisensor Snow and Ice Mapping System (IMS) by Comparison with Ground-Based Measurements over Continental United States

In this study, daily maps of snow cover distribution and sea ice extent produced by NOAA’s interactive multisensor snow and ice mapping system (IMS) were validated using in situ snow depth data from observing stations obtained from NOAA’s National Climatic Data Center (NCDC) for calendar years 2006 to 2010. IMS provides daily maps of snow and sea ice extent within the Northern Hemisphere using data from combination of geostationary and polar orbiting satellites in visible, infrared and microwave spectrums. Statistical correspondence between the IMS and in situ point measurements has been evaluated assuming that ground measurements are discrete and continuously distributed over a 4 km IMS snow cover maps. Advanced Very High Resolution Radiometer (AVHRR) land and snow classification data are supplemental datasets used in the further analysis of correspondence between the IMS product and in situ measurements. The comparison of IMS maps with in situ snow observations conducted over a period of four years has demonstrated a good correspondence of the data sets. The daily rate of agreement between the products mostly ranges between 80% and 90% during the Northern Hemisphere through the winter seasons when about a quarter to one third of the territory of continental US is covered with snow. Further, better agreement was observed for stations recording higher snow depth. The uncertainties in validation of IMS snow product with stationed NCDC data were discussed.

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.

Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data

Satellite remote sensing observations have the potential for efficient and reliable mapping of spatial soil moisture distributions. However, soil moisture retrievals from active microwave remote sensing data are typically complex due to inherent difficulty in characterizing interactions among land surface parameters that contribute to the retrieval process. Therefore, adequate physical mathematical descriptions of microwave backscatter interaction with parameters such as land cover, vegetation density, and soil characteristics are not readily available. In such condition, non-parametric models could be used as possible alternative for better understanding the impact of variables in the retrieval process and relating it in the absence of exact formulation. In this study, non-parametric methods such as neural networks, fuzzy logic are used to retrieve soil moisture from active microwave remote sensing data. The inclusion of soil characteristics and Normalized Difference Vegetation Index (NDVI) derived from infrared and visible measurement, have significantly improved soil moisture retrievals and reduced root mean square error (RMSE) by around 30% in the retrievals. Soil moisture derived from these methods was compared with ESTAR soil moisture (RMSE ~4.0%) and field soil moisture measurements (RMSE ~6.5%). Additionally, the study showed that soil moisture retrievals from highly vegetated areas are less accurate than bare soil areas.

Sea-ice monitoring over the Caspian Sea using geostationary satellite data

A new technique is proposed for sea-ice mapping using observations from geostationary satellite over the Caspian Sea. A two end-member linear-mixture approach has been employed. A neural-network-based approach was used to simulate water and ice reflectances for all possible sun–satellite geometries. The ice-mapping technique incorporates an advanced cloud-detection algorithm with adaptive threshold values. The average percentage of cloud reduction because of the daily compositing ranged from 22% to 25%. Daily maps of ice distribution and concentration with minimal cloud coverage were produced for the winter seasons of 2007 and 2008. The retrieved ice distribution demonstrated a good agreement with Moderate Resolution Imaging Spectroradiometer (MODIS) images and National Oceanic Atmospheric Administration (NOAA) Interactive Multisensor Snow and Ice Mapping System (IMS) snow and ice charts. The obtained correlation coefficients with IMS charts for 2007 and 2008 were 0.92 and 0.83, respectively. The technique has been proposed as one of the candidate ice-mapping techniques for the future Geostationary Operational Environmental Satellite-R Series (GOES-R) Advance Baseline Imager (ABI) instrument.

Sensitivity analysis of b-factor in microwave emission model for soil moisture retrieval: a case study for SMAP mission.

Sensitivity analysis is critically needed to better understand the microwave emission model for soil moisture retrieval using passive microwave remote sensing data. The vegetation b-factor along with vegetation water content and surface characteristics has significant impact in model prediction. This study evaluates the sensitivity of the b-factor, which is function of vegetation type. The analysis is carried out using Passive and Active L and S-band airborne sensor (PALS) and measured field soil moisture from Southern Great Plains experiment (SGP99). The results show that the relative sensitivity of the b-factor is 86% in wet soil condition and 88% in high vegetated condition compared to the sensitivity of the soil moisture. Apparently, the b-factor is found to be more sensitive than the vegetation water content, surface roughness and surface temperature; therefore, the effect of the b-factor is fairly large to the microwave emission in certain conditions. Understanding the dependence of the b-factor on the soil and vegetation is important in studying the soil moisture retrieval algorithm, which can lead to potential improvements in model development for the Soil Moisture Active-Passive (SMAP) mission.

Soil Moisture Retrieval from Radarsat Data: A Neuro-Fuzzy Approach

The spatial dynamic of soil moisture is generally affected by the variation in soil surface characteristics such as: land cover, vegetation density, soil texture, and soil material. The mapping of soil moisture by remote sensing tools has several advantages over the conventional field measurement techniques especially in the case of heterogeneous landscapes. The use of microwave remote sensing offers fast and reliable ways in mapping the spatial distribution of soil moisture. Microwave remote sensing systems is used to measure soil moisture on the basis of a large contrast that exists between the dielectric constant values for dry and wet soils. This study describes the use of non-parametric classifiers for the soil moisture retrieval from active microwave remote sensing data. The difficulty facing the soil moisture retrieval process is due, in large part, to the lack of a precise mathematical description of the observed land cover parameters and to the extent of their variability. Two non-parametric techniques have been used: Neural Networks and Fuzzy Logic. Different configurations of these two classifiers have been tested and compared by assessing the accuracy of soil moisture.

Adjustment to the curve number (NRCS-CN) to account for the vegetation effect on hydrological processes

Abstract This work proposes an approach to automatically adjust the curve number (CN) to account for changes in vegetation density. Precipitation–runoff pairs from the MOdel Parameter Estimation EXperiment (MOPEX) dataset were used to estimate monthly simulated CNs (CNsim). Remotely sensed greenness fraction (GF) was used as a proxy for vegetation density. A relationship was established between CNsim and GF values, and an adjustment factor was introduced. The coefficients of determination (R2) between the simulated and observed runoff when using the unadjusted and adjusted CNs were 0.63 and 0.80, respectively. Likewise, Nash-Sutcliffe coefficients of –0.17 and 0.67, and root mean square error (RMSE) of 5.22 and 2.75 were also obtained for the unadjusted and adjusted CNs, respectively. The results demonstrate how the adjustments compensate for the runoff overestimation when the standard CN (CNstd) is used, and also imply that the adjustment is crucial for improved hydrological modelling, particularly, for flood and flash flood monitoring and forecasting. Editor Z.W. Kundzewicz