ESTIMATION OF SOIL MOISTURE USING MICROWAVE REMOTE SENSING DATA by TARENDRA LAKHANKAR A dissertation submitted to the Graduate Faculty in Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy, The City University of New York 2006 i UMI Number: 3231970 3231970 2006 Copyright 2006 by Lakhankar, Tarendra UMI Microform Copyright All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. ProQuest Information and Learning Company 300 North Zeeb Road P.O. Box 1346 Ann Arbor, MI 48106-1346 All rights reserved. by ProQuest Information and Learning Company. © 2006 TARENDRA LAKHANKAR All Rights Reserved ii This manuscript has been read and accepted for the Graduate Faculty in Engineering in satisfaction of the dissertation requirement for the degree of Doctor of Philosophy. Date Prof. Hosni Ghedira Professor of Civil Engineering Chair of Examining Committee Date Dean Mumtaz K. Kassir Executive Officer Supervisory Committee Prof. Reza Khanbilvardi Prof. Vasil Diyamandoglu Prof. Shayesteh Mahani Prof. Reginald Blake THE CITY UNIVERSITY OF NEW YORK iii Abstract ESTIMATION OF SOIL MOISTURE USING MICROWAVE REMOTE SENSING DATA By Tarendra Lakhankar Adviser: Professor Hosni Ghedira Knowledge of soil moisture helps to derive parameters, such as evaporation, transpiration, infiltration, runoff and drainage classes, which are very useful in several agricultural and hydrological applications. Active and passive remote sensing sensors have shown the capability to estimate soil moisture based on the large contrast between the dielectric properties of wet and dry soil. However, the retrieval of soil moisture from microwaves system is mostly influenced by the characteristics of the vegetation cover. Indeed, having accurate information of the spatial distribution of vegetation (i.e. NDVI and vegetation optical depth) improves the soil moisture retrieval from microwave data. The major objective of this research is to develop an algorithm to produce spatial retrieval of soil moisture using active microwave data. The algorithm will be developed using a combination of parametric and non-parametric tools such as neural networks, fuzzy logic, maximum likelihood etc. The study area is located in Oklahoma (97d35'W, 36d15'N). The active microwave data from RADARSAT-1 acquired in SCANSAR mode were used in combination with the soil moisture data generated from passive iv Electronically Scanned Thinned Array Radiometer (ESTAR) during the SGP97 campaign operated by NASA. This study will evaluate the contribution of vegetation in minimizing its effect on the accuracy of soil moisture retrieval. Based on our research, we found that the presence of higher vegetation cover reduces the accuracy of soil moisture retrieval. The empirical model to limit the effect of vegetation cover to maximize the accuracy of soil moisture retrieval has been proposed. The final product of this study, which has been produced, is a soil moisture map using active microwave data with different level of accuracy. This research also highlights the impact of spatial heterogeneity in land surface conditions on soil moisture retrieval from microwave data. Sensitivity of soil moisture retrieval in spatial heterogeneous area is positively correlated with the type of land-cover. v Acknowledgement First and foremost, I would like to express my deep and sincere gratitude to my supervisor, Professor Hosni Ghedira, for his support and encouragement throughout my doctoral study. Prof. Hosni, you are a fantastic mentor, and a nice person. Without you, this thesis would have been impossible to be complete. I would also like to thank, Professor Reza Khanbilvardi, for his guidance, encouragement and financial support over the last three years. It is my privilege to work with Prof. Reza and NOAA-CREST on such a nice project. I am grateful to Prof. Shayesteh Mahani for carefully following my work and useful comments and corrections. I also would like to thank Professor Vasil Diyamandoglu and Reginald Blake for their remarkable advices and for serving in my PhD committee. Special thanks to Dr. Shakila Merchant for her cheer and encouragement. I wish to express my thanks to my colleagues Amir Azar, Juan Arevalo, Cecelia Hernandez, Heather Glickman, Yajaira Mejia, Bernard Mhando, Kallol Gangul, Rouzbeh Nazari, Nasim and Nasim at City College of New York for all the discussions, cooperation and for the wonderful time we have shared during various conference visits. Heather and Cecelia your last minute help in dissertation correction are very much appreciated. I also would like to thank Yevgeniy Leykin, Sanchia Peterson, Carla and all civil engineering staff for their love. My loving thanks are due to Rabi Khan and his family, for incorporating us in his house and lived like single happy family in New York. Last but certainly not least, I am short of words, to express my loving gratitude to my wife, Aparna, for her patience, understanding and support throughout this study, and to my daughter, Astha, who sacrificed 3 years without her mom and dad, are a never-ending source of love, pride and inspiration to me. This study would not be possible without inspiration and loving support of my parents, brothers and in-laws. vi Table of Content ABSTRACT IV ACKNOWLEDGEMENT VI TABLE OF CONTENT VII LIST OF FIGURES XI LIST OF TABLES XIV NOMENCLATURE XV ACRONYMS XVI 1 INTRODUCTION 1 1.1 BACKGROUND 1 1.2 THESIS OBJECTIVES 4 1.3 THESIS HYPOTHESES 5 1.4 THESIS OVERVIEW 6 2 LITERATURE REVIEW 7 2.1 SOIL MOISTURE MEASUREMENT 8 2.2 SOIL MOISTURE SATELLITE MISSIONS 11 2.3 MICROWAVE REMOTE SENSING 13 2.4 MICROWAVE REMOTE SENSING AND SOIL MOISTURE 15 2.5 ACTIVE MICROWAVE MODELS FOR SOIL MOISTURE RETRIEVAL 16 2.5.1 Theoretical Models 17 2.5.2 Empirical Backscattering Models 17 2.5.3 Semi-empirical Backscattering Models 19 2.5.4 Linear Relationship 22 2.5.5 Modified Linear Relationship 23 2.5.6 Michigan Microwave Canopy Scattering (MIMICS) Model 24 2.5.7 An Optical/Microwave Synergistic Model 25 vii 2.6 EFFECT OF VEGETATION ON SOIL MOISTURE ESTIMATION 26 3 MICROWAVE THEORY AND SOIL MOISTURE 29 3.1 INTRODUCTION 29 3.2 PASSIVE MICROWAVE THEORY 30 3.3 ACTIVE MICROWAVE THEORY 31 3.3.1 Frequency and Wavelength 34 3.3.2 Incidence angle 35 3.3.3 Polarization 37 3.4 SOIL SURFACE PARAMETERS 39 3.4.1 Dielectric Constant 39 3.4.2 Surface Roughness 40 3.4.3 Soil Texture 42 3.4.4 Topography 43 3.4.5 Observation depth 45 3.5 VEGETATION PARAMETERS 46 3.5.1 Normalized Difference Vegetation Index (NDVI) 46 3.5.2 Vegetation Optical Depth 48 3.5.3 Leaf Area Index (LAI) 50 4 NON-PARAMETRIC METHODS 52 4.1 NEURAL NETWORK SYSTEM 52 4.2 FUZZY LOGIC METHOD 56 4.3 REMOTE SENSING AND NEURAL NETWORK SYSTEM 60 5 STUDY AREA AND DATA ACQUISITION 64 5.1 SGP’97 EXPERIMENT 64 5.2 SOIL MOISTURE DATA 66 5.2.1 Field Soil moisture data 66 5.2.2 Truth Soil moisture Data 71 viii 5.3 VEGETATION AND ANCILLARY DATA 73 5.3.1 NDVI 73 5.3.2 Vegetation optical depth 73 5.3.3 Soil Texture 74 5.3.4 Land-cover data 75 5.4 ACTIVE MICROWAVE DATA FROM RADARSAT-1 SATELLITE 75 5.4.1 Data Acquisition 77 5.4.2 Data Pre-processing 78 5.4.3 Image Registration 79 5.5 TEXTURAL ANALYSIS SAR DATA 80 6 METHODOLOGY AND ALGORITHM DEVELOPMENT 85 6.1 INTRODUCTION 85 6.2 NEURAL NETWORK ALGORITHM 85 6.2.1 Neural Network Architecture 86 6.2.2 Neural Network Training 91 6.2.3 Effect of Threshold Limit 96 6.2.4 Neural Network output 99 6.3 FUZZY LOGIC ALGORITHM 100 6.4 MULTIPLE REGRESSION ANALYSIS 108 6.5 ASSESSMENT AND VALIDATION 112 6.5.1 Categorical Assessment 112 6.5.2 Quantitative Assessment 117 6.5.3 Model Validation 121 7 EFFECT OF VEGETATION ON SOIL MOISTURE RETRIEVAL 125 7.1 INTRODUCTION 125 7.2 EFFECT OF NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) 125 7.3 EFFECT OF VEGETATION OPTICAL DEPTH 131 ix [...]... Apart from soil moisture application, microwave remote sensing has been successfully used for rice crop inventory (Chakraborty and Panigrahy 2000) 2.4 Microwave Remote Sensing and Soil Moisture Remote sensing technology is spatial in nature, and creates a greater capability to estimate soil moisture using the microwave region of the electromagnetic spectrum A number of experiments conducted using truck... of NDVI classes on SAR backscattering and soil moisture relationship .126 Figure 51: Effect of NDVI class on soil moisture classification accuracy 127 Figure 52: Effect of NDVI and soil texture as an input on accuracy of soil moisture retrieval 128 Figure 53 Correlation between NDVI and soil moisture retrieval error (July 02, 1997 data) .129 Figure 54 Correlation between NDVI and soil moisture. .. Comparison of soil moisture retrieval from fuzzy model with truth values (July 02 data) 123 Figure 47 Comparison of soil moisture retrieval from fuzzy model with truth values (July 12 data) .123 Figure 48 Comparison of soil moisture retrieval error and its relationship with NDVI (July 02 data) .124 Figure 49 Comparison of soil moisture retrieval error and its relationship with NDVI (July 12 data) .124... demonstrated that soil moisture can be measured accurately from the upper ~5 cm of the soil surface Both active and passive microwave sensors have demonstrated a strong potential to retrieve spatial and temporal variability of soil moisture for different land surface classification The potential of microwave remote sensing in estimating soil moisture is based on the dielectric properties of soil This relationship... heterogeneity of soil surface characteristics The water content of the upper soil layer, or soil moisture, is being increasingly used as input for various hydrological modeling processes Presently, most of the hydrological models that require soil moisture information use point measurements or spatial distribution of soil moisture derived from physically-based models Spatial distribution of soil moisture. .. first -of- a-kind exploratory measurements and aim to measure soil moisture with an accuracy of 0.04 m3 m−3 (4%) The accuracy of satellite-derived soil moisture is usually affected by the presence of vegetation which significantly modifies and attenuates the outgoing microwave radiation of the soil and makes the retrieval of realistic soil moisture from satellite-based sensors difficult and inaccurate Soil. .. distribution of soil moisture over the region, for soil moisture varies in space and in time and its value is generally affected by the variability of soil properties, topography, land cover, evapo-transpiration and precipitation Hence, it is necessary to look for technologies such as remote sensing as an alternative to produce spatial distribution of soil moisture estimates Figure 1: Tensiometer for soil moisture. .. carried out to explore the potential of microwave remote sensing for estimation of soil moisture and other hydrological parameters (Jackson et al 1999; Jacobs et al 2004; O’Neill et al 1993; Rosnaya et al 2006; Schmugge 1998) The details of active microwave sensors that show high capabilities in soil moisture retrieval are given in Table 1 11 Table 1: Details of active microwave sensors Sensor Frequency... probe for soil moisture measurements (Courtesy: United Nations website) Figure 3: Time-Domain Reflectory (TDR) Soil Moisture Probe 10 Since the early sixties, satellite remote sensing has developed as a prominent tool to monitor and compute environmental processes in both spatial and temporal terms In addition to point measurements, soil moisture can be measured using remote sensing Remote sensing methods... a remote sensing context, soil moisture represents the amount of water in the top layer of the soil surface; generally the upper 5 to 10 cm below natural ground surface The temporal and spatial variations of soil moisture represent two key parameters for various hydrological modeling processes With the actual field measurement techniques, it is very difficult to have a spatial measurement of soil moisture, . 2.1 SOIL MOISTURE MEASUREMENT 8 2.2 SOIL MOISTURE SATELLITE MISSIONS 11 2.3 MICROWAVE REMOTE SENSING 13 2.4 MICROWAVE REMOTE SENSING AND SOIL MOISTURE 15 2.5 ACTIVE MICROWAVE MODELS FOR SOIL. YORK iii Abstract ESTIMATION OF SOIL MOISTURE USING MICROWAVE REMOTE SENSING DATA By Tarendra Lakhankar Adviser: Professor Hosni Ghedira Knowledge of soil moisture helps to derive. 4.3 REMOTE SENSING AND NEURAL NETWORK SYSTEM 60 5 STUDY AREA AND DATA ACQUISITION 64 5.1 SGP’97 EXPERIMENT 64 5.2 SOIL MOISTURE DATA 66 5.2.1 Field Soil moisture data 66 5.2.2 Truth Soil moisture