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GUEVARA Udel 0060D 1 COUNTRY TO GLOBAL PREDICTION OF SOIL ORGANIC CARBON AND SOIL MOISTURE USING DIGITAL SOIL MAPPING by Mario Guevara A dissertation submitted to the Faculty of the University of Delaware in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Plant and Soil Sciences Winter 2020 © 2020 Mario Guevara All Rights Reserved COUNTRY TO GLOBAL PREDICTION OF SOIL ORGANIC CARBON AND SOIL MOISTURE USING DIGITAL SOIL MAPPING by Mario Guevara Approved: __________________________________________________________ Erik H. Ervin, Ph.D. Chair of the Department of Plant and Soil Sciences Approved: __________________________________________________________ Mark Rieger, Ph.D. Dean of the College of Agriculture and Natural Resources Approved: __________________________________________________________ Douglas J. Doren, Ph.D. Interim Vice Provost for Graduate and Professional Education and Dean of the Graduate College I certify that I have read this dissertation and that in my opinion it meets the academic and professional standard required by the University as a dissertation for the degree of Doctor of Philosophy. Signed: __________________________________________________________ Rodrigo Vargas, Ph.D. Professor in charge of dissertation I certify that I have read this dissertation and that in my opinion it meets the academic and professional standard required by the University as a dissertation for the degree of Doctor of Philosophy. Signed: __________________________________________________________ Bruce Vasilas, Ph.D. Member of dissertation committee I certify that I have read this dissertation and that in my opinion it meets the academic and professional standard required by the University as a dissertation for the degree of Doctor of Philosophy. Signed: __________________________________________________________ Michela Taufer, Ph.D. Member of dissertation committee I certify that I have read this dissertation and that in my opinion it meets the academic and professional standard required by the University as a dissertation for the degree of Doctor of Philosophy. Signed: __________________________________________________________ Randy Wisser, Ph.D. Member of dissertation committee ACKNOWLEDGMENTS I want to thank to my advisor Dr. Rodrigo Vargas for his guidance and support towards the development of this research. Thanks to Dr. José Sarukhán and to Biol. Jorge Larson for their guidance and support. Thanks to all my colleagues, family and friends. Thanks to my father, to my beautiful wife and daughter. This research is dedicated to the memory of Ana Santamaria Galvan. This research was supported by the Mexican National Council for Science and Technology (Ph.D. fellowship 382790). This research was partially funded by the National Science Foundation (CIF21 DIBBs: PD: Cyberinfrastruture Tools for Precision Agriculture in the 21st Century, 1724847) and the National Aeronautics and Space Administration – Carbon Monitoring Systems Initiative (80NSSC18K0173). “I am impressed both with the quantity and quality of this dissertation. It is an excellent example of how pedometrics can help assess key soil properties and functions for a sustainable world.” Gerard B.M. Heuvelink, Special professor of Pedometrics and Digital Soil Mapping at Wageningen University & Research and ISRIC - World Soil Information. iv TABLE OF CONTENTS LIST OF TABLES ........................................................................................................ ix LIST OF FIGURES ........................................................................................................ x ABSTRACT ................................................................................................................ xiii Chapter 1 INTRODUCTION .............................................................................................. 1 REFERENCES ................................................................................................... 8 2 NO SILVER BULLET FOR DIGITAL SOIL MAPPING: COUNTRY- SPECIFIC SOIL ORGANIC CARBON ESTIMATES ACROSS LATIN AMERICA ........................................................................................................ 13 2.1 Introduction ............................................................................................. 16 2.2 Methods ................................................................................................... 23 2.2.1 SOC observations ........................................................................ 23 2.2.2 SOC error estimates ..................................................................... 24 2.2.3 SOC training data and exploratory analysis ................................ 25 2.2.4 Soil prediction factors .................................................................. 26 2.2.5 Prediction of SOC ........................................................................ 28 2.2.6 Model evaluation and accuracy ................................................... 29 2.2.7 SOC stocks .................................................................................. 32 2.3 Results ..................................................................................................... 33 2.3.1 Descriptive statistics .................................................................... 33 2.3.2 Spatial distribution and point error estimates .............................. 34 2.3.3 Correlation of SOC and its predictors ......................................... 34 2.3.4 SOC-related properties ................................................................ 37 2.3.5 Country-specific SOC predictions ............................................... 41 v 2.4 Model ensembles and SOC maps ............................................................ 43 2.4.1 SOC stocks and model uncertainties ........................................... 46 2.5 Discussion ................................................................................................ 49 2.6 Conclusions ............................................................................................. 58 REFERENCES ................................................................................................. 63 3 SOIL ORGANIC CARBON ACROSS MEXICO AND THE CONTERMINOUS UNITED STATES (1991-2010) ...................................... 72 3.1 Introduction ............................................................................................. 75 3.2 Datasets and methods .............................................................................. 80 3.2.1 SOC observational data ............................................................... 83 3.2.2 Calculation of SOC stocks ........................................................... 87 3.2.3 The environmental covariate space ............................................. 87 3.2.4 Recursive feature elimination ...................................................... 88 3.2.5 Simulated annealing .................................................................... 89 3.2.6 Uncertainty analysis .................................................................... 92 3.2.6.1 Pedotransfer functions for bulk density variance ......... 92 3.2.6.2 Independent datasets for model prediction ................... 93 3.2.7 Spatial autocorrelation of model residuals .................................. 94 3.2.8 Model residual limits ................................................................... 95 3.3 Results ..................................................................................................... 96 3.3.1 Descriptive statistics .................................................................... 96 3.3.2 Recursive feature elimination ...................................................... 96 3.3.3 Simulated annealing .................................................................... 98 3.3.4 SOC residual analysis ................................................................ 100 3.3.5 SOC stocks ................................................................................ 103 3.3.6 Quantile conditional distribution of residuals ........................... 107 3.4 Discussion .............................................................................................. 109 3.4.1 Highest ranked environmental predictors .................................. 110 vi 3.4.2 Uncertainty quantification ......................................................... 111 3.4.2.1 BD pedotransfer functions .......................................... 111 3.4.2.2 Spatial and temporal variations of available data ....... 112 3.4.2.3 Quantile response of residual variance ....................... 114 3.4.3 SOC stocks across CONUS and Mexico ................................... 115 3.4.3.1 SOC across land cover classes ................................... 116 3.4.4 Final remarks ............................................................................. 118 REFERENCES ............................................................................................... 121 4 DOWNSCALING SATELLITE SOIL MOISTURE USING GEOMORPHOMETRY AND MACHINE LEARNING .......................... 135 4.1 Introduction ........................................................................................... 136 4.2 Materials and methods ........................................................................... 142 4.2.1 Datasets and data preparation .................................................... 143 4.2.2 Data exploration ........................................................................ 147 4.2.3 Model building
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