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Pedometric Mapping of Key Topsoil and Subsoil Attributes Using Proximal and Remote Sensing in Midwest Brazil

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Pedometric Mapping of Key Topsoil and Subsoil Attributes Using Proximal and Remote Sensing in Midwest Brazil UNIVERSIDADE DE BRASÍLIA FACULDADE DE AGRONOMIA E MEDICINA VETERINÁRIA PROGRAMA DE PÓS-GRADUAÇÃO EM AGRONOMIA PEDOMETRIC MAPPING OF KEY TOPSOIL AND SUBSOIL ATTRIBUTES USING PROXIMAL AND REMOTE SENSING IN MIDWEST BRAZIL RAÚL ROBERTO POPPIEL TESE DE DOUTORADO EM AGRONOMIA BRASÍLIA/DF MARÇO/2020 UNIVERSIDADE DE BRASÍLIA FACULDADE DE AGRONOMIA E MEDICINA VETERINÁRIA PROGRAMA DE PÓS-GRADUAÇÃO EM AGRONOMIA PEDOMETRIC MAPPING OF KEY TOPSOIL AND SUBSOIL ATTRIBUTES USING PROXIMAL AND REMOTE SENSING IN MIDWEST BRAZIL RAÚL ROBERTO POPPIEL ORIENTADOR: Profa. Dra. MARILUSA PINTO COELHO LACERDA CO-ORIENTADOR: Prof. Titular JOSÉ ALEXANDRE MELO DEMATTÊ TESE DE DOUTORADO EM AGRONOMIA BRASÍLIA/DF MARÇO/2020 ii iii REFERÊNCIA BIBLIOGRÁFICA POPPIEL, R. R. Pedometric mapping of key topsoil and subsoil attributes using proximal and remote sensing in Midwest Brazil. Faculdade de Agronomia e Medicina Veterinária, Universidade de Brasília- Brasília, 2019; 105p. (Tese de Doutorado em Agronomia). CESSÃO DE DIREITOS NOME DO AUTOR: Raúl Roberto Poppiel TÍTULO DA TESE DE DOUTORADO: Pedometric mapping of key topsoil and subsoil attributes using proximal and remote sensing in Midwest Brazil. GRAU: Doutor ANO: 2020 É concedida à Universidade de Brasília permissão para reproduzir cópias desta tese de doutorado e para emprestar e vender tais cópias somente para propósitos acadêmicos e científicos. O autor reserva outros direitos de publicação e nenhuma parte desta tese de doutorado pode ser reproduzida sem autorização do autor. ________________________________________________ Raúl Roberto Poppiel CPF: 703.559.901-05 Email: [email protected] Poppiel, Raúl Roberto Pedometric mapping of key topsoil and subsoil attributes using proximal and remote sensing in Midwest Brazil/ Raúl Roberto Poppiel. -- Brasília, 2020. 105f. : il. Tese (Doutorado em Agronomia) - Universidade de Brasília, Faculdade de Agronomia e Medicina Veterinária, 2019. Orientador: Profa. Dra. Marilusa Pinto Coelho Lacerda Co-orientador: Prof. Titular José Alexandre Melo Demattê Bibliografia 1. Gênese, Levantamento e Classificação de Solos. 2. Geoprocessamento aplicado às Ciências Agrárias. 3. Mapeamento digital de solos. I. Poppiel, Raúl Roberto. II. Universidade de Brasília. Faculdade de Agronomia e Medicina Veterinária. Doutorado em Agronomia. III. Título. IV. Marilusa, Marilusa Pinto Coelho. Doutor iv I THANK The life, Brazil and everyone around me. I DEDICATE To my dear mother, Liliana, my source of inspiration that calms everything, to my family and the memories of my grandparents. I OFFER To the academic community of Brazil. v ACKNOWLEDGEMENTS Firstly, I would like to express my sincere gratitude to my advisor Profa. Marilusa P.C. Lacerda and co-advisor Prof. José A.M. Demattê for the continuous support on my research, motivation and immense knowledge. Besides my advisors, I would like to thank the rest of my thesis examining committee: Dr. Edson E. Sano, Prof. Paulo R. Meneses, Prof. Elpídio I. Fernandes Filho and Prof. Tairone P. Leão for their insightful comments and ideas. I am grateful to my partner Luis Fernando P. Pimentel, for all your affection, patience and emotional support along this intense period. I am also grateful to my family who have supported me along my life… I love you all! I thank the Federal District Research Foundation – FAPDF, who funded my PhD scholarship, and the São Paulo Research Foundation – FAPESP, who funded this research through the grant numbers: 2014/22262-0, 2016/26124-6 and 2016/01597-9. I am also grateful to the Faculty of Agronomy and Veterinary Medicine of the University of Brasilia (FAV/UnB), and to the Department of Soil Science of the “Luiz de Queiroz” College of Agriculture from University of São Paulo (LSO/ESALQ/USP), for the opportunity to develop my PhD. Particularly to my professors at the UnB, I am thankful for the valuable knowledge. A very special gratitude goes out to all my colleagues and friends from the Geotechnologies in Soil Science group (https://esalqgeocis.wixsite.com/english/team) at the ESALQ/USP, and the Geoprocessing and Pedomorphology Laboratory at the FAV/UnB. I am not naming anyone, because you all have always supported me. It was great to share experiences (academic and non-academic) with all of you during the last four years. This accomplishment would not have been possible without you all. Thank you! vi SUMMARY GENERAL ABSTRACT -------------------------------------------------------------------------------- 1 1. INTRODUCTION------------------------------------------------------------------------------------ 3 2. HYPOTHESIS ----------------------------------------------------------------------------------------- 5 3. OBJECTIVES ------------------------------------------------------------------------------------------ 5 3.1. GENERAL OBJECTIVE ------------------------------------------------------------------------------ 5 3.2. SPECIFIC OBJECTIVES ------------------------------------------------------------------------------ 5 4. GENERAL FRAMEWORK ------------------------------------------------------------------------- 5 4.1. WORKING STEPS ----------------------------------------------------------------------------------- 5 4.2. THE THEMATIC PROJECT THAT SUPPORTS THIS THESIS -------------------------------------- 7 5. LITERATURE REVIEW ---------------------------------------------------------------------------- 9 5.1. WHAT IS PEDOMETRICS AND DIGITAL SOIL MAPPING? ------------------------------------ 9 5.2. RECENT ADVANCES IN PEDOMETRIC MAPPING --------------------------------------------- 9 5.3. WHAT IS GOOGLE EARTH ENGINE? ----------------------------------------------------------- 10 5.4. GOOGLE EARTH ENGINE FOR PEDOMETRIC MAPPING ------------------------------------- 12 5.5. MACHINE LEARNING FOR PEDOMETRIC MAPPING ----------------------------------------- 14 5.6. WHY PERFORM PEDOMETRIC MAPPING OF SOIL ATTRIBUTES? ---------------------------- 15 6. REFERENCES ---------------------------------------------------------------------------------------- 17 CHAPTER 1 –– MAPPING AT 30 M RESOLUTION OF PHYSICAL AND CHEMICAL SOIL ATTRIBUTES AT MULTIPLE DEPTHS IN MIDWEST BRAZIL ----------------- 24 1. INTRODUCTION----------------------------------------------------------------------------------- 25 2. MATERIAL AND METHODS ------------------------------------------------------------------- 27 2.1. STUDY AREA AND SOIL DATA ----------------------------------------------------------------- 27 2.2.PREPARING ENVIRONMENTAL COVARIATES ------------------------------------------------- 29 2.2.1. Climate Data -------------------------------------------------------------------------------- 31 2.2.2. Relief and Geology Data ----------------------------------------------------------------- 31 2.2.3 Landsat-Derived Data -------------------------------------------------------------------- 31 2.3. RANDOM FOREST (RF) REGRESSION ---------------------------------------------------------- 34 2.3.1 Calibration and Model Tuning ---------------------------------------------------------- 35 2.3.2 Validation and Variable Importance -------------------------------------------------- 35 2.3.3 Prediction of Continuous Soil Attributes --------------------------------------------- 36 3. RESULTS ---------------------------------------------------------------------------------------------- 36 3.1. SUMMARY AND RELATIONSHIPS BETWEEN SOIL ATTRIBUTES ---------------------------- 36 3.2. SYNTHETIC SOIL IMAGE (SYSI) AND SYNTHETIC VEGETATION IMAGE (SYVI) -------- 38 3.3. MODEL ASSESSMENTS --------------------------------------------------------------------------- 40 3.4. BEST PREDICTORS -------------------------------------------------------------------------------- 42 3.5. SOIL MAPS AT MULTIPLE DEPTHS ------------------------------------------------------------- 43 4. DISCUSSION ---------------------------------------------------------------------------------------- 45 4.1. SOIL DATA ---------------------------------------------------------------------------------------- 45 vii 4.2. MACHINE LEARNING ---------------------------------------------------------------------------- 45 4.3. INTERPRETATION OF COVARIATE LAYERS --------------------------------------------------- 46 4.4. RELIABILITY AND INTERPRETATION OF SOIL MAPS ---------------------------------------- 48 4.5. POSSIBLE APPLICATIONS OF THE MAPS ------------------------------------------------------ 50 5. CONCLUSIONS AND FINAL CONSIDERATIONS ------------------------------------- 50 6. LIST OF ABBREVIATIONS ---------------------------------------------------------------------- 51 7. APPENDIX A ----------------------------------------------------------------------------------------- 51 8. REFERENCES ---------------------------------------------------------------------------------------- 57 CHAPTER 2 –– SOIL COLOR AND MINERALOGY MAPPING USING PROXIMAL AND REMOTE SENSING IN MIDWEST BRAZIL ------------------------------------------- 62 1. INTRODUCTION----------------------------------------------------------------------------------- 63 2. MATERIALS AND METHODS ----------------------------------------------------------------- 66 2.1. STUDY AREA AND SOIL DATA ----------------------------------------------------------------- 66 2.2. REFLECTANCE TO SOIL COLOR ---------------------------------------------------------------- 67 2.3. REFLECTANCE TO SOIL MINERALOGY -------------------------------------------------------- 67 2.3.1. Spectral processing------------------------------------------------------------------------
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