Exploring the potential of sentinel-1, -2, and -3 images for the digital mapping of soil properties based on machine learning algorithms and multi-source environmental data:
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| Format: | Abschlussarbeit Buch |
| Sprache: | English |
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Berlin
[2021?]
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Inhaltsverzeichnis |
| Beschreibung: | Tag der mündlichen Disputation: 19.11.2021 Der Text enthält eine Zusammenfassung in deutscher und englischer Sprache |
| Beschreibung: | xviii, 167 Seiten Illustrationen, Diagramme, Karten (farbig) |
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MARC
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| 100 | 1 | |a Zhou, Tao |e Verfasser |0 (DE-588)1257349686 |4 aut | |
| 245 | 1 | 0 | |a Exploring the potential of sentinel-1, -2, and -3 images for the digital mapping of soil properties based on machine learning algorithms and multi-source environmental data |c von M. Sc. Tao Zhou |
| 264 | 1 | |a Berlin |c [2021?] | |
| 300 | |a xviii, 167 Seiten |b Illustrationen, Diagramme, Karten (farbig) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Tag der mündlichen Disputation: 19.11.2021 | ||
| 500 | |a Der Text enthält eine Zusammenfassung in deutscher und englischer Sprache | ||
| 502 | |b Dissertation |c Humboldt-Universität zu Berlin |d 2021 | ||
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Datensatz im Suchindex
| _version_ | 1804184123257913344 |
|---|---|
| adam_text | CONTENTS
ACKNOWLEDGEMENTS
..................................................................................................................
I
ABSTRACT
...................................................................................................................................
III
ZUSAMMENFASSUNG
...................................................................................................................V
CONTENTS
................................................................................................................................
VII
LIST
OF
FIGURES
........................................................................................................................
XI
LIST
OF
TABLES
.........................................................................................................................
XV
ABBREVIATIONS
......................................................................................................................
XVII
CHAPTER
I:
INTRODUCTION
...........................................................................................................
1
1.
BACKGROUND
.................................................................................................................
2
2.
DIGITAL
SOIL
MAPPING
..................................................................................................
3
3.
THE
ROLE
OF
SATELLITE
REMOTE
SENSING
IN
DIGITAL
SOIL
MAPPING
...............................
7
4.
CONCEPTUAL
FRAMEWORK
............................................................................................
10
4.1.
RESEARCH
QUESTIONS
AND
OBJECTIVES
.............................................................
10
4.2.
STRUCTURE
OF
THE
THESIS
..................................................................................
16
CHAPTER
II:
MAPPING
SOIL
ORGANIC
CARBON
CONTENT
USING
MULTI-SOURCE
REMOTE
SENSING
VARIABLES
IN
THE
HEIHE
RIVER
BASIN
IN
CHINA
.....................................................................
19
ABSTRACT
.........................................................................................................................
20
1.
INTRODUCTION
..............................................................................................................
20
2.
MATERIALS
AND
METHODS
............................................................................................
23
2.1.
STUDY
AREA
.....................................................................................................
23
2.2.
SOIL
DATA
........................................................................................................
24
2.3.
ENVIRONMENTAL
DATA
.....................................................................................
25
2.4.
MODELLING
TECHNIQUES
..................................................................................
27
2.5.
STATISTICAL
ANALYSES
......................................................................................
29
2.6.
MODEL
EVALUATION
........................................................................................
29
3.
RESULTS
AND
DISCUSSION
............................................................................................
31
3.1.
DESCRIPTIVE
STATISTICS
OF
SAMPLED
SOC
CONTENT
........................................
31
3.2.
EVALUATION
OF
MODEL
PREDICTIONS
................................................................32
3.3.
THE
RELATIVE
IMPORTANCE
OF
ENVIRONMENTAL
DATA
.......................................
35
3.4.
THE
SPATIAL
PREDICTION
OF
SOC
CONTENT
......................................................
38
4.
CONCLUSIONS
..............................................................................................................39
ACKNOWLEDGMENTS
........................................................................................................
39
CHAPTER
III:
MAPPING
OF
SOIL
TOTAL
NITROGEN
CONTENT
IN
THE
MIDDLE
REACHES
OF
THE
HEIHE
RIVER
BASIN
IN
CHINA
USING
MULTI-SOURCE
REMOTE
SENSING-DERIVED
VARIABLES41
VII
ABSTRACT
..........................................................................................................................
42
1.
INTRODUCTION
...............................................................................................................42
2.
MATERIALS
AND
METHODS
.............................................................................................45
2.1.
STUDY
AREA
......................................................................................................
45
2.2.
SOIL
DATA
.........................................................................................................
46
2.3.
ENVIRONMENTAL
VARIABLES
.............................................................................
47
2.4.
PREDICTION
MODELS
.........................................................................................
49
2.5.
STATISTICAL
ANALYSES
.......................................................................................
50
2.6.
MODEL
VALIDATION
...........................................................................................50
3.
RESULTS
........................................................................................................................
51
3.1.
DESCRIPTIVE
STATISTICS
....................................................................................
51
3.2.
MODEL
PERFORMANCE
......................................................................................
52
3.3.
RELATIVE
IMPORTANCE
OF
ENVIRONMENTAL
DATA
..............................................54
3.4.
SPATIAL
PREDICTION
OF
SIN
CONTENT
..............................................................
55
4.
DISCUSSION
..................................................................................................................
58
4.1.
MODEL
PERFORMANCE
......................................................................................
58
4.2.
IMPORTANCE
OF
PREDICTOR
VARIABLES
..............................................................
60
4.3.
SPATIAL
PREDICTION
OF
STN
CONTENT
..............................................................
62
5.
CONCLUSIONS
...............................................................................................................
63
ACKNOWLEDGMENTS
.........................................................................................................
64
CHAPTER
IV:
HIGH-RESOLUTION
DIGITAL
MAPPING
OF
SOIL
ORGANIC
CARBON
AND
SOIL
TOTAL
NITROGEN
USING
DEM
DERIVATIVES,
SENTINEL
1
AND
SENTINEL-2
DATA
BASED
ON
MACHINE
LEARNING
ALGORITHMS
...............................................................................................................
65
ABSTRACT
..........................................................................................................................
66
1.
INTRODUCTION
...............................................................................................................
67
2.
MATERIALS
AND
METHODS
.............................................................................................70
2.1.
STUDY
AREA
......................................................................................................
70
2.2.
SOIL
DATA
SOURCE
.............................................................................................
71
2.3.
PREDICTOR
VARIABLES
.......................................................................................
72
2.4.
MODELING
TECHNIQUES
....................................................................................74
2.5.
STATISTICAL
ANALYSES
.......................................................................................
76
2.6.
METHODS
FOR
EVALUATING
MODEL
PERFORMANCE
............................................
76
3.
RESULTS
......................................................................................................................
78
VIII
3.1.
DESCRIPTIVE
ANALYSIS
OF
SOC
AND
STN
......................................................78
3.2.
EVALUATION
AND
COMPARISON
OF
DIFFERENT
MODELS
......................................
79
3.3.
RELATIVE
IMPORTANCE
OF
PREDICTOR
VARIABLES
...............................................
81
3.4.
SPATIAL
CHARACTERISTICS
OF
SOC
AND
STN
MAPS
.........................................
83
4.
DISCUSSION
.................................................................................................................
85
4.1.
PERFORMANCE
OF
PREDICTIVE
MODELS
USING
DEM
DERIVATIVES,
SENTINEL
1
AND
SENTINEL-2
DATA
..............................................................................................
85
4.2.
VARIABLE
IMPORTANCE
.....................................................................................
87
4.3.
SPATIAL
CHARACTERISTICS
OF
SOC
AND
STN
MAPS
.........................................
88
5.
CONCLUSIONS
...............................................................................................................89
ACKNOWLEDGMENTS
........................................................................................................
90
SUPPLEMENTARY
MATERIALS
.............................................................................................
90
CHAPTER
V:
PREDICTION
OF
SOIL
ORGANIC
CARBON
AND
THE
C:N
RATIO
ON
A
NATIONAL
SCALE
USING
MACHINE
LEARNING
AND
SATELLITE
DATA:
A
COMPARISON
BETWEEN
SENTINEL-2,
SENTINEL-3
AND
LANDSAT-8
IMAGES
..................................................................................................................
93
ABSTRACT
..........................................................................................................................
94
1.
INTRODUCTION
...............................................................................................................
94
2.
MATERIALS
AND
METHODS
............................................................................................98
2.1.
STUDY
AREA
......................................................................................................98
2.2.
SOIL
DATASET
....................................................................................................
99
2.3.
ENVIRONMENTAL
DATA
FOR
MODELING
............................................................
100
2.4.
PREDICTIVE
MODELS
.......................................................................................
101
2.5.
STATISTICAL
ANALYSES
.....................................................................................
103
2.6.
ACCURACY
ASSESSMENT
AND
UNCERTAINTY
.....................................................
104
3.
RESULTS
.....................................................................................................................
106
3.1.
DESCRIPTIVE
STATISTICS
OF
SOIL
PROPERTIES
...................................................
106
3.2.
MODEL
EVALUATION
AND
COMPARISON
...........................................................
106
3.3.
RELATIVE
IMPORTANCE
OF
ENVIRONMENTAL
VARIABLES
...................................
ILL
3.4.
SPATIAL
PREDICTION
.......................................................................................
113
4.
DISCUSSION
...............................................................................................................
116
4.1.
PERFORMANCE
OF
SOIL
PREDICTION
MODELS
USING
DIFFERENT
COMBINATIONS
OF
ENVIRONMENTAL
VARIABLES
....................................................................................
116
4.2.
ENVIRONMENTAL
VARIABLES
CONTROLLING
THE
DISTRIBUTION
OF
SOC
CONTENT
AND
C:N
RATIO
IN
SWITZERLAND
...................................................................................
119
4.3.
SPATIAL
DISTRIBUTION
OF
SOC
CONTENT
AND
C:N
RATIO
IN
SWITZERLAND
..
120
5.
CONCLUSIONS
...........................................................................................................
122
ACKNOWLEDGMENTS
.......................................................................................................
122
CHAPTER
VI:
SYNTHESIS
.........................................................................................................
123
1.
SUMMARY
..................................................................................................................
124
2.
MAIN
CONCLUSIONS
....................................................................................................
127
3.
OUTLOOK
....................................................................................................................
129
REFERENCES
.............................................................................................................................
131
EIDESSTATTLICHE
ERKLARUNG
....................................................................................................
167
|
| adam_txt |
CONTENTS
ACKNOWLEDGEMENTS
.
I
ABSTRACT
.
III
ZUSAMMENFASSUNG
.V
CONTENTS
.
VII
LIST
OF
FIGURES
.
XI
LIST
OF
TABLES
.
XV
ABBREVIATIONS
.
XVII
CHAPTER
I:
INTRODUCTION
.
1
1.
BACKGROUND
.
2
2.
DIGITAL
SOIL
MAPPING
.
3
3.
THE
ROLE
OF
SATELLITE
REMOTE
SENSING
IN
DIGITAL
SOIL
MAPPING
.
7
4.
CONCEPTUAL
FRAMEWORK
.
10
4.1.
RESEARCH
QUESTIONS
AND
OBJECTIVES
.
10
4.2.
STRUCTURE
OF
THE
THESIS
.
16
CHAPTER
II:
MAPPING
SOIL
ORGANIC
CARBON
CONTENT
USING
MULTI-SOURCE
REMOTE
SENSING
VARIABLES
IN
THE
HEIHE
RIVER
BASIN
IN
CHINA
.
19
ABSTRACT
.
20
1.
INTRODUCTION
.
20
2.
MATERIALS
AND
METHODS
.
23
2.1.
STUDY
AREA
.
23
2.2.
SOIL
DATA
.
24
2.3.
ENVIRONMENTAL
DATA
.
25
2.4.
MODELLING
TECHNIQUES
.
27
2.5.
STATISTICAL
ANALYSES
.
29
2.6.
MODEL
EVALUATION
.
29
3.
RESULTS
AND
DISCUSSION
.
31
3.1.
DESCRIPTIVE
STATISTICS
OF
SAMPLED
SOC
CONTENT
.
31
3.2.
EVALUATION
OF
MODEL
PREDICTIONS
.32
3.3.
THE
RELATIVE
IMPORTANCE
OF
ENVIRONMENTAL
DATA
.
35
3.4.
THE
SPATIAL
PREDICTION
OF
SOC
CONTENT
.
38
4.
CONCLUSIONS
.39
ACKNOWLEDGMENTS
.
39
CHAPTER
III:
MAPPING
OF
SOIL
TOTAL
NITROGEN
CONTENT
IN
THE
MIDDLE
REACHES
OF
THE
HEIHE
RIVER
BASIN
IN
CHINA
USING
MULTI-SOURCE
REMOTE
SENSING-DERIVED
VARIABLES41
VII
ABSTRACT
.
42
1.
INTRODUCTION
.42
2.
MATERIALS
AND
METHODS
.45
2.1.
STUDY
AREA
.
45
2.2.
SOIL
DATA
.
46
2.3.
ENVIRONMENTAL
VARIABLES
.
47
2.4.
PREDICTION
MODELS
.
49
2.5.
STATISTICAL
ANALYSES
.
50
2.6.
MODEL
VALIDATION
.50
3.
RESULTS
.
51
3.1.
DESCRIPTIVE
STATISTICS
.
51
3.2.
MODEL
PERFORMANCE
.
52
3.3.
RELATIVE
IMPORTANCE
OF
ENVIRONMENTAL
DATA
.54
3.4.
SPATIAL
PREDICTION
OF
SIN
CONTENT
.
55
4.
DISCUSSION
.
58
4.1.
MODEL
PERFORMANCE
.
58
4.2.
IMPORTANCE
OF
PREDICTOR
VARIABLES
.
60
4.3.
SPATIAL
PREDICTION
OF
STN
CONTENT
.
62
5.
CONCLUSIONS
.
63
ACKNOWLEDGMENTS
.
64
CHAPTER
IV:
HIGH-RESOLUTION
DIGITAL
MAPPING
OF
SOIL
ORGANIC
CARBON
AND
SOIL
TOTAL
NITROGEN
USING
DEM
DERIVATIVES,
SENTINEL
1
AND
SENTINEL-2
DATA
BASED
ON
MACHINE
LEARNING
ALGORITHMS
.
65
ABSTRACT
.
66
1.
INTRODUCTION
.
67
2.
MATERIALS
AND
METHODS
.70
2.1.
STUDY
AREA
.
70
2.2.
SOIL
DATA
SOURCE
.
71
2.3.
PREDICTOR
VARIABLES
.
72
2.4.
MODELING
TECHNIQUES
.74
2.5.
STATISTICAL
ANALYSES
.
76
2.6.
METHODS
FOR
EVALUATING
MODEL
PERFORMANCE
.
76
3.
RESULTS
.
78
VIII
3.1.
DESCRIPTIVE
ANALYSIS
OF
SOC
AND
STN
.78
3.2.
EVALUATION
AND
COMPARISON
OF
DIFFERENT
MODELS
.
79
3.3.
RELATIVE
IMPORTANCE
OF
PREDICTOR
VARIABLES
.
81
3.4.
SPATIAL
CHARACTERISTICS
OF
SOC
AND
STN
MAPS
.
83
4.
DISCUSSION
.
85
4.1.
PERFORMANCE
OF
PREDICTIVE
MODELS
USING
DEM
DERIVATIVES,
SENTINEL
1
AND
SENTINEL-2
DATA
.
85
4.2.
VARIABLE
IMPORTANCE
.
87
4.3.
SPATIAL
CHARACTERISTICS
OF
SOC
AND
STN
MAPS
.
88
5.
CONCLUSIONS
.89
ACKNOWLEDGMENTS
.
90
SUPPLEMENTARY
MATERIALS
.
90
CHAPTER
V:
PREDICTION
OF
SOIL
ORGANIC
CARBON
AND
THE
C:N
RATIO
ON
A
NATIONAL
SCALE
USING
MACHINE
LEARNING
AND
SATELLITE
DATA:
A
COMPARISON
BETWEEN
SENTINEL-2,
SENTINEL-3
AND
LANDSAT-8
IMAGES
.
93
ABSTRACT
.
94
1.
INTRODUCTION
.
94
2.
MATERIALS
AND
METHODS
.98
2.1.
STUDY
AREA
.98
2.2.
SOIL
DATASET
.
99
2.3.
ENVIRONMENTAL
DATA
FOR
MODELING
.
100
2.4.
PREDICTIVE
MODELS
.
101
2.5.
STATISTICAL
ANALYSES
.
103
2.6.
ACCURACY
ASSESSMENT
AND
UNCERTAINTY
.
104
3.
RESULTS
.
106
3.1.
DESCRIPTIVE
STATISTICS
OF
SOIL
PROPERTIES
.
106
3.2.
MODEL
EVALUATION
AND
COMPARISON
.
106
3.3.
RELATIVE
IMPORTANCE
OF
ENVIRONMENTAL
VARIABLES
.
ILL
3.4.
SPATIAL
PREDICTION
.
113
4.
DISCUSSION
.
116
4.1.
PERFORMANCE
OF
SOIL
PREDICTION
MODELS
USING
DIFFERENT
COMBINATIONS
OF
ENVIRONMENTAL
VARIABLES
.
116
4.2.
ENVIRONMENTAL
VARIABLES
CONTROLLING
THE
DISTRIBUTION
OF
SOC
CONTENT
AND
C:N
RATIO
IN
SWITZERLAND
.
119
4.3.
SPATIAL
DISTRIBUTION
OF
SOC
CONTENT
AND
C:N
RATIO
IN
SWITZERLAND
.
120
5.
CONCLUSIONS
.
122
ACKNOWLEDGMENTS
.
122
CHAPTER
VI:
SYNTHESIS
.
123
1.
SUMMARY
.
124
2.
MAIN
CONCLUSIONS
.
127
3.
OUTLOOK
.
129
REFERENCES
.
131
EIDESSTATTLICHE
ERKLARUNG
.
167 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Zhou, Tao |
| author_GND | (DE-588)1257349686 |
| author_facet | Zhou, Tao |
| author_role | aut |
| author_sort | Zhou, Tao |
| author_variant | t z tz |
| building | Verbundindex |
| bvnumber | BV048290395 |
| classification_rvk | RB 10232 RB 10165 RR 69165 RP 35165 RK 30165 |
| ctrlnum | (OCoLC)1327677448 (DE-599)DNB1250182166 |
| discipline | Geographie |
| discipline_str_mv | Geographie |
| format | Thesis Book |
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| geographic | China Nordwest (DE-588)4461752-5 gnd Slowenien (DE-588)4055302-4 gnd Schweiz (DE-588)4053881-3 gnd |
| geographic_facet | China Nordwest Slowenien Schweiz |
| id | DE-604.BV048290395 |
| illustrated | Illustrated |
| index_date | 2024-07-03T20:03:25Z |
| indexdate | 2024-07-10T09:34:20Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033670409 |
| oclc_num | 1327677448 |
| open_access_boolean | |
| owner | DE-11 |
| owner_facet | DE-11 |
| physical | xviii, 167 Seiten Illustrationen, Diagramme, Karten (farbig) |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| record_format | marc |
| spelling | Zhou, Tao Verfasser (DE-588)1257349686 aut Exploring the potential of sentinel-1, -2, and -3 images for the digital mapping of soil properties based on machine learning algorithms and multi-source environmental data von M. Sc. Tao Zhou Berlin [2021?] xviii, 167 Seiten Illustrationen, Diagramme, Karten (farbig) txt rdacontent n rdamedia nc rdacarrier Tag der mündlichen Disputation: 19.11.2021 Der Text enthält eine Zusammenfassung in deutscher und englischer Sprache Dissertation Humboldt-Universität zu Berlin 2021 Digitale Daten (DE-588)4535099-1 gnd rswk-swf Satellitenfernerkundung (DE-588)4224344-0 gnd rswk-swf Vergleich (DE-588)4187713-5 gnd rswk-swf Bodenkartierung (DE-588)4131289-2 gnd rswk-swf China Nordwest (DE-588)4461752-5 gnd rswk-swf Slowenien (DE-588)4055302-4 gnd rswk-swf Schweiz (DE-588)4053881-3 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Bodenkartierung (DE-588)4131289-2 s Digitale Daten (DE-588)4535099-1 s Satellitenfernerkundung (DE-588)4224344-0 s Vergleich (DE-588)4187713-5 s DE-604 China Nordwest (DE-588)4461752-5 g Slowenien (DE-588)4055302-4 g Schweiz (DE-588)4053881-3 g B:DE-101 application/pdf https://d-nb.info/1250182166/04 Inhaltsverzeichnis DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033670409&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Zhou, Tao Exploring the potential of sentinel-1, -2, and -3 images for the digital mapping of soil properties based on machine learning algorithms and multi-source environmental data Digitale Daten (DE-588)4535099-1 gnd Satellitenfernerkundung (DE-588)4224344-0 gnd Vergleich (DE-588)4187713-5 gnd Bodenkartierung (DE-588)4131289-2 gnd |
| subject_GND | (DE-588)4535099-1 (DE-588)4224344-0 (DE-588)4187713-5 (DE-588)4131289-2 (DE-588)4461752-5 (DE-588)4055302-4 (DE-588)4053881-3 (DE-588)4113937-9 |
| title | Exploring the potential of sentinel-1, -2, and -3 images for the digital mapping of soil properties based on machine learning algorithms and multi-source environmental data |
| title_auth | Exploring the potential of sentinel-1, -2, and -3 images for the digital mapping of soil properties based on machine learning algorithms and multi-source environmental data |
| title_exact_search | Exploring the potential of sentinel-1, -2, and -3 images for the digital mapping of soil properties based on machine learning algorithms and multi-source environmental data |
| title_exact_search_txtP | Exploring the potential of sentinel-1, -2, and -3 images for the digital mapping of soil properties based on machine learning algorithms and multi-source environmental data |
| title_full | Exploring the potential of sentinel-1, -2, and -3 images for the digital mapping of soil properties based on machine learning algorithms and multi-source environmental data von M. Sc. Tao Zhou |
| title_fullStr | Exploring the potential of sentinel-1, -2, and -3 images for the digital mapping of soil properties based on machine learning algorithms and multi-source environmental data von M. Sc. Tao Zhou |
| title_full_unstemmed | Exploring the potential of sentinel-1, -2, and -3 images for the digital mapping of soil properties based on machine learning algorithms and multi-source environmental data von M. Sc. Tao Zhou |
| title_short | Exploring the potential of sentinel-1, -2, and -3 images for the digital mapping of soil properties based on machine learning algorithms and multi-source environmental data |
| title_sort | exploring the potential of sentinel 1 2 and 3 images for the digital mapping of soil properties based on machine learning algorithms and multi source environmental data |
| topic | Digitale Daten (DE-588)4535099-1 gnd Satellitenfernerkundung (DE-588)4224344-0 gnd Vergleich (DE-588)4187713-5 gnd Bodenkartierung (DE-588)4131289-2 gnd |
| topic_facet | Digitale Daten Satellitenfernerkundung Vergleich Bodenkartierung China Nordwest Slowenien Schweiz Hochschulschrift |
| url | https://d-nb.info/1250182166/04 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033670409&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT zhoutao exploringthepotentialofsentinel12and3imagesforthedigitalmappingofsoilpropertiesbasedonmachinelearningalgorithmsandmultisourceenvironmentaldata |
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Inhaltsverzeichnis