Agro-geoinformatics: Theory and Practice (Springer Remote Sensing/Photogrammetry)

Contents
Chapter 1: Introduction to Agro-Geoinformatics: Theory and Practices
Chapter 2: Remote Sensing for Agriculture
	2.1 Introduction
	2.2 Major Agriculture-Related Remote Sensing Data Sources
	2.3 Agricultural Applications
		2.3.1 Crop Type Identification
		2.3.2 Crop Phenology Mapping
		2.3.3 Crop Yield Estimation
		2.3.4 Crop Evapotranspiration (ET) and Water Use
		2.3.5 Soil Moisture Retrieval
	2.4 Summary
	References
Chapter 3: GIS Fundamentals for Agriculture
	3.1 Introduction
	3.2 GIS: The Geospatial Approach
	3.3 GIS Application in Agriculture
		3.3.1 GIS Mapping and Analytical Techniques
		3.3.2 Spatial Database for Agricultural Systems
		3.3.3 GIS-Based Modeling in Agricultural Application
			3.3.3.1 Environment Models Linked to GIS
			3.3.3.2 Crop Yield Prediction Based on GIS
			3.3.3.3 Agricultural Management Models Using GIS
		3.3.4 Decision Support System
			3.3.4.1 Traditional Decision Support Systems
			3.3.4.2 New Direction and Trends in Decision Support System
	3.4 Conclusion
	References
Chapter 4: Agro-geoinformatics Data Sources and Sourcing
	4.1 Introduction
	4.2 Data Sources
		4.2.1 Satellite
		4.2.2 Airborne Camera
		4.2.3 In Situ Sensors
		4.2.4 Manual Reports
		4.2.5 Summary
	4.3 Sourcing
		4.3.1 Conventional Sourcing
		4.3.2 Cloud Sourcing
		4.3.3 Crowdsourcing
	4.4 Conclusion
	References
Chapter 5: Standards and Interoperability
	5.1 Introduction
	5.2 Standard Organizations
		5.2.1 ISO
		5.2.2 OGC
		5.2.3 CEN
		5.2.4 ANSI
	5.3 Typical Standard Development Process
	5.4 Types of Standards
		5.4.1 Data Content or Encoding Standard
		5.4.2 Metadata Content and Encoding Standard
		5.4.3 Data Service Standard
		5.4.4 Statistical Standards and Methodological Guidelines
	5.5 Conclusion
	References
Chapter 6: Image Processing Methods in Agricultural Observation Systems
	6.1 Introduction
	6.2 The Fundamentals of Digital Image Processing
		6.2.1 Origins and Definitions
		6.2.2 Basic Steps in Image Processing
	6.3 Hardware and Software
		6.3.1 Image Processing Hardware
		6.3.2 Image Processing Software
		6.3.3 Mobile Device-Based Image Processing
		6.3.4 Cloud-Based Image Processing
	6.4 Agricultural Image Data Collection
		6.4.1 In Situ Data Collection
		6.4.2 Airborne-Based Data Collection
		6.4.3 Space-Borne-Based Data Collection
		6.4.4 Big Data Challenge in Agricultural Image Data Collection
	6.5 Agro-Geoinformation Extraction from Image
		6.5.1 Knowledge-Based Expert System
		6.5.2 Machine Learning-Based Decision Tree
		6.5.3 Artificial Neural Network
		6.5.4 A Case Study
	6.6 Summary
	References
Chapter 7: Data Fusion in Agricultural Information Systems
	7.1 Introduction
	7.2 Agricultural Information Systems
	7.3 Regression Model Example for Real-Time Yield Efficiency Monitoring
		7.3.1 Phenological Stage-Based Data Segmentation
		7.3.2 Agrometeorological Indices and Regression-Based Data Fusion for Yield Estimation.
	7.4 Neural Networks for Data Fusion
	7.5 Wavelets in Data Fusion
	7.6 Convolutional Neural Networks
	7.7 Conclusion
	Appendix
	References
Chapter 8: Big Data and Its Applications in Agro-Geoinformatics
	8.1 Introduction
		8.1.1 Challenges in Modern Agriculture
		8.1.2 The Role of Big Data in Agriculture
	8.2 Agricultural Big Data
		8.2.1 Special Features of Agro-Big Data
		8.2.2 State-of-the-Art Analysis Methods
	8.3 Agro-Geoinformatics
		8.3.1 Definition
		8.3.2 Agro-Geoinformatics: Connecting Agro-Big Data to Agricultural Applications
		8.3.3 Related Research
	8.4 Examples of Big Data Application in Agro-Geoinformatics
		8.4.1 Agro-Sensor Web
		8.4.2 GADMFS
		8.4.3 CropScape
		8.4.4 VegScape
		8.4.5 RF-Class
		8.4.6 SMAP Explorer
		8.4.7 GeoFairy
		8.4.8 CyberConnector COVALI
		8.4.9 Geoweaver
	8.5 Conclusion
	References
Chapter 9: Land Parcel Identification
	9.1 Introduction
	9.2 Land Parcel and Agricultural Land Parcel
		9.2.1 What Is Land Parcel?
		9.2.2 Land Parcel in Agriculture
		9.2.3 Techniques to Identify Land Parcel
	9.3 Managing Land Parcel Information in Agro-Geoinformation Systems for Local Governments, Agencies, and Companies
	9.4 Managing Land Parcel Information in Agro-Geoinformation Systems at State and National Levels
	9.5 Approaches to Manage Land Parcel Information in Globe Agro-Geoinformation Systems - International Standards
	9.6 Conclusion and Discussion
	References
Chapter 10: Crop Pattern and Status Monitoring
	10.1 Introduction
	10.2 Crop Pattern Mapping
		10.2.1 Statistical Approach
		10.2.2 Remote Sensing Approach
		10.2.3 Case Study - Operational National Cropland Mapping Programs
			10.2.3.1 USA Cropland Data Layer
			10.2.3.2 Canada Crop Inventory
		10.2.4 Limitations and Perspectives
	10.3 Crop Status Monitoring
		10.3.1 Statistical Approach
		10.3.2 Remote Sensing Approach
		10.3.3 Case Study - Operational Remote Sensing Crop Condition Monitoring
			10.3.3.1 National Crop Progress Monitoring System
			10.3.3.2 Global Agricultural Monitoring
			10.3.3.3 Other Operational Crop Status Monitoring Systems
		10.3.4 Limitations and Perspectives
	10.4 Conclusions
	References
Chapter 11: Crop Growth Modeling and Yield Forecasting
	11.1 Introduction
	11.2 Statistical Modeling
	11.3 Physiological/Physical-Based Modeling
	11.4 Remote Sensing Monitoring of Crop Growth
	11.5 Data Assimilation
		11.5.1 Sequential Data Assimilation Algorithms
	11.6 Conclusion
	References
Chapter 12: Spatial and Temporal Monitoring System for Agriculture
	12.1 Introduction
	12.2 Related Work
	12.3 Spatial and Temporal Monitoring Systems for Agriculture
		12.3.1 Web Service-Based Near-Real-Time Global Agricultural Drought Monitoring System
		12.3.2 Web Service-Based Near-Real-Time US Vegetation Condition Monitoring System
		12.3.3 Web Service-Based Near-Real-Time US Flood and Progress Monitoring System
	12.4 Conclusion
	References
Chapter 13: Spatial Data Usage in Turkish Agriculture
	13.1 Introduction
	13.2 Parcel-Based Support Payment System
	13.3 Land Parcel Identification System
		13.3.1 Orthophoto Production
			13.3.1.1 Geodetic Works
			13.3.1.2 Post-processing of Aerial Imagery (AI)
			13.3.1.3 DEM Production for the AI Areas
			13.3.1.4 Orthophoto Production, Mosaicking, and Tile Cutting from the AI and SI
			13.3.1.5 Radiometric Enhancement
			13.3.1.6 Pan-Sharpening
		13.3.2 Constraints for Aerial Imagery
			13.3.2.1 Cloud Cover Percentage
			13.3.2.2 Sun Angle
			13.3.2.3 Crop Phenology
		13.3.3 Orthophoto Features
		13.3.4 Vector Data in LPIS
			13.3.4.1 Generation
			13.3.4.2 Controls
		13.3.5 Usage of LPIS Data with Collaboration of Cadastre
	13.4 Potential Usage of Spatial Database
	13.5 Geostatistics through Spatial Database
		13.5.1 Interpolation Methodology
	13.6 Conclusion
	References
Chapter 14: Geospatial Land Use and Land Cover Data for Improving Agricultural Area Sampling Frames
	14.1 Introduction
	14.2 Background
		14.2.1 Related Work
		14.2.2 NASS Area Sampling Frames
		14.2.3 NASS Cropland Data Layer
		14.2.4 NASS Cultivated Layer
	14.3 Study areas
	14.4 Automated Stratification Methodology
		14.4.1 Stratification Method
		14.4.2 Automatic Stratification Analysis and Results Evaluation
		14.4.3 Comparison of Traditional and Automatic Stratification Results
	14.5 Integration of Automatic Stratification into NASS Operations
		14.5.1 Ancillary Data for Manual Review and Editing Process
		14.5.2 Integration Process
	14.6 Integration Results
		14.6.1 Stratification Accuracy
		14.6.2 Mean Stratum Percent Cultivation Range, Standard Deviations, and PSU Size
	14.7 Integration Discussion
		14.7.1 Stratification Accuracy
		14.7.2 Mean Stratum Percent Cultivation Range, Standard Deviations, and PSU Size
		14.7.3 Labor Cost
	14.8 Conclusion
	References
Chapter 15: Mapping and Monitoring of Soil Moisture, Evapotranspiration, and Agricultural Drought
	15.1 Introduction
	15.2 Soil Moisture
		15.2.1 Methodology
		15.2.2 Data
		15.2.3 Results
	15.3 Evapotranspiration
		15.3.1 Methods
		15.3.2 Data
		15.3.3 Results
			15.3.3.1 Validation at US-Skr
			15.3.3.2 Validation at ARM-SGP Stations
	15.4 Agricultural Drought
		15.4.1 Normalized Difference Vegetation Index (NDVI)
		15.4.2 Vegetation Condition Index (VCI)
		15.4.3 Results
	15.5 Conclusions
	References
Chapter 16: Flood Monitoring and Crop Damage Assessment
	16.1 Introduction
	16.2 Remote Sensing on Flood Event Monitoring
		16.2.1 Traditional Gauge-Based Flood Monitoring
		16.2.2 Remote Sensing-Based Flood Monitoring
			16.2.2.1 Remote Sensing in Flood Forecasting
			16.2.2.2 Remote Sensing in Flood Mapping
		16.2.3 GIS-Based Flood Modeling and Early Warning System
		16.2.4 Event and Duration of the Flood
	16.3 Flood Crop Damage Assessments
		16.3.1 Classification Method
		16.3.2 Band Ratioing (Vegetation Indices)
	16.4 Case Study: NDVI-Based Corn Loss Assessment through Regression Model
		16.4.1 Flood Event
		16.4.2 Data
		16.4.3 Study Area
		16.4.4 Method
			16.4.4.1 Pure Pixel Selection
			16.4.4.2 Normal NDVI
			16.4.4.3 NDVI Smoothing
			16.4.4.4 Area under the Curve
			16.4.4.5 Regression Model
		16.4.5 Result
			16.4.5.1 Regression Result
			16.4.5.2 Model Estimation
	16.5 Conclusion
	References
Chapter 17: Remote Sensing-Based Mapping of Plastic-Mulched Land Cover
	17.1 Introduction
	17.2 A Decision-Tree Classifier for Extracting PML Using Landsat Imagery
		17.2.1 Methodology
			17.2.1.1 The Detectable Features of PML
			17.2.1.2 Construction of the Decision-Tree Classifier
		17.2.2 A Specific Example
			17.2.2.1 Data Sets and Preprocessing
			17.2.2.2 Experiment Results
	17.3 A Threshold Model for Mapping PML Using MODIS Time Series Data
		17.3.1 Methodology
		17.3.2 A Specific Example
			17.3.2.1 Data Sets and Preprocessing
			17.3.2.2 Determination of Threshold Condition and Value
			17.3.2.3 Detecting and Mapping PML
	17.4 Subpixel Mapping of PML from MODIS Imagery Using Spatial Attraction Models
		17.4.1 Methodology
			17.4.1.1 Subpixel Mapping Theory
			17.4.1.2 Subpixel/Pixel Spatial Attraction Model (SPSAM)
			17.4.1.3 MSPSAM and MSAM
			17.4.1.4 Improved Spatial Attraction Model (ISAM)
		17.4.2 A Specific Example
			17.4.2.1 Data Sets and Preprocessing
			17.4.2.2 Experiment Results
	17.5 Conclusion
	References
Chapter 18: Design and Implementation of Geospatial Data Services for Agriculture
	18.1 Introduction
	18.2 Geospatial Data for Agriculture
		18.2.1 Data Categories
			18.2.1.1 CDL Data
			18.2.1.2 Vegetation Index Data
			18.2.1.3 Hydrological Data
			18.2.1.4 Temperature Data
		18.2.2 Data Life Cycle
	18.3 Geospatial Interoperability and Standardization
		18.3.1 Geospatial Web Service Interoperability Standards
		18.3.2 Content Interoperability Standards
	18.4 Geospatial Web Service Architecture for Agriculture
		18.4.1 A Specific Example: CropScape
			18.4.1.1 Application Layer
			18.4.1.2 Service Layer
			18.4.1.3 Data Layer
	18.5 Geospatial Data Service Functionalities for Agriculture
		18.5.1 Agricultural Data Management
		18.5.2 Agricultural Data Analytics
	18.6 Conclusion
	References
Index