Advanced Remote Sensing: Terrestrial Information Extraction and Applications

Cover
Advanced Remote Sensing: Terrestrial Information Extraction and
Applications
Copyright
Contributors of the second edition
Foreword to the first edition
Preface to the first edition
Preface to the second edition
1 - A systematic view of remote sensing
	1.1 Introduction
	1.2 Platform and sensor systems
		1.2.1 Geostationary satellites
		1.2.2 Polar-orbiting satellites
		1.2.3 Overview of major satellite missions and programs
			1.2.3.1 USA
			1.2.3.2 Europe
			1.2.3.3 China
		1.2.4 Small satellites and satellite constellations
		1.2.5 Sensor types
		1.2.6 Data characteristics
			1.2.6.1 Spatial resolution
			1.2.6.2 Spectral resolution
			1.2.6.3 Temporal resolution
			1.2.6.4 Radiometric resolution
	1.3 Data transmission and ground receiving system
	1.4 Data processing
		1.4.1 Radiometric calibration
		1.4.2 Geometric processing
		1.4.3 Image quality enhancement
		1.4.4 Atmospheric correction
		1.4.5 Image fusion and product integration
	1.5 Mapping category variables
	1.6 Estimating quantitative variables
		1.6.1 Forward radiation modeling
			1.6.1.1 Scene generation
			1.6.1.2 Surface radiation modeling
			1.6.1.3 Atmospheric radiative transfer
			1.6.1.4 Sensor modeling
		1.6.2 Inversion methods
			1.6.2.1 Statistical analysis and machine learning techniques
				1.6.2.1.1 Artificial neural network
				1.6.2.1.2 Support vector machine
				1.6.2.1.3 Regression tree
				1.6.2.1.4 Random forest
				1.6.2.1.5 Multiple adaptive regression spline function
			1.6.2.2 Optimization algorithms
			1.6.2.3 Look-up table algorithms
			1.6.2.4 Direct estimation methods
			1.6.2.5 Data assimilation methods
			1.6.2.6 Spatial and temporal scaling
			1.6.2.7 Regularization method
		1.6.3 Use of multisource data
		1.6.4 Use of a prior knowledge
		1.6.5 Space–time constraints
		1.6.6 Algorithm ensemble
	1.7 Production, archiving, and distribution of high-level products
	1.8 Product validation
	1.9 Remote sensing applications
	1.10 Conclusion
	References
2 - Geometric processing and positioning techniques
	2.1 Overview
	2.2 In-orbit geometric calibration of satellite remote sensing imagery
		2.2.1 Systematic error sources of satellite remote sensing imagery
			2.2.1.1 The Earth curvature correction
			2.2.1.2 Atmospheric refraction correction
			2.2.1.3 The Earth rotation correction
			2.2.1.4 The CCD manufacture error correction
		2.2.2 In-orbit geometric calibration model
		2.2.3 In-orbit geometric calibration of the ZiYuan-3 satellite
			2.2.3.1 Calibration accuracy analysis of the CCD-detector look angles
			2.2.3.2 Direct georeferencing accuracy analysis
			2.2.3.3 Extrapolated georeferencing accuracy analysis
	2.3 Geometric rectification of a single remote sensing image
		2.3.1 Image geometric rectification models
			2.3.1.1 Rigorous geometric processing models
			2.3.1.2 Empirical geometric processing models
				2.3.1.2.1 The general polynomial model
				2.3.1.2.2 The direct linear transformation model
				2.3.1.2.3 The affine transformation model
				2.3.1.2.4 The rational function model
		2.3.2 Layout of ground control points
			2.3.2.1 Principles of selecting ground control points
			2.3.2.2 Distribution requirements of ground control points
		2.3.3 Image resampling
			2.3.3.1 Nearest-neighbor interpolation
			2.3.3.2 Bilinear interpolation
			2.3.3.3 Bicubic convolution
		2.3.4 Accuracy evaluation
	2.4 Geometric registration of satellite remote sensing imagery
		2.4.1 Automatic extraction of image registration points
			2.4.1.1 Image matching based on image gray
				2.4.1.1.1 Correlation coefficient matching
				2.4.1.1.2 Least squares matching
			2.4.1.2 Image matching based on features
				2.4.1.2.1 Detecting the extreme values of the scale space
				2.4.1.2.2 Direction distribution of key points
				2.4.1.2.3 The description of feature points
				2.4.1.2.4 Feature matching
				2.4.1.2.5 The elimination of mismatched conjugate points
		2.4.2 Mathematical models of image registration
			2.4.2.1 Linear transformation models
			2.4.2.2 Nonlinear transformation models
	2.5 Construction of a digital terrain model
		2.5.1 The concept of the DEM and structure of the model
			2.5.1.1 The regular grid DEM
			2.5.1.2 The triangulated irregular network
		2.5.2 Preprocessing of DEM data
			2.5.2.1 DEM data collection
			2.5.2.2 Blunder detection of original data
				2.5.2.2.1 The stereoscopic manual visual inspection method
				2.5.2.2.2 Gross error detection based on the fitting curved surface
			2.5.2.3 Filtering the source data
		2.5.3 Interpolating of DEM data
	2.6 Orthoimage production
		2.6.1 Digital differential rectification of frame perspective imagery
			2.6.1.1 Principles of digital differential rectification
			2.6.1.2 Digital differential rectification based on the inverse method
			2.6.1.3 Digital differential rectification based on the forward method
		2.6.2 Digital differential rectification of linear array remote sensing imagery
			2.6.2.1 Digital differential rectification based on the rigorous geometric processing model
			2.6.2.2 Digital differential rectification based on the RFM
		2.6.3 The orthoimage mosaic
			2.6.3.1 Image dodging and tone balance
				2.6.3.1.1 Image dodging in a single remote sensing image
				2.6.3.1.2 Tone balance among different images
			2.6.3.2 Image mosaic
				2.6.3.2.1 Mosaic line searching
				2.6.3.2.2 Image filling
	2.7 Summary
	2.8 Questions
	References
	Further reading
3 - Compositing, smoothing, and gap-filling techniques
	3.1 Multitemporal compositing techniques
		3.1.1 Maximum vegetation index composite
		3.1.2 Minimum band reflectance composite
		3.1.3 Maximum surface temperature composite
		3.1.4 Mixing criteria compositing
		3.1.5 MODIS vegetation index compositing technique
	3.2 Time series data smoothing and gap filling
		3.2.1 Curve fitting method
			3.2.1.1 Adaptive SG filtering
			3.2.1.2 Asymmetric Gaussian function and double logistic function fitting
		3.2.2 Ecosystem-dependent temporal interpolation technique
		3.2.3 Temporal spatial filter algorithm
		3.2.4 Smoothing and gap-filling algorithm based on the wavelet transform
		3.2.5 Time series surface reflectance reconstruction
			3.2.5.1 Surface reflectance screening
			3.2.5.2 Surface reflectance composition
			3.2.5.3 NDVI reconstruction
			3.2.5.4 Cloud detection of surface reflectance
			3.2.5.5 Surface reflectance reconstruction
	3.3 Summary
	References
4 - Atmospheric correction of optical imagery
	4.1 Atmospheric effects
		4.1.1 Atmospheric characterization in a quantitative remote sensing model
		4.1.2 Atmospheric composition
		4.1.3 Interaction between electromagnetic waves and the atmosphere
		4.1.4 Major aspects of atmospheric correction
			4.1.4.1 Internal sensor error
			4.1.4.2 External errors caused by environmental factors
	4.2 Correcting the aerosol impact
		4.2.1 Spectral information–based correction method
			4.2.1.1 Midinfrared dark target method
				4.2.1.1.1 Selection of dark pixels
				4.2.1.1.2 Determining the surface reflectance of dark pixels
				4.2.1.1.3 Determination of the aerosol model
				4.2.1.1.4 Calculating aerosol optical thickness over dark targets
			4.2.1.2 Near-infrared dark target method
			4.2.1.3 Deep blue method
				4.2.1.3.1 Pixel selection
				4.2.1.3.2 Determination of surface reflectance
				4.2.1.3.3 Determining aerosol models
				4.2.1.3.4 Obtaining aerosol optical thickness
		4.2.2 Temporal information–based correction method
			4.2.2.1 Linear regression method
			4.2.2.2 Improved multitemporal imaging method
		4.2.3 Angular information–based correction method
		4.2.4 Polarization information–based correction method
		4.2.5 Multisensor cooperative inversion algorithm
		4.2.6 Joint inversion of atmospheric surface parameters
	4.3 Correcting the impact of water vapor
	4.4 Correcting the impacts of other constituents
	4.5 Commonly used models and software
		4.5.1 MODTRAN model
		4.5.2 6S Model
		4.5.3 FLAASH
		4.5.4 ACTOR
		4.5.5 ACORN
	4.6 Application of GF-1 WFV atmospheric correction
		4.6.1 Radiation calibration
		4.6.2 Geometric correction and angle-assisted data calculation
		4.6.3 Atmospheric parameter acquisition
		4.6.4 Atmospheric correction
	4.7 Conclusions
	References
5 - Solar radiation
	5.1 Basic concepts
		5.1.1 Solar radiation spectrum
		5.1.2 Solar constant
		5.1.3 Shortwave radiation and photosynthetically active radiation
		5.1.4 Attenuation of solar radiation
		5.1.5 Earth radiation budget
	5.2 Observation network of land surface radiation
	5.3 Surface radiation estimation based on satellite remote sensing and GCM
		5.3.1 Empirical model
			5.3.1.1 Simple empirical model
				5.3.1.1.1 Lacis and Hansen model
				5.3.1.1.2 Gueymard model
			5.3.1.2 Relative sunshine duration model
		5.3.2 Parameterization method
			5.3.2.1 Inputs for parameterization models
			5.3.2.2 Clear-sky model
				5.3.2.2.1 Broadband model
					5.3.2.2.1.1 Modified Bird model
				5.3.2.2.2 Spectral model
					5.3.2.2.2.1 Iqbal spectral model
			5.3.2.3 Cloudy-sky model
				5.3.2.3.1 Broadband model
				5.3.2.3.2 Dual-band model
		5.3.3 Lookup table method
		5.3.4 Machine learning methods
	5.4 Current existing products and long-term variations
		5.4.1 Existing products and evaluation
		5.4.2 Temporal and spatial patterns of solar radiation
	5.5 Summary
	Nomenclature
	Acknowledgements
	References
6 - Broadband albedo
	6.1 Land surface bidirectional reflectance modeling
		6.1.1 Definition of land surface bidirectional reflectances and broadband albedo
			6.1.1.1 Bidirectional reflectance distribution function
			6.1.1.2 The definition of BRDF, reflectance, reflectance factor, and albedo
			6.1.1.3 Definitions of relative physical quantities
				6.1.1.3.1 Bidirectional reflectance factor
				6.1.1.3.2 Diffuse hemispherical–directional reflectance factor
				6.1.1.3.3 Hemispherical–directional reflectance factor
				6.1.1.3.4 Directional–hemispherical reflectance
				6.1.1.3.5 Diffuse hemispherical–hemispherical reflectance
				6.1.1.3.6 Hemispherical–hemispherical reflectance
				6.1.1.3.7 Broadband albedo
		6.1.2 Observations data of surface bidirectional reflection
			6.1.2.1 Laboratory and field observations
				6.1.2.1.1 Bidirectional reflective characteristics of vegetation canopy
				6.1.2.1.2 Bidirectional reflective characteristics of bare soil
				6.1.2.1.3 Bidirectional reflective characteristics of ice/snow
			6.1.2.2 Remote sensing observation data
		6.1.3 Surface bidirectional reflectance model
			6.1.3.1 Physical model
				6.1.3.1.1 Radiative transfer models
				6.1.3.1.2 Geometric optical models
				6.1.3.1.3 Geometric–radiative transfer mixed model
				6.1.3.1.4 Real scene computer simulation model
			6.1.3.2 Empirical model
				6.1.3.2.1 Minnaert model
				6.1.3.2.2 Shibayama model
				6.1.3.2.3 Walthall model and modified Walthall model
			6.1.3.3 Semiempirical models
				6.1.3.3.1 Kernel-driven model
					6.1.3.3.1.1 RossThick kernel
					6.1.3.3.1.2 RossThin kernel
					6.1.3.3.1.3 RossHotspot kernel (modified RossThick kernel)
					6.1.3.3.1.4 LiSparse and LiSparseR kernels
					6.1.3.3.1.5 LiDense kernel
					6.1.3.3.1.6 LiTransit kernel
					6.1.3.3.1.7 Roujean geometric kernel
				6.1.3.3.2 RPV model
	6.2 The albedo-estimation method based on bidirectional reflectance model inversion
		6.2.1 Inversion of the bidirectional reflectance model and derivation of narrowband albedo
			6.2.1.1 Bidirectional reflectance model and data fitting
			6.2.1.2 Albedo from integration of bidirectional reflectance
		6.2.2 Narrowband-to-broadband albedo conversion
			6.2.2.1 Vegetation and soil
			6.2.2.2 Snow cover
	6.3 The direct estimation of surface albedo
		6.3.1 Overview of the direct-estimation method
		6.3.2 Albedo-estimation method based on surface bidirectional reflectance data
			6.3.2.1 General concept
			6.3.2.2 Building the training dataset
				6.3.2.2.1 The fitting and interpolation method of the POLDER-BRDF database
				6.3.2.2.2 Land cover classification
				6.3.2.2.3 Band conversions from POLDER to MODIS
			6.3.2.3 Regression method
			6.3.2.4 The results of the AB1 algorithm
		6.3.3 The TOA reflectance–based method
			6.3.3.1 Atmospheric radiative transfer simulation
			6.3.3.2 The result of the AB2 algorithm
	6.4 Global land surface albedo products and validation
		6.4.1 Global surface albedo products from satellites
			6.4.1.1 MODIS albedo
			6.4.1.2 POLDER albedo
			6.4.1.3 VIIRS albedo
			6.4.1.4 Meteosat albedo
			6.4.1.5 CLARA-SAL
			6.4.1.6 CERES albedo
			6.4.1.7 GLOBALBEDO
			6.4.1.8 GLASS albedo
			6.4.1.9 MuSyQ albedo
		6.4.2 Issues in validating the remote sensing albedo products
			6.4.2.1 The scale matching method in the validation of land surface albedo products
			6.4.2.2 Uncertainties in the validation and their assessment
			6.4.2.3 The issue of albedo scaling in the mountainous areas
	6.5 Temporal and spatial analysis of the global land surface albedo
		6.5.1 The method to calculate regional average and monthly average albedo
		6.5.2 Temporal variation of global albedo
		6.5.3 The surface albedo of different latitudinal zones
		6.5.4 The comparison of different albedo products
		6.5.5 Surface albedo of different land types
		6.5.6 Change trend of annual average albedo
	6.6 Problems and prospects in the study of broadband albedo
	References
7 - Land surface temperature and thermal infrared emissivity
	7.1 The definitions of land surface temperature and land surface emissivity
		7.1.1 The definition of land surface temperature
			7.1.1.1 Thermodynamic or kinetic temperature (Norman and Becker, 1995)
			7.1.1.2 Brightness temperature
			7.1.1.3 Radiometric temperature (Becker and Li, 1995)
			7.1.1.4 Equivalent or average temperature
		7.1.2 Definition of land surface temperature
			7.1.2.1 Spectral emissivity
			7.1.2.2 e-Emissivity (Norman and Becker, 1995)
			7.1.2.3 r-Emissivity (Norman and Becker, 1995)
			7.1.2.4 Equivalent emissivity for a nonisothermal surface (Li et al., 1999)
			7.1.2.5 Component effective emissivity
	7.2 The estimation of average land surface temperature
		7.2.1 Single-channel algorithms
			7.2.1.1 The radiative transfer equation method
			7.2.1.2 The single-channel algorithm
			7.2.1.3 Generalized single-window algorithms
		7.2.2 Split-window algorithms for thermal infrared sensors
		7.2.3 Multichannel algorithms
			7.2.3.1 The temperature-independent spectral index method
			7.2.3.2 The MODIS day/night algorithm
			7.2.3.3 The integrated retrieval algorithm
			7.2.3.4 Algorithms for hyperspectral data from meteorological satellites
		7.2.4 Microwave methods
	7.3 LSE estimation methods
		7.3.1 Emissivity measurement methods
		7.3.2 Classification-based methods
		7.3.3 NDVI-based methods
		7.3.4 Multichannel methods
			7.3.4.1 The normalized emissivity method
			7.3.4.2 The α residual method
			7.3.4.3 The MMD method
			7.3.4.4 The TES algorithm for ASTER
			7.3.4.5 Optimization methods
		7.3.5 Retrieval algorithms for hyperspectral data
			7.3.5.1 The iterative spectrally smooth temperature and emissivity separation algorithm
			7.3.5.2 Correlation-based algorithms
			7.3.5.3 Downward radiance residue index algorithms
			7.3.5.4 Multiscale wavelet–based temperature and emissivity separation algorithm
		7.3.6 The calculation of the surface longwave broadband emissivity
		7.3.7 The retrieval of the surface longwave broadband emissivity
	7.4 LSE and LST products
	7.5 Fusion of land surface temperature products
	7.6 Summary
	Acronyms
	References
	Further reading
8 - Surface longwave radiation budget
	8.1 Surface downward longwave radiation
		8.1.1 Background
		8.1.2 Profile-based methods
		8.1.3 Hybrid methods
			8.1.3.1 The general framework of the hybrid methods
			8.1.3.2 Clear-sky surface downward longwave radiation model for MODIS
				8.1.3.2.1 Surface downward longwave radiation model in North America
				8.1.3.2.2 Surface downward longwave radiation model in globe
			8.1.3.3 Clear-sky surface downward longwave radiation models for GOES Sounders and GOES-R ABI
			8.1.3.4 Surface downward longwave radiation hybrid models for CERES
		8.1.4 Meteorological parameter–based methods
			8.1.4.1 Bayesian model averaging
			8.1.4.2 Clear-sky parameterizations
			8.1.4.3 All-sky parameterizations
			8.1.4.4 Verification based on ground measurement data
				8.1.4.4.1 Verification in clear skies
				8.1.4.4.2 Verification in cloudy skies
	8.2 Surface upwelling longwave radiation
		8.2.1 Temperature-emissivity method
		8.2.2 Hybrid methods
			8.2.2.1 MODIS linear surface upwelling longwave radiation model
			8.2.2.2 Dynamic learning neural network model
			8.2.2.3 Surface upwelling longwave radiation models for VIIRS
			8.2.2.4 Surface upwelling longwave radiation models for GOES Sounders and GOES-R ABI
	8.3 Surface net longwave radiation
		8.3.1 Estimation of surface net longwave radiation in clear sky
		8.3.2 Estimation of surface net longwave radiation in cloudy sky
			8.3.2.1 Methods
				8.3.2.1.1 Linear model
				8.3.2.1.2 MARS model
			8.3.2.2 Results
				8.3.2.2.1 Validation of the linear model
				8.3.2.2.2 Validation of MARS model
		8.3.3 Global surface net longwave radiation product generation
	8.4 Ground validation networks and existing satellite-derived surface longwave radiation budget products
		8.4.1 Existing surface longwave radiation budget products
		8.4.2 Spatiotemporal variation analysis of surface downward longwave radiation
	8.5 Summary
	Acknowledgments
	References
	Further reading
9 - Canopy biochemical characteristics
	9.1 Overview of principles and methods
		9.1.1 Remote sensing of plant biochemical parameters
			9.1.1.1 Leaf structure and its biological, physical, and chemical properties
			9.1.1.2 Spectral characteristics of biochemicals
		9.1.2 Introduction to theories and methods
			9.1.2.1 Empirical and semiempirical methods
			9.1.2.2 Radiative transfer models
				9.1.2.2.1 N-stream models
				9.1.2.2.2 Random model
				9.1.2.2.3 Ray tracing model
				9.1.2.2.4 Plate model
				9.1.2.2.5 Conifer leaf model LIBERTY
	9.2 Empirical and semiempirical methods
		9.2.1 Extraction of biochemical concentration on the leaf scale
			9.2.1.1 Cellulose concentration
			9.2.1.2 Lignin concentration
			9.2.1.3 Carbon concentration
			9.2.1.4 Nitrogen concentration
		9.2.2 Extraction of chlorophyll concentration
			9.2.2.1 Spectral index
			9.2.2.2 Chlorophyll concentration on the leaf scale
			9.2.2.3 Chlorophyll concentration of crops on the canopy scale
	9.3 Extraction using physical models
		9.3.1 Overview of the retrieval methods
			9.3.1.1 The cost function in retrieval
			9.3.1.2 Retrieval algorithm
			9.3.1.3 Retrieval strategy
		9.3.2 Leaf-scale biochemical parameter retrieval
			9.3.2.1 Unbiased data retrieval
				9.3.2.1.1 PROSPECT model retrieval
				9.3.2.1.2 LIBERTY model retrieval
			9.3.2.2 Extraction from noisy data
			9.3.2.3 Extraction from observed data
		9.3.3 Canopy-scale biochemical parameter retrieval
			9.3.3.1 Retrieval from simulated data: multiple-phase retrieval of biochemical parameters at the canopy scale
			9.3.3.2 Retrieval of biochemical parameters from real-observed data
		9.3.4 The influence of spectral resolution and band selection
			9.3.4.1 The influence of spectral resolution on the retrieval of biochemicals
			9.3.4.2 Band selection specifically for the retrieval of biochemicals
	9.4 Extraction of vertical distribution of biochemical components in vegetation using hyperspectral lidar
		9.4.1 Study on vertical extraction of vegetation characteristics using hyperspectral lidar
		9.4.2 Experiment and data processing of hyperspectral lidar instruments
			9.4.2.1 Hyperspectral lidar instruments
			9.4.2.2 Experiments and data processing
			9.4.2.3 Hyperspectral lidar point cloud data
		9.4.3 Inversion method and results of vertical distribution of vegetation biochemical components
			9.4.3.1 Relationship between biochemical components and hyperspectral lidar
			9.4.3.2 Vertical distribution of vegetation index of hyperspectral lidar
			9.4.3.3 Vertical distribution of biochemical components
	9.5 Summary
	References
	Further reading
10 - Leaf area index
	10.1 Definitions
		10.1.1 Direct leaf area index measurement
		10.1.2 Indirect leaf area index estimation
			10.1.2.1 Indirect contact method
			10.1.2.2 Indirect optical method
	10.2 Statistical methods
	10.3 Canopy model inversion methods
		10.3.1 Radiative transfer modeling
			10.3.1.1 A brief introduction to the models
			10.3.1.2 SAILH model–based simulation
			10.3.1.3 The 3D radiative transfer model
		10.3.2 Optimization techniques
			10.3.2.1 Minimization in one or multiple dimensions
			10.3.2.2 Nonderivative and derivative methods
		10.3.3 Neural networks
			10.3.3.1 CYCLOPES leaf area index algorithm
			10.3.3.2 GLASS LAI algorithm
		10.3.4 Genetic algorithms
			10.3.4.1 Introduction
			10.3.4.2 The application of the GA in LAI retrieval
		10.3.5 Bayesian networks
			10.3.5.1 A brief introduction to Bayesian networks
			10.3.5.2 The application of Bayesian network in LAI retrieval
		10.3.6 Lookup table methods
	10.4 Data assimilation methods
		10.4.1 Variational assimilation methods
		10.4.2 The sequential data assimilation algorithm
	10.5 LAI retrieval from lidar data
		10.5.1 Retrieving LAI from FAVD
		10.5.2 Retrieving leaf area index from gap fraction
	10.6 Global and regional leaf area index products
		10.6.1 Major global moderate-resolution leaf area index products
		10.6.2 Leaf area index climatology
	10.7 Summary
	References
11 - Fraction of absorbed photosynthetically active radiation
	11.1 Introduction
	11.2 FAPAR estimation method
		11.2.1 Empirical methods
		11.2.2 MODIS FAPAR product algorithm
		11.2.3 JRC_FAPAR product algorithm
		11.2.4 Four-stream radiative transfer model
		11.2.5 GLASS FAPAR algorithm
	11.3 FAPAR product intercomparison and validation
		11.3.1 Intercomparison of FAPAR products over the globe
		11.3.2 Intercomparisons over different land cover types
		11.3.3 Comparison with FAPAR values derived from high-resolution reference maps
	11.4 Spatiotemporal analysis and applications
	11.5 Summary
	References
12 - Fractional vegetation cover
	12.1 Introduction
	12.2 Field measurements of fractional vegetation cover
		12.2.1 Visual estimation
			12.2.1.1 The traditional method
			12.2.1.2 The digital image method
			12.2.1.3 The grid method
		12.2.2 Sampling method
			12.2.2.1 The belt transect sampling method
			12.2.2.2 The point count sampling method
				12.2.2.2.1 Needle sampling method
				12.2.2.2.2 Square frame sampling method
				12.2.2.2.3 Lookup sampling method
			12.2.2.3 The shadow sampling method
			12.2.2.4 The canopy projection method
		12.2.3 Optical measuring instruments
			12.2.3.1 Spatial quantum sensor and traversing quantum sell
			12.2.3.2 Digital photography
			12.2.3.3 LAI-2000 indirect measurement
		12.2.4 Examples of field measurement
			12.2.4.1 Examples of noninstrumental measurements
				12.2.4.1.1 Grassland
				12.2.4.1.2 Forested land
				12.2.4.1.3 Shrubbery
			12.2.4.2 Examples of digital photography measurement
				12.2.4.2.1 Selecting the photography environment
				12.2.4.2.2 Fractional vegetation cover extraction from the classification of digital images
	12.3 The remote sensing retrieval
		12.3.1 Regression models
			12.3.1.1 The linear regression model method
			12.3.1.2 The nonlinear regression model method
		12.3.2 The linear unmixing model
		12.3.3 Machine learning methods
			12.3.3.1 The neural network method
			12.3.3.2 The decision tree method
			12.3.3.3 The random forests regression method
			12.3.3.4 The support vector machines
	12.4 Current remote sensing products
	12.5 Spatiotemporal change analysis of fractional vegetation cover
		12.5.1 Challenges and prospects for fractional vegetation cover estimation
	References
	Further reading
13 - Vegetation height and vertical structure
	13.1 Field measurement of vegetation height and vertical structure
		13.1.1 Height of a single tree
		13.1.2 Relationship between height and diameter at breast height
		13.1.3 Estimation of average tree height at forest stand level
			13.1.3.1 Conditioned average height
			13.1.3.2 Average height weighted by basal area
			13.1.3.3 Dominant average height
	13.2 Small footprint lidar data
		13.2.1 Principle of small footprint lidar
		13.2.2 Segmentation of single tree and parameters estimation
		13.2.3 Estimation of forest parameters at forest stand level
		13.2.4 Large footprint lidar data
			13.2.4.1 Principle of large footprint lidar and its application in forestry
		13.2.5 Estimation of forest parameters from lidar waveform data
	13.3 Vegetation canopy height and vertical structure from SAR data
		13.3.1 Principle of interferometric SAR
		13.3.2 Forest height estimation using multifrequency InSAR data
		13.3.3 Retrieval of vegetation vertical structure from PolInSAR data
			13.3.3.1 The principle of polarimetric SAR interferometry
			13.3.3.2 Mode inversion for forest height estimation
				13.3.3.2.1 Randomly oriented volume model
				13.3.3.2.2 ROVG model with specular reflection from ground
				13.3.3.2.3 Three-step method of forest height estimation
				13.3.3.2.4 Polarization coherence tomography
		13.3.4 Forest height from radargrammetry
	13.4 Vegetation canopy height and vertical structure from airborne stereoscopic images
	13.5 Future perspectives
	References
14 - Aboveground biomass
	14.1 Introduction
	14.2 Allometric methods
	14.3 Optical remote sensing methods
		14.3.1 Using vegetation indices
		14.3.2 Multivariate regression analysis
		14.3.3 kNN methods
			14.3.3.1 Overview
			14.3.3.2 Assumption
			14.3.3.3 Method description
			14.3.3.4 Number of neighbors
		14.3.4 Artificial neural networks
			14.3.4.1 Principle
			14.3.4.2 Limitations
	14.4 Active and stereoscopic remote sensing methods
		14.4.1 Lidar data
			14.4.1.1 Small-footprint lidar
			14.4.1.2 Large-footprint lidar
		14.4.2 SAR data
			14.4.2.1 Backscattering coefficients
			14.4.2.2 Interferometric SAR
		14.4.3 Spaceborne stereoscopic images
	14.5 Synthesis methods of multisource data
		14.5.1 Regression models
		14.5.2 Nonparametric algorithms
			14.5.2.1 Segmentation and biomass allocation
			14.5.2.2 Random forest method
			14.5.2.3 Maximum entropy model
			14.5.2.4 Support vector regression
		14.5.3 Multisource remote sensing data
	14.6 Future perspective
	References
	Further reading
15 - Estimate of vegetation production of terrestrial ecosystem
	15.1 Concept of vegetation production
	15.2 Ground observation of vegetation production
		15.2.1 Biological approach
			15.2.1.1 Measurement of primary production of vegetation in grassland ecosystem
			15.2.1.2 Measurement of primary production of vegetation in forest ecosystem
				15.2.1.2.1 Measurement of primary production in tree layer
				15.2.1.2.2 Measurement of primary production of shrub layer
				15.2.1.2.3 Measurement of primary production of herbaceous layer
		15.2.2 Eddy covariance
	15.3 Statistical models based on vegetation index
	15.4 Light use efficiency model based on remote sensing data
		15.4.1 Principles for light use efficiency model
		15.4.2 Major light use efficiency model
			15.4.2.1 CASA model
			15.4.2.2 CFix model
			15.4.2.3 CFlux model
			15.4.2.4 EC-LUE model
			15.4.2.5 GLO-PEM
			15.4.2.6 MODIS-GPP product
			15.4.2.7 VPM
			15.4.2.8 Two-leaf model
		15.4.3 Disparities among diverse light use efficiency models
		15.4.4 Defects of light use efficiency models
			15.4.4.1 Difficulty in estimating net primary production
			15.4.4.2 Difference in light use efficiency under the effect of scattering and direct solar radiation
			15.4.4.3 Influence of forest disturbance on GPP estimates
	15.5 Potential of sun-induced chlorophyll fluorescence for vegetation production estimates
	15.6 Dynamic global vegetation models
		15.6.1 Brief introduction to dynamic global vegetation models
		15.6.2 Application of remote sensing data in dynamic global vegetation models
			15.6.2.1 Land cover map
			15.6.2.2 Leaf area index
			15.6.2.3 Model-driven data
	15.7 Temporal and spatial distribution pattern of global vegetation productivity
	15.8 Global gross primary production product
		15.8.1 Input data
		15.8.2 Brief introduction to global gross primary production product
			15.8.2.1 General information
			15.8.2.2 Model algorithm description
			15.8.2.3 Model validation and accuracy
	15.9 Summary
	References
16 - Precipitation
	16.1 Introduction
	16.2 Surface measurement techniques
		16.2.1 Rain gauge network
		16.2.2 Ground-based radar
	16.3 Estimation from satellite data
		16.3.1 VIS/IR algorithms
		16.3.2 Passive microwave algorithms
		16.3.3 Active microwave algorithms
		16.3.4 Multisensor algorithms
	16.4 Global and regional datasets
		16.4.1 Tropical Rainfall Measuring Mission
		16.4.2 Global Satellite Mapping of Precipitation
		16.4.3 Global Precipitation Climatology Project
		16.4.4 Global Precipitation Measurement
		16.4.5 Climate Prediction Center Morphing
	16.5 Global precipitation climatology
	16.6 Summary
	References
17 - Terrestrial evapotranspiration
	17.1 Introduction
	17.2 Basic theories of λE
		17.2.1 The Monin–Obukhov similarity theory
		17.2.2 The Penman–Monteith equation
	17.3 Satellite λE algorithms
		17.3.1 One-source models
		17.3.2 Two-source models
		17.3.3 Ts-VI space methods
		17.3.4 Empirical models
		17.3.5 The empirical Penman–Monteith equation
		17.3.6 Assimilation methods and temporal scaling up
	17.4 Observations for algorithm calibration and validation
		17.4.1 Eddy covariance technique
		17.4.2 Energy balance Bowen ratio method
		17.4.3 The scintillometer method
		17.4.4 Terrestrial water budget method
	17.5 The spatiotemporal characteristics of global and regional λE
	17.6 Conclusions and discussion
	Acknowledgments
	References
18 - Soil moisture contents
	18.1 Introduction
	18.2 Conventional SMC measurement techniques
	18.3 Microwave remote sensing methods
		18.3.1 Passive microwave remote sensing
			18.3.1.1 Basic principles
			18.3.1.2 Satellite sensors
			18.3.1.3 Inversion algorithms
				18.3.1.3.1 AMSE-R instrument algorithm
				18.3.1.3.2 Land Parameter Retrieval Model (LPRM)
		18.3.2 Active microwave remote sensing
			18.3.2.1 Basic principles
			18.3.2.2 Satellite sensors
			18.3.2.3 Inversion methods
	18.4 Optical and thermal infrared remote sensing methods
		18.4.1 The triangle method
		18.4.2 The trapezoid method
		18.4.3 Temperature–vegetation dryness index
		18.4.4 The thermal inertia method
	18.5 Estimation of soil moisture profile
	18.6 Comparison of different remote sensing techniques
	18.7 Available datasets and spatial and temporal variations
		18.7.1 Ground point measurements
		18.7.2 Microwave remote sensing
			18.7.2.1 AMSR-E/Aqua daily L3 surface soil moisture
			18.7.2.2 VUA-NASA soil moisture products
			18.7.2.3 Scatterometer-derived soil moisture product from the Vienna University of Technology
			18.7.2.4 Soil Moisture and Ocean Salinity
			18.7.2.5 Soil Moisture Active and Passive
			18.7.2.6 ESA soil moisture ECV products
		18.7.3 LSM estimates with observation-based forcing
	18.8 Conclusions
	References
19 - Snow water equivalent
	19.1 Snow water equivalent ground measurement method
	19.2 Snow microwave scattering and emission modeling
		19.2.1 Semiempirical models
			19.2.1.1 Helsinki University of Technology model
			19.2.1.2 Microwave emission model for layered snowpack model
		19.2.2 Analytical models
		19.2.3 Numerical models
	19.3 Microwave snow water equivalent retrieval techniques
		19.3.1 Snow water equivalent inversion techniques using passive microwave remote sensing
			19.3.1.1 Semiempirical algorithms
				19.3.1.1.1 Static algorithms
					19.3.1.1.1.1 The Chang (1987) algorithm (the NASA algorithm)
					19.3.1.1.1.2 The Foster et al. (1997) algorithm (the NASA 96 algorithm)
					19.3.1.1.1.3 The Foster et al. (2005) algorithm
					19.3.1.1.1.4 The Derksen et al. (2005) algorithm (the Canada algorithm)
					19.3.1.1.1.5 The snow depth estimation algorithms in China
				19.3.1.1.2 Dynamic algorithms
					19.3.1.1.2.1 The temperature gradient index dynamic algorithm
					19.3.1.1.2.2 The Kelly et al. (2003) dynamic algorithm
					19.3.1.1.2.3 Later development of the TGI algorithm: a combined static and dynamic algorithm
					19.3.1.1.2.4 Kelly (2009) dynamic algorithm
			19.3.1.2 Physically based statistical algorithm
			19.3.1.3 Iterative algorithms
			19.3.1.4 Lookup table algorithms
			19.3.1.5 Machine learning algorithms
			19.3.1.6 Data assimilation methods
			19.3.1.7 The mixed-pixel problem in the passive microwave SWE retrieval
		19.3.2 Active snow water equivalent inversion algorithms
			19.3.2.1 Snow water equivalent inversion algorithm based on physical backscattering models
				19.3.2.1.1 Snow water equivalent retrieval algorithm based on multifrequency (L/C/X) radar observations
				19.3.2.1.2 Snow water equivalent retrieval algorithm based on X- and Ku-band radar observations
			19.3.2.2 Estimation of SWE and its variation by repeat-pass interferometric SAR
	19.4 Optical remote sensing techniques
		19.4.1 Snow cover fraction estimation using subpixel decomposition method
		19.4.2 The empirical algorithm to estimate snow depth
		19.4.3 The SWE reconstruction algorithm combined with the snowmelt model
	19.5 Snow water equivalent product and applications
		19.5.1 Snow water equivalent products
		19.5.2 Snow spatiotemporal distribution characteristics
		19.5.3 Snow water equivalent Product application
			19.5.3.1 Hydrological applications
			19.5.3.2 Meteorological applications
			19.5.3.3 Biological applications
			19.5.3.4 Economical applications
	19.6 Summary
	References
	Further reading
20 - Water storage
	20.1 Introduction
	20.2 Water balance–based estimation
	20.3 Surface parameter–based estimation
		20.3.1 Principles
		20.3.2 Satellite-derived water surface area
			20.3.2.1 Optical satellite sensors
			20.3.2.2 Active microwave sensors
			20.3.2.3 Passive microwave sensors
			20.3.2.4 Combination of multisatellite sensors
		20.3.3 Satellite-derived water level
			20.3.3.1 Water level/area relationship method
			20.3.3.2 Land–water contact method
			20.3.3.3 Satellite altimetry method
		20.3.4 Applications
	20.4 GRACE-based estimation
		20.4.1 GRACE satellite
		20.4.2 Principles
		20.4.3 GRACE dataset and applications
	20.5 Summary
	References
21 - High-level land product integration methods
	21.1 Introduction
		21.1.1 Overview of product integration methods
		21.1.2 A toy model of product integration
	21.2 Geostatistics methods
		21.2.1 Introduction to stochastic process
		21.2.2 Optimal interpolation
			21.2.2.1 Application of optimal interpolation in product integration
			21.2.2.2 A case study
		21.2.3 Bayesian maximum entropy
			21.2.3.1 Application of Bayesian maximum entropy to product integration
	21.3 Multiresolution tree
		21.3.1 Methodology
		21.3.2 A case study with leaf area index
		21.3.3 A case study with albedo
	21.4 Empirical orthogonal function–based methods
		21.4.1 Introduction to Data Interpolating Empirical Orthogonal Functions method
		21.4.2 Application of DINEOF in product integration
			21.4.2.1 A case study on leaf area index
	21.5 Summary
	References
22 - Data production and management system
	22.1 Remote sensing ground system
		22.1.1 NASA\'s Earth Observation System Data and Information System
		22.1.2 European remote sensing satellite ground system
	22.2 Data production system
		22.2.1 Production task management
			22.2.1.1 Task list formulation
			22.2.1.2 Task list checking
			22.2.1.3 Task list execution
			22.2.1.4 Task list resetting
			22.2.1.5 Task list cancellation
			22.2.1.6 Task list priority setting
			22.2.1.7 Display of task list execution status
			22.2.1.8 Display of computational resource status
		22.2.2 High-performance computing
		22.2.3 Data quality inspection
			22.2.3.1 Construction of quality inspection database
			22.2.3.2 Algorithm module integration
			22.2.3.3 Construction of user feedback mechanism
		22.2.4 System monitoring
		22.2.5 Data management
		22.2.6 Product data management
		22.2.7 Product metadata management
	22.3 Cloud computing–based integration of data management and analytics
		22.3.1 Components of the Google Earth Engine system
			22.3.1.1 Data management system
			22.3.1.2 Calculation engine system
			22.3.1.3 Programming interface
			22.3.1.4 User interface system
	22.4 Summary
	References
	Further reading
23 - Urbanization: monitoring and impact assessment
	23.1 Introduction
	23.2 Urban area monitoring
		23.2.1 Mapping urban areas
			23.2.1.1 Mapping by optical remote sensing
			23.2.1.2 Mapping by nighttime remotely sensed light data
		23.2.2 Monitoring urban growth
	23.3 Urban ecological environment monitoring
		23.3.1 Urban vegetation monitoring
		23.3.2 Estimation of carbon storage and sequestration by urban forests
	23.4 Study on the impact of urbanization
		23.4.1 The effects of urbanization on vegetation growth season
		23.4.2 Impact of urbanization on net primary productivity
		23.4.3 The influence of urbanization on land surface parameters and environment
		23.4.4 Urban heat island effects
		23.4.5 Impact of urbanization on air quality
	23.5 Summary
	References
24 - Remote sensing application in agriculture
	24.1 Introduction
	24.2 Cropland information extracting
		24.2.1 Cropland mapping
		24.2.2 Monitoring cropland change
		24.2.3 Agricultural irrigation
	24.3 Crop yield prediction
		24.3.1 Rice yield prediction by using NOAA-AVHRR NDVI and historical rice yield data
		24.3.2 A production efficiency model–based method for satellite estimates of corn and soybean yields
	24.4 Drought monitoring of crop
		24.4.1 Analysis of agricultural drought using vegetation temperature condition index
		24.4.2 Monitoring agricultural drought using multisensor remote sensing data
	24.5 Crop residue monitoring
		24.5.1 Crop residue cover
		24.5.2 Crop residue burning
	24.6 The impact from cropland
		24.6.1 Irrigation impacts on land surface parameters
		24.6.2 Impacts of cropland on surface temperature
		24.6.3 Impact of crop residue burning
	24.7 Response of crops to climate change
		24.7.1 Effects of extreme heat on wheat growth
		24.7.2 Effects of changes in humidity and temperature on crops
	24.8 Summary
	References
	Further reading
25 - Forest cover changes: mapping and climatic impact assessment
	25.1 Introduction
	25.2 Mapping forest change
		25.2.1 Change detection based on forest cover mapping
		25.2.2 Techniques using temporal landsat imagery
		25.2.3 MODIS vegetation continuous fields products
		25.2.4 FAO FRA 2010 remote sensing survey
	25.3 Qualifying the climatic effects of forest change
		25.3.1 Greenhouse gases
		25.3.2 Temperature
		25.3.3 Precipitation
	25.4 Case studies
		25.4.1 Deforestation in the Amazon Basin
			25.4.1.1 Deforestation in the Amazon Basin
			25.4.1.2 The drivers of deforestation
			25.4.1.3 Deforestation alters the energy and water balance
			25.4.1.4 Deforestation case
		25.4.2 Forest disturbance in China
			25.4.2.1 Historical forest cover change
			25.4.2.2 Forestry influence in China
	25.5 Conclusions
	References
Index
	A
	B
	C
	D
	E
	F
	G
	H
	I
	J
	K
	L
	M
	N
	O
	P
	Q
	R
	S
	T
	U
	V
	W
	X
	Z
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