Note: Intro -- Satellite-based Applications on Climate Change -- Foreward -- Preface -- Contents -- Chapter 1: An Introduction to Satellite-Based Applications and Research for Understanding Climate Change -- 1.1 Introduction -- 1.2 Satellites and Changes over Time -- 1.3 International Satellite Collaboration and Coordination -- 1.4 Modern Satellite Era -- 1.5 Using Satellite Data to Understand Climate -- 1.6 Book Overview -- 1.6.1 Part I Overview of Satellite-Based Measurements and Applications -- 1.6.2 Part II Atmospheric and Climate Applications -- 1.6.3 Part III Hydrological and Cryospheric Applications -- 1.6.4 Part IV Land Surface and Ecological Applications -- References -- Chapter 2: Calibrating a System of Satellite Instruments -- 2.1 Introduction -- 2.2 Satellite Instrument Calibration Methodologies -- 2.3 Challenges in Calibrating Heritage Satellite Instruments for Climate Change Detection -- 2.4 Inter-satellite Instrument Calibration -- 2.5 Future Developments -- 2.6 Concluding Remarks -- References -- Chapter 3: MODIS Instrument Characteristics, Performance, and Data for Climate Studies -- 3.1 Introduction -- 3.2 MODIS Instrument Characterization and Performance -- 3.3 MODIS Data Products -- 3.4 The MODIS Operational Follow-On Instrument: VIIRS -- 3.5 Summary and Concluding Remarks -- References -- Chapter 4: Evaluation of the Temperature Trend and Climate Forcing in the Pre- and Post Periods of Satellite Data Assimilation -- 4.1 Introduction -- 4.2 Data and Methodology -- 4.2.1 NCEP/NCAR Reanalysis -- 4.2.2 ERA-40 -- 4.2.3 MSU (Microwave Sounding Unit) -- 4.2.4 Climate Forcings: Solar, ENSO, QBO, and Stratospheric Aerosols -- 4.2.5 Methodology -- 4.2.5.1 Multiple Linear Regression Analysis -- 4.3 Trend of Global Temperature -- 4.3.1 Stratosphere -- 4.3.2 Troposphere -- 4.4 Multiple Linear Regression Analysis -- 4.4.1 Solar Response. , 4.4.2 ENSO, QBO, and Stratospheric Aerosol Response -- 4.5 Summary -- References -- Chapter 5: Development of the Global Multispectral Imager Thermal Emissive FCDRs -- 5.1 Introduction -- 5.2 Data and Technical Methods -- 5.3 Results and Analysis -- 5.3.1 Validation of Spectral Mapping -- 5.3.2 Band Transfer for FCDR Generation -- 5.4 Conclusion and Discussions -- References -- Chapter 6: Global Precipitation Monitoring -- 6.1 Introduction -- 6.2 Satellite Precipitation Retrieval Methods -- 6.2.1 Visible and Infrared Methods -- 6.2.2 Passive Microwave Methods -- 6.2.3 Active Microwave Methods -- 6.3 Multisensor Global Rainfall Products -- 6.4 Summary and Future -- References -- Chapter 7: Developing a Historical Precipitation Record -- 7.1 Introduction -- 7.2 Historical Reconstructions -- 7.3 An Improved EOF-Based Reconstruction -- 7.4 An Improved CCA-Based Reconstruction -- 7.5 Merged Reconstruction -- 7.6 Conclusions -- References -- Chapter 8: Atmospheric Temperature Climate Data Records from Satellite Microwave Sounders -- 8.1 Introduction -- 8.2 Methodology for Consistent MSU/AMSU FCDR Development -- 8.2.1 MSU/AMSU Level-1c Calibration -- 8.2.2 Solar Heating-Related Instrument Temperature Variability -- 8.2.3 A SNO Technique for Inter-satellite Calibration and FCDR Development -- 8.2.4 Data Assimilation of Radiance FCDR in Climate Reanalysis -- 8.3 Atmospheric Temperature TCDR from Merged MSU/AMSU-A Data -- 8.3.1 Antenna Pattern Correction -- 8.3.2 Limb Adjustment -- 8.3.3 Diurnal Drift Correction -- 8.3.4 Residual Inter-satellite Bias Correction -- 8.3.5 Correction of the Earth-Location-Dependent Biases -- 8.3.6 Frequency Differences Between MSU and AMSU Channels -- 8.3.7 Well-Merged NOAA Version 2.0 MSU/AMSU Atmospheric Temperature TCDR -- 8.4 Conclusion and Data Availability -- References -- Chapter 9: Monitoring Change in the Arctic. , 9.1 Introduction -- 9.2 Winds -- 9.3 Clouds -- 9.4 Surface Temperature and Albedo -- 9.5 Radiative Fluxes and Cloud Forcing -- 9.6 Sea Ice -- References -- Chapter 10: Assessing Hurricane Intensity Using Satellites -- 10.1 Introduction -- 10.2 The Dvorak Tropical Cyclone Intensity Estimation Method -- 10.2.1 Operational Dvorak Technique -- 10.2.2 Improved Objective Dvorak Approaches -- 10.3 Satellite Microwave Intensity Estimation Techniques -- 10.3.1 Microwave Sounder Applications -- 10.3.2 Microwave Imagery Applications -- 10.4 Other Wind Estimation Techniques -- 10.5 Forecaster Applications -- 10.6 Future Outlook -- References -- Chapter 11: Satellite-Based Ocean Surface Turbulent Fluxes -- 11.1 Introduction -- 11.2 Transfer at the Air-Sea Interface -- 11.3 Satellite Estimation of Input Parameters -- 11.3.1 Wind Stress -- 11.3.2 Sea Surface Temperature -- 11.3.3 Surface-Air Temperature and Humidity -- 11.4 Satellite-Based Flux Data Sets -- 11.4.1 HOAPS -- 11.4.2 J-OFURO -- 11.4.3 GSSTF -- 11.4.4 Combined Approach -- 11.5 Error Estimates and Uncertainties -- 11.6 Summary and Outlook -- References -- Chapter 12: Satellite-Monitored Snow Cover in the Climate System -- 12.1 The Role of Snow Cover in the Climate System -- 12.2 Satellite Snow Monitoring -- 12.3 Snow-Climate Interaction -- 12.4 Numerical Simulations -- 12.5 Snow-Atmosphere Coupling Experiment -- 12.6 Summary -- References -- Chapter 13: Evapotranspiration Estimates from Remote Sensing for Irrigation Water Management -- 13.1 Introduction -- 13.2 Objective and Approach -- 13.3 Climate and Water Availability in Morocco -- 13.4 Methodology for Estimating Evapotranspiration -- 13.4.1 Introduction to METRIC -- 13.4.2 METRIC Development History -- 13.4.3 Calibration via Reference Evapotranspiration -- 13.4.4 Calculation of Evapotranspiration. , 13.5 METRIC Applications for Morocco Water Management -- 13.5.1 Study Area -- 13.5.2 Method -- 13.5.3 ET Estimates -- 13.6 Water Balance Analysis -- 13.6.1 Method -- 13.6.2 Water Balance Analysis Results -- 13.7 Summary and Conclusion -- References -- Chapter 14: Snow Cover -- 14.1 Introduction -- 14.2 Interactive Snow Mapping Technique and Product -- 14.3 Snow Retrievals with Microwave Sensors Data -- 14.4 Automated Snow Remote Sensing in Optical Spectral Bands -- References -- Chapter 15: Climate-Scale Oceanic Rainfall Based on Passive Microwave Radiometry -- 15.1 Introduction -- 15.2 Background -- 15.2.1 Atmospheric Model -- 15.2.2 Statistical Rain Field Model -- 15.2.3 Beamfilling Correction -- 15.3 Data Product -- 15.3.1 Data Processing -- 15.3.2 Sampling -- 15.3.3 Product Evaluation -- 15.3.3.1 Rainfall Rate (R) (Unconditional) -- 15.3.3.2 Conditional Rain Rate (rcond) -- 15.3.3.3 Freezing Level Height (FL) -- 15.3.3.4 Rain Frequency (p) -- 15.4 Applications -- 15.4.1 GPCP Merging -- 15.4.2 Climate ``Trend´´ and Variations -- 15.4.3 TRMM Applications -- 15.4.4 TRMM Boost -- 15.5 Summary and Discussions -- References -- Chapter 16: Integrating Landsat with MODIS Products for Vegetation Monitoring -- 16.1 Introduction -- 16.2 Algorithm Integration -- 16.3 Data Fusion Approach -- 16.3.1 STARFM Approach -- 16.3.2 The Enhanced STARFM Approach -- 16.3.2.1 STAARCH for Mapping Reflectance Change -- 16.3.3 Products Normalization -- References -- Chapter 17: Satellite Applications for Detecting Vegetation Phenology -- 17.1 Introduction -- 17.2 Method -- 17.2.1 Physical Principles for Deriving Phenology from Satellite Measurements -- 17.2.2 Approaches for Deriving Phenology from Satellite Measurements -- 17.3 Applications -- 17.3.1 Landsat-Derived Vegetation Phenology -- 17.3.2 AVHRR-Derived Vegetation Phenology. , 17.3.3 MODIS-Derived Vegetation Phenology -- 17.3.4 Applications of Other Sensors -- 17.4 Summary -- References -- Chapter 18: Monitoring a Sentinel Species from Satellites: Detecting Emiliania huxleyi in 25Years of AVHRR Imagery -- 18.1 Introduction -- 18.2 Methods -- 18.2.1 AVHRR Imagery -- 18.2.2 SeaWiFS Imagery -- 18.2.3 Detecting E. huxleyi Blooms -- 18.2.4 Geophysical Data and Climate Indices -- 18.3 Results and Discussion -- 18.3.1 Extent of E. huxleyi Blooms -- 18.3.2 Relationship to Environmental Variables and Climatic Indices -- 18.4 Conclusion -- References -- Chapter 19: Land Surface Temperature (LST) Retrieval from GOES Satellite Observations -- 19.1 Introduction -- 19.1.1 Literature Review -- 19.1.1.1 Importance of Skin Temperature -- 19.1.1.2 LST Derivation from Satellites Under Clear Conditions -- 19.1.2 LST Derivation from Satellites Under Cloudy Conditions -- 19.1.3 Ill-Posed Problem -- 19.1.4 Validation Issues -- 19.2 LST Retrieval from Geostationary Satellites -- 19.2.1 GOES Instrument Characteristics -- 19.3 Theoretical Description -- 19.3.1 Physical Description -- 19.3.2 Mathematical Description of the LST Algorithm -- 19.3.2.1 GOES Split-Window Algorithm -- 19.3.2.2 Some Other Traditional Split-Window-Type LST Algorithms -- 19.3.2.3 Triple-Window LST Algorithm -- 19.3.2.4 LST Algorithms for GOES M (12)-Q Series -- Dual-Window Algorithm -- One-Channel Algorithm -- 19.4 Forward Simulations and Regression Coefficients -- 19.4.1 Forward Simulations -- 19.4.2 Coefficients Derivation -- 19.4.3 Simulation Analyses -- 19.5 Tests and Applications with Real GOES Observations -- 19.5.1 Diurnal Temperature Range Derivation and Studies -- 19.5.2 Comparison of Dual-Window and Split-Window Algorithms -- 19.5.3 Precision and Accuracy Estimates -- 19.5.4 Error Sources -- 19.5.4.1 Large Viewing Angle -- 19.5.4.2 Water Vapor Uncertainty. , 19.5.4.3 Emissivity Uncertainty.