This book is unique in its ambitious and comprehensive coverage of earth system land surface characterization, from observation and modeling to data assimilation, including recent developments in theory and techniques, and novel application cases. The contributing authors are active research scientists, and many of them are internationally known leading experts in their areas, ensuring that the text is authoritative.
This book comprises four parts that are logically connected from data, modeling, data assimilation integrating data and models to applications. Land data assimilation is the key focus of the book, which encompasses both theoretical and applied aspects with various novel methodologies and applications to the water cycle, carbon cycle, crop monitoring, and yield estimation.
Readers can benefit from a state-of-the-art presentation of the latest tools and their usage for understanding earth system processes. Discussions in the book present and stimulate new challenges and questions facing today's earth science and modeling communities.

Foreword v

Preface ix

Acknowledgements xiii

Part 1: Observation 1

Chapter 1. Remote Sensing Data Products for Land Surface Data Assimilation System Application 3

Yunjun Yao, Shunlin Liang and Tongren Xu

1.1 Introduction 3

1.2 Atmospheric Forcing Data 4

      1.2.1 Radiation data 4

      1.2.2 Air temperature 8

      1.2.3 Water vapor 10

      1.2.4 Precipitation 11

1.3 Land Surface Remote Sensing Data Products 14

      1.3.1 Land Surface Temperature 15

      1.3.2 Land surface albedo 17

      1.3.3 Leaf area index 20

      1.3.4 Fraction of absorbed photosynthetically active radiation 24

      1.3.5 Vegetation indices 27

      1.3.6 Soil moisture 29

1.4 Data for Parameterization of Models 31

      1.4.1 Land cover types 31

1.5 Summary 32

References 33

Chapter 2. Second-Generation Polar-Orbiting Meteorological Satellites of China: The Fengyun 3

Series and Its Applications in Global Monitoring 45

Peng Zhang

2.1 Historical Review of Chinese Meteorological Satellites 45

2.2 Mission of the Fengyun 3 Series 46

2.3 The Payloads on FY-3A and FY-3B 47

2.4 Ground Segment Designs for FY-3A and FY-3B 50

2.5 Standard Product in Level 1 and Level 2 52

2.6 Data Archives and Service 53

2.7 Demonstration of Multidisciplinary Data Utilization 55

      2.7.1 Synoptic weather monitoring 55

      2.7.2 Typhoon monitoring 55

      2.7.3 NWP by data assimilation 58

      2.7.4 Ozone monitoring 60

      2.7.5 Air quality monitoring 61

2.8 Conclusions 63

Acknowledgments 64

References 64

Chapter 3. NASA Satellite and Model Land Data Services: Data Access Tutorial 67

Suhung Shen, Gregory Leptoukh and Hongliang Fang

3.1 Introduction of NASA Land Products 67

      3.1.1 NASA satellite missions on land observations 67

      3.1.2 NASA satellite land products, processing levels, resolutions, and data format 68

      3.1.3 NASA land assimilation model products 70

3.2 Search and Order NASA Earth Science Data Products 71

      3.2.1 NASA earth science data centers 71

      3.2.2 Find and access data from the centralized systems 71

      3.2.3 Find land data from data archive centers 73

      3.2.4 Access Landsat data 74

      3.2.5 Access data from GES DISC 75

3.3 NASA Online Visualization Services 78

      3.3.1 Giovanni 78

      3.3.2 MODIS Rapid Response System 79

      3.3.3 NASA Earth Observations (NEO) 79

      3.3.4 NASA Earth Observatory 79

      3.3.5 NASA visible earth 80

3.4 Support Research Projects and Sample Usage of Data and Services 80

      3.4.1 NASA data to support research projects: NEESPI and MAIRS 80

      3.4.2 Sample plots by using Giovanni 81

3.5 Summary 87

Acknowledgments 88

References 88

Part 2: Modeling 91

Chapter 4. Land Surface Process Study and Modeling in Drylands and High-Elevation Regions 93

Yingying Chen and Kun Yang

4.1 Brief Review of Land Surface Models 93

4.2 Issues in Land Surface Modeling of Drylands and High-Elevation Regions 95

      4.2.1 Thermal coupling between land and atmosphere in drylands 96

      4.2.2 Soil stratification beneath alpine grassland 100

      4.2.3 Soil surface resistance for evaporation 100

4.3 Parameterization Schemes for Arid and High-Elevation Regions 102

      4.3.1 A novel thermal roughness length scheme and its validation 102

      4.3.2 Inverse analysis of the role of soil vertical heterogeneity 107

      4.3.3 A soil surface resistance scheme for evaporation 110

4.4 Land Surface Modeling Improvements 111

      4.4.1 Modeling improvements in drylands 111

      4.4.2 Improvements considering soil vertical stratification in alpine grasslands 117

      4.4.3 Improvements by introduction of soil surface resistance for evaporation in LSM 119

4.5 Summary and Remarks 120

References 120

Chapter 5. Review of Parameterization and Parameter Estimation for Hydrologic Models 127

Soroosh Sorooshian and Wei Chu

5.1 Overview 127

5.2 Review of Hydrologic Models 128

      5.2.1 Basic concepts of a hydrologic model 128

      5.2.2 Trends of modern hydrologic modeling . 130

5.3 Review of Parameter Estimation Methods 133

      5.3.1 Automatic calibration requirements 133

      5.3.2 Choice of calibration criteria 134

      5.3.3 State-of-the-art algorithms of optimization for hydrologic models 135

5.4 Conclusions 138

Acknowledgments 138

References 139

Part 3: Data Assimilation 141

Chapter 6. Assimilating Remote Sensing Data into Land Surface Models: Theory and Methods 143

Xin Li and Yulong Bai

6.1 Theory of Data Assimilation 144

      6.1.1 Uncertainties of modeling 144

      6.1.2 Uncertainties of observation 146

      6.1.3 Rationales for land data assimilation 148

6.2 Methods of Data Assimilation 149

      6.2.1 Classification of data assimilation methods 149

      6.2.2 Bayesian theoretical foundation for data assimilation 150

      6.2.3 EnKF 156

6.3 Case Studies of Land Data Assimilation 160

      6.3.1 Retrieving soil temperature profile by assimilating MODIS land surface temperature products with EnKF 160

      6.3.2 Assimilation of passive microwave remote sensing data for active layer soil temperature estimation 163

6.4 Summary 166

Acknowledgments 167

References 168

Chapter 7. Estimating Model and Observation Error Covariance Information for Land Data Assimilation Systems 171

Wade T. Crow

7.1 Introduction 171

7.2 Background 173

7.3 Application to a Modern LSM 178

7.4 Remaining Challenges 183

      7.4.1 Auto-correlated observation errors 185

      7.4.2 Uncertainty in the source and structure of model error 189

      7.4.3 Speed of adaptive filter convergence 193

7.5 Potential Solutions 194

      7.5.1 Use of triple collocation to estimate R 194

      7.5.2 Robust filtering strategies 198

7.6 Summary 199

Appendix A. Innovation Properties in an Optimal KF 200

References 203

Chapter 8. Inflation Adjustment on Error Covariance Matrices for Ensemble Kalman Filter Assimilation 207

Xiaogu Zheng, Guocan Wu, Xiao Liang and Shupeng Zhang

8.1 Introduction 207

8.2 Inflation Adjustment on Error Covariance Matrices in EnKF 210

      8.2.1 Ensemble Kalman filter 210

      8.2.2 Inflation adjustment on error covariance matrices in the case of linear observation operator 212

      8.2.3 Inflation adjustment on error covariance matrices in the case of nonlinear observation operator 213

      8.2.4 Statistics to verify assimilation methods 215

8.3 Introduction of Simplified Ideal Models for Verification 216

      8.3.1 Lorenz-96 model 216

      8.3.2 Two-dimensional SWE model 217

8.4 Verification Results Using Linear Observation 218

      8.4.1 The case of time-dependent inflation 218

      8.4.2 The case of time-independent inflation 220

      8.4.3 The case of inaccurate observation error covariance matrix 221

      8.4.4 The case of time-dependent inflation factor of observation error covariance matrix 224

8.5 Verification Results Using Nonlinear Observation 225

      8.5.1 Sensitivity analysis on the degree of tangent linearity of observation operator 226

      8.5.2 Comparison of several inflation adjustment schemes in the case of tangent linear observation operator 227

8.6 Discussion and Main Conclusion 229

Appendix A: Li et al.’s Estimation of the Inflation Factor of Forecast Error Covariance Matrix λt 230

Appendix B: A Calculation Method of Determinant det(HtλtPf t HTt + Rt) 231

References 232

Chapter 9. A Review of Error Estimation in Land Data Assimilation Systems 235

Yulong Bai, Xin Li and Qianlong Chai

9.1 Introduction 235

9.2 Error Problems in Modern DA Methods 238

      9.2.1 Error definitions and their sources 238

      9.2.2 Error definitions in sequential DA methods 240

      9.2.3 Error definitions in variational DA methods 244

9.3 Error Estimation Issues 245

      9.3.1 Model error 245

      9.3.2 Observation error 248

      9.3.3 Algorithm errors in ensemble DA 249

      9.3.4 Summary for error estimation 249

9.4 Error Handling Methods in Ensemble DA 249

      9.4.1 Multiplicative inflation methods 250

      9.4.2 Additive inflation methods 251

      9.4.3 The relaxation-to-prior method 251

      9.4.4 Evolutionary algorithm-based error parameterization methods 252

      9.4.5 Experiments designed with crossover error parameterization methods 254

9.5 Summary and Discussions 264

Acknowledgments 267

Chapter 10. An Introduction to Multi-scale Kalman Smoother-Based Framework and Its Application to Data Assimilation 275

Daniel E. Salas and Xu Liang

10.1 Introduction 275

10.2 Traditional Kalman Filter 278

10.3 MKS and Its Extension 279

      10.3.1 Upward sweep 285

      10.3.2 Downward sweep 300

10.4 EM Algorithm for Parameter Estimation 304

      10.4.1 E step 305

      10.4.2 M Step 305

10.5 Application of the MKS-Based Framework with EM Method for Data Assimilation 306

      10.5.1 Algorithm complexity 306

10.6 Example 308

10.7 Symbols 330

Acknowledgments 331

References 331

Part 4: Application 335

Chapter 11. Overview of the North American Land Data Assimilation System (NLDAS) 337

Youlong Xia, Brian A. Cosgrove, Michael B. Ek, Justin Sheffield, Lifeng Luo, Eric F. Wood, Kingtse Mo and the NLDAS team

11.1 Introduction 338

      11.1.1 Background of LDAS 339

      11.1.2 NOAA-NASA-University collaborations and the development of NLDAS 340

      11.1.3 Other LDAS activities around the world 343

11.2 NLDAS History 343

      11.2.1 NLDAS-1 343

      11.2.2 NLDAS-2 351

11.3 Summary and Concluding Remarks 368

Acknowledgments 370

References 370

Chapter 12. Soil Moisture Data Assimilation for State Initialization of Seasonal Climate Prediction 379

Wenge Ni-Meister

12.1 Introduction 380

12.2 Brief History of Soil Moisture Data Assimilation 381

12.3 Basic Concepts of Soil Moisture Data Assimilation 383

12.4 Soil Moisture Assimilation — A Case Study 386

      12.4.1 Data assimilation algorithm development 386

      12.4.2 Assimilation of SMMR data into CLSM 388

12.5 Conclusions and Discussion 397

Acknowledgments 399

References 399

Chapter 13. Assimilation of Remote Sensing Data and Crop Simulation Models for Agricultural Study: Recent Advances and Future Directions 405

Hongliang Fang, Shunlin Liang and Gerrit Hoogenboom

13.1 Introduction 406

13.2 Crop Growth Modeling 407

13.3 Data Assimilation Methods 409

      13.3.1 Direct input approach 410

      13.3.2 Sequential assimilation approach 411

      13.3.3 Variational assimilation approach 413

13.4 Remote Sensing Data and Preprocessing 414

      13.4.1 Visible and near-infrared (NIR) information 415

      13.4.2 Microwave information 417

      13.4.3 Thermal infrared information 419

13.5 Corn Yield Estimation at a Regional Level 420

      13.5.1 Sensitivity study 422

      13.5.2 Cost function construction 423

      13.5.3 Corn yield estimation 424

      13.5.4 Water balance studies 426

13.6 Challenges and Future Studies 429

13.7 Conclusions 430

References 431

Chapter 14. Simultaneous State-Parameter Estimation for Hydrologic Modeling Using Ensemble Kalman Filter 441

Xianhong Xie

14.1 Introduction 441

14.2 EnKF with State-Augmentation Technique 443

14.3 Case Study for a Simple Rainfall-Runoff Model 445

14.4 Application to a Distributed Hydrologic Model 448

      14.4.1 SWAT model 448

      14.4.2 Data assimilation procedure 449

      14.4.3 Result 451

14.5 Discussion 456

14.6 Conclusion 460

Acknowledgments 461

References 462

Index 465

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