Review of Land Surface Observation, Modeling and Data Assimilation by Shulin Liang, Xin Li, and Xianhong Xie

These days. remote sensing and satellite technologies are providing unprecedented data on land surface processes, which permit modeling of land surface hydrology at large spatial scales, ranging from watershed, to regional, to continental. This book that grew out of a summer school organized in July 2010 at Bejing Normal University, Beijing, China, is a timely publication. It deals with several aspects that are central to hydrologic modeling in general and large-scale modeling in particular. Although this is not a hydrology book, nonetheless it is very useful for hydrologic investigations and studies.

The book comprises 14 chapters that are organized in four sections. The first section is on observation, encompassing three chapters. The first chapter deals with remote sensing data products for land surface data assimilation system application. It discusses atmospheric forcing data (including radiation, air temperature, water vapor and precipitation), land surface remote sensing (including land surface temperature, land surface albedo, leaf area index, adsorbed radiation, vegetation indices and soil moisture), and data for model parameterization (including land cover types). The chapter is well-written and is a good summary of remote sending data products.

The second chapter focuses on China’s Fengyun 3 series (second-generation polar orbiting meteorological satellites) and its application in global monitoring. Proving a short historical review of the Chinese meteorological satellites, it goes on to discussing the mission of the Fengyun (FY) 3 series, payloads on FY-3 A and FY-3B, ground segment designs for FY-3 A and FY-3B, standard product on Levels 1 and 2, data archives and services, and utilization of multidisciplinary data (demonstrated through synoptic weather monitoring, typhoon monitoring, numerical weather prediction by data assimilation, ozone monitoring, and air quality monitoring). This is a good chapter and provides insights into the Chinese meteorological satellite program of which people outside of China may not be aware.

Chapter 3 offers a tutorial on data access to the National Aeronautics and Space Administration (NASA) satellite and model land data services. Introducing NASA land products with reference to satellite mission on land observations; satellite land products, processing levels, resolutions, and data format; and land assimilation model products, it goes on to present search-and-order NASA earth science data products, including earth science data centers, finding and accessing data from centralized systems, finding data from data archiving centers, accessing Landsat data, and accessing data from the Goddard earth sciences data and information services center. Then it discusses NASA online visualization services (including Giovanni, MODIS rapid response system, NASA earth observations, NASA earth observatory, and NASA visible earth), and support research projects and sample usage (of data and services), encompassing NASA data to support research projects and sample plots by the use of Giovanni. This is a well-written chapter.

Section 2, comprising two chapters, is on modeling. Chapter 4 deals with land surface process study and modeling in drylands and high elevation regions. The subject matter of this chapter covers land surface models, issues in land surface modeling (such as thermal coupling between land and atmosphere in drylands, soil stratification beneath alpine grasslands, and soil surface resistance), parameterization schemes (such as thermal roughness length scheme and its validation, inverse analysis of the role of soil vertical heterogeneity, and a soil surface resistance scheme), and land surface improvements (including modeling improvements in drylands, consideration of soil vertical stratification, and introduction of soil surface resistance for evaporation in land surface modeling). The discussion in this chapter is presented well.

Parameterization and parameter estimation for hydrologic models are presented in Chapter 5. Providing a review of hydrologic models, including concepts of a hydrologic model and trends of modern hydrologic modeling, it discusses parameter estimation methods, including automatic calibration requirements, choice of calibration criteria, and state-of-art algorithms of optimization for hydrologic models. It is a good review chapter.

Data assimilation constitutes the theme of Section 3 that consists of five chapters. Chapter 6 presents theory and methods of assimilating remote sensing data into land surface models. The theory of data assimilation discusses uncertainties modeling, uncertainties of observation, and the rationale for land data assimilation. Methods of data assimilation encompass classification of data assimilation methods and Bayesian theoretical foundation for data assimilation. Chapter 6 is concluded with a discussion of case studies of land data assimilation, including retrieving soil temperature profile by assimilating MODIS land surface temperature products and assimilation of passive microwave remote sensing data for active layer soil temperature estimation. It is a good chapter.

Chapter 7 deals with estimation model and observation error covariance information for land data assimilation systems. It first provides a background and then discusses application to a modern land surface model (LSM), challenges including autocorrelated observation errors, uncertainty in the source and structure of model error, speed of adaptive filter convergence, and potential solutions with the use of triple collocation to estimate R, where R means true observation error covariance, and robust filtering strategies.

Inflation adjustment on error covariance matrices for the ensemble Kalman filter assimilation is discussed in Chapter 8. Providing a short introduction, the chapter discusses the ensemble Kalman filter, inflation adjustment in linear observation operator and nonlinear observation operator, simplified ideal models for verification, and verification results using linear observation and nonlinear observation. It is a well-written chapter.

Error estimation in land data assimilation systems is reviewed in Chapter 9. It discusses error definition and sources, including sequential data assimilation (DA) and variational DA methods, error estimation issues (including model error, observation error, and algorithmic errors in ensemble DA), and error handling methods in ensemble DA (including multiplicative inflation methods, additive inflation methods, relaxation-to-prior method, evolutionary algorithm-based error parameterization methods, and experiments designed with crossover error parameterization methods).

Chapter 10 introduces the multiscale Kalman smoother (MKS)-based framework and its application to data assimilation. It first introduces the Kalman filter and then presents the MKS and its extension, including upward sweep and downward sweep, the expectation maximization (EM) algorithm for parameter estimation (including the expectation step and maximization step), and application of the MKS-based framework with the EM method for data assimilation, including algorithmic complexity. The chapter is concluded with an example. The chapter is well-written.

The last section is Section 4 on application comprising four chapters. Chapter 11 provides an overview of the North American land data assimilation system (NLDAS). It first provides a background of land data assimilation data system (LDAS) and describes National Oceanic and Atmospheric Administration (NOAA)-NASA-university collaborations, and the development of LDAS and other LDAS activities around the world. Then it presents NLDAS history, including NLDAS-1 and NLDAS-2. It is a good overview.

Soil moisture data assimilation for state initialization of seasonal climate prediction is the theme of Chapter 12. Providing a brief history of soil moisture data assimilation, it presents basic concepts of soil moisture assimilation and a case study of soil moisture assimilation, including data assimilation algorithm development and assimilation of scanning multifrequency microwave radiometer (SMMR) data in catchment-based land surface model (CLSM). The chapter is well-written.

Chapter 13 deals with recent advances and future directions in the area of assimilation of remote sensing data and crop simulation models for agricultural study. It first discusses crop growth models and goes on to discussing data assimilation methods including the direct input approach, sequential assimilation approach, and variational assimilation approach; remote sensing data and preprocessing, including the visible and near-infrared approach, microwave information, and thermal infrared information; and corn yield estimation at a regional level, including sensitivity analysis, cost function construction, corn yield estimation, water balance, and challenges and future studies.

The last chapter, Chapter 14, discusses simultaneous state-parameter estimation for hydrologic modeling using the ensemble Kalman filter. Providing a brief background, it presents the ensemble Kalman filter with the state-augmentation technique, and a case study for a simple rainfall-runoff model. It then discusses application to a distribution hydrologic model, the soil water assessment tool (SWAT), and a data assimilation procedure.

On the whole, the book is well-written and presents plenty of useful information for hydrologic modeling and inferences. The chapter authors are accomplished researchers and are known in their areas. The book will be particularly useful for graduate students and faculty.