定量遥感技术在地表监测中的应用

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"Quantitative RS，即定量遥感，是遥感科学的一个重要分支，主要关注对地表特征的精确测量和分析。定量遥感技术利用卫星或航空遥感器收集的数据，通过数学模型和算法来量化地表参数，如植被覆盖、土壤湿度、水体面积等。本书《Quantitative Remote Sensing of Land Surfaces》由Shunlin Liang撰写，属于Wiley Series in Remote Sensing系列，旨在深入探讨定量遥感的理论与应用。在遥感领域，定量遥感具有以下几个关键知识点： 1. 数据采集：遥感系统，如光学和微波传感器，用于捕捉地表反射或辐射的能量。这些数据包含了地表特性信息，例如反射率、辐射亮度和热红外发射率。 2. 数据处理：包括预处理（如辐射校正、几何校正）和后处理（如图像分类、目标检测）。这些步骤旨在减少噪声，提高数据质量，以便后续的定量分析。 3. 物理模型：如大气校正模型（如MODTRAN、6S），用于校正大气对地表反射的影响，使测量结果更接近地表真实情况。 4. 算法开发：包括指数法、植被指数、机器学习算法（如支持向量机、随机森林）等，用于提取地表信息，如植被覆盖度、作物生长状态、土地利用变化等。 5. 定量化参数估计：例如，通过归一化差异植被指数（NDVI）估算植被覆盖，通过热红外数据计算地表温度，或者通过雷达回波强度估计土壤湿度。 6. 雷达遥感：涉及到合成孔径雷达（SAR）系统和信号处理，适用于穿透云层和夜间观测，能提供地表的结构和动态信息。 7. 多光谱和高光谱遥感：通过多个波段的数据，可以进行更精细的地表特征识别和分析。 8. 统计方法：在遥感数据分析中，统计方法用于描述和解释数据分布，如雷达截面的统计描述，以及异常检测和不确定性评估。 9. 应用领域广泛：定量遥感被广泛应用在环境监测（如气候变化、灾害响应）、城市规划、农业管理、自然资源调查等多个领域。本书作者Shunlin Liang的著作深入探讨了以上这些概念和技术，为读者提供了理解和应用定量遥感的全面视角。通过阅读本书，读者可以掌握定量遥感的基本理论，以及如何将其应用于实际问题的解决中。"

xiv

CONTENTS

13.4

Carbon Cycle Studies

13.4.1

Background

13.4.2

“Bottom-up” Approach

13.4.3

Case Demonstrations

13.5

Land-Atmosphere Interaction

13.6

Summary

References

APPENDIX

CD-ROM

Content

Data Directory

Software Directory

504

506

509

51 2

51 5

525

Index

528

Preface

Remote sensing has entered a new era in the twenty-first century with a

series

operating satellites from the NASA Earth Observing System (EOS)

program, other international programs, and commercial programs. Since

November 2000 the first civilian spaceborne hyperspectral sensor, Hyperion,

has observed land surfaces on the NASA Earth Observer-1 (EO-1) platform.

The first hyperspatial commercial satellite, IKONOS, produces global im-

agery at the spatial resolutions of

m (panchromatic band) and

(multispectral bands) operationally. An increasing number

people are

processing and analyzing vast amounts of optical remotely sensed observa-

tions for monitoring land surface processes and other applications on both

local and global scales.

One basic characteristic of optical remote sensing in the twenty-first

century is the extensive use

quantitative algorithms for estimating Earth

surface variables. New sensors have much higher measurement precision with

new technology and are far better calibrated. Empirical statistical models are

being replaced by physically based models developed mostly since the early

1980s.

the NASA Earth Vision by 2020, environmental prediction is the

primary goal. The reliable prediction relies on the physical dynamic models

of land surface processes whose variables must be quantitatively estimated

from Earth observing data.

This book emphasizes both the basic principles of optical remote sensing

(0.4-14

pm) and practical algorithms for estimating land surface variables

quantitatively from remotely 5ensed observations. It presents the current

physical understanding of remote sensing as a system with a focus on

radiative transfer modeling of the atmosphere, canopy, soil, and snow, and

also the state-of-the-art quantitative algorithms for sensor calibration, atmos-

pheric and topographic correction, estimation

a variety of biophysical

variables (e.g., leaf area index, fraction of photosynthetically active radiation

absorbed by vegetation) and geophysical variables (e.g., broadband albedo,

emissivity, and skin temperature), and four-dimensional data assimilation.

This book evolved from the lecture notes for a graduate-level remote

sensing course that

offered at the University

Maryland, but it can serve

as a valuable reference book for advanced undergraduates and professionals

XVi

PREFACE

who are involved in remotely sensed data in different scientific disciplines.

This book does not assume a special mathematical background beyond a

good working knowledge of statistics, calculus, and linear algebra on an under-

graduate level. Although some detailed formulas are given in certain sections

as important references, the derivations of these formulas are not provided.

aid the rcader, each chapter starts with a brief overview of its content and

concludes with a summary containing

overview of open research issues

and pointers to relevant references. Extensive verbal “explanations” of what

is bcing addressed in the corresponding formulas are also given.

This book should be studied in sequential order, but readers who are

interested primarily

practical algorithms might follow the shorter sequence

of Chapters

and

5-13.

Book

chapters are conceptually organized into two

parts-Chapters 2--4 and 5-12-plus an introductory chapter (Chapter

and a final chapter (Chapter

13)

on applications. Chapter

introduces some

basic concepts and presents an overview of an optical remote sensing model-

ing systcm that links surface environmental variables to remotely sensed data.

Major components of the systems are outlined as a background

the

following chapters. Chapter

describes many different examples demon-

strating how quantitative remote sensing techniques can be used to address

scientific issues and solve practical societal problems.

Quantitative cstimation of land surface variables relies greatly on physical

understanding of the remotely sensed data and their relation to these

variables. In Chapters 2-4,

summarize major physical modeling methods

atmosphere, vegetation canopy, soil, and snow media in the reflective spec-

trum with an emphasis

radiative transfer modeling. Radiative transfer

modeling in the thermal infrared spectrum in conjunction with the estimation

of land surface skin temperature is discussed in Chapter

10.

Chapter 2 introduces the basic principles of radiative transfer theory and

its connections with atmospheric properties with numerical and approximate

solutions to the atmospheric radiative transfer equation. Chapter

presents

three types of canopy reflectance models: radiative transfer models, geomet-

ric

optical

models, and computer simulation models. In

rudiatiue

transfer

modeling,

different leaf models are also introduced, different methods for

solving one-dimensional

canopy radiative transfer equation are pre-

sented, and methods for handling spatial heterogeneity of the canopy field

are outlined.

geometric

optical

modeling,

major models are outlined.

simply but widely mentioned Li-Strahler model is presented in greater

detail. In

computer

sinzulutiorz

models,

the basic principles of both Monte

Carlo ray tracing and radiosity modeling methods are presented. Chapter 4

presents the modeling methods for soil and snow media. The emphasis is on

radiative transfer modeling, but geometric optical modeling methods are also

outlined.

Chapters

present the practical algorithms for estimating land surface

variables from optical remotely sensed data. It starts with sensor calibration

and ends with product validation. Chapter

discusses the need for sensor

PREFACE

XVii

radiometric calibration and various postlaunch vicarious calibration methods.

The calibration coefficients for land remote sensing satellite thematic map-

ping (Landsat TM) and National Oceanic and Atmospheric Administration

advanced very high-resolution radiometer (NOAA AVHRR) data are pro-

vided for easy reference. Chapter

presents the basic principles and practical

methods of atmospheric correction that convert top-of-atmosphere (TOA)

radiance to surface reflectance. It focuses mainly on estimating aerosol

optical properties from single-viewing remotely sensed data, but multiangle

imaging spectroradiometric

(MISR)

aerosol estimation methods representing

multiangular remote sensing and those for estimating total column water

vapor content of the atmosphere are also discussed. Chapter

discusses

various topographic correction algorithms that remove the disturbance caused

by the variable land surface topography. The current status of digital eleva-

tion model (DEM) data is also evaluated. Chapter

presents various meth-

ods for estimating land surface variables, such

leaf area index, fraction

the photosynthetically active radiation (FAPAR) absorbed by vegetation

canopies, the fraction of vegetation coverage, and biochemical concentra-

tions. Statistical algorithms (based mainly

vegetation indices from multi-

spectral and hyperspectral remotely sensed data) and physical inversion

methods (based mainly on process models introduced in the first few chap-

ters of the book) are presented.

Chapters

and 10 discuss various methods for estimating land surface

radiation budget components, such

broadband albedo, broadband emissiv-

ity, and skin temperature.

calculating broadband albedo, we focus mainly

the conversion algorithms from narrowband to broadband. In estimating

skin temperature, split-window algorithms

as well

other temperature and

emissivity separation algorithms are presented. Chapter

presents four-di-

mensional data assimilation algorithms and applications. This

a quantita-

tive, objective method for inferring the state of the dynamic system from

heterogeneous, irregularly distributed, and temporally inconsistent observa-

tional data with differing accuracies. One unique feature is the ability to

estimate environmental variables that are not related to any remotely sensed

radiometric quantities. The basic principles and the typical algorithms are

introduced, and the applications to hydrology and crop growth are demon-

strated. Chapter

discusses the validation of the estimated land surface

variables and various methods of spatial scaling. Since land surface variables

drive various models related to land surface processes, the accuracy of the

remotely sensed products will largely affect the model outputs and the final

conclusions. The validation rationale, NASA

EOS

validation programs, and

some validation methodologies are presented. Because of its importance in

remote sensing besides validation, spatial scaling is presented separately. The

emphasis is on downscaling methods, including linear unmixing, generating

continuous fields, decomposition of normalized difference vegetative index

(NDVI) temporal profiles, multiresolution data fusion, and statistical down-

scaling global

(or

general) circulation model

(GCM)

outputs.

XViii

PREFACE

This book builds on many journal papers and books cited in the references

at the end of each chapter, which is at best representative but by no means

exhaustive. Many references are made in the text to websites that serve as

references and as sources of complimentary information. As most users of

the Internet know, uniform resource locators (URLs) are often changed,

while some are even dropped and removed. We have strived

make these

current as possible, but as time progresses some will disappear or otherwise

be altered without our knowledge.

This book cites more than

1300

references. Some useful computer pro-

gram codes and valuable datasets are also included in the CD-ROM for easy

reference.

ACKNOWLEDGMENTS

Many people substantially contributed to this book, both directly and indi-

rectly. First and foremost,

am greatly indebted to Professors Alan Strahler

at Boston University and John Townshend at the University of Maryland for

their consistent encouragement and guidance. Parts

the

book

were written

during my sabbatical leave while visiting the Center for Remote Sensing and

Geographic Information Science, Beijing Normal University (BNU), China;

the Earth Ohervation Research Center, National Space Development Agency

of Japan (NASDA); CSIRO Land and Water at Canberra, Australia; and

USDA Beltsville Agricultural Research Service (BARC), Maryland. I greatly

appreciate all the support from Professors Xiaowen Li, Qijiang Zhu, and

Jindi Wang at BNU; Dr. Tamotsu Igarashi at NASDA; Dr. Tim McVicar at

CSIRO; and Drs. Craig Daughtry and Charlie Walthall at BARC. I’d like to

thank Drs. Mat Disney, Philip Lewis, and David Jupp for their kindly

providing me with some of the illustrations used in this book, and my

co-authors of the journal papers that largely build this book. I am indebted to

my students who made great contributions to this book, particularly Chad

Shuey, Hongliang Fang, and Lynn Thorp for their help in preparing the

manuscript.

gratefully acknowledge the assistance of George Telecki (Edi-

tor), Rosalyn Farkas (Production Editor), and other staff at Wiley. I would

also like to acknowledge the research funding support from several agencies,

particularly from NASA through its Terrestrial Ecology,

EOS,

EO-1

and

other programs. Finally,

would like to express my most heartfelt thanks to

my lovely wife, Jie, for her endless support and encouragement. Without her

sacrifice,

his project would never come to the end.

SHUNLIN

LIANG

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