phd dissertation defense tracking daily land surface albedo and reflectance anisotropy with...

PhD Dissertation Defense

Tracking Daily Land Surface Albedo and Reflectance Anisotropy with MODerate-resolution Imaging

Spectroradiometer (MODIS)

Yanmin Shuai

Department of Geography and Environment, Boston University

Dissertation CommitteeCrystal B. Schaaf

Alan H. Strahler

Curtis E. Woodcock

Xiaowen Li

Qijiang Zhu

David Roy

Xiaoyang Zhang

Yanmin Shuai - MODIS BRDF/Albedo

Contents1. Introduction

1. Background

2. Research Interest

3. Research Topics• Quantify uncertainty in MODIS BRDF/Albedo Retrievals

• Daily Land Surface Reflectance Anisotropy and Albedo from MODIS

• Daily Vegetation Monitoring with the MODIS Reflectance Anisotropy and Albedo Products

4. Concluding Remarks

2

Backscattering vs. Forward Scattering

Background: Reflectance anisotropy

Isotropic reflectance Directional reflectance

Forward scatteringregion

Backscattering(hot-spot) region

The forward and backscattering regions have been recognized as structural identifiers of vegetation canopies; e.g. the amplitude of the hot-spot region shows a good agreement with the expected values for the leaf reflectance.

© 1992 - 2009 Jennifer Vranes http://www.jensart.com/

Photograph by Don Deering.

•Describe the intrinsic reflective properties of land surface.

•Characterized by the Bidirectional Reflectance Distribution Function (BRDF)

http://www.jensart.com/

Yanmin Shuai - MODIS BRDF/Albedo 4

(a) backward scattering (sun behind observer), where the sun and the viewer are on the same side, hiding most of the shadows;(b) nadir view, where a maximum of the background can be seen; (c) forward scattering (sun opposite observer) where the sun and the viewer are on the opposite side.

BRDF Example

BRDF rendering of a forest canopy composed of opaque cone and cylindrical objects viewed from three directions:

http://www.ccrs.nrcan.gc.ca/optic/coarse/beps/scale_e.php

A B C

BRDF Applications• Deriving various reflectance quantities, such as

albedo (Lucht et al.,2000; Strahler et al.,1995)

• Characterize the surface energy budget (Dickinson1983,1990)

• Correction of view and illumination angle effects (Roy et al., 2002, Schaaf et al., 2002)

• Land cover classification (Friedl et al.,2002) • Vegetation Phenology (Zhang 2003,2006…)

• Cloud detection (Roy et al.,2006)

• Atmospheric correction (Hu et al.,1999)


Current algorithm

• The operational MODIS BRDF/albedo algorithm uses high-quality, multi-day, cloud free atmospherically-corrected surface reflectances to provide global 500m reflectance quantities every 8day.

• The retrieval quality is assessed by two measures: WoD and RMSE.


• Forward model: kernel based BRDF model(RTLS-R)

Isotropic kernel Geometric kernel (surface scattering)

Volumetric kernel

0 0 1 1 2 2( , , , ) ( ) ( , , ) ( ) ( , , ) ( ) ( , , )i i i i i i i i i i i iR f K f K f K

-Roujean et al., 1992

Temporal BRDF change


Selected biomes:•Deciduous broadleaf forest (DBF)•Evergreen broadleaf forest (EBF)•Irrigate cropland •Sandy area

•Temporal BRDF shapes for 4 typical biomes

Research Interests• Optimize MODIS BRDF/Albedo Products for Regional

users

• Specify algorithm retrieval characteristics for various biomes

• Develop a daily algorithm for Direct Broadcast users

• Apply daily algorithm to track fine scale phenology


Research Topics - I.

Quantify Uncertainty in MODIS BRDF/Albedo Retrieval System


Shuai Y., C. B. Schaaf, A. H. Strahler, J. Liu, and Z. Jiao (2008), Quality assessment of BRDF/albedo retrievals in MODIS operational system, Geophys. Res. Lett.,35, L05407, doi:10.1029/2007GL032568.

Part I. Motivation

• Investigate the quality of model-fit and angular sampling in the BRDF inversion

• Evaluate the two quality measures: RMSE (Root Mean Squared Error) and WoD (Weight of Determination)

• Determine biome-appropriate thresholds for the MODIS 500m BRDF/albedo operational system


• Forward model: kernel based BRDF model(RTLS-R)


Isotropic kernel Geometric kernel (surface scattering)

Volumetric kernel

• Inversion with given n reflectance observations by a least-squares method ,where

( , , , )i i i

0 0 1 1 2 2( , , , ) ( ) ( , , ) ( ) ( , , ) ( ) ( , , )i i i i i i i i i i i iR f K f K f K

2

0 ( )k

ek = 0,1,2

f

22

1

( )n

i ii

e R

i.e. 2

0 0, 1 1, 2 2, 0,10

2

0 0, 1 1, 2 2, 1,11

2

0 0, 1 1, 2 2, 2,12

2 [ ( )] 0

2 [ ( )] 0

2 [ ( )] 0

n

i i i i ii

n

i i i i ii

n

i i i i ii

ef K f K f K K

f

ef K f K f K K

f

ef K f K f K K

f

20, 0, 1, 0, 2, 0,

1 1 1 10

21, 0, 1, 1, 2, 1 1,

1 1 1 1

22

2, 0, 2, 1, 2, 2,1 1 1 1

n n n n

i i i i i i ii i i i

n n n n


n n n n


K K K K K K

f

K K K K K f K

f

K K K K K K

1

20, 0, 1, 0, 2, 0,

1 1 1 10

21 1, 0, 1, 1, 2, 1,

1 1 1 1

22

2, 0, 2, 1, 2, 2,1 1 1 1

n n n n


n n n n


n n n n


K K K K K K

f

f K K K K K K

f

K K K K K K

The Inverse Matrix

M-1 Reference: Strahler et .al ,1999; Lucht et. al, 2000b

Part I. WoD (Weight of Determination)Where U is a vector composed of the

weighting of the kernels


T -1WoD = U M U• For WoD_f0, UT=(1,0,0)

• For WoD_WDR at given sun-view geometry (45°,0°),

• For WoD_BSA ,

• For WoD_WSA, UT=

• WoD describe the behavior of the kernel-driven linear models under conditions of limited and varying angular sampling.

• We use both WoD-WDR and WoD-WSA in the operational MODIS BRDF inversion

1 2U ( 1, ( , , ), ( , , ) )T K K

2 / 2 2 / 2

1 2

0 0 0 0

U ( 1, ( , , )sin cos , ( , , )sin cos ) )TLSN LSNK d d K d d

/2 2 / 2 /2 2 / 2

1 2

0 0 0 0 0 0

U (1, ( ( , , )sin cos )sin cos , ( ( , , )sin cos )sin cos ))T K d d d K d d d

Reference: Lucht et. al, 2000a


Sampling pattern A B C D E

Number of observation 26 6 7 6 3

WoD-WSA 0.1209 2.1562 1.8533 0.7352 1.9905

WoD-WDR 0.1843 1.2496 1.1668 0.5271 1.2761

WSA* 0.2607 0.3419 0.2873 0.2444 0.2237

Deviation** 0 0.0812 0.0266 -0.0163 -0.037Data source: Gao et 2001

• A lower WoD means a higher confidence with the angular sampling pattern.

• Therefore adequate angular sampling produces low WoDs, while poor angular sampling produces high WoD values

Part I. Ability of WoDs


Sampling Patterns A B C D

Number of Observation 7 21 21 14

WoD-WSA 5.007 0.4381 0.3074 0.4523

WoD-WDR 4.002 1.2492 0.7328 0.4987

RMSE *** 0.0352 0.0905 0.0288

Part I. Ability of WoDs (Cont.)

Better angular sampling results in lower WoDs

Part I. RMSE (Root Mean Squared Error)


2

1

i

(( ( , , , ) ( , , , )) )

3 Where is the weight assigned to each availabel observation.

n

i i i i i i ii

R wRMSE

n

RMSE describes the deviation of the RTLS-R model-fit from the clear observations.• The larger the RMSE, the higher the error in the model-fitting

Part I. WoD and RMSE characteristics for various biomes

• Selected Biomes according to IGBP classification– Tropical forest

– Boreal forest

– Grass

– Shrub

– Cropland

– Desert

– Snow

• Range of RMSE and WoD values Yanmin Shuai - MODIS BRDF/Albedo 16

(a) Range of test variable for red and near infrared band in one tile.(b) Percent of the total number of available pixels for red and near infrared band in one tile.


A. Red B. NIR

• RMSEs in both bands display similar histogram behaviors, and are generally between 0.05 and 0.15 (except for the two Greenland tiles).

• Indicates that good model fits occur in all locations except for the Greenland tiles.


A. Red

•The high WoD-WDRs generally occur in the region (0.3, 0.7), and generally rapidly decrease in frequency beyond a value of 1.0 .

•Again the Greenland tiles are exceptional. More observations are obtained but never a nadir observation at 45°SZN

•WoD-WDR increase in biomes from barren to forest.

B. NIR


•For WoD-WSA, the Greenland tiles display similar trends with others.• Highest WoD-WSAs occur in the region (0.20, 1.80).

A. Red

B. NIR


Mean and standard deviation for red and NIR bands

•We specify a range of retrieval values for various biomes

•RMSE captures the quality of the model fit

•WoD-WDR and WoD-WSA indicate the quality of angular sampling

•We suggest improved thresholds values

–Slight tightening of RMSE

–Relax the WoD-WDR

–Keep the WoD-WSA


Part I. Summary

Research Topics - II.

Daily Retrieval of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface BRDF

and Reflectance Quantities

for Direct Broadcast Application


Part II. Introduction


• Over 120 DB stations worldwide• Require regional rapid response systems• Numerous near real time applications

Allocate resources for fire fighting (Coronado,2006)Burned area estimation (Roy 2003;2007)Fire hotspot, peat-lands distribution (Maier 2007)Flooding, oil leak, logging in Siberia (Gershenzon 2007)Improve weather and hurricane tracking (Coronado,2006)


Originally envisioned as a daily product

Long term archive constraints limited product to current 8-day retrieval

DB application allows implementation of daily algorithm in a regional context

DB application allows the algorithm to be fine turned for specific regions or biomes

•MODIS BRDF/Albedo product

Part II. Introduction (Cont.)


•IMAPP (Liam et al.,2007)

•MOD09_SPA (V5.3.18)

•We provided the gridding algorithm (with R. Wolfe) so that multi-date data could be binned. This is a requirement for:

Part II. DB algorithm

all multi-date models processes

Burned area

VI composites

Albedo/BRDF


Part II. Algorithm

Three modules in the process•Quality assessment (A): to evaluate the angular sampling and assign weights based on the quality, age, and observation coverage of each observation •Full inversion (B): attempt to make a high quality BRDF retrieval with RTLS-R model if sufficient high quality observations are acquired• Backup inversion (C): perform a magnitude retrieval based on a backup inversion if observations are insufficient or poor angular sampling happened

• Quality weight by a gradiant function• Age Weight• Observation coverage weight


Part II. Algorithm for DB -weight strategy

cov( ) ( )t q d d obs pt t

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

tp (obscov)

Wob

scov

1( )

cov ( )b

a pobs pt e

( )d dt =a*td+b


•Strategy 1: Daily rolling

Part II. Algorithm for DB -inversion strategies

drop the oldest observation and add the newest one weight the observation by measured quality, without the consider of age and “obscov” as a baseline strategy


Part II. Algorithm for DB -inversion strategies (Cont.)

Strategy 2: Daily rolling with enhanced weight rules


Part II. Algorithm for DB -inversion strategies (Cont.)

•Strategy 3: Daily magnitude based on the BRDF retrieved by strategy 1

•Strategy 4: Daily magnitude based on a priori BRDFs retrieved by strategy 2

Part II. Evaluation-location• Location:

– Bartllet Experimental Forest, NH (44.0646°N, 71.2881°W)– Dominant Species: Red maple, American beech, paper birch,

and Eastern hemlock

• Data


2005-2008 Ground-based albedo (Kipp and Zonen CM3 broadband albedometer)

MODIS AOD (MOD04 10km)

Weather records


•Spatial representativeness for Bartlett site using ETM+ band 7,4 and 2The geostatistical quantities (Román et al., 2009) suggest that the direct assessments between the tower-based measures and MODIS retrievals are feasible

Part II. Evaluation-location representativeness


SW _albedo comparison between ground measures and MODIS daily retrievals•The operational 8day broadband albedos performs well •However, the daily strategy 2 appears to provide the best results•In all cases, the daily retrievals reveal fine scale variability

Part II. Fullfill the system by Strategy2Temporal spectral albedo over Bartlett


Yanmin Shuai - MODIS BRDF/Albedo 35From http://www.foliage.net


Part II. Temporal EVI over Bartlett Forest area

• Temporal EVI of small area (30km by 30km) centered

Part II. Summary• A daily rolling strategy and particularly that of

weighting the input reflectances by their quality, observations coverage, and age within the period (as well as using the most recent full retrieval as the a priori BRDF information for magnitude inversions), appears to provide the best retrievals.

• Bartlett tower and MODIS albedo measurements match well (and capture the onset of fall foliage)

• The DB daily rolling BRDF/albedo algorithm has undergone beta testing at a number of direct broadcast sites and is being currently prepared for release to the general MODIS direct broadcast community Yanmin Shuai - MODIS BRDF/Albedo 37

Research Topics - III.

Winter Wheat Monitoring with the Daily MODIS Reflectance Anisotropy and Albedo Product


Part III. Introduction• Land surface phenology is defined as the seasonal pattern of

variation in the properties of vegetated land surfaces on the regional and global scale, and is typically characterized using satellite remote sensing products (Friedl et al.2006).


• Vegetation phenology events are important parameters for biogeochemical and dynamic vegetation models, reflecting and capturing the relationship between vegetation and environment factors. (Morisette et al., 2009; Menzel et al. 2005; Kathuroju et al. 2007; Schwartz, 1998; Zhang et al., 2004,2007; Parmesan and Yohe, 2003; Myneni et al.,1997)

Temperature (Schwartz, 1998; Zhang et al., 2004) Water availability, Precipitation, and rainfallLength of regional growth season vs. climatic and ecological changes

Part III. Research area

• Yucheng Experiment Site located at (36.8290ºN, 116.5704ºE)

• Data Ground pyranometer measurements every 10 minutes

for year 2005 (5 meter tower) Daily MODIS L2 aerosol optical depths (at 550nm) (MOD04 10km) Extensive field-based phenology event records


N

Yucheng

http://soso.nipic.com/search.asp?kw=%E5%9B%BE%E4%BE%8B

Part III. Spatial representativeness of Yucheng


•Spatial representativeness using ETM+ band 7,4 and 2


Part III. Daily MODIS NBAR, albedo and VIs

•AOD varied from 0.1-0.9 indicating cloud contamination or haze was periodically affecting both tower albedos and MODIS retrievals.

•Despite this the temporal daily shortwave albedo is consistent with the ground-measures within 0.028RMSE during the winter wheat growing season

•NIR NBAR shows the characteristic behavior as the foliage changes with the season

•Red and blue NBAR decrease as the brighter soil is obscured by the crop foliage

Part III. Phenology event detection


1 2

NIR red

NIR red blue

EVI GC C L

NIR red

NIR red

NDVI

Where L=1, C1=6, C2=7.5,G =2.5 (Huete et al. 2002)

• Vegetation indices (VIs):

Part III. Temporal EVI of small area


• Location: a 30km by 30km area centered on the YCES

•Relative low EVI values in Yucheng city-area (upper-left)

• Regional phenology featureGreenup during day79-89Maturity appears from day120+Increasing senescence from 143+Harvest from 165+

Data source: YuCheng Experiment Site (YCES), Chinese Academy of Sciences (CAS).http://www.cern.ac.cn


According to the field criterion:

• “Start” of a growth stage means that ~10% of the individual crops has entered into the growth phase;

• “End” indicates that ~90% crops have completed this stage

The “Start” date of each phenological stage for winter wheat at YCES

Wheat Variety (#13)

Growth stages Year-DOY Date (mm/dd/yyyy)

Seeding 2004-284 10/10/2004

Emergence 2004-295 10/21/2004

Tiller 2004-313 11/8/2004

Turning green 2005-074 3/15/2005

Erect growth 2005-081 3/22/2005

First node visible 2005-097 4/7/2005

Boot stage 2005-109 4/19/2005

Heading 2005-122 5/2/2005

Beginning flower 2005-127 5/7/2005

Ripening 2005-132 5/12/2005

Milky ripe 2005-142 5/22/2005

Mealy ripe 2005-157 6/6/2005

Kernel hard 2005-163 6/12/2005

Harvest 2005-166 6/15/2005

Part III. Phenology event detection


Part III. Phenology event detection (Cont.)

• Piecewise Logistic fitting methodology (Zhang et al., 2003;2006)

•Transition dates detected from time series of daily NBAR-EVI values•Daily detected onsets Vs. ground measures

Daily detected(onset)

Ground measures start

GreenupDay 77

Turning greenDay 74

MaturityDay 126

Heading Day 122Beginning flower Day

127

SenescenceDay 143

Milky ripeDay 142

DormancyDay 169

Harvest Day 166

Part III. Summary


• The daily rolling DB algorithm is used to monitor winter wheat at the Yucheng Experiment Site in China

•The spectral NBAR and resulting NDVI and EVI capture subtle daily variations

•The transition dates detected with the Zhang’s piecewise logistic methodology correlate closely with the transition dates recorded in the field

•The daily algorithm shows great potential in capturing fine scale crop phenology

Final remarks• An improved MODIS BRDF/albedo algorithm is introduced in order to meet the

near real time requirements of regional and DB users

• To assess the inversion quality of regional retrievals, we investigated the range of two measures (RMSE & WoDs) for various biomes, and suggested improved thresholds to more accurately capture these biomes:

– [0.061,0.097] for RMSE

– [1.431,1.848] for WoD-WDR

– [2.122,2.736] for the WoD-WSA

• A daily rolling strategy is developed which weights the input reflectances by their quality, observations coverage, and age within the period, as well as the use of the most recent high quality retrieval as the a priori BRDF.

– An evaluation for Bartlett

– The software system has undergone beta testing at several direct broadcast sites, and is being currently prepared for release to the general MODIS direct broadcast community

• A case study for the monitorin of the phenology of winter wheat is investigated at the Yucheng Experimental Site, China

– The spectral NBAR products and resulting VIs same to capture subtle daily variations

– The transition dates detected by the logistic methodology are highly correlated with field records

– The daily algorithm shows its potential in capturing fine scale details


Backup slice 1 (Swath & Obscov)


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phd dissertation defense tracking daily land surface albedo and reflectance anisotropy with...

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reflectance anisotropythe

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forward scatteringbackground