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 Committee Crystal B. Schaaf Alan H. Strahler Curtis E. Woodcock Xiaowen Li Qijiang Zhu David Roy Xiaoyang Zhang

Yanmin Shuai - MODIS BRDF/Albedo Contents 1.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 reflectanceDirectional reflectance Forward scattering region 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. © Jennifer Vranes Photograph by Don Deering. Describe the intrinsic reflective properties of land surface. Characterized by the Bidirectional Reflectance Distribution Function (BRDF)

Yanmin Shuai - MODIS BRDF/Albedo4 (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: 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 ) Yanmin Shuai - MODIS BRDF/Albedo5

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. Yanmin Shuai - MODIS BRDF/Albedo6 Forward model: kernel based BRDF model(RTLS-R) Isotropic kernel Geometric kernel (surface scattering) Volumetric kernel -Roujean et al., 1992

Temporal BRDF change Yanmin Shuai - MODIS BRDF/Albedo7 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 Yanmin Shuai - MODIS BRDF/Albedo8

Research Topics - I. Quantify Uncertainty in MODIS BRDF/Albedo Retrieval System Yanmin Shuai - MODIS BRDF/Albedo9 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: /2007GL

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 Yanmin Shuai - MODIS BRDF/Albedo10

Forward model: kernel based BRDF model(RTLS-R) Yanmin Shuai - MODIS BRDF/Albedo11 Isotropic kernel Geometric kernel (surface scattering) Volumetric kernel Inversion with given n reflectance observations by a least-squares method,where i.e. 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 Yanmin Shuai - MODIS BRDF/Albedo12 For WoD_f 0, U T =(1,0,0) For WoD_WDR at given sun-view geometry (45°,0°), For WoD_BSA, For WoD_WSA, U T = 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 Reference: Lucht et. al, 2000a

Yanmin Shuai - MODIS BRDF/Albedo13 Sampling pattern ABCDE Number of observation WoD-WSA WoD-WDR WSA* Deviation** Data 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

Yanmin Shuai - MODIS BRDF/Albedo 14 Sampling PatternsABCD Number of Observation WoD-WSA WoD-WDR RMSE*** Part I. Ability of WoDs (Cont.) Better angular sampling results in lower WoDs

Part I. RMSE (Root Mean Squared Error) Yanmin Shuai - MODIS BRDF/Albedo15 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/Albedo16 (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.

Yanmin Shuai - MODIS BRDF/Albedo17 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.

Yanmin Shuai - MODIS BRDF/Albedo18 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

Yanmin Shuai - MODIS BRDF/Albedo19 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

Yanmin Shuai - MODIS BRDF/Albedo20 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 Yanmin Shuai - MODIS BRDF/Albedo21 Part I. Summary

Research Topics - II. Daily Retrieval of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface BRDF and Reflectance Quantities for Direct Broadcast Application Yanmin Shuai - MODIS BRDF/Albedo22

Part II. Introduction Yanmin Shuai - MODIS BRDF/Albedo23 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)

Yanmin Shuai - MODIS BRDF/Albedo24  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.)

Yanmin Shuai - MODIS BRDF/Albedo25 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

Yanmin Shuai - MODIS BRDF/Albedo26 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 Yanmin Shuai - MODIS BRDF/Albedo27 Part II. Algorithm for DB -weight strategy =a*t d +b

Yanmin Shuai - MODIS BRDF/Albedo28 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

Yanmin Shuai - MODIS BRDF/Albedo 29 Part II. Algorithm for DB -inversion strategies (Cont.) Strategy 2: Daily rolling with enhanced weight rules

Yanmin Shuai - MODIS BRDF/Albedo30 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 ( °N, °W) – Dominant Species : Red maple, American beech, paper birch, and Eastern hemlock Data Yanmin Shuai - MODIS BRDF/Albedo31  Ground-based albedo (Kipp and Zonen CM3 broadband albedometer)  MODIS AOD (MOD04 10km)  Weather records

Yanmin Shuai - MODIS BRDF/Albedo32 Spatial representativeness for Bartlett site using ETM+ band 7,4 and 2  The 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

Yanmin Shuai - MODIS BRDF/Albedo33 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 Strategy2 Temporal spectral albedo over Bartlett Yanmin Shuai - MODIS BRDF/Albedo34

Yanmin Shuai - MODIS BRDF/Albedo35 From

Yanmin Shuai - MODIS BRDF/Albedo 36 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/Albedo37

Research Topics - III. Winter Wheat Monitoring with the Daily MODIS Reflectance Anisotropy and Albedo Product Yanmin Shuai - MODIS BRDF/Albedo38

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). Yanmin Shuai - MODIS BRDF/Albedo39 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 rainfall  Length of regional growth season vs. climatic and ecological changes

Part III. Research area Yucheng Experiment Site located at ( ºN, ºE) 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 Yanmin Shuai - MODIS BRDF/Albedo40 N Yucheng

Part III. Spatial representativeness of Yucheng Yanmin Shuai - MODIS BRDF/Albedo 41 Spatial representativeness using ETM+ band 7,4 and 2

Yanmin Shuai - MODIS BRDF/Albedo42 Part III. Daily MODIS NBAR, albedo and VIs AOD varied from 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 Yanmin Shuai - MODIS BRDF/Albedo43 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 Yanmin Shuai - MODIS BRDF/Albedo 44 Location: a 30km by 30km area centered on the YCES Relative low EVI values in Yucheng city-area (upper-left) Regional phenology feature  Greenup during day79-89  Maturity appears from day120+  Increasing senescence from 143+  Harvest from 165+

Data source: YuCheng Experiment Site (YCES), Chinese Academy of Sciences (CAS). Yanmin Shuai - MODIS BRDF/Albedo45 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 stagesYear-DOYDate (mm/dd/yyyy) Seeding /10/2004 Emergence /21/2004 Tiller /8/2004 Turning green /15/2005 Erect growth /22/2005 First node visible /7/2005 Boot stage /19/2005 Heading /2/2005 Beginning flower /7/2005 Ripening /12/2005 Milky ripe /22/2005 Mealy ripe /6/2005 Kernel hard /12/2005 Harvest /15/2005 Part III. Phenology event detection

Yanmin Shuai - MODIS BRDF/Albedo46

Yanmin Shuai - MODIS BRDF/Albedo 47 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 Greenup Day 77 Turning green Day 74 Maturity Day 126 Heading Day 122 Beginning flower Day 127 Senescence Day 143 Milky ripe Day 142 Dormancy Day 169 Harvest Day 166

Part III. Summary Yanmin Shuai - MODIS BRDF/Albedo48 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 Yanmin Shuai - MODIS BRDF/Albedo49

Backup slice 1 (Swath & Obscov) Yanmin Shuai - MODIS BRDF/Albedo50 一个观 测 地表栅格单元