Development of the surface reflectance Fundamental Climate Data

Development of the surface reflectance Fundamental Climate Data Record from the Landsat archive, the LDCM mission and future Landsats Principal Investigator Dr. Eric Vermote NASA GSFC Code 619 Co. P.I. Dr. Chris Justice Department of Geographical Sciences University of Maryland Collaborators Dr. Richard Santer University of Littoral, Wimereux, France Olivier Hagolle CESBIO-CNES, Toulouse, France .

Objectives to provide an algorithm and code for a Surface Reflectance Standard Product for integration into the LDCM processing system. prior to LDCM launch, to test the proposed approach using similar dataset (Formosat-2) to validate the resulting LDCM product, using data from AERONET to provide a cloud masking and cloud shadow screening algorithm for LDCM

to undertake vicarious calibration of the LDCM instrument to advise on and contribute to the LDCM program outreach and explore the potential synergy with other high resolution international earth observation programs (e.g. through the LCLUC, GLAM GOFC/GOLD and IGOL programs). to be an active contributor to the LDCM Team, participating in telecons, science team meetings and working groups as appropriate. Basis/Rationale for the Landsat Surface Reflectance Product The Surface Reflectance standard product developed for MODIS provides the basis for a number of higher order land products for global change and applications research The fully automated and robust approach used for MODIS has been adapted for Landsat missions The code (LEDAPS variation) has been made available to a large community of users and could be delivered to LDCM ground

stations. Higher order products (LAI/FPAR, Forest Cover Change, Automatic First Stage Classification) have been already generated from SR products. Validation/Evaluation plan is clearly defined and underway (AERONET) Automatic Quality Assesment and accuracy verification is also achieved (GFCC project) Improvements have been implemented (or are underway) in optimized version of the original code (WELD, Google Earth Engine) in particular with aerosol model and use of lookup tables Approach for the surface reflectance product Atmospheric correction consistent with the MODIS, AVHRR and VIIRS approach, ensuring consistent reflectance data across resolutions based on rigorous radiative transfer

http://6s.ltdri.org http://rtcodes.ltdri.org/ MODIS AQUA vs MOBY IKONOS vs TARP Calibration MODIS calibration accuracy at ~2%, methods have been developed to crosscalibrate other instruments. MODIS/Terra & ASTER: Simultaneous Nadir Observation VIIRS & MODIS/Aqua: Near simultaneous, (BRDF corrected surface reflectance comparison) ASTER Calibration Approach: Use coincident MODIS surface reflectance data (CMG) over

desert site to atmospherically correct ASTER (mainly for water vapor, ozone, molecular scattering effect) MODIS CMG ASTER Results for band2 (0.67mic) Saturation ASTER calibration trends for Red (excluding obvious saturation) Using MODIS to develop global generalized BRDF correction and improved NDVI Time series NDVI Directional Normalized reflectance

Directional reflectance RED / NIR Noise = 20% and 22% Noise = 5.7% BRDF Normalization Noise = 4.2% and 2.8% Noise = 2.8% VIIRS SR

Results for 3/06/2012 Site over Australia: Data normalized for BRDF effect (nadir, sun zenith angle= 45deg) NIR bands: band 2 MODIS, band M7 VIIRS Aqua SR Theoretical uncertainties for thr surface reflectance MODIS product Validation and uncertainties estimates. Theoretical error budget, comprehensive evaluation. FOREST Belterra

Clear Average Hazy l [ nm] r x10000 Drx10000 470 120 52 51 52 550 375 49 55 64 645 240 52 59 65 870 2931 40

152 246 1240 3083 38 110 179 1650 1591 29 52 84 2130 480 41 28 42 D NDVIx1000 NDVI x1000

849 30 34 40 SAVANNA SEMI-ARID SUMMARY Skukuza Clear Average Hazy l [ nm] r x10000 Drx10000 470 400 52

52 53 550 636 52 58 64 645 800 53 62 67 870 2226 35 103 164 1240 2880 38 97

158 1650 2483 35 66 104 2130 1600 40 36 53 D NDVIx1000 NDVI x1000 471 22 28 33

Sevilleta Clear Average Hazy l [ nm] r x10000 Drx10000 470 700 51 53 55 550 1246 51 70 85 645 1400 57 74 85 870

2324 41 95 146 1240 2929 45 93 148 1650 3085 55 81 125 2130 2800 56 60 87 D NDVIx1000

NDVI x1000 248 11 15 19 Error in ~0.5% in reflectance unit Comprehensive analysis of performance using the AERONET network 2000-2007 Results (25542 cases) Version 2 AERONET (i.e. with Background correction and spheroid) Toward a quantitative assessment of performances (APU) 1,3 Millions 1 km pixels were analyzed for each

band. Red = Accuracy (mean bias) Green = Precision (repeatability) Blue = Uncertainty (quadatric sum of A and P) On average well below magenta theoretical error bar On going assessment of LEDAPS ETM+ surface reflectance product WELD (D. Roy) 120 acquisitions over 23 AERONET sites (CONUS) Junchang Ju, David P. Roy, Eric Vermote, Jeffrey Masek, Valeriy Kovalskyy, Continental-scale validation of MODIS-based and LEDAPS Landsat ETM+ atmospheric correction methods, Remote Sensing of Environment (2012), Available online 10 February 2012, ISSN 0034-4257, 10.1016/j.rse.2011.12.025. Google Earth Engine : Acquisitions over 120 AERONET sites (global) In progress going for 300

GFCC: Comparison with MODIS SR products GLS 2000 demonstration Min Feng, Chengquan Huang, Saurabh Channan, Eric F. Vermote, Jeffrey G. Masek, John R. Townshend, Quality assessment of Landsat surface reflectance products using MODIS data, Computers & Geosciences, Volume 38, Issue 1, January 2012, Pages 9-22, ISSN 0098-3004, 10.1016. GLS 2005 (TM and ETM+) Min Feng Joseph O. Sexton, Chengquan Huang, Jeffrey G. Masek, Eric F. Vermote, Feng Gao, Raghuram Narasimhan, Saurabh Channan, Robert E. Wolfe, John R. Townshend ,Global, long-term surface reflectance records from Landsat: a comparison of the Global Land Survey and MODIS surface reflectance datasets. Remote Sensing of the Environment (in review) WELD/LEDAPS results (Red-band3) LEDAPS WELD uses MODIS aerosol

Top of the atmosphere (A) GLS 2000 ETM+ vs. MODIS/Terra daily SR (B) GLS 2005 ETM+ vs. MODIS/Terra daily SR Landsat/LDCM spatial resolution offer better validation opportunity Courtesy of Feng Gao The Internal cloud/cloud shadow mask

Performed in two stages (TOA first / SR second stage) Evaluated for 157 Landsat scenes covering a variety of conditions Cloud mask comparison ACCA cloud mask SRBM (Surface reflectance Based Mask): Internal cloud mask based on SR product VCM :Truth Validation Cloud Mask (operator made) Metrics for cloud detection versus VCM Rate of omission of cloud %: Leakage Rate of commission of cloud % : False detection As far as leakage the internal cloud mask, SRBM, is superior to ACCA/ In

term of commission ACCA has better performance than SRBM SRBM performance were confirmed by the comparison with Zhe et al. Cloud Mask over 143 scenes. LEDAPS SRB shadow algorithm needs improvements % Leakage LEAKAGE RATE comparison Scene index % Commission COMMISSION RATE Comparison Scene index

Sentinel 2 Have similar spectral bands than LDCM/Landsat enabling the last version of aerosol retrieval and surface reflectance to be implemented Validation protocols are well defined and could be implemented Inter-comparison of products should be looked before launch (near coincidence, spectral differences etc) Cf to Gutman/Masek presentation (today 4:30) about and LDCMSentinel-2 Data Merging Plans Cross-comparison of MODIS SR with product derived using independent approach 1/2 Comparison of aggregated FORMOSAT-2 reflectance and MODIS reflectance. No BRDF

correction. Density function from light grey (minimum) to black (maximum); white = no data. Comparison of aggregated FORMOSAT-2 reflectance and BRDF corrected MODIS reflectance. Corrections were performed with Vermote al. (2009) method using for each day of acquisition, the angular configuration of FORMOSAT-2 data. From Claverie et al., 2013 (in preparation) Cross-comparison of MODIS SR with product derived using independent approach 2/2 Spectral Bands of MODIS and FORMOSAT-2 Comparison of simulated FORMOSAT-2 and MODIS reflectance performed with PROSAIL model. The simulated dataset is the same as the one described in Baret et al. (2007).

Conclusions Surface reflectance algorithm is mature and pathway toward validation and automated QA is clearly identified. Algorithm is generic and tied to documented validated radiative transfer code enabling easier inter-comparison and fusion of products from different sensors (MODIS,VIIRS,AVHRR, LDCM, Landsat, Sentinel 2 )

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