1. Land Surface Reflectance Approaches Ian Grant Australian Bureau of Meteorology

2. Why Surface Reflectance? Surface reflectance Produced by atmospheric correction of top-of-atmosphere (TOA) reflectances Produced for many sensors: MODIS, AVHRR, SGLI, SEVIRI, … Better temporal and spatial consistency than TOA reflectance – the variable atmosphere is removed

3. Why Surface Reflectance? Basis of many land surface products Fractional cover (green/dead/bare) Vegetation properties: NDVI, LAI, … Grassland curing (dead fraction for bushfire management) Albedo Anisotropy of reflectance (BRDF)

4. Surface reflectance from Himawari-8 AHI has 6 of the 7 MODIS land bands Complementary to LEO (MODIS, VIIRS, SGLI, …) Disadvantage Coarser spatial resolution Advantages 10-minute frequency potentially improves coverage and quality More complete coverage in broken cloud Less noise due to more observations View direction fixed Sun direction varies through day but repeats each day

5. Surface reflectance algorithms GOES-R / ABI Land Surface Albedo & Surface Reflectance Algorithm Lead by Shunlin Liang (Univ Maryland) Simultaneously optimises: −Atmospheric correction −Surface directional reflectance (BRDF) modelling −Albedo estimation ABI AOD used as first guess; estimates AOD over bright surfaces End-of-day calculation of BRDF model parameters Near-real-time calculation of surface reflectance and albedo "Works on both bright and dark surfaces" However, funding ended before completion of development

6. Surface reflectance algorithms MAIAC Simultaneous retrieval of −Surface reflectance −BRDF −Atmospheric parameters Applied to MODIS, VIIRS, DSCOVR/EPIC Lead by Alexei Lyapustin (NASA/GSFC) "Works for GOES-R … Interested in applying to Himawari-8"

7. Recent Developments: Amazonia - Select high NDVI (>0.75) pixels; - Get average  0.66 for 20-50th percentile (N>20); filter residual clouds (~0.005) and shadows; - If sufficient angular sampling during 10 days (Terra+Aqua), then RTLS inversion; - Joint inversion of RGB and 2.1  m channels for more robust retrieval; - Get pixel’s BRDF by scaling using single good observation BRDF shape update every 1-2 weeks to track geometry and phenology changes Morton et al. (Nature, 2014): BRDF retrieval @pixel in Amazon doesn’t happen often Equatorial Amazon, 150km: Rare 3-day period of low cloud cover (BRDF variation >> spatial variation) Meso-scale (50km) RTLS retrieval TOACM AOTNDVI  0.84-0.9 0.660.66  0.870.87  RGB 0.02-0.040.25-0.35 Himawari-8 BRDF in one (clear) day?

8. Bias vs. Surface Reflectance -MAIAC and VIIRS comparable at sfc. reflectances below 0.05 -Similar slope (opposite sign) from 0.05 - 0.1, then VIIRS bias increases dramatically. VIIRS AOT IP vs MODIS MAIAC (25km) (S. Kondragunta, S. Superczynski (NOAA), study for NASA GeoCAPE project)

9. Surface reflectance algorithms Look Up Tables from radiative transfer models MODIS MOD09 (LUT from 6S) DLR's AVHRR "TIMELINE" project Simple Model of Atmospheric Correction (SMAC) Landsat atmospheric correction Geoscience Australia experience Joint GEO+LEO? Directions: (Fixed view & varying sun) + (varying view & fixed sun) Resolution: Complementary temporal and spatial For example, SGLI has finer spatial resolution that MODIS or VIIRS

10. Ancillary inputs Aerosol Model (MACC, …) Satellite: AHI? Simultaneous retrieval Total Water Vapour NWP (ERA-Interim, NCEP, MACC, ACCESS, JMA, …) Total Ozone Model (MACC, …) Satellite: AHI? Blended?

11. Validation Comparisons with other satellites MODIS, VIIRS, … SGLI (fine resolution at more bands) AERONET Surface Reflectance Validation Network (ASRVN) Surface reflectance from accurate atmospheric correction at AERONET sites Created by Alexei Lyapustin Use approach at CSIRO and BoM AOD sites?

13. MINERAL RESOURCES FLAGSHIP Thermal infrared mapping and monitoring of surface particle size: clay loss related desertification Tom Cudahy 1 and Bihong Fu 2 1 CSIRO, Perth, Western Australia 2 CAS-RADI, China HIMAWARI-8 workshop – Land Surface Processes| Brisbane, 25-26 th August 2015|Clay loss monitoring - Tom Cudahy & Bihong Fu

14. Background Measurement of surface particle size is key for tracking clay-loss driven desertification across the world’s drylands EO sensing of silicate information using satellite ASTER TIR bands can help provide this particle size information unlike radiometric data (K, Th, U) HIMAWARI-8 shares three similar TIR bands with ASTER (11, 13 &14) Band 11 is highly sensitive to both silicate minerals and SO 2 Opportunity Potential to use HIMAWARI-8 for monitoring surface particle size across Australia’s and Asia’s drylands as part of National reporting of clay (soil) loss related process of dryland desertification Clay loss monitoring| Tom Cudahy CSIRO 14 | LOI% sand% clay ASTER SI (B 13 /B 10 )0.590.740.57 K0.030.050.01 Th0.050.120.05 U0.120.110.02 Laboratory NGSA results (N=168)

15. ASTER surface particle size across Australia 3.5-286 286-369 369-414 414-428 428-440 440-452 452-462 Si (g/kg) ASTER Silica Index (SI) 5.2-35.9 35.9-59.2 59.2-76.5 76.5-88.0 88.0-93.6 93.6-97.7 97.7-100.0 1.0 not quartz sand 1.4 quartz sand limit of arid zone % sand Combination of satellite ASTER Silica Index (3500 scenes) and field/laboratory geochemical data from Geoscience Australia’s NGSA Clay loss monitoring| Tom Cudahy CSIRO 3 |

16. Number of pixels (offset for clarity) Etadunna Station 9 th Oct 2006 13 th Sep 2000 22 nd Oct 2005 SI 2001 /SI 2000 SI 2002 /SI 2001 SI 2004 /SI 2002 SI 2005 /SI 2004 SI 2006 /SI 2005 SI 2007 /SI 2006 SI 2008 /SI 2007 ASTER SI (B 13 /B 10 ) 2000 2001 2002 2004 2005 2006 2008 2007 111.5 mm (2 days) 93 mm (9 days) 76.4 mm (4 days) 100 mm (1 day) 1.0 1.5 clay increasing sand content 50 c de f g h i clay dust deposit c-i Local rainfall event Temporal monitoring with ASTER – HIMAWARI-8? Clay loss monitoring| Tom Cudahy CSIRO 4 |

17. Opportunity To evaluate HIMAWARI-8 data over a period of peak dust activity (e.g. 2015-2016 summer) at moderate temporal frequency (average daily) across the drylands of Australia and China Requires access to HIMAWARI-8 data (yet to be negotiated) Requires HIMAWARI-8 data to be pre-processed to: Surface radiance (kinetic temperature) TIR emissivity (i.e. requires TES) standard map projection Work could be completed as part of an existing 3-year collaborative project between CSIRO and CAS-RADI on EO for desertification mapping and monitoring. Ref: T.J. Cudahy et al. Satellite-derived mineral mapping of weathering, deposition and erosion. Nature Science Reports (in review) 17 | Clay loss monitoring| Tom Cudahy CSIRO

18. Tom Cudahy CSIRO Mineral Resources Flagship Australian Resources Research Centre Kensington, Western Australia t+61 8 6436 8630 m+61 407 662 369 eThomas.Cudahy@csiro.au whttp://c3dmm.csiro.auhttp://c3dmm.csiro.au MINERAL RESOURCES FLAGSHIP