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Guangjian Yan State Key Laboratory of Remote Sensing Science,

wp-2 : Satellite observations of vegetation cover, of surface albedo and temperature over the Plateau. Guangjian Yan State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University 2009-6-29. Objectives.

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Guangjian Yan State Key Laboratory of Remote Sensing Science,

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  1. wp-2: Satellite observations of vegetation cover, of surface albedo and temperature over the Plateau Guangjian Yan State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University 2009-6-29

  2. Objectives • Develop algorithms to retrieve vegetation cover, LAI, surface albedo, emissivity and land surface temperatures from measurements by polar and/or geostationary satellites; • Produce a consistent data set for the land surface variables over the region of Tibetan Plateau.

  3. Partners LSIIT - Université Louis Pasteur (ULP) The International Institute for Geo-information Science and Earth Observation (ITC) Alterra Green World Research - Wageningen University and Research Centre (Alterra) University of Valencia(UVEG) Beijing Normal University(BNU) Institute of Geographic Sciences and Natural Resources Research (IGSNRR) Institute of Remote Sensing Applications(IRSA)

  4. Tasks • Task 2.1.A generic algorithm for the retrieval of important surface properties from a multitude satellite sensors(ULP,ITC, IRSA) • Task 2.2Developing an algorithm for simultaneous retrievals of atmospheric variables, land surface variables using AATSR bi-angular/multi-spectral radiometric observations (Alterra, IGSNRR). • Task 2.3. Improving MODIS LAI and albedo products by combining remote sensing model and dynamic process model (BNU, IRSA) • Task 2.4. Developing an algorithm to produce a consistent LST from polar satellites (LSIIT, UVEG, BNU, IGSNRR, IRSA). • Task 2.5. Estimation of LSE, LST and albedo from Geostationary Satellite (GS) data (LSIIT, Alterra, IGSNRR)

  5. Task 2.1.A generic algorithm for the retrieval of important surface properties from a multitude satellite sensors Model Algorithm data parameter ★ ★ ★ ★ Partly finished

  6. Task 2.2. Developing an algorithm for simultaneous retrievals of atmospheric variables, land surface variables using AATSR data Model Algorithm data parameter

  7. Task 2.3. Improving MODIS LAI and albedo products by combining remote sensing model and dynamic process model Model Algorithm data parameter ★ ★ ★ ★ ★ ★ Partly finished

  8. Task 2.4. Developing an algorithm to produce a consistent LST from polar satellites Model Algorithm data parameter ★ ★ ★ ★ Partly finished

  9. Task 2.5. Estimation of LSE, LST and albedo from Geostationary Satellite (GS) data Model Algorithm data parameter

  10. Elevation retrieval - ICESat Facts Main goals: • measuring ice sheet mass balance, • cloud and aerosol heights, • land topography and vegetation characteristics. Organization: NASA Altitude: 600km Launch: 2003 Life time: 5 years Payload: GLAS instrument

  11. Greenland 5 km DEM

  12. Processing steps Waveform Dataset Pre-processing Gaussian Fitting Initial estimation of Gaussians • Conversion • Bin-ASCII • Counts-Voltage GLA01 GLA14 Gaussian Fitting Normalization Extraction of Elevation Points Smoothing Parameters Parameters Applications

  13. Results of Gaussian decomposition Red: raw waveform Green: Gaussian components First mode: left most Gaussian component Last mode: right most

  14. Albedo retrieval using HJ-1 data • Angular&spectral kernel model to describe land surface BRDF • Here,C0CgCv,are kernel coefficients independent from wavelength and stand for the weights of different scattering parts. • They are all related to the structure of canopy or mixed pixel. • Although all the kernel coefficients have clear physical meaning, they can also be treated as empirical parameters.

  15. 90m resolution DEM over the Plateau Store the results in database Compute the pixel-average slope and aspect angle for each 500m grid Compute the pixel-average slope and aspect angle for each 5km grid Compute the subpixel correction factor T for each 5km grid Flow chart for setting up a database for topography effect correction.

  16. 500m/5km resolution direction reflectance from MODIS or HJ-1A/1B Database of topographic parameters Correct the pixel level topography effect,using slope and aspect angle. Inversion of ASK BRDF model Derive spectral albedo and broadband albedo 500m/5km resolution topography corrected albedo Flow chart for topography effect correction for 500m albedo products.

  17. LAI retrieval -- a priori knowledge based inversion parameter space time series

  18. NW5 NW4 NW2 NW1 NW3 Shunyi county, Beijing, 2001.

  19. NW5 NW4 NW2 NW1 NW3 LAI maps without VI-based a priori knowledge (using only red band） (using only NIR band)

  20. NW5 NW4 NW2 NW1 NW3 (using both red and NIR but without VI-based a priori knowledge) (using 2 bands with VI-based a priori knowledge)

  21. Time series LAI retrieval by coupling crop growth model Relative leaf area index (RLAI: LAI/LAImax) from 865 ground measurements in Shunyi and Changping of Beijing were fitted with relative accumulation temperature (DVS). Empirical LOGISTIC model for crop growth

  22. Time series LAI retrieval -- flowchart

  23. Result Validation using ground measured LAI values in Shunyi, Beijing, 2001

  24. LAI retrieval -- Data assimilation • Developing a priori LAI trend from several years’ MODIS LAI product • the adaptive Savitzky-Golay filtering to eliminate the contaminated pixels. • the SARIMA method is used to construct the dynamic model. • The Ensemble Kalman Filter technique is discussed to estimate real-time LAI from time series MODIS reflectance data.

  25. Adaptive Savitzky-Golay (SG)

  26. NDVI temporal profiles with circles to mark the contaminated data

  27. Dynamic model • the following dynamic model is constructed based on the climatology to evolve LAI in time and used to provide the short-range forecast of LAI

  28. Tonzi Ranch Bondville Linze Yingke

  29. (MODIS+MISR)

  30. θ 1-S S Fractional vegetation cover retrieval – angular correction • A general formula to calculate fCover • A simple model for mixing pixles:

  31. Model inversion using multitude remote sensing data with two spatial resolutions • MODIS observations are used for their angular information • Higher resolution images (HJ-1, TM) are used to get S • solve equations for a, b, LAIe, then calculate fcover

  32. Study area (Heihe basin) Gebi desert Zhangye City

  33. Angular effects corrected fCover (Mar. 07 2009)

  34. LAI and fCover generation using high resolution images To evaluate the uncertainty in the prediction of the biophysical variables (LAI and fCover) due to the coarse spatial resolution of the MODIS sensor by using high resolution optical data Particular relevance will be given to the temporal profile of LAI and fCover along the different seasons and according to the main and back-up MODIS algorithms.

  35. LAI and fCover estimation procedure High resolution LAI and fCover maps LUT based RTM inversion (PROSPECT+SAILH) High resolution TOC reflectance

  36. Comparison with MODIS LAI and fCover products Validation procedure Uncertainties assessment Field measurements ? High resolution LAI and fCover maps Aggregated LAI and fCover maps More data available ?

  37. Comparison:MODIS – high resolution data Probability density functions (pdf) of LAI and fCover estimated from high resolution data are determined for a sufficient number of MODIS pixels of different land use. Based on these analyses, uncertainty assessment of parameter retrievals is carried out separately for the four major native pasturelands, identified from the southeast to northwest: bush-meadow, alpine meadow, alpine grassland and desert grassland.

  38. LST retrieval -- Modeling • Physics-based Radiation Transfer Canopy Model for All Growth Stages Top view of the wheat canopy

  39. a periodic function to discuss the mutual overlaps between the neighboring rows. • simplify the row planted wheat canopy as foliage gather whose density is unchanged along row direction and in the vertical direction, and is changed gradually in the crossing-row direction.

  40. Brightness temperature directional distribution simulated by the new model.

  41. Directional thermal radiation from rugged terrain geometric effect on surface radiation shadowing effect obstruct sky radiance affect spatial distribution of vegetation increase environmental radiation temperature difference on shadowed and sunlit surfaces

  42. A parameterization scheme for coarse resolution pixels • 1）emitted radiation of surface • 2）environmental and sky radiation introduce an effective view angle calculate the statistics in a coarse pixel average ratio that obstruction surroundings occupied in view hemisphere of surface averaged sky factor

  43. θ S account for emitted directional radiation of a coarse pixel a simple model for angular anisotropy effect supposed L(θ) has a linear relationship with cos , with Hapke formula (1993)

  44. parameterization for sky factor vd and terrain configuration factor Ct slope S, azimuth A is the horizon angle for direction （Dozier and Frew, 1990）。integrating over the coarse pixel pdf of horizon angle Hφ pdf of slope angle pdf of elevation

  45. preliminary validation error caused by simplized form of surface emitted radiance (K) error of radiance measured at surface (K) 1、Followed by a statistical parameterization scheme, a model was developed for remote sensing retrieval. 2、All the simplifications calculate statistics of the topographic effects exerted on radiative transfer. 3、 The parameterized model cost much less time with an acceptable accuracy lost.

  46. LST retrieval using HJ-1 data • HJ-1 150m LST products was proposed by the IRSA/CAS as a daytime land product over the Tibet Plateau. • A view angle dependent single channel LST algorithm has been developed for correcting atmospheric and emissivity effects for all land cover types. HJ satellite constellation CCD resolution：30m bands： 0.43-0.52μm，0.52-0.60μm， 0.63-0.69μm，0.76-0.90μm swath： ≥700km IRS resolution：150m（NIR MIR）/300m（TIR） bands： 0.75-1.10μm，1.55-1.75μm， 3.50-3.90μm，10.5-12.5μm swath： 720km

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