# Presentation: 763184

## ANALYSIS OF MODIS - MISR CROSS-CALIBRATION

*A. Lyapustin, Y. Wang (UMBC), J. Xiong, R. Wolfe (GSFC),*
*R. Kahn, C. Bruegge (JPL), K. Thome (UA), A. Ignatov (NOAA),*
*S. Platnick (GSFC)*

## AERONET-based Surface Reflectance Validation Network (ASRVN) 

_**THEORETICAL BASIS**_** **
-  3D Radiative Transfer (**Lyapustin & Knyazikhin, *****Appl. Opt.*****, 2001; 2002**)
-  variable anisotropic surface;
-  arbitrary spatial resolution;
-  semi-analytical, accurate and fast due to parameterizations.
-  Accurate Modeling of Gaseous Absorption
-  Inversion with *MRPV*MISR and *LSRT*MODIS BRF Models
_**PRODUCTS**_
-  BRF, Albedo (spectral & SW broadband)
-  Surface Radiative Fluxes, PAR
-  Spectral Regression Coefficients (2.1 mBlue, Red)
_**APPLICATIONS**_
-  Validation of BRF & Albedo over Heterogeneous Surfaces
-   Calibration Analysis
- Vicarious calibration
- Cross-calibration of different sensors
- Detection of calibration trend based on a time series of surface reflectance (climate quality).

- _MISR_
-        _MODIS_
-               _ETM+_
- 	        VIIRS

**AERONET-based Surface Reflectance Validation Network (ASRVN) **

*A. Lyapustin, Y. Wang *(GEST UMBC/NASA GSFC), *B. Holben, J. Privette *(GSFC)

**Goal – development of globally representative dataset of surface reflectance**
-    Surface BRF and albedo are important EOS products, not yet validated globally.
-    Knowledge of SR is required by CM, cloud properties, and aerosol algorithms.

## Cross-Calibration Analysis

- Comparison of MISR (top) and MODIS TERRA (bottom) ASRVN albedo over Mongu, Zambia

## MODIS vs MISR Albedo – cont. (GSFC, USA)

- Artifacts of measurements

- Cloudy pixels

## Regression of TOA Reflectance (3535 km2, >50% cloudy)

## Simulation Model:

**SHARM_IPC_Mie code**:
-  arbitrary band-pass function (MISR; MODIS; VIIRS; ETM+);
-  absorption of 7 major atmospheric gases (H2O, CO2, O3, CH4, NO2, CO, N2O): LBL absorption is modeled using HITRAN-2000 database and Voigt profile, continuum absorption model of AER (Clough et al.);
-  atmospheric profiles of Standard Models, solar irradiance model of Kurucz (MODTRAN3.0);
-  spectral resolution 0.01 – 1 cm-1;
-  full multiple scattering RT (code SHARM) with exactly calculated single scattering, Delta-M method for clouds;
**Aerosol (Mie) model**:
-  bi-modal log-normal size distribution (urban-industrial, biomass burning, dust/maritime models from Dubovik et al., 2002).
**Surface models**:
-  spectral albedo from ASTER and USGS spectral libraries;
**Water cloud models**:
-  log-normal size distribution, r=6, 10, 15, 20 m, =0.1 m; c=3.7 – 130.

- Band-pass functions of MISR (red)
- and MODIS land (blue) spectral
- channels

## Simulated Regression of TOA Reflectance for Water Clouds

**Table 1.** Dependence of slope on droplet size.
- Simulations were done with Hc=2km, RH=2cm,
- 1976 US Standard Atmospheric Profile.

**Table 2.** Effect of cloud top height and column water vapor on the slope of regression.

_**Simulated Regression of TOA Reflectance for Water Clouds**_

## Summary

- We developed two independent methods to evaluate calibration bias between MODIS TERRA and MISR:
-      The first one derives bias as a difference between observed and theoretical regression coefficients for the TOA 	reflectance.
-      The second one evaluates bias based on statistical matching of the ASRVN albedo products from MODIS and from 	MISR over a large number of AERONET sites. The estimates from both methods agree well, except in the 	red band, where the albedo matching technique predicts about twice as high difference.
- 		**Conclusions for the first method**:
-  Clouds prove to be a reliable stable target for the cross-calibration analysis.
-  Comparison of MODIS-MISR regression lines obtained from measurements and from simulations allows to evaluate 	the difference in the gain coefficient.
-  Our analysis suggests the following band gain difference:  Blue – 5.8%, Green – 3.1%, Red – 1.2%.

_**Conclusions**_

|  | AB | BB | AG | BG | AR | BR | ANir | BNir |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1. Measured (151 points) | 1.0466 | 0.0135 | 1.0256 | 0.002 | 1.0534 | -0.0002 | 1.0092 | 0.0005 |
| 2. Simulated | 0.989 | 0.0043 | 0.995 | -0.0001 | 1.041 | 0.0007 | 1.009 | 0.0007 |
| 3. Difference | 0.0577 | 0.0092 | 0.0306 | 0.002 | 0.0124 | 0.000 | 0.000 | 0.000 |

**Table 3.** Summary of regression coefficients