# Presentation: 071923

## Status of the “A 0.05 degree global climate/interdisciplinary long term data set from AVHRR, MODIS and VIIRS” REASoN CAN

**Nazmi Saleous**
- SAIC
- and
- NASA GSFC – Code 614.5

## A 0.05 degree global climate/interdisciplinary long term data set from AVHRR, MODIS and VIIRS

*NASA GSFC*: Ed Masuoka (PI), Nazmi Saleous, Jeff Privette, Jim Tucker & Jorge Pinzon.
*UMD*: Eric Vermote, David Roy & Steve Prince.

*Collaborator*:  Chris Justice (UMD).
*NASA Study Manager*: Dr. Diane Wickland.

## Long Term Land Data Record

-  Use a MODIS-like operational production approach including an operational QA team.
-  Set up an advisory process.
-  Make intermediate versions of the data sets available to the community through a web interface and solicit input from users.
-  Hold community workshops for outreach and feedback.
-  Prototype the development and production of a climate quality data record.

## Proposed LTLDR Products

- 		Surface reflectance		Vegetation Indices		Surface temperature and emissivity		Snow 		LAI/FPAR		BRDF/Albedo		Aerosols		Burned area
- Products and formats will be modified based on feedback from the User Community Workshops.

## Project milestones

- Jun 04

- Dec 04

- Jun 05

- Dec 05

- Dec 06

- Dec 07

- Jun 06

- Jun 07

- Jun 08

- 1st user workshop

- 3rd user workshop

- Beta AVHRR/MODIS datasets produced

- Provisional AVHRR/MODIS datasets

- Beta VIIRS dataset

- Validate AVHRR/MODIS

- AVHRR calibration

- Evaluation of existing data sets

- Implement Surface reflectance Algorithm (Atmospheric Correction)

- Implement VI, LAI/FPAR / Albedo algorithms

- AVHRR BRDF correction.

- Evaluate Beta data set

- Implement Surface temperature / Snow algorithms

- Evaluate AVHRR/MODIS continuity (LAI/FPAR)

- Intermediate data set for evaluation

- Implement Burned area Algorithm

- AVHRR/MODIS harmonization

- Evaluation/Validation AVHRR and MODIS

- 2nd user workshop

## Data Sources

- 81

- 82

- 83

- 84

- 85

- 87

- 88

- 89

- 90

- 91

- 86

- 92

- 93

- 94

- 95

- 96

- 98

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- 00

- 01

- 02

- 97

- 04

- 05

- 03

- 07

- 09

- 10

- 08

- 06

- Terra

- Aqua

- NPP

- NPOESS

- 11

- AVHRR

- MODIS

- VIIRS

**N07**

**N09**

**N11**

**N14**

**N16**

**N17**

**N09**

## Existing Production Systems

**NPP/NPOESS**:
- under development.

**MODIS Terra and Aqua**:
- Level 1 produced and distributed by GSFC DAAC.
- Land Level 2 and higher products are generated in MODAPS at GSFC-Code 922 (Ed Masuoka) and distributed from the ECS DAACs.
- Products created in this system are validated to stage 2 and have published accuracies.

**AVHRR**:
- Pathfinder AVHRR Land (PAL) data set produced and distributed by GSFC DAAC.
- NOAA (GVI).
- Others: e.g. GIMMS.
- Differences in these products due to different processing approaches.
- The most widely used is the PAL data set. However, it uses a suboptimal radiometric degradation assumption and includes partial atmospheric correction.

## AVHRR and MODIS Production Systems

- Calibration
- Cloud/Shadow Screening
- Atmospheric Correction

- Gridding

- Land products

- Gridding

- Land products

- AVHRR GAC L1B
- 1981 - present

- MODIS coarse resolution
- surface reflectance
- 2000 - present

- Geolocation
- Calibration
- Cloud/Shadow Screening
- Atmospheric Correction

- AVHRR products

- MODIS products

**List of potential products**:
- Surface Reflectance, VI,
- Surface Temperature and emissivity,
- Snow, LAI/FPAR, BRDF/Albedo,
- Aersols, burned area

**Format**:
- HDF-EOS
- Geographic projection 1/20 deg resolution
- Daily, multi-day, monthly

- MODIS Level 0
- 2000 - present

- MODIS standard products
- (Full resolution and CMG)

## AVHRR data set

- Lacks onboard calibration.
- Limited set of spectral bands reduces the accuracy of atmospheric parameters retrieval and correction (water vapor and aerosols).
- Broad spectral bands lead to contamination by the atmosphere.
- Orbital drift leads to substantial variation in the solar geometry throughout the mission.

## Significant Earth Science findings based on AVHRR

- Phenology –lengthening snow-free season in arctic
- Increased NPP in North America
- Widely-used information
- NDVI
- NPP, agricultural yield
- Phenology
- Land cover
- Burned area

## Are the AVHRR observations adequate to justify these Earth Science conclusions?

- Approaches to the question:
- What accuracy and precision in the AVHRR data is assumed by users when reporting “significance” of results? Any independent verification?
- 2.	How does the implied data quality compare with the results of best available estimates?
- 2.1 Analytical estimates of quality – global, generalized
- 2.2 Analytical estimates based on local observations

## Example of AVHRR data use

- Time series of peak NDVI derived from 8-km resolution AVHRR data from 1981 to 2001 (a) and SWI over the past 22–50 years (b) among bioclimate subzones. Dashed lines are linear regressions. The shaded area highlights the period of SWI covered by NDVI data

- From: Jia, G.J., Epstein, H.E. and Walker, D.A., 2003. Greening of arctic Alaska, 1981–2001. GEOPHYSICAL RESEARCH LETTERS, VOL. 30, NO. 20, 2067, doi:10.1029/2003GL018268.

- Significant NDVI over 21 years
- =0.056±0.0032  to  0.082±0.028

**Notes:**
SWI=annual sum of average monthly temperature > 0

## Generating Improved AVHRR products

- Reliable and consistent calibration across the different NOAA platforms.
- Apply MODIS algorithms to AVHRR where possible, e.g.: the MODIS aerosol retrieval and atmospheric correction approach.
- BRDF correction to address differences in the solar and viewing geometry.
- Coincident AVHRR/MODIS to evaluate and improve AVHRR products and quantify accuracy.

## First year activities

- Evaluated existing data sets and identify areas where improvement is critical.
- Adapted the Vermote/Kaufman AVHRR vicarious calibration approach for AVHRR-3 and used it to calibrate NOAA-07 – NOAA-16.
- Evaluated the vicarious calibration approach using coincident MODIS and NOAA-16 observations over invariant targets.
- Presented planned work and calibration to NOAA (Andy Heidinger’s group) and provided group with our derived calibration for NOAA-07 - 16.

## First year activities (cont.)

- Established a theoretical error budget for AVHRR and MODIS surface reflectance.
- Studied limitations of the surface temperature derived from AVHRR.
- Held a Long Term Data Records session at the fall AGU conference to present the project and solicited feedback.
- Developed a list of potential evaluators for our Beta data set.
- Presented project activities at the ESIPS Federation Meetings and participated in the SEEDS working groups (software reuse, metrics and standards).

##  ndvi of 0.02 ---> 2-3 pg NPP yr-1

**PAL NDVI anomaly slope = 0.002/year**

**GIMMS NDVI anomaly slope = 0.0007/year**

**For 20 years:**
**      ****PAL ******** ndvi = 0.04**
**GIMMS ******** ndvi = 0.014**

****** ****ndvi of 0.02 ---> 2-3 pg NPP yr****-1**

- PAL

- GIMMS

## AVHRR Data set improvements

- Radiometric VIS/NIR Calibration
- Atmospheric Correction

## Consistent AVHRR calibration across platforms

- Degradation in channel 1
- (from Ocean observations)

- Channel1/Channel2 ratio
- (from Clouds observations)

- - Use the Vermote/Kaufman calibration approach (1995)

**0.8**

**0.85**

**0.9**

**0.95**

**1**

**1.05**

**1.1**

**1980**

**1985**

**1990**

**1995**

**2000**

**2005**

**Year**

**NOAA7**

**NOAA9**

**NOAA11**

**NOAA14**

**NOAA16**

**Notes:**
The Vermote/Kaufman vicarious calibration approach relies on high reflective clouds to determine the degradation ration between AVHRR channels 1 & 2 assuming that the clouds response is neutral in their wavelength range. It also relies on accurate Rayleigh using 6S to predict the reflectance over clear ocean targets in channel 1 at slant viewing angles. This method uses only AVHRR and meteorological data that are available for the AVHRR record. It can be applied to AVHRR on all of NOAA’s platforms.

## Approach used to validate N16 calibration with MODIS

- Characterize the reflectance spectral variation using MODIS narrow bands.
- Use 2 years of data to characterize the site BRDF using the simple linear kernel model used in the MODIS BRDF product.
- Rigorous cloud screening is applied to the data.
- Exclude observations within 15deg of backscattering conditions to avoid the hot spot.
- Exclude off-nadir observations (viewing zenith angle > 50 deg) where the pixel size variation makes it difficult to select coincident observations.

## Evaluating AVHRR calibration using MODIS

- Ch1

- Ch2

## Use of MODIS to improve AVHRR atmospheric corrections

- Use coincident MODIS/AVHRR data to develop an approach for water vapor retrieval from AVHRR.

## Theoretical Error Budget

## Error Budget: MODIS TOA simulations

| Parameter | Values |
| --- | --- |
| Geometrical conditions |  |
| Aerosol optical depth | 0.05 (clear) 0.30 (average) 0.50 (high) |
| Aerosol model | urban clean,urban polluted, smoke low absorption 
smoke high absorption |
| Water vapor content
[g/cm2] | 1.0, 3.0 and 5.0  uncertainties +/-0.2 |
| Ozone content 
[cm.atm] | 0.25 , 0.3, 0.35 uncertainties +/- 0.02 |
| Pressure 
[mb] | 1013mb, 930mb, 845mb uncertainties +/-10mb |

| Solar Zenith | View Zenith | Relative
Azimuth | Case
Name |
| --- | --- | --- | --- |
| 30 | 0 | 0 | A |
| 30 | 30 | 0 | B |
| 30 | 30 | 180 | C |
| 30 | 60 | 0 | D |
| 30 | 60 | 180 | E |
| 60 | 0 | 0 | F |
| 60 | 30 | 0 | G |
| 60 | 30 | 180 | H |
| 60 | 60 | 0 | I |
| 60 | 60 | 180 | J |

## Error Budget: MODIS surface reflectance and NDVI summary

|   | Forest |  |  |  | Savanna |  |  |  | Semi-arid |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Reflectance/ | value | Aerosol Optical Depth |  |  | value | Aerosol Optical Depth |  |  | value | Aerosol Optical Depth |  |  |
| NDVI |   | clear | avg | hazy |   | clear | avg | hazy |   | clear | avg | hazy |
| 3 (470 nm) | 0.012 | 0.0052 | 0.0051 | 0.0052 | 0.04 | 0.0052 | 0.0052 | 0.0053 | 0.07 | 0.0051 | 0.0053 | 0.0055 |
| 4 (550 nm) | 0.0375 | 0.0049 | 0.0055 | 0.0064 | 0.0636 | 0.0052 | 0.0058 | 0.0064 | 0.1246 | 0.0051 | 0.007 | 0.0085 |
| 1 (645 nm) | 0.024 | 0.0052 | 0.0059 | 0.0065 | 0.08 | 0.0053 | 0.0062 | 0.0067 | 0.14 | 0.0057 | 0.0074 | 0.0085 |
| 2 (870 nm) | 0.2931 | 0.004 | 0.0152 | 0.0246 | 0.2226 | 0.0035 | 0.0103 | 0.0164 | 0.2324 | 0.0041 | 0.0095 | 0.0146 |
| 5 (1240 nm) | 0.3083 | 0.0038 | 0.011 | 0.0179 | 0.288 | 0.0038 | 0.0097 | 0.0158 | 0.2929 | 0.0045 | 0.0093 | 0.0148 |
| 6 (1650 nm) | 0.1591 | 0.0029 | 0.0052 | 0.0084 | 0.2483 | 0.0035 | 0.0066 | 0.0104 | 0.3085 | 0.0055 | 0.0081 | 0.0125 |
| 7 (2130 nm) | 0.048 | 0.0041 | 0.0028 | 0.0042 | 0.16 | 0.004 | 0.0036 | 0.0053 | 0.28 | 0.0056 | 0.006 | 0.0087 |
| NDVI | 0.849 | 0.03 | 0.034 | 0.04 | 0.471 | 0.022 | 0.028 | 0.033 | 0.248 | 0.011 | 0.015 | 0.019 |

| Parameter | Accuracy |
| --- | --- |
| Calibration | 2% absolute, 1% band to band |
| Pressure | ±10 mbars |
| Water vapor | 0.2 g.cm-2 (Differential absorption approach) |
| Ozone | ±20 Dobson (EP-TOMS) |
| SWIR/VIS relation | ± 0.005 reflectance units |
| Aerosol type | Smoke low/high absorption, urban polluted |

## Error Budget: AVHRR surface reflectance and NDVI summary

|  | AVHRR Pathfinder-like processing | With LTLDR improvements |
| --- | --- | --- |
| Calibration | 10% absolute, 4% band to band | 4% absoute, 2% band to band |
| Pressure | ±10 mbars | ±10 mbars |
| Water vapor | 0.7 g.cm-2 (NCEP or None) | 0.3 g.cm-2 (split window) |
| Ozone | ±30 Dobson (LONDON) | ±10 Dobson (EP-TOMS) |
| Aerosols | No Correction | 0.01 error in predicting red refl. from 3.75 m |

|   | Forest |  |  |  | Savanna |  |  |  | Semi-arid |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Reflectance/ | value | Aerosol Optical Depth |  |  | value | Aerosol Optical Depth |  |  | value | Aerosol Optical Depth |  |  |
| NDVI |   | clear | avg | hazy |   | clear | avg | hazy |   | clear | avg | hazy |
|  Ch1 (VIS) | 0.0448 | 0.0056 | 0.051 | 0.0803 | 0.086 | 0.009 | 0.0457 | 0.073 | 0.143 | 0.0149 | 0.039 | 0.0628 |
|  Ch2 (NIR) | 0.237 | 0.020 | 0.0217 | 0.0338 | 0.196 | 0.0164 | 0.0225 | 0.037 | 0.217 | 0.0179 | 0.02 | 0.0349 |
|  Ch3 (MIR) | 0.045 | 0.002 | 0.0026 | 0.0031 | 0.086 | 0.0042 | 0.0044 | 0.0046 | 0.143 | 0.0073 | 0.0074 | 0.0074 |
| NDVI | 0.682 | 0.033 | 0.195 | 0.266 | 0.392 | 0.042 | 0.124 | 0.168 | 0.206 | 0.046 | 0.068 | 0.090 |
|  Ch1 (VIS) | 0.0448 | 0.0101 | 0.01 | 0.01 | 0.086 | 0.0101 | 0.0101 | 0.01 | 0.143 | 0.0106 | 0.0104 | 0.0104 |
|  Ch2 (NIR) | 0.237 | 0.0085 | 0.0133 | 0.0196 | 0.196 | 0.0075 | 0.0101 | 0.0141 | 0.217 | 0.0081 | 0.0097 | 0.0132 |
|  Ch3 (MIR) | 0.045 | 0.0014 | 0.0015 | 0.0025 | 0.086 | 0.0020 | 0.0022 | 0.0026 | 0.143 | 0.003 | 0.0033 | 0.0037 |
| NDVI | 0.682 | 0.056 | 0.058 | 0.064 | 0.392 | 0.043 | 0.047 | 0.054 | 0.206 | 0.03 | 0.033 | 0.038 |

## Production and Distribution

-  Use a MODAPS-like environment for production.
-  Benefit from the MODIS production experience.
- Data products will be kept online and distributed by ftp and  through a web page.
- Make intermediate data sets available for evaluators.
- Transition the data sets to the DAAC later in the project  when the datasets are validated.

## Quality Assessment

**Global Browse**

**Known Issues Tracking**

**Time series analysis**

**Building on the MODIS Land QA approach**

## Community Outreach

** ****Request users input through the project’s web site.**
**Workshops/Sessions held throughout the project to refine requirements and provide feedback on products.**
** ****Publish team’s evaluation of existing and intermediate datasets on the web and request input and comments from users. **
**Participation in scientific conferences and peer reviewed publications.**

## The creation of  a Long Term Land Surface Data record with documented and comparable accuracy across instruments is feasible.
The long term trend observed with precursor AVHRR datasets needs to be verified.
A beta version of the AVHRR data set will become available for evaluation in June 2005.
The user community involved in the definition and evaluation of the data sets (Pathfinder approach).
Incremental release of the products (Beta => Provisional => Validated) as they are generated (MODIS approach).

- The long term trend observed with precursor AVHRR datasets needs to be verified.
- A beta version of the AVHRR data set will become available for evaluation in June 2005.
- The user community involved in the definition and evaluation of the data sets (Pathfinder approach).
- Incremental release of the products (Beta => Provisional => Validated) as they are generated (MODIS approach).

- Summary