# Presentation: 276661

## Biophysical products from Landsat

**LANDSAT Science Team Meeting**
**January 9-11, 2007**

**Ramakrishna Nemani**
- NASA Ames Research Center
**Ranga Myneni**
- Boston University
**Jennifer Dungan**
- NASA Ames Research Center

## Planned Contribution

**Develop an operational capability to produce vegetation green leaf area index (LAI) from Landsat data by adapting a physically based approach conceived and implemented by the MODIS Science team.**
**LAI- Leaf Area Index is defined as one-sided green leaf area per unit ground area in broadleaf canopies and as the projected needle leaf area in coniferous forests.**
**LAI is an important variable for quantifying the cycling of water, carbon and nutrients through ecosystems.**
** **

## LAI from MODIS (1km)

**LAI from MODIS (1km)**

## MODIS LAND PRODUCTS

**MODIS LAND PRODUCTS**

**LAI and FPAR have large user community**

**Basic parameter to monitor vegetation activity prior to the Terra launch **

**Measurable or physically well-defined parameters are the preferred options**

## Need for biophysical products from Landsat

**Development of 30-m LAI product from LANDSAT is timely as it will fill gap between coarse resolution products and ground measurements. Currently available high resolution LAI products are few, and benchmark data set is clearly required to unify efforts from multiple developers**
**High resolution LAI product will be of high demand in multiple remote sensing applications (such as validation of coarse resolution products, etc.), in local and regional ecological applications and numerous climate and hydrology studies**

## Estimating LAI is the second most common application of Landsat TM data in vegetation studies (mapping vegetation is first)

- Based on Google Scholar analysis

## Slide 7

## Application of RHESSys to the Lake McDonald

**Application of RHESSys to the Lake McDonald**
**watershed in the Glacier National Park, Montana**

## RHESSys simulations of snowpack, streamflow and

**RHESSys simulations of snowpack, streamflow and**
**forest growth for Lake McDonald watershed**

## Adapting MODIS algorithm to LandsatMODIS LAI/FPAR Retrievals: Main and Back-Up Algorithms 

_**Main algorithm**_**: during retrievals, surface reflectances predicted by RT model are compared with MODIS channel data (Red and NIR) and when agree corresponding LAI and FPAR are retrieved. The RT simulations are performed with the Stochastic RT model which accounts for 3D effects of vegetation heterogeneity with pair-correlation function. RT simulations are parameterized with vegetation type, leaf optical properties, soil reflectance patterns. Main algorithm delivers most accurate retrievals, based on best quality input. **
_**Back-Up algorithm: **_**If Main algorithm fails due to input (or RT model) uncertainties, the back-up algorithm retrieves LAI/FPAR from NDVI. Those are low accuracy retrievals, based on low precision input. **

**Main algorithm**

**Back-Up algorithm**

## MODIS LAI and FPAR Production

**Notes:**
The algorithm finds LAI and FPAR values given sun and view  directions, Bidirectional Reflectance Factor (BRF) for each MODIS band, band uncertainties, and  six biome land cover classes. The algorithm are executed on a daily basis. The daily LAI/FPAR values are composited over an eight day period. These 1 km 8-day products are made available to the user. The processing is done by NASA. The products are distributed from the EDC Data Center. This flow chart shows one of the main themes of my talk. The closed loop between product analysis, validation and algorithm refinement - key elements for improving product  quality in each successive re-processing.

## Preprocessing

- 2000, 1990
- GeoCover TM/ETM+

- 1975
- GeoCover MSS

- Calibration
- Atm. Correction
- Cloud//Snow mask

**MODIS**
**Aerosols**

-  IT Normalization

**Radiometrically Consistent Surface Reflectance Dataset (1975-2000)**

- Other Landsat Data

-  Orthorectification
-  Precision Correction
-  Invariant Target
-   Normalization or  Atm. Correction

- Analysis

**QA/**
**Validation**

**Landsat Preprocessing and Analysis**

-  Disturbance Rate, Type
-  Land Cover Conversion Rate
-  Spectral Unmixing for Fractional Change

**Disturbance/Recovery **
**Products for Carbon Assessments**

- Aggregation

- LEDAPS, Masek et al.,

**Notes:**
To  render these data sets radiometrically compatible for highly automated analysis, we will begin with the most recent 2000 ETM+ data, which is calibrated, use MODIS estimates of aerosol concentrations to atmospherically correct the landsat scenes, in mountainous regions correct for terrain illumination effects, and cloud and snow mask the images.

The 1975 and 1990 era images would then be rendered radiometrically consistent with the 2000 era scenes, using one of a  number of existing algorithms to match digital count values between scenes of invariant bright and dark targets in the images -- a sort of vicarious calibration.

In some cases where we might need more than the one landsat scene in the EarthSat collection, we would also go into the EDC archive and match additional scenes.

Once we have three decades of radiometrically consistent scenes, all kind of possibilities open up for disturbance mapping.  For example, as s hown in this little diagram to the left here, and blown up in the next slide,.

## Landsat Leaf Area Index Production

**such as NLCD**

**LEDAPS**

**TM specific**

**Notes:**
The algorithm finds LAI and FPAR values given sun and view  directions, Bidirectional Reflectance Factor (BRF) for each MODIS band, band uncertainties, and  six biome land cover classes. The algorithm are executed on a daily basis. The daily LAI/FPAR values are composited over an eight day period. These 1 km 8-day products are made available to the user. The processing is done by NASA. The products are distributed from the EDC Data Center. This flow chart shows one of the main themes of my talk. The closed loop between product analysis, validation and algorithm refinement - key elements for improving product  quality in each successive re-processing.

## LAI from LANDSAT Data

**Optimization of retrievals over woody vegetation with MODIS SWIR data**
**Multiple studies indicate that the use of SWIR data improves correlation of satellite data and LAI. However the retrieval approaches were implemented mostly with empirical methods **
**We will investigate the possibility to implement retrievals with ****Red-NIR-SWIR**** data. **

## Relevancy

**A standard biophysical product**: This effort would represent the first of its kind by producing an 		operational biophysical product from the Landsat program thereby leveraging twenty years of EOS 		research.
**Desirable spatial resolution**: Though many of the EOS products revolutionized the way remote sensing 		data are used in research and operations, one significant limitation is their limited spatial resolution. We 		suggest that a 30m LAI product will bring a vast number of new users into the Landsat community.
**Links to modeling**: Scientists interested in using Landsat data for modeling purposes often resort to building 		their own empirical relations between LAI and remote sensing data. This is evident from the hundreds of 		studies in literature covering a variety of vegetation types and soil conditions. A standard method for 		producing 	LAI from Landsat will give a head start to users.
**A match to NASA strategic interests**: High resolution LAI data are of interest to NASA’s strategic 	roadmap scientific objectives relating to Atmospheric Composition, Climate and Weather, and Water due 	to its significance in coupling land-atmosphere interactions.  In applied sciences, 30m LAI will have a large 	impact on watershed studies dealing with water resources, carbon sequestration, air pollution and erosion 	studies among others.
**Facilitation of rigorous cross-sensor and cross-scale comparisons**: Implementing the 	same physically-based algorithm at 30m-250m-1000m resolutions should help in scaling studies that are 	often difficult to implement at multiple resolutions. Estimates at various resolutions over a target area 	should provide a desirable independent information source to compare with ground-based to statistical 	sampling used in studies such as BigFoot (Cohen and Justice 1999).
**Leveraging the team’s experience**: Our team has considerable experience in the fields of radiative 	transfer modeling, ecosystem modeling and statistical prediction techniques. We have considerable 	experience with MODIS sensor, data processing and product generation. We believe this experience will 	be useful and constructive in the deliberations of the LDCM.