Biophysical products from Landsat

1. Biophysical products from Landsat Ramakrishna Nemani NASA Ames Research Center Ranga Myneni Boston University Jennifer Dungan NASA Ames Research Center LANDSAT Science Team Meeting January 9-11, 2007

2. 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.

3. LAI from MODIS (1km)

4. MODIS LAND PRODUCTS LAI and FPAR have large user community Measurable or physically well-defined parameters are the preferred options Basic parameter to monitor vegetation activity prior to the Terra launch

5. 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

6. Estimating LAI is the second most common application of Landsat TM data in vegetation studies (mapping vegetation is first) Based on Google Scholar analysis

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

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

10. Adapting MODIS algorithm to LandsatMODIS LAI/FPAR Retrievals: Main and Back-Up Algorithms Back-Up algorithm Main algorithm • 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.

11. MODIS LAI and FPAR Production

12. Undisturbed broadleaf deciduous Undisturbed evergreen needleleaf Disturbed 2000 1977 1980 1977 NDVI x 1990 x 1975 x x o o o o o o x x Phenological Stage Landsat Preprocessing and Analysis 2000, 1990 GeoCover TM/ETM+ 1975 GeoCover MSS Other Landsat Data MODIS Aerosols • Orthorectification • Precision Correction • Invariant Target • Normalization or Atm. Correction • Calibration • Atm. Correction • Cloud//Snow mask • IT Normalization Preprocessing Radiometrically Consistent Surface Reflectance Dataset (1975-2000) • Disturbance Rate, Type • Land Cover Conversion Rate • Spectral Unmixing for Fractional Change Analysis Aggregation QA/ Validation Disturbance/Recovery Products for Carbon Assessments LEDAPS, Masek et al.,

13. Landsat Leaf Area Index Production such as NLCD LEDAPS TM specific

14. 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.

15. 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.