Remote Sensing in Drought Monitoring: Myths and Realities

1. Remote Sensing in Drought Monitoring: Myths and Realities Jesslyn F. Brown SAIC, contractor for USGS at the Center for Earth Resources Observation and Science (EROS) 5th U.S. Drought Monitor Forum, Oct. 10-11, 2007

2. EROS Mike Budde Yingxin Gu Calli Jenkerson Ron Lietzow Susan Maxwell Shahriar Pervez Gail Schmidt Gabriel Senay NDMC Karin Callahan Eric Hunt Soren Scott Tsegaye Tadesse Brian Wardlow Special Acknowledgements Funding provided by USGS, USDA Risk Management Agency, and NASA

3. Topics • Status of EROS research and products • Vegetation Drought Response Index (VegDRI) • eMODIS • Irrigated area mapping (MODIS) • Normalized Difference Water Index (MODIS) • Simple Energy Balance/Evapotranspiration (MODIS) MODIS = Moderate Resolution Imaging Spectroradiometer

4. Topics • Bigger Picture—What is the current and future role of remote sensing in drought monitoring in the U.S.? “Myths and Realities” Disclaimer: the views I express here are my own and in no way should they be construed as representing the official policies or opinions of either the US Geological Survey, the Department of the Interior, Executive Branch of the United States Government, any of the officials of these offices, of these entities in aggregate, nor of those of any other person, or organization. They are my opinions, mine alone, and I am responsible for them.

5. AVHRR MODIS EROS Land Change System: Drought Monitoring Georegistration Compositing Surface Reflectance Existing Stacking Smoothing Anomaly Detection Metrics Calculation (SOS, SG, PASG) Intercalibration Subscribers get “Regular data over the Nation served quickly” VegDRI models 2008 VegDRI Satellite Data User/Decision Support System Remote Sensing Data Services

6. Current Year VegDRI Products July 30, 2007

7. VegDRI Automation and Software Efficiencies • Programming: VegDRI system software • Gained efficiencies: trimmed ~2 hours off of processing times from last year (still hampered by AVHRR composite processing issues) • Server improvements: stability and disk • Testing automated system based on historical AVHRR input • In Spring 2008, will test system in near-real time based on AVHRR and MODIS satellite inputs

8. Input Data Data Mining and Information Discovery Subsystem Modeling and Dissemination Subsystems MODIS Vegetation Indices CART analysis of input variables Iterate with sample analyses Derive variable coefficients and weights Search archives Data retrieval Mosaic Reprojection Derive metrics AVHRR NDVI Data rescaling Image Extract samples Produce Text File Database of Model Variables Model Parameters And Rules Reproject 1992 National Land Cover Database (NLCD) Calc dominant LC Subset Image Model Input Data Model Results Extract samples Produce Text File Reports State Soil Geographic (STATSGO) Database Extract Attributes Calculate Variables Maps Omernik EPA Level III Ecoregions Rasterize Reproject Apply Model to Calculate Vegetation Drought Response Index (VegDRI) Subset Image Extract samples Produce Text File % of Land in Farms in Irrigation (USDA) Calculate variables Polygon encoding U.S. Drought Monitor DSS Standardized Precipitation Index (bi-weekly) Data retrieval Subset Compile database Reproject Segment seasons Rescale data Palmer Drought Severity Index (bi-weekly) Interpolate surface Image Extract samples Produce Text File Dynamic Inputs New input datasets Model Formulation and Implementation; Recurring Operations Static Inputs Dependent variable Processes to be automated or improved

9. Schedule for expansion Summer 2006 Spring 2007 Winter 2007 Summer 2008

10. 2007 VegDRI Map Access http://gisdata.usgs.gov/website/Drought_Monitoring/viewer.php QuickViews http://www.drought.unl.edu/vegdri/VegDRI_Main.htm Dynamic Map Viewer

12. Green Grass Dark Soil Dry Grass UV VISIBLE NEAR IR SHORTWAVE INFRARED NOAA AVHRR 1970s> 1 2 4 1 2 6 3 5 7 MODIS 2000> LANDSAT ETM+ 1990s> 1 2 3 4 5 7 • MODIS sensor shows spectral and spatial resolution improvements compared to the widely used AVHRR sensor The channels and the optical spectral sensitivities for MODIS, AVHRR, and LANDSAT

13. eMODIS Concept Product Characteristics Expedited Historic Instruments Aqua and Terra MODIS Extent Continental U.S. (CONUS) Spatial Resolutions 250, 500, and 1000 meters Product Latency ~ 1 day after last input < 30 days after last input Archive Persistence 90 days Indefinitely Composite Period 7-day, Rolling 7-day, Interval Example eMODIS product: Terra MODIS 1000m NDVI CONUS composite for August 2-8, 2006 Layers NDVI, Surface Refl. Bands, Quality, Acq. Date Projection/Format Lambert Equal Area Azimuthal / GeoTIFF Processing Flow Expedited L1B (NOAA NRT) Long Term Archive and Web-enabled Access Cloud Mask Processing (MODIS PGE 03) Historic L1B (NASA LAADS) Surface Refl. Processing (MODIS PGE 11) Composite Processing (NDVI, CONUS) Ancillary Data

14. VegDRI Model Improvements • Improve quality and resolution of irrigated agricultue

15. VegDRI Inputs: Original Irrigation Layer Census of Agriculture statistics + land cover mask

16. Legend Irrigated crop land Vegetation Prototype 2002 Irrigated Lands Map

17. Irrigated Agriculture Methodology MODIS Annual Peak NDVI County irrigated area statistics Land cover mask

18. MODIS 250 meter NDVI time-series

19. Satellite Derived Vegetation Indices • NDVI related to energy absorbed by the vegetative canopy to fuel photosynthesis (integrated measure) • NDWI responds to changes in the water content (absorption of SWIR radiation) and spongy mesophyll in vegetation canopies

20. Green Grass Dark Soil Dry Grass UV VISIBLE NEAR IR SHORTWAVE INFRARED NOAA AVHRR 1970s> 1 2 4 1 2 6 3 5 7 MODIS 2000> LANDSAT ETM+ 1990s> 1 2 3 4 5 7 • NDWI: MODIS sensor shows spectral and spatial resolution improvements compared to the widely used AVHRR sensor The channels and the optical spectral sensitivities for MODIS, AVHRR, and LANDSAT

22. East Amarillo Fire

23. Soil moisture sites in the OK Mesonet

24. Simplified Energy Balance Approach to Monitor Actual Evapotranspiration Step 1: Use MODIS thermal data to develop ET fractions (comparable to the combination of crop coefficient Kc and soil stress Coefficient Ks) Step 2: Produce actual ET estimates by multiplying the ET fractions by a global reference ET (ETo), produced at EROS ET = ETfrac * ETo Thot – Tx ETfrac = --------- Thot - Tcold

30. Future Plans for the Simple Energy Balance Work • Collect feedback on 2006 products • Continue to process central Great Plains data (2000 – 2006) for flash drought investigation • Part of the U.S. Water Census?

31. Drought Monitoring at County and Sub-county Scales • A major goal is to improve the detail of decision-making products • This is stated in the 2004 Western Governors’ Association Report “A clear way to accomplish this is to incorporate gridded inputs at higher resolutions, that is remote sensing products.”

32. Bigger Picture—What is the role of remote sensing in drought monitoring in the U.S.? “Myths and Realities” • One goal is to improve the detail of decision-making products to sub-county • This is stated in the 2004 Western Governors’ Association Report “A clear way to accomplish this is to incorporate gridded inputs at higher resolutions, aka remote sensing.”

33. Principal Drought Monitor Inputs CPC Daily Soil Model USGS Streamflow Palmer Drought Index 30-day Precip. USDA Soil Ratings Satellite Veg Health

34. Principal Drought Monitor Inputs CPC Daily Soil Model USGS Streamflow Palmer Drought Index 30-day Precip. USDA Soil Ratings

35. Remote Sensing in Drought Monitoring: Myths and Realities ? Are there barriers to using remote sensing products in the USDM? ? What are they and can they be eliminated or minimized?

36. Remote Sensing Myths and Realities Related to “period of record” Related to “product latency” or “production schedule” Related to “mismatch between the product design and the drought application”

37. “DRAFT” USDM Requirements for Remote Sensing Vegetation Input Data • Consistent • Reliable • In anomalous vegetation signal, drought effects separated from non-drought • Seasonality/phenology taken into account • Familiar drought-like categorization • Timely delivery (to USDM authors by Mon/Tues) • Current information (latency not more than 48 hours—ideally within 24 hours)

38. Strengthen Partnerships <<<Improve communication on both sides>>> ** NIDIS Knowledge Assessment Workshop (probably next February) “Contributions of Satellite Remote Sensing to Drought Monitoring” • Planned workshop report: a compendium of current and upcoming national remote sensing products, their characteristics and uncertainties

39. The remote sensing community can: • Improve response to drought community requirements • Document/provide improved explanations for products that are tailored to the users • Increase direct involvement with the drought monitoring community by requesting feedback

40. The drought community can: • Increase feedback (especially constructive criticism) on products • Test and evaluate products • Facilitate understanding that, just like drought indices, there is no single remote sensing product that will track both short-term and long-term drought signals

41. Jesslyn Brown 605.594.6003 jfbrown@usgs.gov Comments and Questions?