The Future of Remote Sensing Applications for Drought Monitoring

Dr. Brian WardlowRemote Sensing Specialist / GIScience Program Area Leader

National Drought Mitigation Center (NDMC)

Inter-Regional Workshop on Indices and Early Warning Systems for DroughtLincoln, NE

December 10, 2009

Satellite-based remote sensing has been widely used over the past 20+ years for national to global-scale many terrestrial monitoring activities (e.g., land use/land cover mapping, crop yield estimates, and ecological applications), including drought monitoring.

Remote Sensing for Terrestrial Monitoring

1. Synoptic Earth observations by satellite-basedremote sensing instruments of the Earth’s land surface at regional to global scales.

2. High temporal frequency of repeat satellite observations over the same geographic area.

3. Spatially continuous measurementsacross the landscape provide detailed information about land surface patterns and conditions. Typical pixel size (ground area sampled) by remote sensing instruments used for large-area monitoring range from1-km to 4 degrees.

The Remote Sensing PerspectiveImportant Characteristics for Drought Monitoring

Geographic patterns of seasonal vegetation greenness for the U.S. as observed from a time-series NOAA AVHRR NDVI data. (Animation produced by NOAA /NESDIS).

Remotely sensed data from a series of the National Oceanic and Atmospheric Administration’s (NOAA) Advanced Very High Resolution Radiometer (AVHRR) instruments has been commonly used for national to global-scale drought monitoring activities since the 1980s.

Traditional Remote Sensing-Based Drought Monitoring

Global vegetation greenness patterns for mid-summer 1995 derived from AVHRR normalized difference vegetation index (NDVI) data. (Courtesy of NOAA)

1. Normalized difference vegetation index (NDVI), which provides a general measure of the state and health of vegetation, was one of the first remote sensing-based indicators usedfor drought detection and monitoring.

NDVI data calculated from AVHRR observations provided global-scale information on a weekly/bi-weekly basis that was optimal for this type of applications.

Tucker, C. J., C. L. Vanpraet, M. J. Sharman, and G. van Ittersum. 1985a. Satellite remote sensing of total herba-ceous biomass production in the Senegalese Sahel: 1980- 1984. Remote Sensing of Environment 17:233-249.

Courtesy of NASA

Drought impacted area in 2002

Traditional Remote Sensing-Based Drought Monitoring

AVHRR NDVI anomaly map highlights drought impacted areas (depicted by brown color) across much of Africa’s Sahel region in 2002.

Courtesy of NASA

Traditional Remote Sensing-Based Drought Monitoring

2. Vegetation Health Index (VHI) integrates both NDVI- (VCI – Vegetation Condition Index) and thermal-based (TCI – Temperature Condition Index) indicators derived from AVHRR data to depict drought stress as function of vegetation canopy greenness and temperature.

Global products generated weekly by NOAA.

VHI = a*VCI + b*TCIa* & b* = coefficients to quantify share of NDVI-based VCI (Vegetation Condition Index) & TCI (Temperature Conditions Index) contribution in total vegetation health.

http://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/index.php

Progress in Remote Sensing• Over the past 10 years, many advanced

remote sensing instruments have been launched that collect information that can be used monitor different aspects of drought.

• Development of advanced processing and analysis techniques and improved computing capabilities have resulted in new remote sensing-based approaches that could be used for drought monitoring.

Examples of New Remote Sensing Instruments

Vegetation Conditions

Evapotranspiration (ET)

Soil MoistureGroundwater Fluxes

Remote Sensing-based Estimates of:

Vegetation

Vegetation Drought Response Index (VegDRI)VegDRI is a new ‘hybrid’ drought index that integrates:

- satellite-based observations of vegetation conditions- climate-based drought index data- biophysical characteristics of the environment

to produce 1-km spatial resolution maps that depict ‘drought-related vegetation stress’.

Operational production of VegDRI maps for the continental U.S. once every two weeks across the year. In 2010, production will transition to weekly updates using MODIS data inputs.

VegDRI – An Integrated Approach

Satellite-based 1-km NDVI (normalized difference vegetation index) observations provide spatially detailed information regarding the general state and condition of vegetation.

Variables:- percent annual seasonal

greenness (PASG)- start of season anomaly

(SOSA)

Measure of ‘broad scale’geographic patterns of dryness.

Variables:- standardized precipitation

index (SPI)- Palmer drought severity

index (PDSI)

Environmental characteristics that influence climate-vegetation interactions.

Variables:- land use/land cover type - soil characteristics- elevation- ecological setting

Remote Sensing Component Climate Component Biophysical Component

+ +

Brown, J.F., B.D. Wardlow, T. Tadesse, M.J. Hayes, and B.C. Reed, 2008. The vegetation drought response index (VegDRI): a new integrated approach for monitoring drought stress in vegetation. GIScience and Remote Sensing, 45(1):16-46.

http://www.drought.unl.edu/vegdri/VegDRI_Main.htm

Vegetation Outlook ( VegOut)A new experimental tool that provides a series of maps depicting future outlooks (2 to 6 weeks) of general vegetation conditions (seasonal greenness) based on an analysis of:

1) satellite-based observations of vegetation2) climate-based drought indices3) general biophysical characteristics of the environment4) oceanic indicators.

Tadesse, T. and B. Wardlow, 2007. The Vegetation Outlook (VegOut): A New Tool for Providing Outlooks of General Vegetation Conditions Using Data Mining Proceedings of the Seventh IEEE International Conference on Data Mining , Techniques, pp.667-672.

VegOut Maps for the Central U.S.

4-week VegOut

6-week VegOut

2-week VegOut

Standardized Seasonal Greenness (SSG)

High

Intermediate

Low

Out of season

(b) 2-week SSG prediction for August 11

(c) 4-week SSG prediction for August 25

(d) 6-week SSG prediction for September 8

(e) Observed SSG for August 11

(g) Observed SSG for September 8

(f) Observed SSG for August 25

(a) Observed SSG forJuly 28, 2008

2-weekVegOut

4-weekVegOut

6-weekVegOut

Fraction of Absorbed PhotosyntheticallyActive Radiation (fAPAR)

fAPAR is a measure of the canopy’s energy absorption capacity (how large a fraction of the sunlight leaves absorb in the 0.4–0.7 μm spectrum).

‘Physical-based’ indicator of the state and productivity of vegetation.

European Drought Observatory (EDO) is using 1-km fAPAR maps produced by the European Space Agency (ESA) from Medium Resolution Imaging Spectrometer (MERIS).- 10-day composited fAPAR maps- fAPAR anomaly maps for each 10-day period (deviation from

historical fAPAR conditions from 1997 to present)

fAPAR within EDO

fAPAR anomaly, 1-10 September 2009

Courtesy: Stefan Niemeyer, JRC/EDO

Evaluation: MERIS fAPAR as a drought indicatorPerformances fAPAR anomalies VS NDVI anomalies compared to precipitation

anomalies (3-week SPI) over the Iberia peninsula

Positive correlation on 95.5% of the area

Positive correlation on 79.4% of the area

MERIS fAPAR –SPI3 MERIS NDVI –SPI3

Courtesy: Stefan Niemeyer, JRC/EDO

Evapotranspiration (ET)

Actual Evapotranspiration (ETa)

Land Surface Temperature (LST)

ETfraction

Weather DataRadiation

Temp, Wind, & Relative Humidity

aETo

ETa

Normalized Difference Vegetation Index (NDVI)

Digital Elevation Model (DEM)

A simplified surface energy balance (SSEB) model is applied to remote sensing inputs and other weather and ancillary data to generate 1-km resolution maps of actual ET. Utilizes a ‘hot’ and ‘cold’ pixel approach (identified in the LST

image) adapted from SEBAL (Bastiaanssen et al., 1998) and METRIC (Allen et al., 2005). ETa estimates can be

calculated at multiple timesteps: weekly, monthly, seasonal, or annual summaries. Applied in the U.S. by U.S.

Geological Survey (USGS) and internationally through the Famine and Early Warning System (FEWS) network.

Senay, et al., 2007. Sensors, 7, 979-1000.

Actual ET Results for Nebraska

ETa estimates can be used to understand water usage demands and identify areas that may be experiencing changes in their ET regimes due to drought conditions or other environmental factors.

Courtesy: Gabriel Senay, USGS / FEWS

WetterDrierSEASONAL ANOMALIES (9-year record)

April – September 2008

Evaporative Stress Index (ESI)

U.S. Drought Monitor (USDM)

Courtesy: M. Anderson USDA ARS (2008)

ESI

DM

Evaporative Stress Index (ESI)ESI depicts moisture stress based on ET flux estimates from a ‘two-source’(soil and veg. canopy) land surface energy balance model (ALEXI & DisALEXI) that relies primarily on remotely sensed thermal observations.

ESI = 1 – ETa / ETP

Developed by U.S. Department of Agriculture’s (USDA) Agricultural Research Division (ARS) and is being applied for the U.S. and tested in other countries.

Anderson, M.C., Norman, J.M., Mecikalski, J.R., Otkin, J.A., Kustas, W.P. 2007. A climatological study of evapotranspiration and moisture stress across the continental U.S. based on thermal remote sensing. II. Surface moisture climatology. Journal of Geophysical Research. 112, D11112. http://dx.doi.org/10.1029/2006JD007507.

Evaporative Stress Index (ESI) Advantages of the ESI1.ESI can be derived separately for the vegetation canopy (ESIc) and soil surface (ESIs) using the two-source land surface model.2.ESI can be calculated from data from many different instruments to provide range of calculations across multiple spatial and temporal scales.

Minutes to Hourly: GOES- & Meteosat-derived 5- to 10-km ESI maps

Daily to weekly: MODIS-derived 1-km ESI maps

Bi-weekly (16-days): Landsat 60- and 120-m ESI maps

Courtesy: M. Anderson USDA ARS (2008)

Soil Moisture & Groundwater

Soil Moisture Change (SMC)

Soil moisture changes (SMC) estimated using backscatter data acquired from NASA’s active microwave scatterometer(QuikSCAT).

NASA is currently producing experimental SMC products for the continental U.S. SMC Product Characteristics:

- Nominal spatial resolution of 25-km- Weekly updates of maps

QSCAT Soil Moisture Change (upper 5 cm)

Courtesy of Son Nghiem, NASA JPL

Localized positive SMC associated with locally heavy snowfall event (8-10 inches).

Terrestrial Water Storage (TWS)Terrestrial Water Storage (TWS) is the sum of :

1) surface water2) snow3) soil moisture4) groundwater

Calculated from gravity data acquired by the new GRACE (GravityRecovery and Climate Experiment) instrument that is processed

through Land Data Assimilation System (LDAS) to downscale data to higher spatial resolution outputs.

GRACE Observed TWS

GRACE + LDAS Modeled TWSData A

ssimilation

Courtesy: Matt Rodell, NASA GSFC

Zaitchik, B.F., M. Rodell, and R.H. Reichle, 2008: Assimilation of GRACE Terrestrial Water Storage Data into a Land Surface Model: Results for the Mississippi River Basin. J. Hydrometeor., 9, 535–548.

Monthly TWS Anomaly (Jan. – Dec. 2003)(60-year TWS record generated by LDAS for historical mean)

TWS and Anomaly Products Spatial resolution is ~4 degrees.

Generated at multiple time steps (weekly, monthly, seasonally, & annually).

TWS results can be ‘vertically disaggregated’ to estimate changes in surface water, soil moisture, and groundwater.

Experimental products currently generated for North America.

Courtesy: Matt Rodell, NASA GSFC

1) Long-term continuity of data acquisition from a specific instrument to maintain production of a standard suite of informational products.

- Necessary to establish a long-term data record to establish a meaningful historical context for anomalies that are detected.- Challenge is many instruments are designed for research rather than

operations.

Current Consideration for Remote Sensing and Operational Drought Monitoring

1) Long-term continuity of data acquisition from a specific instrument to maintain production of a standard suite of informational products.

- Necessary to establish a long-term data record to establish a meaningful historical context for anomalies that are detected.- Challenge is many instruments are designed for research rather than

operations.

2) Validation of remote sensing-based products for drought application.- Challenge to link a point-based measurement (e.g., soil moisture probe) to remote sensing estimate that may be calculated over larger area (e.g., pixel size area of 1-km).- Lack of ground observations collected over large areas (e.g., national network) and many time periods (e.g., weekly collection across the year).

Current Consideration for Remote Sensing and Operational Drought Monitoring

3) Maintain a strong linkage between the remote sensing scientists & the end user of the information (i.e., drought monitoring community).

- Develop informational product requirements • Delivery schedule for new product updates (e.g., day of week; weekly or monthly)• File formats• Specific types of products (e.g., change maps and percentile rankings)

Current Consideration for Remote Sensing and Operational Drought Monitoring

Thank you for your attention. If you have questions or would like additional information on a

specific remote sensing tool, please contact:

Dr. Brian WardlowNational Drought Mitigation Center (NDMC)

bwardlow2@unl.edu