1. 1 MODIS Evapotranspiration Project (MOD16) Kenlo Nishida Core science development, NTSG, Univ. Montana / Univ. of Tsukuba Steve Running and Rama Nemani Project directors, NTSG, Univ. Montana Joe Glassy System development, Lupine Logic Inc. Comments? → kenlo@ntsg.umt.edu

2. 2 Why we need evapotranspiration (ET)? ET = “How wet is there?” Water resources management & drought monitoring - Water resource crisis is biggest problem in 21 st century. ---- ET consumes precious water resources!!! - Fire risk assessment Improve Models - ET serves as input data or validation data for climate model. - Validation of ecosystem model (Biome-BGC, RHESSys, etc.) Diagnose environment - Urbanization  change energy budget  hot city? (like Tokyo in summer) - Cut/plant forest  change energy budget  change climate & productivity - Global warming  ET increase? Decrease?

3. 3 Why ET by satellite? Score table of three approaches to ET -------------------------------------------------------------------------------------------------------- Observationmodelsatellite RS -------------------------------------------------------------------------------------------------------- Time coverage Good!Good! Poor… Spatial coverage Poor…So-so. Good! Accuracy Good!So-so. So-so. Cost efficiency Poor…So-so. So-so. -------------------------------------------------------------------------------------------------------- Particular advantage of satellite RS: - Not influenced by water re-distribution (e.g., irrigation) - Strong at phenology

4. 4 Why is Aqua/MODIS good? - Frequent global coverage (every day + combination with Terra/MODIS) - Afternoon overpass … dry condition becomes clearer than morning - High-spatial resolution (1km) cf. Microwave remote sensing - High-precision of temperature and vegetation indices cf. NOAA/AVHRR - albedo and emissivity are available. cf. NOAA/AVHRR - Atmospheric information is available from other sensors on Aqua -AIRS/AMSU/HSB …… accurate atmospheric profile!!!

5. 5 Final Product EF (Evaporation Fraction) values - 8 day-period, 1km resolution, globally - by Aqua (EOS-PM)/MODIS Requirements for the algorithm - Stand alone: It can operate only with optical satellite sensor data - Flexible: It can ingest any other reliable data, if available. - Simple: It is simply constructed to reduce computational load. - Scalable: It can give daily information of ET from instants data. - Versatility: It can operate regardless of climate and biome. - Sensor Independence: It can co-operate with other sensors with same logic. Outline of the Project

6. 6 What is Evaporation Fraction? Net radiation (radiation absorbed on the land) Ground heat transferAvailable Energy - Fractional value is representative for “wetness”. - Scalability of instantaneous observation to longer period. - Accurate estimate of Q is difficult. Potential for coupling with Terra-MODIS (AM overpass) From: Crago, 1996, “Scaling up in Hydrology using Remote Sensing”

7. 7 Actual landscape: mixture of forest, farm, grassland, road, etc. Simplification MODIS-ET Model Landscape Fraction of vegetation: f veg Fraction of bare soil: 1.0 - f veg ET = f veg ET veg + (1 – f veg ) ET soil ET veg ET bare Q veg Q bare EF = f veg  EF veg + (1 – f veg )  EF bare Q Q

8. 8 We want this! - Vegetation Index …NDVI or EVI (S) - Temperature on vegetation: T veg (S) - Incoming solar radiation: PAR (T) -VI-Ts diagram (S) Note: (S) …. Derived from Satellite (T) …. Estimated theoretically How do we get it? Q veg Q bare EF = f veg  EF veg + (1 – f veg )  EF bare Q Q - radiative transfer of atmosphere (T)

9. 9 Estimating EF veg (1) Concept Central concept: Evaporation from vegetation (transpiration) is mostly controlled by stomata opening (canopy resistance). α Δ EF veg =  Δ + γ ( 1 + r c / 2 r a ) Assuming complementary relationship ( ET + PET PM = 2PET PT ; PET PM =Penman’s PET; PET PT =Priestley-Taylor’s PET ), we can get: Psychrometric constant (slightly change with T) Derivative of saturated vapor pressure curve (change with T) Constant. 1.26 Canopy resistance Aerodynamic resistance

10. 10 1 / r c = f 1 (T) f 2 (VPD) f 3 (PAR) f 4 (  ) / r cMIN Soil water Solar radiation Humidity Temperature 1 / r c = f 1 (T) f 3 (PAR) / r cMIN Ideally, Actually, Change of VI Estimating EF veg (2) Canopy Resistance Model

11. 11 Estimating EF bare T bare max – T bare EF bare =  T bare max – T bare min VI-T s diagram (Nemani & Running, 1989; 1993) VI TsTs VI max VI min T veg =T bare min T bare max T bare VI Window satellite image Warm Edge Air temperature Wind speed Q bare0 – ET T bare =  + T a 4εσT a 3 (1- C G ) +  C p /r a bare

12. 12 Channel reflectance Thermal IR Satellite data f veg VI-T s diagram TararcTararc T a PAR T bare max T bare T a EF bare EF veg VI albedo Orbit T a R d PAR Q bare Q bare0 Q veg EF U 50m r a rcrc Data Stream TsTs Radiative transfer model Radiation budget Energy budget Conductance model Penman-Monteith & Complementary relation VI T s VI R d T a albedo Q bare T bare max T a

13. 13 Prototype -NOAA/AVHRR 14-day composite (1km resolution) -Window size: 21km*21km - Validation 13 sites of AmeriFlux

14. 14

15. 15  site (symbol) type data size R 2 bias standard error  Harvard Forest DBF 28 0.75 -0.05 0.13 Walker Branch DBF 29 0.88 0.01 0.13 Willow Creek DBF 8 0.80 -0.14 0.18 WLEF Tower DBF 17 0.89 -0.18 0.20 Blodgett ENF 11 0.30 -0.12 0.21 Duke Forest ENF 13 0.70 0.04 0.19 Howland ENF 20 0.84 -0.02 0.10 Metolius ENF 15 0.20 -0.12 0.23 Bondville Crop 37 0.81 -0.07 0.19 Ponca Crop 6 0.36 -0.10 0.31 Little Washita Grass 20 0.86 0.04 0.14 Shidler Grass 10 0.91 -0.03 0.12 Ski Oaks Shrub 16 0.29 -0.07 0.17 all sites --- 230 0.74 -0.05 0.17  Validation Comparison of estimated EF by AVHRR and observed EF at AmeriFlux sites

16. 16 Only NDVI Full algorithm Test of simplified algorithmValidation for each landcover type

17. 17 Day233, 2000 North USA (Tile H10-12V04) Prototype with Terra/MODIS