Crop Monitoring with Land Data Assimilation and Remote Sensing Michael Marshall Climate Hazards Group (FEWSNET) UC Santa Barbara (office), (fax)

2 Synopsis and Problem Statement  More than 30% of people (primarily children) in sub-Saharan Africa are undernourished  Climatic shocks drive domestic food prices and production  Crop monitoring and early warning is an effective mitigation tool  Remote sensing and surface reanalysis modeling techniques enhance crop monitoring and early warning  Crop stress (proportional to moisture in the root zone) can lead to significant declines in crop yield How can remotely sensed estimates of evapotranspiration (ET) be integrated with surface reanalysis data to augment crop monitoring?

3 Indices of Crop Stress  Precipitation –PDSE, PDSE-z, and CMI (Palmer 1965) – SPI (McKee et al. 1993)  Vegetation –NDVI and VHI (Kogan 1995)  Evapotranspiration (ET) –WRSI (Doorenbos and Pruitt 1977) –ESI (Anderson 2007)  Soil Moisture (Koster and Suarez 1996)

4 Study Area

5 ET Model (Marshall et al. 2010) f c = m 2 NDVI + b 2 f g = m 1 EVI + b 1 / m 2 NDVI + b 2 f t = f m = f APAR / f APARmx f wet = RH 10 (Betts et al. 1997) (Chen et al. 1996) (Fisher et al. 2008)

6  (NDVI, EVI): MODIS 16-day VI at 0.05° resolution  (VPD max, RH min, R N, T max, PAR, LE s,i ): GLDAS NOAH 3-hourly surface reanalysis at 0.25° resolution  (Crop production and area): Department of Resource Surveys and Remote Sensing of the Ministry of Planning and National Development district-level maize statistics for the “long rains”  (Food security reports): FEWSNET annual online reports  Spearmen’s rank correlation  Qualitative analysis: SPI and MODIS LST in EWX Data Handling and Processing

7 ρ = 0.55 ρ = 0.74

8 Evaporative Stress Index (Canopy) Crop stress is directly proportional to the amount of moisture in the root zone (transpiration). Therefore evaporation from the canopy and soil is negligible: Assuming evaporation from the canopy and soil is negligible, ESI can be derived in terms of Fisher transpiration: R N and PET (two highly uncertain ET terms) are eliminated.

11 Implementation of ET a and ESI c in Crop Monitoring  ESI c skewed- gamma or other standardization  Visualization of ESI c (post) with SPI (pre) in EWX  Forecast tool in semi-arid areas (Marsabit, Wajir, and West Pokot)  African Data Dissemination Service (ADDS)  Lagged vegetation/precipitation relationship and backcasting  Substitution of current ET a method in WRSI

12 THANK YOU