Operational products for crop monitoring Hervé Kerdiles, JRC MARS
Outline Meteo products Rainfall, temperature, global radiation, ETa, snow depth Biomass indicators & crop yield prediction NDVI & other VIs, LAI, fapar, phenology, Dry Matter Productivity (DMP), (ASI) Water balance products: Water Satisfaction Index (WSI) Land cover masks
Meteo products From global circulation models e.g. ECMWF Subscription needed for NRT data (0.125 ) from the operational HRES model (OPE); also forecast for the next 10 days, 15 /32 / 181 days (based on 51 ensemble members) monthly and seasonal forecasts to be assessed ERA Interim data at 0.75 freely available for research (1979 NRT with delay of 3 months) JRC distributes R, Temp, ETP, Rg at dekadal timestep (see http://spirits.jrc.ec.europa.eu), resampled to 0.25 with correction bias for temperature and Rg from ERA Interim; no correction for rainfall 25 March 2015 3
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ECMWF rainfall Consistency between ERA Interim OPE rainfall over 2008 2012 5
ECMWF rainfall Consistency between ERA Interim OPE rainfall over 2008 2012 Strong bias between ERA Interim and OPE rainfall Conclusion: Cannot use ERA Interim (2000-2007) and OPE (2008-now) rainfall together as a predictor for yield 6
Meteo products From satellites Meteosat: ETa, Land Surface temp (Land-SAF Eumetcast), rainfall (various products), Rg (global radiation)? MODIS e.g. ETa ERS/AMI, ASCAT, AMSR-E (mainly 25-50 km resolution, interruption since 2013): soil surface moisture -> Focus on rainfall estimates Case study: Malawi floods of Jan 2015 Do we see abnormal rainfall over Malawi and if so, when and where? 25 March 2015 7
Satellite Rainfall Estimates Shortage of raingauge observations in many places in Africa due to low density of meteo stations and to gaps in the rainfall observations. Rainfall estimates may replace raingauge observations for monitoring the crop season. Rainfall estimates may be derived from Numerical Weather Prediction models (e.g. ECMWF) or from satellite data, in particular thermal data (e.g. Meteosat data), passive microwave data (e.g. SSM/I data from the Tropical Rainfall Monitoring Mission), sometimes in combination with raingauge data.
Examples of Precipitation Estimates Product Temporal resolution Spatial resolution Period covered ECMWF ERA Interim Daily, Ten-daily 0.25 deg 01/1989 12/2010 (JRC archive) ECMWF OPE Daily, Ten-daily 0.125 deg 01/2008 present (JRC archive, 0,25 deg) Tamsat V2.0 Ten-daily 0.0375 deg 01/1983 present NOAA RFE V2.0 Daily, Ten-daily 0.05 deg 03/2000 present NOAA RFE ARC2 Daily 0.1 deg 01/1983 present CHIRPS Pentad, ten-daily 0.05 deg 01/1981-present NOAA CMORPH 30 mn / 3 hourly (daily 0.0727 deg / 8 km at Equator, archive for last 2 weeks) 0.25 deg for 3h and daily 12/2002 present TRMM 3B42 RT V7 3 hourly 0.25 deg 01/1998 present GPCC monthly 0.5 deg to 1 deg (maybe 0.25 deg) Full data product: 1950 2004 Monitoring product: 1986 present Many PrEst are available => Need to determine the best PrEst for a given area and period (validation study)
Comparison NOAA RFE, Tamsat, ERA Interim NOAA RFE Tamsat Interim Mean 1996-2012 yearly rainfall sum
Comparison NOAA RFE, Tamsat, ERA Interim Tamsat-RFE Tamsat-ERA Scale: -2500 -> +900 RFE-ERA
Algeria Tamsat: algorithm not suitable for non convective rainfalls
Angola Poor Tamsat calibration due to too few gauges over Angola?
Burkina Faso & Niger ECMWF systematically lower
Madagascar West Centre N East South Which is the closest to reality?
Example of validation study: Uganda To intercompare (validate) the various PrEst, we need gauge data (10 daily rainfall data) Gauges shown by red dots Only grid squares with at least one gauge are used in the validation (shaded) See Maidment et al., 2012
Example of validation study: Uganda Validating rainfall products against kriged gauge data (resolution: 0.5 x0.5 ) Period of study: 2001 to 2005 for first rainy season (Feb June) ERA-40 TAMSAT ERA-Interim RFE 2.0 Product Bias/mm RMSD/mm R 2 ERA-40-9.86 18.09 0.41 ERA-Interim TAMSAT 14.06-1.01 26.55 10.41 0.39 0.72 RFE 2.0 GPCP -1.73 0.16 11.00 11.56 0.74 0.72 GPCP Mean dekadal rainfall Courtesy of R. Maidment et al., 2012
Example of validation study: Uganda Seasonal cycle Courtesy of R. Maidment et al., 2012
Example of validation study: Uganda Example of conclusions derived (Maidment et al., 2012) From the products studied, satellite algorithms performed much better than reanalysis model data All three satellite algorithms produce similar statistics TAMSAT tends to underestimate high rainfall at single grid square scale RFE 2.0 and GPCP tend to overestimate high rainfall at single grid square scale RFE 2.0 and GPCP may include some of gauges used in validation Model products ERA Interim overestimates strongly - particularly at height of season ERA-40 underestimates strongly
Biomass indicators Vegetation indices Global NDVI products (except emodis-africa only?) Product Spatial Resolution Temporal frequency Years Spot VGT 1 km 10 days 1998-05/2014 ProbaV 300m 1km 10 days 06/2014 - MODIS 250m, 500m, 1 km 16 days 02/2000 - emodis 1 km 10 days 01/2001- MetopAVHRR 1 km 10 days 2007- Other vegetation indices EVI: Modis VCI, VPI: Spot VGT/ProbaV, NOAA AVHRR ProbaV products: http://land.copernicus.eu/global/products/ndvi 20
Biomass indicators NDVI Vegetation/ProbaV vs emodis Good agreement VGT/ProbaV & emodis (except for Bechar arid area), with max NDVI higher for VGT 25 March 2015 21
Biomass indicators fapar, LAI: Spot VT/ProbaV, MSG (Africa, 2008-), MODIS (1km, 8 days) Phenology: MODIS (500m, 2001-2012), home-made DMP: Spot VGT/ProbaV (VITO/Copernicus), MODIS (1km, 8 days, 2000-2010) Monteith s efficiencies model for DMP t=harvest DMP = Σε b (temp, water stress).ε a (t).ε c.r g (t) t=sowing fapar PAR 22
Water balance indicators Actual Evapotranspiration anomaly Fewsnet monthly product http://earlywarning.usgs.gov/fews/downloads 25 March 2015 23
Water balance indicators Actual Evapotranspiration anomaly ETa anomaly for 02/2015 ET derived from the Operational Simplified Energy Balance model (SSEBop Senay et al. 2013) based on Modis 10-daily temperature 25 March 2015 24
Water Satisfaction Index Rationale: relation between crop yield & water satisfaction FAO water balance model (Fewsnet, MARS) W t = W t-1 + R t ETM t where: W t : available soil water at end of dekad t [mm] W t-1 : available soil water at end of dekad t-1 [mm] R t : cumulated rainfall during dekad t [mm] ETM t : cumulated maximum evapotranspiration during dekad t for given crop [mm.dekad -1 ]= ET0 t * K c,t * WUEred t with K c,t crop coefficient (type and stage specific) and WUE crop water use efficiency reduction due to suboptimal temperature ETM t water requirement (WR) 25
Water Satisfaction Index MARS WSI W t = W t-1 + R t ETM t IF W t > storage capacity: => water surplus IF W t < 0: => Deficit D = W t else D=0 WSI = 100 1 WSI = 100% season without drought stress WSI < 100% season with drought stress n t= 1 n t= 1 D WR At given dekad during crop season, use weather scenario (e.g. dry, wet or median weather) to finish the crop season > WSI: indicator of water satisfaction at the end of the season 26
Water Satisfaction Index WSI requires: crop specific mask & crop parameters (K c,wue red ), soil map (for soil water holding capacity and max rooting depth) Initial soil moisture (simulation till convergence) Phenology (sowing date rule + crop season length) Weather data (ECMWF or Tamsat Rain, ECMWF ET0 & Temp) 27
Crop yield estimation from NDVI Crop yields related to cumulated fapar at regional / national level Assumptions: The inter-annual variability of yields can be explained by crop photosynthetic activity along the season (rainfed crops) The analysis is valid for predominant crops (typically wheat/barley) Statistical model requiring reliable yield statistics Crop acreage is stable (winter Vs summer cereals), i.e. change in NDVI due to change in cop biomass, not in crop area Integration period is optimized through a regression between historical yield and fapar data
Wheat yield forecast in Morroco and Ukraine With VGT NDVI 1999-2011 Morocco (Wheat) Evolution of regression reliability along the season Ukraine (Wheat) 29
Crop mask MARS Crop mask (2011) Static mask, synthesis of various masks 30
Crop mask Ethiopia 50 km N-NW of Addis MARS Crop mask Red = cropland Black = no cropland 31
Crop mask MARS Crop mask (ftp://mars.jrc.ec.europa.eu/ Public/cropmask/) X X X X X X No cropland Other crop masks - FAO GLC Share 2014 http://www.glcn.org/datab ases/lc_glcshare_en.jsp - New USA 30m: http://www.globallandcover. com/glc30download/index. aspx - Chinese LC 2000 and 2010 at 30m Validation needed for each country 32
Conclusion Many products - >Need to identify the most relevant for each country/area of interest See length of time series (for LTA, VCI, calibration of yield prediction models) Improvement in spatial resolution to come (but archive?) In the near future, high resolution (10m) could be one of the products for monitoring individual fields (same problem with archive) 33