Selected results of FoodSat research … Food: what’s where and how much is there? 2 Topics: Exploring a New Approach to Prepare Small-Scale Land Use Maps using Multi-Temporal NDVI SPOT-Images  what’s where Combined Use of Polar Orbiting and Geo-Stationary Satellites to Improve Time Interpolation in Dynamic Crop Models for Food Security Assessment  how much is there

Used for mapping (Nizamabad, India): Time series of 147 SPOT-4 Vegetation decadal NDVI images (declouded) at 1-km 2 resolution. Strong emphasis on temporal aspects reflecting vegetation phenology and practiced crop calendars. What’s where … by compiling existing data!!

Output: a 1-km 2 NDVI unit map with 11 NDVI-profiles as preliminary legend. Result-1: Unsupervised classification (ISODATA clustering) followed by supervised grouping (based on profile behaviour)

crossing Result-2: Using an existing (old) land cover map to define what each NDVI-unit represents. Output: Distinct sets of cover complexes by NDVI- unit.

Result-3: Using agricultural statistics by admin.area to define crops grown by NDVI-unit (through multiple regression). Output: Data of fraction by NDVI-unit and season, planted to specific crops.

Result-4: Combining results 1,2,3 and literature on crop calendars practiced, to prepare the final legend = final output. Section of the full legend ….

Mar May NDVI Rainfall (mm/month) P (apr-dec): 13, Extra result: Monitoring land use modifications of irrigated areas, where during winter, rice is the dominant crop. The NDVI-curves mostly reflect changes in areas cropped by year; changes could not be related directly to rainfall, but …by lack of power to run pumps!!

Key findings:  existing available data are combined to delineate / describe ‘what is where’ through data mining,  the generated map has 1 km 2 pixels, and consists of ‘mixels’ representing unique land cover and land use complexes,  map units are defined on the basis of their behaviour in time that tallies with signals provided by instruments used for monitoring,  the map does not have a ‘salt-and-pepper’ appearance, but has clear delineable map units, while the method used was not sensitive to the size of identified units,  the supervised grouping following the unsupervised classification, yielded very good results regarding the generaliza- tion and stratification of the large spatial data set,  no specific field verification was required, though local expert knowledge of the authors matched the results.

How much is there…by spatial modelling!! Defined by a production function as included in a crop growth model: P,Y = f (light, temperature, C3/C4, canopy heating) gainslosses

Canopy heating: a proxy for crop stress ΔT < 0 ΔT > 0 Plant temperatures increase following reduced transpiration rates, caused by: deficit of water (water stress), reduction of biomass (by diseases or pestes), high salinity in the soil water, nutrient deficiencies and toxicities, etc. Plant temperatures can be estimated through Thermal Infrared Satellite Imagery.

Result-1: Combining TI-images of both polar orbiting and geo- stationary satellites and a crop growth model. Output: Software that pre-processes images of various sources and use them as input in a ‘spatial’ crop growth model (PSn).

Result-2: Cross-calibration of multi-sensor data. Output: Procedures that carry out sensor, time and place specific atmospheric and radiometric correction to generate sensor a-specific temperature measurements (split-window algorithm).

Result-3: duration/severity of crop stress detected using a temperature-based remotely sensed index (cf H2O ; China) A(bove): NOAA data alone B(elow): NOAA/GMS-5 combined Duration first stress period: 1a: 2 days 1b: 6 days. Dry matter growth curves simulated with the PS-n model on the basis of canopy-ambient air temperature differences.

Key findings:  the method greatly reduces the computational data needs of crop growth models,  the method allows estimation of not just the H 2 O-limited production, but of “actual farmer’s production”,  drawbacks of polar-orbiters (rough temporal resolution) and geo-stationary satellites (poor radiometric signal) are eliminated by combining the two,  multi-sensor data can only be combined when cross-calibrated,  uncertainties in modeled losses are less when combining data from various RS- platforms.