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Agriculture 2011 Developing a High Spatial and Temporal Resolution Database for Meteorological Based Agronomical Models Dr. Offer Beeri and Shay Mey-tal money on soil/plant sensing and sampling. The main goal of this project was to replace this method of hand sampling with a computer-based system. To achieve this goal, satellite imagery was integrated with climate dataset in a geographic information system (GIS), allowing for the collection of the data and the processing of daily reports for field crops in the project. Figure 1. Noon-time temperature, 28-March-2009, as captured by climate stations network (A) and satellite imagery (B). Local meteorology is an important part of agriculture crop monitoring as correct management incorporates crop growth and growth rate with weather data in order to determine irrigation amounts and timing. The most important climate data for this monitoring are growing degree-days (GDD) and evapo-transpiration (ET), where the former represents the accumulated temperature required for crop growth, and the latter characterizes the loss of water to the atmosphere. Both are necessary to ensure that the supplied water amounts are calculated based on the current growth rate and the loss of water. Most farmers are dependent upon climate stations located 30-50 km (20-30 miles) from each other (Figure 1A). With local changes in topography, soil and drainage, the huge spatial variability between each pair of stations does not allow for effective monitoring of local crop fields (Figure 1B). As a result, farmers invest large amounts of time and 5 Scientific background: The main method to determine the amounts of water during any crop irrigation is calculating the potential ET, multiplied by crop coefficients that are based on crop growing models. These variables are calculated from weather stations and known experimental data to represent the day-to-day changes. Yet, as the spatial distribution is greater than the average field size, local differences are not recognizable. Numerous researchers have attempted to resolve this issue by integrating remote imagery. These projects have illustrated that as crops become dryer and require more water, the greater the difference between crop and air temperatures (Moran, 1994). To ensure that crop growth stages do not affect this model, the vegetation temperature is normalized by the vegetation vigor, both mapped by satellite imagery. This index represents the vegetation resistance to transpiration (Nemani and Running, 1989) with higher values indicating water-stress. Integration of this method will allow for the mapping of differences between neighboring crop fields, as well as inside any plot, and agriculture growth models that use crop coefficients for monitoring will enable updated irrigation amounts for each field. 30
Agriculture 2011 In order to test the possibility of using satellite imagery for day-to-day irrigation decision making, the suggested GIS module combines data from satellite imagery and local weather stations. The Moderate Resolution Imaging Spectroradiometer (MODIS) is used with its Red and Near-Infrared 250-m pixel and surface temperature 1000-m products. The latter is scaled-down to 250-m (Hassen et al 2007). Weather stations are used for calibration while the other weather station is utilized for verification. The spatial variability of the GIS product is tested on cotton and tomatoe crops and in natural forests. The research goals were (2009 season): 1. To test the variability among temperature calculated from weather stations and mapped by temperature integrating surface temperature imagery, Red and Near-Infrared images and weather station data. 2. To test the variability among GDD calculated by weather stations and GDD mapped by surface temperature imagery. 3. To build a GIS module for agriculture monitoring by satellite imagery and weather stations. The research results were: Remote sensing temperature (Surface), was well correlated to temperatures calculated from weather stations (Air at 2m). We found variability of GDD between growing fields based on GDD mapped by surface temperature imagery. We have built remote sensing and GIS models for agriculture monitoring by satellite imagery and weather stations. Summary: We have built a low cost remote sensing model enabling precise field-scale irrigation amount and timing calculation. This model can be used to improve and make other agricultural models more precise, such as insect growth models (IPM), harvest timing etc…anywhere around the globe. In 2010, we improved our model with the integration of other satellite data (higher spatial resolution), and checked it with more crops and needs. For more details please contact as at info@agam-ag.com NDVI (biomass index) used in our model to improve spatial resolution from 100Ha/pixel to approximately 5Ha/pixel. It can also be used to monitor crop growth rate. Minimum air temperatures in Celsius, calculated by the weather station (2m) calibrating model. Daily average temperatures and GDD can be calculated based on minimum and maximum (same calibration model) temperature data. References Allen, R. Pereira, LS. Smith, M. Raes, D. and Wright, JL. (2005), FAO-56 Dual crop coefficient method for estimating evaporation from soil and application extension, Journal of Irrigation and Drainage Engineering, 131, 1-12 Hassan QK. Bourque CPA. Meng F. and Richards W. (2007), Spatial mapping of growing degree days: an application of MODIS-based surface temperatures and enhanced vegetation index, Journal of Applied Remote Sensing, 1, 1-12 (DOI: 10.1117/1.2740040) Moran MS (1994) Irrigation management in Arizona using satellites and airplanes. Irrigation Sciences, 15, 35-44. Nemani R. and Running S. (1989) Estimation of regional surface resistance to Evapotranspiration from NDVI and thermal-IR AVHRR data, Journal of Applied Meteorology, 28, 276-284. MODIS website: http://modis.gsfc.nasa.gov/ info@agam-ag.com 31
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