In agriculture, soil and crop conditions change from day to day and throughout the growing season. Agricultural targets also vary spatially with differences observed from field to field, as well as within individual fields. The heterogeneity of corn-growing conditions in Mexico makes accurate data for crop type, crop condition and crop yield prediction difficult to obtain. Yield predictions are needed by the federal government to estimate, ahead of harvest time, the amount of corn required to be imported in order to meet the expected domestic shortfall. In this project a methodology for the estimation of corn yield ahead of harvest time is developed which uses radar and optical remote sensing and which specifically considers the corn-growing situation in Central Mexico. Radar based crop type classification requires data sets with multiple polarizations. Recent research to assess relative classification accuracies of multi-polarized combinations for target crops using airborne data has been reported. In addition to identifying crop type and variety, identifying crop growth stage is valuable. Crop condition, loosely defined as the vigor or health of a crop in a particular growth stage, is related to crop productivity and yield; however, the relationship is complex. Main crop condition indicators include biomass, height, leaf area and contents of plant water, chlorophyll and nitrogen. Crop-type and crop-condition mapping are among the applications that are expected to benefit the most from the technical enhancements embodied by RADARSAT-2. The potential of RADARSAT-1 data for these applications has been rated as "limited", whereas for RADARSAT-2 data this potential is anticipated to be "strong". The Science and Operational Applications Research for RADARSAT-2 Program (SOAR) is promoting the evaluation of Synthetic Aperture Radar (SAR) capabilities by providing images to selected research projects which include the present one.objectives of this project are: a) use RADARSAT-2 data and optical data to determine cultivated areas and monitor crop condition for obtaining better estimations of crop yield; b) obtain polarization signatures from RADARSAT-2 data for corn and relate these to Leaf Area Index and photosynthetic active radiation (PAR) crop parameters and vegetation indexes, to establish indicators of crop condition and produce estimates for crop yield; c) use field data collected for three key corn crop growth stages over 300 pilot plots during 2001-2006, and increase the number of plots to build a database to support accuracy studies using RADARSAT-2 data.The expected benefits of this project are: to obtain knowledge about crop type, crop condition and crop yield with better accuracy than with current methodologies; to support national corn farmers associations; to design agriculture related activities within State agriculture plans; to support the corn product industry and aid government decision making. Relevant results and economical impact will imply operational usage of RADARSAT- 2 data in the agricultural sector in Mexico.
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