Improve Agricultural Monitoring Systems through Satellite Imagery for Iran

Component 2

Sustainable methods and tools for crop area and yield estimation through integration of remote sensing are developed

The project will review and evaluate the current agriculture monitoring methodology within the MJA and establish a baseline of current approaches, gaps and limitations in procedures and, identify the main sustainable methods and tools, and areas for improvement.

Analysis and assessment will be undertaken for the development of a strategy to produce consolidated crop area and production statistics utilizing statistically sound, harmonized methodology that leverages data collected by provincial agricultural offices.

All kinds of available existing agricultural, geographic and administrative information on crops (estimates of acreage of main crops in the area, ground data collected in the previous years in the area, cropping calendars, satellite data etc.) will be collected and validated.

The methodology includes the development of crop masks, area frame construction and sampling in the field for generation of agricultural statistics. The most recent accessible real-time satellite-based data like Sentinel 2, Landsat 8, Proba-V, etc will be used to generate cropland information.
A cropland map based on FAO Land Cover Classification System (LCCS) methodology is being developed from the project. The existing Land Cover database integrated with the most recent Sentinel 2 satellite imagery is being used to extract the agriculture and crop areas that will support the development of Area Frame Sampling and stratification. Cropland Information will be enriched with the main crop types and the main seasonal crop dynamics based on the integration of multi temporal satellite information and derived indicators (NDVI). The project will develop an efficient and low-cost stratification of area frames for agricultural estimates derived from cropland cover integrated with remote sensing and field sampling.

Key approaches are:

  • Access the most recent high-resolution images in the study area
  • Develop multi-temporal profiles of vegetation indexes
  • Based on agricultural intensity, develop a stratification of the sampling frame
  • Propose a comprehensive national agricultural monitoring strategy through the development of a UTF project proposal based on the identified approach and tested methodology