Rice is considered as one of the most strategic plants in Iran. The production of rice has been confronted with new challenges since the year of 2000, so the necessity of supporting policies implemented by all relevant authorities has been revealed more and more. Average rainfall records in Gilan province, a rice-favored tropical region, often show a range of 800 ml – 2000 ml with the latter number knowns as the desirable quantity for rice farming. Generally, rice farming needs humidity of 70-80 per cent and Gilan province is well-known for its tropical climate that formulates the necessities for successful productive rice farming industry in this specified district. Long tedious hours of work roughly reach out to 1050-1400 hours for each hectare of rice farm so that all three stages of the rice farming process could be completed respectively. This long hours of work commands stretched hours of pay for the staff that may leave the business overdrawn in the long run. With population overgrowth and lack of unlimited resources, a need for research and development in terms of optimized rice productivity, is inevitably on the rise. Each advanced plan that guarantees higher product yield with better quality boosts the country one step closer to economical and political independence. A comprehensive knowledge over rainfall locations and its seasonal changes enhances the farming productivity and plays key role in the agricultural risk management and its crops insurance. Considering rice dominance in Iran’s farming, use of old fashioned methods in evaluation of the damages imposed by different hazards; such as heavy rainfalls, seems defectively inefficient due to the fact that the surveys are conducted in a very short period of time with a great deal of cost. 
Recent developments in the field of Remote Sensing (RS) have popularized the technique; as it offers unique vast insight, high levels of data transition speed, and availability of field-specialized software/hardware. In this research, TRMM_3B42 data which is known as TRMM data (version 07), is utilized for measuring precipitation.
In an attempt to evaluate the efficiency of TRMM in reporting the damages occurred to Gilan’s province rice farms, Pearson correlation coefficient, was compared among processed data and the gathered observational results separately across the larger states where rice farms cover the area of 15 hectares and less than that, also few villages in Rasht district so that the results indicate the promising use of method in Gilan’s province ultimately since Pearson correlation coefficient between calculations and observations equals to 0.945, so it is meaningful in the statistical level of 1%, although in the occasion of smaller villages its efficiency was graded as poor.