One goal of the Remote Sensing based Information and Insurance for Crops in emerging Economies (RIICE) project is to estimate, on an operational basis, rice production at national scale in primis targeted to food security and crop insurance purposes. There are two unique elements to this proposed service: 1. Multi-year, annual, and seasonal SAR data are acquired from all existing operational spaceborne systems are used and complemented by MODIS 250/500 m 16-/8-days composite data. This solution: - overcomes the spatial-temporal problem, hence assuring an appropriate temporal repetition at an adequate scale (i.e. spatial resolution) even over large areas; - provides sensor independent operational monitoring with sufficient data redundancy to ensure information delivery. 2. A crop growth simulation model estimates yield and hence production using dedicated remote sensing products in addition to the usual meteorological, soil, and plant parameters. This remote sensing-crop model approach to yield estimation: - uses relevant remote sensing derived information on rice phenology to initialize the model on the correct date; - uses remote sensing parameters as measurements of the crop's response to the environment and management thus reducing the reliance on other input data to the model that would impossible to obtain over wide geographic areas; - considers the spatial distribution of rice fields; - improves the yield estimation figures by forcing the model towards actual rather than attainable yields. Initial results and experiences gained in the past two years in seven Asian countries are presented and discussed.

AN OPERATIONAL REMOTE SENSING BASED SERVICE FOR RICE PRODUCTION ESTIMATION AT NATIONAL SCALE

Mirco Boschetti;Giacinto Manfron;
2013

Abstract

One goal of the Remote Sensing based Information and Insurance for Crops in emerging Economies (RIICE) project is to estimate, on an operational basis, rice production at national scale in primis targeted to food security and crop insurance purposes. There are two unique elements to this proposed service: 1. Multi-year, annual, and seasonal SAR data are acquired from all existing operational spaceborne systems are used and complemented by MODIS 250/500 m 16-/8-days composite data. This solution: - overcomes the spatial-temporal problem, hence assuring an appropriate temporal repetition at an adequate scale (i.e. spatial resolution) even over large areas; - provides sensor independent operational monitoring with sufficient data redundancy to ensure information delivery. 2. A crop growth simulation model estimates yield and hence production using dedicated remote sensing products in addition to the usual meteorological, soil, and plant parameters. This remote sensing-crop model approach to yield estimation: - uses relevant remote sensing derived information on rice phenology to initialize the model on the correct date; - uses remote sensing parameters as measurements of the crop's response to the environment and management thus reducing the reliance on other input data to the model that would impossible to obtain over wide geographic areas; - considers the spatial distribution of rice fields; - improves the yield estimation figures by forcing the model towards actual rather than attainable yields. Initial results and experiences gained in the past two years in seven Asian countries are presented and discussed.
2013
Istituto per il Rilevamento Elettromagnetico dell'Ambiente - IREA
Synthetic Aperture Radar (SAR)
Aqua and Terra MODIS
rice
cultivated area and extent
phenology
Leaf Area Index (LAI)
yield
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/248531
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