2 February 2007 designed by Okamoto, K., EID/NIAES 1 農業分野での高時間分解能 農業分野での高時間分解能 衛星リモート・センシング・データの利用 衛星リモート・センシング・データの利用 Application of satellite remote sensing data with Application of satellite remote sensing data with high time resolution to agricultural issues high time resolution to agricultural issues 岡本勝男・坂本利弘 農業環境技術研究所 生態系計測研究領域 OKAMOTO, Katsuo, and SAKAMOTO, Toshihiro, Ecosystem Informatics Division, National Institute for Agro-Environmental Sciences 1. Contents 1. Contents 農業分野で衛星リモート・センシングに求めるもの What can satellite remote sensing provide for agriculture sector? 事例紹介: Introduction of case studies: – 作物の収穫量を推定する: Estimating the yield of crops – 作物季節を推定する: Defining the phenology of crops これからの衛星リモート・センシングに求めるもの Having the cheek to ask more ...
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Application of satellite remote sensing data with Application of satellite remote sensing data with high time resolution to agricultural issueshigh time resolution to agricultural issues
岡本勝男・坂本利弘農業環境技術研究所 生態系計測研究領域
OKAMOTO, Katsuo, and SAKAMOTO, Toshihiro,Ecosystem Informatics Division, National Institute for
Agro-Environmental Sciences
1. Contents1. Contents
農業分野で衛星リモート・センシングに求めるもの
What can satellite remote sensing provide for agriculture sector?
事例紹介: Introduction of case studies:– 作物の収穫量を推定する: Estimating the yield of crops– 作物季節を推定する: Defining the phenology of crops
これからの衛星リモート・センシングに求めるもの
Having the cheek to ask more ...
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22. . 衛星リモート・センシングに求めるもの衛星リモート・センシングに求めるものWhat can satellite remote sensing provide for agriculture sectorWhat can satellite remote sensing provide for agriculture sector??
農業で知りたいことWe want to know ...– どの作物が– the area with crops and
• 作付分類– 離散的データ:栽培期間中に数シーン
– どれくらい収穫できるか– the yield
• 生育診断,収量推定– 連続的データ:栽培期間中に,短い間隔でたくさん
• 事例:生育程度,収穫量,旱魃被害,植物季節
光利用効率(光乾物変換係数)RUE (LUE): Radiation (Light) Use Efficiency
33..作物の作物の生育生育を推定するを推定するDetermining the growth of cropsDetermining the growth of crops
Shibayama, M., and Akiyama, T., 1989, Seasonal visible, near-infrared and mid-infrared spectra of rice canopies in relation to LAI and above-ground dry phytomass. Remote Sensing of Environment, 27, 119-127.
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4. 4. Case study: detection of Case study: detection of drought damagedrought damage
Why did we focus on drought as a case study?– Drought and flood affect largely the agriculture– Drought damage spread widely
Detection of drought in the northeastern part of China– a tendency to be drought from spring to summer 2001
Normalized Difference Vegetation Index (NDVI)-based detection– NDVI=(NIR-R)/(NIR+R)– NDVI is proportional to vegetative biomass
44--1. 1. Comparison of NDVI between Comparison of NDVI between 2000 and 2001:B/May2000 and 2001:B/May--AugAug
遼寧省遼東半島 遼寧省遼東半島
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44--2. 2. Comparison of NDVI between Comparison of NDVI between normal years and 2001: paddy fieldnormal years and 2001: paddy field
Seasonal change in NDVI on paddy field
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44--3. 3. Comparison of NDVI between Comparison of NDVI between normal years and 2001: croplandnormal years and 2001: cropland
出典: Sakamoto et al., 2006, Spatio-temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers. Remote Sensing of Environment, 100, 1-16.
出典: Sakamoto et al., 2006, Spatio-temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers. Remote Sensing of Environment, 100, 1-16.
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77.. これからのこれからの衛星リモート・センシングに衛星リモート・センシングに
求めるもの求めるものHaving the cheek to ask more ...Having the cheek to ask more ...
衛星リモート・センシングの長所を活かした農業・環境研究へのデータの提供
Sensors on board the satellite can measure the agricultural parameters and the environment–広域性 widely,–反復性 repeatedly,– (観測の)均質性 homogeneously and–隔測性 remotely