K
Kiyoshi Honda
Researcher at Chubu University
Publications - 72
Citations - 1331
Kiyoshi Honda is an academic researcher from Chubu University. The author has contributed to research in topics: Normalized Difference Vegetation Index & Sensor Observation Service. The author has an hindex of 21, co-authored 71 publications receiving 1183 citations. Previous affiliations of Kiyoshi Honda include Mie University & National Institute of Advanced Industrial Science and Technology.
Papers
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Artificial neural network analysis of laboratory and in situ spectra for the estimation of macronutrients in soils of Lop Buri (Thailand)
TL;DR: In this article, an artificial neural network (ANN) is implemented to estimate soil organic matter, phosphorous, and potassium from the VNIR spectrum (400-1100 nm) from 41 bare soil reflectances of Lop Buri province, Thailand.
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Combining remote sensing-simulation modeling and genetic algorithm optimization to explore water management options in irrigated agriculture
TL;DR: In this paper, the authors presented an approach to explore water management options in irrigated agriculture considering the constraints of water availability and the heterogeneity of irrigation system properties using remote sensing (RS) data.
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Estimating Canopy Nitrogen Concentration in Sugarcane Using Field Imaging Spectroscopy
TL;DR: This research demonstrated that the estimation model developed by SMLR yielded a higher correlation coefficient with nitrogen content, which indicated that the sensitive spectral wavelengths for quantifying nitrogen content existed mainly in the visible, red edge and far near-infrared regions of the electromagnetic spectrum.
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Discrimination of irrigated and rainfed rice in a tropical agricultural system using SPOT VEGETATION NDVI and rainfall data
TL;DR: In this article, a technique, a peak detector algorithm, was proposed to discriminate between rainfed and irrigated rice crops in Suphanburi province, Thailand using a three-year time series of Satellite pour l'Observation de la Terre (SPOT) VEGETATION S10 Normalized Difference Vegetation Index (NDVI) data.
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On quantifying agricultural and water management practices from low spatial resolution RS data using genetic algorithms: A numerical study for mixed-pixel environment
TL;DR: In this article, a genetic algorithm-based methodology to quantify agricultural and water management practices from remote sensing (RS) data in a mixed-pixel environment was presented, where the mixing parameters were parameterized by data assimilation using evapotranspiration and leaf area index.