Abstract
We present FYMOS, an all-aluminum, robust, light weight, freeform based, near infrared hYperspectral imager for MOisture Sensing. FYMOS was designed and built to remotely measure moisture content using spectral features from 0.7-1.7µm integrating an InGaAs sensor. The imaging system, operating at F/2.8, is based on the three-concentric-mirror (Offner) spectrograph configuration providing a spectral resolution of 8 nm optimized for broad spectral coverage with sufficient resolution to make assessments of water levels. To optimize the optical performance, whilst minimizing weight and size, the design incorporates a bespoke freeform blazed grating machined on a commercial 5 axis ultra precision diamond machine. We achieve a 30% improvement on the RMS wavefront error in the spatial and spectral fields compared to a conventional Offner-Chrisp design with similar aperture and the monolithic Primary/Tertiary mirror eases the manufacturing assembly whilst minimizing weight. We demonstrate the performance of FYMOS by measuring the evaporation rate of water on a soil sample and results are processed with a physical multilayer radiative transfer model (MARMIT) to estimate the mean water thickness.
Highlights
Over the last two decades, hyperspectral imaging has become an essential tool in a wide variety of fields, from medical diagnoses, food quality assurance and mineral mapping. [1,2,3] One particular area where hyperspectral imagers have great potential, is in monitoring for precision agriculture enabling direct and remote acquisition of information on crop, soils, pests and weeds
A comprehensive study of the di erent technologies related to soil moisture measurements is given in [8]
Due to the low penetration of light into soil, the measurements are limited to surface moisture measurements, but with prior knowledge of the dry soil surface reflectance, it is possible to recover the soil moisture content (SMC) using, physical or empirical models with various degrees of confidence depending on the soil type [10,11]
Summary
Over the last two decades, hyperspectral imaging has become an essential tool in a wide variety of fields, from medical diagnoses, food quality assurance and mineral mapping. [1,2,3] One particular area where hyperspectral imagers have great potential, is in monitoring for precision agriculture enabling direct and remote acquisition of information on crop, soils, pests and weeds. [1,2,3] One particular area where hyperspectral imagers have great potential, is in monitoring for precision agriculture enabling direct and remote acquisition of information on crop, soils, pests and weeds. The in-field techniques o er direct in-situ information, at a defined and controlled depth and provide the most accurate measurement. They do not allow for large scale surveys with high spatial or temporal resolution and require a large number of operators. Based sensing systems generally operate by comparing relative reflectance in di erent wavelength bands to determine soil moisture content (SMC). Due to the low penetration of light into soil, the measurements are limited to surface moisture measurements, but with prior knowledge of the dry soil surface reflectance, it is possible to recover the SMC using, physical or empirical models with various degrees of confidence depending on the soil type [10,11]
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