Multispectral Optical Sensor Research Articles

GCOM-C Data Validation Plan for Land, Atmosphere, Ocean, and Cryosphere

Japan Aerospace Exploration Agency (JAXA) will launch an Earth observing satellite for climate studies named “Global Change Observation Mission – Climate (GCOM-C)” in 2017 which carries a multi-spectral optical sensor named Second-Generation Global Imager (SGLI). The GCOM-C satellite will observe various geophysical variables such as vegetation, land surface temperature, aerosol, clouds, ocean color, sea surface temperature, snow cover extent, snow grain size and so on. The objectives of the SGLI observations are to elucidate the roles of the geophysical variables in the recent changing Earth's climate system and to establish long-term satellite data record of the variables. Three kinds of target accuracies to be achieved for the SGLI observations are defined for evaluating the success of the GCOM-C satellite mission. First one is the minimum thresholds to be achieved for the first SGLI data release at one year after the launch of GCOM-C. Second and third thresholds are the standard and goal accuracies to be achieved at five years after the launch for evaluating the full and extra success of the mission. The target accuracies for individual SGLI products are available at the GCOM-C web site (http://suzaku.eorc.jaxa.jp/GCOM_C/index.html). The quality and accuracies of SGLI products are planned to be evaluated and maintained through validation activities organized by the SGLI validation team consisting of JAXA and SGLI principal investigators (PIs). Uncertainties of the SGLI data products will be characterized through the comparison with in-situ observations, similar products derived from other satellites, climatological data, and/or numerical model simulations. This paper summarizes the overall validation plan for the SGLI geophysical variable products.

Remote Sensing Evaluation of Two-spotted Spider Mite Damage on Greenhouse Cotton.

The objective of this study was to evaluate a ground-based multispectral optical sensor as a remote sensing tool to assess foliar damage caused by the two-spotted spider mite (TSSM), Tetranychus urticae Koch, on greenhouse grown cotton. TSSM is a polyphagous pest which occurs on a variety of field and horticultural crops. It often becomes an early season pest of cotton in damaging proportions as opposed to being a late season innocuous pest in the mid-southern United States. Evaluation of acaricides is important for maintaining the efficacy of and preventing resistance to the currently available arsenal of chemicals and newly developed control agents. Enumeration of spider mites for efficacy evaluations is laborious and time consuming. Therefore, subjective visual damage rating is commonly used to assess density of spider mites. The NDVI (Normalized Difference Vegetation Index) is the most widely used statistic to describe the spectral reflectance characteristics of vegetation canopy to assess plant stress and health consequent to spider mite infestations. Results demonstrated that a multispectral optical sensor is an effective tool in distinguishing varying levels of infestation caused by T. urticae on early season cotton. This remote sensing technique may be used in lieu of a visual rating to evaluate insecticide treatments.

Remote Sensing Evaluation of Two-spotted Spider Mite Damage on Greenhouse Cotton

The objective of this study was to evaluate a ground-based multispectral optical sensor as a remote sensing tool to assess foliar damage caused by the two-spotted spider mite (TSSM), Tetranychus urticae Koch, on greenhouse grown cotton. TSSM is a polyphagous pest which occurs on a variety of field and horticultural crops. It often becomes an early season pest of cotton in damaging proportions as opposed to being a late season innocuous pest in the mid-southern United States. Evaluation of acaricides is important for maintaining the efficacy of and preventing resistance to the currently available arsenal of chemicals and newly developed control agents. Enumeration of spider mites for efficacy evaluations is laborious and time consuming. Therefore, subjective visual damage rating is commonly used to assess density of spider mites. The NDVI (Normalized Difference Vegetation Index) is the most widely used statistic to describe the spectral reflectance characteristics of vegetation canopy to assess plant stress and health consequent to spider mite infestations. Results demonstrated that a multispectral optical sensor is an effective tool in distinguishing varying levels of infestation caused by T. urticae on early season cotton. This remote sensing technique may be used in lieu of a visual rating to evaluate insecticide treatments.

Evaluation of selected acaricides against twospotted spider mite (Acari: Tetranychidae) on greenhouse cotton using multispectral data.

Twospotted spider mite (TSSM), Tetranychus urticae Koch, is an early season pest of cotton in the mid-southern USA and causes reduction in yield, fiber quality and impaired seed germination. Objectives of this study were to investigate the efficacy of abamectin and spiromesifen with two divergent LC50 values against TSSM in a computer-operated spray table which simulated aerial application parameters. Combined with a pressure of 276kPa and a speed of 8km/h, a 650033 nozzle delivered a spray rate of 18.7 L/ha. The active ingredient rates were 1/8, 1/4, 1/2 and the lowest label recommended rates for early season cotton. The intent was to study efficacy relative to deposition characteristics at active ingredient rates equal to and lower than those recommended by the label. Spectral reflectance values from a multispectral optical sensor were used to calculate the Normalized Difference Vegetation Index which numerically described the surface reflectance characteristics of cotton canopies concomitant to damage caused by T. urticae in the greenhouse. Water sensitive paper samplers described spray droplet spectra parameters (Dv0.1, Dv0.5 and Dv0.9, µm) and percent spray coverage. The volume median diameter (Dv0.5, µm) for abamectin and spiromesifen were respectively, 218 and 258 at one-half rate of the lowest label rate. These spray droplets were well above the driftable portions of the spray volume (<141µm) for both abamectin and spiromesifen. Efficacy evaluations indicated that spiromesifen was more effective than abamectin in controlling T. urticae on early season cotton at one-half rate of the lowest label rate. Results reported herein demonstrate that the multispectral optical sensor in lieu of manually counting T. urticae appears to be a promising tool for efficacy evaluations against acaricides for early season plants grown in greenhouses.

Optical‐Mechanical System for On‐Combine Segregation of Wheat by Grain Protein Concentration

Grain segregation by grain protein concentration (GPC) may help growers maximize revenues in markets that offer protein premiums. Our objective was to develop an on‐combine system for automatically segregating wheat (Triticum aestivum L.) by GPC during harvest. A multispectral optical sensor scans the grain as it is conveyed by the combine’s grain bin‐filling auger. Light from the optical probe is transmitted through a fiber optic cable to a spectrometer, which determines the spectral characteristics of the grain. This information is processed by the instrument control software that is programmed to calculate GPC from a chemometric model. The continuous GPC output is simultaneously fed to a binary computer algorithm for triggering a logic circuit and operating a mechanical diverter valve that diverts the grain into either one of two bins. Field tests of the system were conducted during harvest of hard red spring wheat using a Case IH 1470 combine modified with front and rear bins. Front and rear bins were compared in terms of the mean and frequency distribution of the optically sensed GPC. In addition, the grain in each bin was manually sampled and tested in the laboratory for GPC. Results showed that it is possible to use the GPC measured by an optical sensor to effectively control a mechanical diverter valve for routing the grain into one of two bins on a combine. An advantage of this approach is that prior knowledge of harvesting zones is not required.

Material classification through distance aware multispectral data fusion

Safety applications require fast, precise and highly reliable sensors at low costs. This paper presents signal processing methods for an active multispectral optical point sensor instrumentation for which a first technical implementation exists. Due to the very demanding requirements for safeguarding equipment, these processing methods are targeted to run on a small embedded system with a guaranteed reaction time T < 2 ms and a sufficiently low failure rate according to applicable safety standards, e.g., ISO-13849. The proposed data processing concept includes a novel technique for distance-aided fusion of multispectral data in order to compensate for displacement-related alteration of the measured signal. The distance measuring is based on triangulation with precise results even for low-resolution detectors, thus strengthening the practical applicability. Furthermore, standard components, such as support vector machines (SVMs), are used for reliable material classification. All methods have been evaluated for variants of the underlying sensor principle. Therefore, the results of the evaluation are independent of any specific hardware.

Comparison of optical sensors discrimination ability using spectral libraries

In remote sensing, the ability to discriminate different land covers or material types is directly linked with the spectral resolution and sampling provided by the optical sensor. Previous studies have shown that spectral resolution is a critical issue, especially in complex environments. In spite of the increasing availability of hyperspectral data, multispectral optical sensors onboard various satellites are acquiring every day a massive amount of data with a relatively poor spectral resolution (i.e. usually about four to seven spectral bands). These remotely sensed data are intensively used for Earth observation regardless of their limited spectral resolution. In this article, we studied seven of these optical sensors: Pleiades, QuickBird, Système Pour l'Observation de la Terre 5 (SPOT5), IKONOS, Landsat Thematic Mapper (TM), FORMOSAT, and Medium Resolution Imaging Spectrometer (MERIS). This study focuses on the ability of each sensor to discriminate different materials according to its spectral resolution. We used four different spectral libraries which contain around 2500 spectra of materials and land covers with a fine spectral resolution. These spectra were convolved with the relative spectral responses (RSRs) of each sensor to create spectra at the sensors' resolutions. Then, these reduced spectra were compared using separability indices (divergence, transformed divergence (TD), Bhattacharyya, and Jeffreys-Matusita) and machine learning tools. In the experiments, we highlighted that the configuration of spectral bands could lead to important differences in classification accuracy according to the context of application (e.g. urban area).

Analysis of bar morphology using multi-temporal and multi-sensor satellite images: Example from the Han Estuary, Korea

In the present paper, we report a new powerful technique of studying bar morphology using multi-sensor and multi-temporal satellite images with examples from the inaccessible field sites of the Han Estuary in Korea. The technique utilizes the ability of multi-spectral optical sensors for classifying the types of land cover, and the ability of synthetic aperture radar (SAR) for acquiring radar imagery during day and night, irrespective of cloud cover. Combining the data acquired by both types of sensors at different times, the bar morphology of the Han Estuary is analyzed in this study. The Han Estuary is situated on the North Limit Line (NNL) dividing North and South Korea, and considered to have retained its rich ecological value. However, because of the limited access to the estuary, little information is available on the morphology of the Han Estuary, and the technique based on satellite remote sensing described in this paper is the most suitable and only approach to obtaining information on such inaccessible areas. In the present paper, the bar morphology of the Han Estuary from the years 2000 to 2009 is investigated using multi-temporal spaceborne optical sensors including those on board of SPOT-5, LANDSAT-7, QuickBird, KOMPSAT-2, and OrbView-3, and also C-band synthetic aperture radar (SAR) on board of RADARSAT-1 and ENVISAT with limited field survey data. Sixteen sets of optical data and twelve sets of SAR data were used to study the spatial and temporal variations of bar morphology and the effects of tide and precipitation. The results show that there was some variation in shape and size of bars depending on the location. The tidal effect is large on the bars closer to the open Yellow Sea, and the effect of precipitation was observed on the bars located in the mouths of rivers. Based on the changes of the bar–water boundary, a topographical map was produced, showing the relative height of bars. Using the technique of satellite remote sensing, this paper has shown, for the first time, the long-term bar morphological characteristics of the restricted Han Estuary on the NLL, and the authors anticipate that the results together with the relative height map of bars will constitute the basis of future morphological and ecological studies in this important estuary.

Ground-Based Sensing System for Cotton Nitrogen Status Determination

A ground-based sensing system was developed for determination of nitrogen (N) status in cotton plants. The system consists of a multi-spectral optical sensor, an ultrasonic sensor, and a data-acquisition and processing unit. The optical sensor's light source provides modulated panchromatic illumination of a plant canopy with light-emitting diodes. The sensor measures plant reflectance at four spectral wavebands (400 to 500 nm, 520 to 570 nm, 610 to 710 nm, and 750 to 1100 nm). The ultrasonic sensor is used to determine plant height. The data-acquisition and processing unit is based on a single-board computer that collects data from the multi-spectral optical sensor and the ultrasonic sensor, and spatial information from a Global Positioning System receiver. Field tests of the system over two years involved measuring spectral reflectance and plant height simultaneously in real time in situ. An artificial neural network was developed to predict N status in cotton plants based on data from the sensing system. The network was trained with actual leaf N concentration data that corresponded to sensor spectral data and plant height. Results showed that the spectral information and plant height measured by the sensing system had significant correlation with leaf N concentration of the cotton plants. Trained neural networks were able to predict N status of the cotton plants at 90% accuracy when N status was divided into two categories: deficiency and non-deficiency.

Advances in defining fine- and micro-scale pattern in marine plankton

Since the June 1995 ICES Symposium on Fisheries and Plankton Acoustics in Aberdeen (MacLennan and Holliday, 1996), the use of acoustics for studying zooplankton has seen important advances. Acoustical monitoring of small-scale zooplankton distributions can now be done at intervals of a fraction of a minute. Resolution at vertical spatial scales of tens of centimeters is now easily achieved with commercially available sensors. Multiple-frequency echo-ranging sensors (TAPS™) have been deployed in an up-looking mode on the bottom, and on moorings looking up, down and horizontally. Real-time telemetry provides data on plankton distributions at ranges up to tens of meters from the sensors for periods of weeks to months. These sensors allow one to estimate total zooplankton biomass and the size-abundance spectrum of the animals in the water column at different depths and times. When a profiling CTD and multi-spectral optical sensors were used to define the physical environment and phytoplankton distributions near an acoustical zooplankton profiler, strong relationships were observed between measured spatial and temporal patterns. New methods in zooplankton acoustics are illustrated with data collected from these sensors while monitoring thin, sub-meter thick layers of plankton and diel migrations of benthopelagic crustaceans.

Simultaneous Microwave and Optical Wavelength Observations of Agricultural Targets

SUMMARYA 13.3 GHz scatterometer and a nadir-viewing radiometer measuring reflected radiance in the LANDSAT bands were flown simultaneously over a number of fields near Ottawa containing eleven different forage crops. The purpose of the experiment was to compare the relative ability of the optical and microwave sensors to differentiate the various crop types, and to investigate the advantages of combining optical and microwave measurements for crop discrimination.The coherent illumination and detection inherent in the scatterometer operation results in analysis techniques and statistical uncertainties which are quite different from those associated with the more traditional optical techniques. The data reduction procedure and resulting uncertainties are discussed in some detail.Confusion matrices were calculated for both the optical and the microwave data. Microwave and optical sensors provide complementary information which, when combined, permit the most accurate (88.4 ± 8.0 (s.d.) % for 9 classes) classifications to be achieved. The better single sensor based on classification accuracy is a multispectral optical sensor. We found that the most significant features derivable from the scatterometer data were the dual-polarized scattering coefficients at nadir and the linear slopes of the scattering coefficients as a function of observation angle. We recommend as follow-on to this experiment an experiment in which a comparison of imaging radar data with multispectral scanner data is made.