Heterodyne radiometer instrument concept studies

This report presents an analysis of the atmospheric characteristics in the terahertz spectral region (frequencies from 300 GHz to 10 THz, wavelengths from 30 μm to 1 mm), with particular attention in the range 1 to 5 THz. This interval is the spectral range of interest in the framework of DIAST project. Historically the THz spectral interval has been characterized by a relative lack of convenient radiation sources, detectors and transmission technology. This document considers the designs of different spectroradiometers and the simulation of their instrumental responses. The simulations take into account the scenarios presented in the document: “REPORT - DIAST Project 1 - Typical atmospheric scenarios in the 0.6 - 5 THz wavelength range”, in which the atmospheres have been chosen to be representative of a realistic working scenario in different acquisition geometries, taking into account the typical gaseous components and pollutants of terrestrial atmosphere

A method for atmosphere characterization using mesoscale models, satellite data, and ground measurements

This thesis describes a new procedure for computing irradiances for the Earth's atmosphere including the three-dimensional structure of the local atmosphere around the point of observation. Simulated gridded model atmospheres are used as input for a radiative transfer procedure (based on libRadtran radiative transfer package) for irradiance and radiance determination. The atmospheric structure is obtained from the MM5 meteorological model providing gridded three-dimensional atmospheric profiles and cloud position. Simulated data are compared with ground sensors in UV and visible bands and Meteosat Second Generation satellite images. A fast algorithm is also developed for treating multilayer direct transfer with scattering. The effects of a variety of atmospheric constituents (including new characterizations for aerosols and clouds) are discussed

Estimation of canopy attributes in beech forests using true colourdigital images from a small fixed-wing UAV

Accurate estimates of forest canopy are essential for the characterization of forest ecosystems. Remotely-sensed techniques provide a unique way to obtain estimates over spatially extensive areas, but their application is limited by the spectral and temporal resolution available from these systems, which is often not suited to meet regional or local objectives. The use of unmanned aerial vehicles (UAV) as remote sensing platforms has recently gained increasing attention, but their applications in forestry are still at an experimental stage. In this study we described a methodology to obtain rapid and reliable estimates of forest canopy from a small UAV equipped with a commercial RGB camera. The red, green and blue digital numbers were converted to the green leaf algorithm (GLA)and to the CIE L∗a∗b∗colour space to obtaine stimates of canopy cover, foliage clumping and leaf area index (L) from aerial images. Canopy attributes were compared with in situ estimates obtained from two digital canopy photographic techniques (cover and fisheye photography).The method was tested in beech forests. UAV images accurately quantified canopy cover even in very dense stand conditions, despite a tendency to not detecting small within-crown gaps in aerial images, leading to a measurement of a quantity much closer to crown cover estimated from in situ cover photography. Estimates of L from UAV images significantly agreed with that obtained from fisheye images, but the accuracy of UAV estimates is influenced by the appropriate assumption of leaf angle distribution. We concluded that true colour UAV images can be effectively used to obtain rapid, cheap and meaningful estimates of forest canopy attributes at medium-large scales. UAV can combine the advantage of high resolution imagery with quick turnaround series, being therefore suitable for routine forest stand monitoring and real-time applications.L'articolo è disponibile sul sito dell'editore www.elsevier.com/locate/ja

The SURPRISE demonstrator: a super-resolved compressive instrument in the visible and medium infrared for Earth Observation

While Earth Observation (EO) data has become ever more vital to understanding the planet and addressing societal challenges, applications are still limited by revisit time and spatial resolution. Though low Earth orbit missions can achieve resolutions better than 100 m, their revisit time typically stands at several days, limiting capacity to monitor dynamic events. Geostationary (GEO) missions instead typically provide data on an hour-basis but with spatial resolution limited to 1 km, which is insufficient to understand local phenomena. In this paper, we present the SURPRISE project - recently funded in the frame of the H2020 programme – that gathers the expertise from eight partners across Europe to implement a demonstrator of a super-spectral EO payload - working in the visible (VIS) - Near Infrared (NIR) and in the Medium InfraRed (MIR) and conceived to operate from GEO platform -with enhanced performance in terms of at-ground spatial resolution, and featuring innovative on-board data processing and encryption functionalities. This goal will be achieved by using Compressive Sensing (CS) technology implemented via Spatial Light Modulators (SLM). SLM-based CS technology will be used to devise a super-resolution configuration that will be exploited to increase the at-ground spatial resolution of the payload, without increasing the number of detector’s sensing elements at the image plane. The CS approach will offer further advantages for handling large amounts of data, as is the case of superspectral payloads with wide spectral and spatial coverage. It will enable fast on-board processing of acquired data for information extraction, as well as native data encryption on top of native compression. SURPRISE develops two disruptive technologies: Compressive Sensing (CS) and Spatial Light Modulator (SLM). CS optimises data acquisition (e.g. reduced storage and transmission bandwidth requirements) and enables novel onboard processing and encryption functionalities. SLM here implements the CS paradigm and achieves a super-resolution architecture. SLM technology, at the core of the CS architecture, is addressed by: reworking and testing off-the-shelf parts in relevant environment; developing roadmap for a European SLM, micromirror array-type, with electronics suitable for space qualification. By introducing for the first time the concept of a payload with medium spatial resolution (few hundreds of meters) and near continuous revisit (hourly), SURPRISE can lead to a EO major breakthrough and complement existing operational services. CS will address the challenge of large data collection, whilst onboard processing will improve timeliness, shortening time needed to extract information from images and possibly generate alarms. Impact is relevant to industrial competitiveness, with potential for market penetration of the demonstrator and its components

Compressive Sensing Imaging Spectrometer for UV-Vis Stellar Spectroscopy: Instrumental Concept and Performance Analysis

Compressive sensing (CS) has been proposed as a disruptive approach to developing a novel class of optical instrumentation used in diverse application domains. Thanks to sparsity as an inherent feature of many natural signals, CS allows for the acquisition of the signal in a very compact way, merging acquisition and compression in a single step and, furthermore, offering the capability of using a limited number of detector elements to obtain a reconstructed image with a larger number of pixels. Although the CS paradigm has already been applied in several application domains, from medical diagnostics to microscopy, studies related to space applications are very limited. In this paper, we present and discuss the instrumental concept, optical design, and performances of a CS imaging spectrometer for ultraviolet-visible (UV–Vis) stellar spectroscopy. The instrument—which is pixel-limited in the entire 300 nm–650 nm spectral range—features spectral sampling that ranges from 2.2 nm@300 nm to 22 nm@650 nm, with a total of 50 samples for each spectrum. For data reconstruction quality, the results showed good performance, measured by several quality metrics chosen from those recommended by CCSDS. The designed instrument can achieve compression ratios of 20 or higher without a significant loss of information. A pros and cons analysis of the CS approach is finally carried out, highlighting main differences with respect to a traditional system

A Fast Iterative Procedure for Adjacency Effects Correction on Remote Sensed Data

This paper describes a simple, iterative atmospheric correction procedure based on the MODTRAN®5 radiative transfer code. Such a procedure receives in input a spectrally resolved at-sensor radiance image, evaluates the different contributions to received radiation, and corrects the effect of adjacency from surrounding pixels permitting the retrieval of ground reflectance spectrum for each pixel of the image. The procedure output is a spectral ground reflectance image obtained without the need of any user-provided a priori hypothesis. The novelty of the proposed method relies on its iterative approach for evaluating the contribution of surrounding pixels: a first run of the atmospheric correction procedure is performed by assuming that the spectral reflectance of the surrounding pixels is equal to that of the pixel under investigation. Such information is used in the subsequent iteration steps to estimate the spectral radiance of the surrounding pixels, in order to make a more accurate evaluation of the reflectance image. The results are here presented and discussed for two different cases: synthetic images produced with the hyperspectral simulation tool PRIMUS and real images acquired by CHRIS–PROBA sensor. The retrieved reflectance error drops after a few iterations, providing a quantitative estimate for the number of iterations needed. Relative error after the procedure converges is in the order of few percent, and the causes of remaining uncertainty in retrieved spectra are discussed

Experimental Tests on TIR Multispectral Images for Temperature-Emissivity Separation by Using the MaxEnTES Algorithm

Satellite images in the TIR are relevant for several Earth Observation applications. The retrieval of temperature and emissivity from the emitted radiance, however, requires the use of suitable algorithms, such as the MaxEnTES that uses the concept of maximum entropy to solve the Temperature-Emissivity Separation problem. Here we discuss the performance of MaxEnTES when applied to TIR images with a limited number of channels, specifically simulated HyspIRI multispectral images and real multispectral images by ASTER. The results were respectively compared with the original temperatures used for the simulations and with the temperatures obtained by using the ASTER TES algorithm

Radiometric and signal-to-noise ratio properties of multiplex dispersive spectrometry

Recent theoretical investigations have shown important radiometric disadvantages of interferential multiplexing in Fourier transform spectrometry that apparently can be applied even to coded aperture spectrometers. We have reexamined the methods of noninterferential multiplexing in order to assess their signal-to-noise ratio (SNR) performance, relying on a theoretical modeling of the multiplexed signals. We are able to show that quite similar SNR and radiometric disadvantages affect multiplex dispersive spectrometry. The effect of noise on spectral estimations is discussed

Assessing the daedalus sensor's performance by means of spectral mixture analysis in the Migliarino, San Rossore, Massaciuccoli Regional Park (Italy)

Coastal areas represent relevant zones for environmental monitoring. They are characterized by several habitats that coexist and interact in a condition of dynamic equilibrium. Moreover they are sites of human settlements and important economic and commercial activities. Therefore, an accurate environmental characterization of these complex systems require a large amount of information and different levels of analysis. To date, the contribution by remote sensing to study coastal zones is widely accepted as it provides high quality tools and products to investigate and monitor these fragile ecosystems. In this framework, the aim of this work is to test the performance of the multispectral Daedalus Airborne Thematic Mapper (ATM-2) sensor for the interpretation and analysis of geo-environmental features of the Migliarino, San Rossore, Massaciuccoli Regional Park's coastal area

Assessing The Daedalus Sensor’s Performance By Means Of Spectral Mixture Analysis In The Migliarino, San Rossore, Massaciuccoli Regional Park (Italy).

Coastal areas represent relevant zones for environmental monitoring. They are characterized by several habitats that coexist and interact in a condition of dynamic equilibrium. Moreover they are sites of human settlements and important economic and commercial activities. Therefore, an accurate environmental characterization of these complex systems require a large amount of information and different levels of analysis. To date, the contribution by remote sensing to study coastal zones is widely accepted as it provides high quality tools and products to investigate and monitor these fragile ecosystems. In this framework, the aim of this work is to test the performance of the multispectral Daedalus Airborne Thematic Mapper (ATM-2) sensor for the interpretation and analysis of geo-environmental features of the Migliarino, San Rossore, Massaciuccoli Regional Park's coastal area