Diurnal emissivity dynamics in bare versus biocrusted sand dunes

Rozenstein, Offer; Agam, Nurit; Serio, Carmine; Masiello, Guido; Venafra, Sara; Achal, Stephen Binal; Puckrin, Eldon; Karnieli, Arnon

doi:10.1016/j.scitotenv.2014.11.035

Cited by 31 publications

(17 citation statements)

References 61 publications

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“…Applications of the methodology to surface emissivity have been explicitly dealt with [23,50,51]. In its most updated version (see e.g., [52][53][54]), the full IASI spectrum (8461 channels) is used to simultaneously retrieve, the surface temperature (T s ) and emissivity spectrum (ε), the atmospheric profiles of temperature (T), water vapour (Q), ozone (O), HDO, and CO 2 , and the column amount of N 2 O, CO, CH 4 , SO 2 , HNO 3 , NH 3 , OCS and CF 4 .…”

Section: Retrieval Methodologymentioning

confidence: 99%

Physical Retrieval of Land Surface Emissivity Spectra from Hyper-Spectral Infrared Observations and Validation with In Situ Measurements

et al. 2018

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A fully physical retrieval scheme for land surface emissivity spectra is presented, which applies to high spectral resolution infrared observations from satellite sensors. The surface emissivity spectrum is represented with a suitably truncated Principal Component Analysis (PCA) transform and PCA scores are simultaneously retrieved with surface temperature and atmospheric parameters. The retrieval methodology has been developed within the general framework of Optimal Estimation and, in this context, is the first physical scheme based on a PCA representation of the emissivity spectrum. The scheme has been applied to IASI (Infrared Atmospheric Sounder Interferometer) and the retrieved emissivities have been validated with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. It has been found that the retrieved emissivity spectra are independent of background information and in good agreement with in situ observations.

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Section: Retrieval Methodologymentioning

confidence: 99%

Physical Retrieval of Land Surface Emissivity Spectra from Hyper-Spectral Infrared Observations and Validation with In Situ Measurements

et al. 2018

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“…To obtain and assess the direct approaches to assess spatial and multitemporal surface soil moisture data, in addition to microwave RS techniques, there are also various optical sensors such as MODIS [315], Landsat [316], hyperspectral RS sensors (HyMap, [317]), as well as thermal infrared sensors (Landsat, Sentinel-3, or SEVIRI [318], Table 2). Since soil moisture is subject to a very high spatial-temporal variability, the suitability of optical and thermal sensors to derive soil moisture related information very much depends on the spatial, spectral, and temporal resolution of the RS sensors.…”

Section: Direct and Indirect Measurements By Optical And Thermal Sensorsmentioning

confidence: 99%

Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics

Lausch

Baade

Bannehr³

et al. 2019

Remote Sensing

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In the face of rapid global change it is imperative to preserve geodiversity for the overall conservation of biodiversity. Geodiversity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystem organization. Despite the great importance of geodiversity, there is a lack of suitable monitoring methods. Compared to conventional in-situ techniques, remote sensing (RS) techniques provide a pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, as well as standardized monitoring of continuous geodiversity on the local to global scale. This paper gives an overview of the state-of-the-art approaches for monitoring soil characteristics and soil moisture with unmanned aerial vehicles (UAV) and air- and spaceborne remote sensing techniques. Initially, the definitions for geodiversity along with its five essential characteristics are provided, with an explanation for the latter. Then, the approaches of spectral traits (ST) and spectral trait variations (STV) to record geodiversity using RS are defined. LiDAR (light detection and ranging), thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soil characteristics and soil moisture are also presented. Furthermore, the paper discusses current and future satellite-borne sensors and missions as well as existing data products. Due to the prospects and limitations of the characteristics of different RS sensors, only specific geotraits and geodiversity characteristics can be recorded. The paper provides an overview of those geotraits.

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“…The retrieval of surface emissivity and temperature from space has been predominantly focus on statistical approaches and we refer the interested reader to [8,9] for reviews. Apart from SEVIRI, physically based ε, T s retrieval algorithms have been considered by various authors for applications to imagers and hyper-spectral infrared sounders (among many other we quote [10][11][12][13][14][15][16]).…”

Section: Introductionmentioning

confidence: 99%

SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval

Masiello

Serio

Venafra

et al. 2019

Preprint

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Timely processing of observations from hyper-spectral imagers, such as SEVIRI (Spinning Enhanced Visible and Infrared Imager), largely depends on fast radiative transfer calculations. This paper mostly concerns the development and implementation of a new forward model for SEVIRI to be applied to real time processing of infrared radiances for the physical retrieval of surface temperature and emissivity. The new radiative transfer model improves computational time by a factor of ≈ 7 compared to the previous versions and makes it possible to process SEVIRI data at nearly real time. The new forward model has been applied for the simultaneous retrieval of surface temperature and emissivity in three infrared channels (8.7, 10.8, 12 μm). The inverse scheme relies on a Kalman filter approach, which allows us to exploit a sequential processing of SEVIRI observations. Based on the new forward model, the paper also presents a validation retrieval performed with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. Furthermore, a comparison with IASI (Infrared Atmospheric Sounder Interferometer) emissivity retrievals has been performed as well. It has been found that the retrieved emissivities are in good agreement with each other and with in situ observations, i.e. average differences are generally well below 0.01.

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Diurnal emissivity dynamics in bare versus biocrusted sand dunes

Cited by 31 publications

References 61 publications

Physical Retrieval of Land Surface Emissivity Spectra from Hyper-Spectral Infrared Observations and Validation with In Situ Measurements

Physical Retrieval of Land Surface Emissivity Spectra from Hyper-Spectral Infrared Observations and Validation with In Situ Measurements

Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics

SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval

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