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Journal articles on the topic 'CHRIS hyperspectral'

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Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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1

Verrelst, Jochem, Juan Rivera Caicedo, Jorge Vicent, Pablo Morcillo Pallarés, and José Moreno. "Approximating Empirical Surface Reflectance Data through Emulation: Opportunities for Synthetic Scene Generation." Remote Sensing 11, no. 2 (January 16, 2019): 157. http://dx.doi.org/10.3390/rs11020157.

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Collection of spectroradiometric measurements with associated biophysical variables is an essential part of the development and validation of optical remote sensing vegetation products. However, their quality can only be assessed in the subsequent analysis, and often there is a need for collecting extra data, e.g., to fill in gaps. To generate empirical-like surface reflectance data of vegetated surfaces, we propose to exploit emulation, i.e., reconstruction of spectral measurements through statistical learning. We evaluated emulation against classical interpolation methods using an empirical field dataset with associated hyperspectral spaceborne CHRIS and airborne HyMap reflectance spectra, to produce synthetic CHRIS and HyMap reflectance spectra for any combination of input biophysical variables. Results indicate that: (1) emulation produces surface reflectance data more accurately than interpolation when validating against a separate part of the field dataset; and (2) emulation produces the spectra multiple times (tens to hundreds) faster than interpolation. This technique opens various data processing opportunities, e.g., emulators not only allow rapidly producing large synthetic spectral datasets, but they can also speed up computationally intensive processing routines such as synthetic scene generation. To demonstrate this, emulators were run to simulate hyperspectral imagery based on input maps of a few biophysical variables coming from CHRIS, HyMap and Sentinel-2 (S2). The emulators produced spaceborne CHRIS-like and airborne HyMap-like surface reflectance imagery in the order of seconds, thereby approximating the spectra of vegetated surfaces sufficiently similar to the reference images. Similarly, it took a few minutes to produce a hyperspectral data cube with a spatial texture of S2 and a spectral resolution of HyMap.

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2

Fan, W., X. Xu, X. Liu, B. Yan, and Y. Cui. "Accurate LAI retrieval method based on PROBA/CHRIS data." Hydrology and Earth System Sciences Discussions 6, no. 6 (November 12, 2009): 7001–24. http://dx.doi.org/10.5194/hessd-6-7001-2009.

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Abstract. Leaf area index (LAI) is one of the key structural variables in terrestrial vegetation ecosystems. Remote sensing offers a chance to derive LAI in regional scales accurately. Variations of background, atmospheric conditions and the anisotropy of canopy reflectance are three factors that can strongly restrain the accuracy of retrieved LAI. Based on the hybrid canopy reflectance model, a new hyperspectral directional second derivative method (DSD) is proposed in this paper. This method can estimate LAI accurately through analyzing the canopy anisotropy. The effect of the background can also be effectively removed. So the inversion precision and the dynamic range can be improved remarkably, which has been proved by numerical simulations. As the derivative method is very sensitive to the random noise, we put forward an innovative filtering approach, by which the data can be de-noised in spectral and spatial dimensions synchronously. It shows that the filtering method can remove the random noise effectively; therefore, the method can be performed to the remotely sensed hyperspectral image. The study region is situated in Zhangye, Gansu Province, China; the hyperspectral and multi-angular image of the study region has been acquired from Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/PROBA), on 4 and 14 June 2008. After the pre-processing procedures, the DSD method was applied, and the retrieve LAI was validated by the ground truth of 11 sites. It shows that by applying innovative filtering method, the new LAI inversion method is accurate and effective.

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3

Fan, W. J., X. R. Xu, X. C. Liu, B. Y. Yan, and Y. K. Cui. "Accurate LAI retrieval method based on PROBA/CHRIS data." Hydrology and Earth System Sciences 14, no. 8 (August 10, 2010): 1499–507. http://dx.doi.org/10.5194/hess-14-1499-2010.

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Abstract. Leaf area index (LAI) is one of the key structural variables in terrestrial vegetation ecosystems. Remote sensing offers an opportunity to accurately derive LAI at regional scales. The anisotropy of canopy reflectance, variations in background characteristics, and variability in atmospheric conditions constitute three factors that can strongly constrain the accuracy of retrieved LAI. Based on a hybrid canopy reflectance model, a new hyperspectral directional second derivative method (DSD) is proposed in this paper. This method can estimate LAI accurately through analyzing the canopy anisotropy. The effect of the background can also be effectively removed. With the aid of a widely-accepted atmospheric model, the influence of atmospheric conditions can be minimized as well. Thus the inversion precision and the dynamic range can be markedly improved, which has been proved by numerical simulations. As the derivative method is very sensitive to random noise, we put forward an innovative filtering approach, by which the data can be de-noised in spectral and spatial dimensions synchronously. It shows that the filtering method can remove random noise effectively; therefore, the method can be applied to hyperspectral images. The study region was situated in Zhangye, Gansu Province, China; hyperspectral and multi-angular images of the study region were acquired via the Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/PROBA), on 4 June 2008. After the pre-processing procedures, the DSD method was applied, and the retrieved LAI was validated by ground reference data at 11 sites. Results show that the new LAI inversion method is accurate and effective with the aid of the innovative filtering method.

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4

Garcia Millan, Virginia, and Arturo Sanchez-Azofeifa. "Quantifying Changes on Forest Succession in a Dry Tropical Forest Using Angular-Hyperspectral Remote Sensing." Remote Sensing 10, no. 12 (November 22, 2018): 1865. http://dx.doi.org/10.3390/rs10121865.

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The tropical dry forest (TDF) is one the most threatened ecosystems in South America, existing on a landscape with different levels of ecological succession. Among satellites dedicated to Earth observation and monitoring ecosystem succession, CHRIS/PROBA is the only satellite that presents quasi-simultaneous multi-angular pointing and hyperspectral imaging. These two characteristics permit the study of structural and compositional differences of TDFs with different levels of succession. In this paper, we use 2008 and 2014 CHRIS/PROBA images from a TDF in Minas Gerais, Brazil to study ecosystem succession after abandonment. Using a −55° angle of observation; several classifiers including spectral angle mapper (SAM), support vector machine (SVM), and decision trees (DT) were used to test how well they discriminate between different successional stages. Our findings suggest that the SAM is the most appropriate method to classify TDFs as a function of succession (accuracies ~80 for % for late stage, ~85% for the intermediate stage, ~70% for early stage, and ~50% for other classes). Although CHRIS/PROBA cannot be used for large-scale/long-term monitoring of tropical forests because of its experimental nature; our results support the potential of using multi-angle hyperspectral data to characterize the structure and composition of TDFs in the near future.

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5

Wang, Qiang, Yong Pang, Weiwei Jia, Haowei Zhang, and Chaoyang Wang. "Effective and Universal Pre-Processing for Multi-Angle CHRIS/PROBA Images." Journal of the Indian Society of Remote Sensing 49, no. 7 (March 16, 2021): 1581–91. http://dx.doi.org/10.1007/s12524-020-01288-0.

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AbstractCompared with traditional nadir observations, multi-angle hyperspectral remote sensing can obtain more spatial and spectral information and improve the inversion precision of structure information on the Earth’s surface. However, processing multi-angle remote-sensing images presents new challenges. Owing to the multi-angle sensors used to obtain multi-angle images, there are major differences in the spatial and spectral information between each angle in these images. Data from the Compact High-resolution Imaging Spectrometer (CHRIS) on Project for On-Board Autonomy (PROBA) should be pre-processed to extract the BRDF (Bidirectional Reflectance Distribution Functions). Given the limitations of the pre-processing software currently available for CHRIS/PROBA images, and the lack of metadata and auxiliary origin schedules, some CHRIS multi-angle images cannot be pre-processed correctly. In the study, to promote the application of multi-angle data, a formula for calculating key parameters according to in-orbit geometric imaging relationships is derived to design a multi-angle image process flow including image rollovers, bad-line repairs, orthorectification and atmospheric corrections accounting for terrain effects. Test results indicate that the pre-processing method can quickly and effectively recover multi-angle hyperspectral information and obtain spectral characteristics of multi-angle observations.

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6

Gürsoy, Ö., A. C. Birdal, F. Özyonar, and E. Kasaka. "Determining and Monitoring the Water Quality of Kizilirmak River of Turkey: First Results." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 30, 2015): 1469–74. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-1469-2015.

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Water resources are getting more and more important with each passing day in case of survival of humanity. For this reason, assessing water resources’ quality and also monitoring them have attracted lots of attention in the recent years. Remote sensing has been growing widely in the last decade and its resources are very usable when it comes to water resources management. In this study, by using remote sensing technology, satellite images that have 350 to 1050 nanometres wavelength band sensors (e.g. CHRIS Proba) are used to determine the quality of the Kizilirmak River’s water. Kizilirmak River is born and also pours out to sea in country limits of Turkey. It is the longest river of the country by the length of 1355 kilometres. Through the river’s resources, ground based spectral measurements are made to identify the quality differences of the water at the test spots that have been determined before. In this context at Imranli, where the river contacts civilization for the first time, which is located in Sivas city of Turkey, samples are gathered in order to do ground based spectroradiometer measurements. These samples are gathered simultaneously with the image acquiring time of CHRIS Proba satellite. Spectral signatures that are obtained from ground measurements are used as reference data in order to classify CHRIS Proba satellite’s hyperspectral images over the study area. Satellite images are classified based on Chemical Oxygen Demand (COD), Turbidity and Electrical Conductivity (EC) attributes. As a result, interpretations obtained from classified CHRIS Proba satellite hyperspectral images of the study area are presented.

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7

Sahithi, V. S., and S. Agrawal. "Sub pixel location identification using super resolved multilooking CHRIS data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 463–68. http://dx.doi.org/10.5194/isprsarchives-xl-8-463-2014.

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CHRIS /Proba is a multiviewing hyperspectral sensor that monitors the earth in five different zenith angles +55°, +36°, nadir, −36° and −55° with a spatial resolution of 17 m and within a spectral range of 400–1050 nm in mode 3. These multiviewing images are suitable for constructing a super resolved high resolution image that can reveal the mixed pixel of the hyperspectral image. In the present work, an attempt is made to find the location of various features constituted within the 17m mixed pixel of the CHRIS image using various super resolution reconstruction techniques. Four different super resolution reconstruction techniques namely interpolation, iterative back projection, projection on to convex sets (POCS) and robust super resolution were tried on the −36, nadir and +36 images to construct a super resolved high resolution 5.6 m image. The results of super resolution reconstruction were compared with the scaled nadir image and bicubic convoluted image for comparision of the spatial and spectral property preservance. A support vector machine classification of the best super resolved high resolution image was performed to analyse the location of the sub pixel features. Validation of the obtained results was performed using the spectral unmixing fraction images and the 5.6 m classified LISS IV image.

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8

Duca, Riccardo, and Fabio Del Frate. "Hyperspectral and Multiangle CHRIS–PROBA Images for the Generation of Land Cover Maps." IEEE Transactions on Geoscience and Remote Sensing 46, no. 10 (October 2008): 2857–66. http://dx.doi.org/10.1109/tgrs.2008.2000741.

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9

Wang, Qing, Zhengke Zhang, Zengzhou Hao, Bingling Liu, and Jilian Xiong. "Optical Classification of Coastal Water Body in China using Hyperspectral Imagery CHRIS/PROBA." IOP Conference Series: Earth and Environmental Science 668, no. 1 (February 1, 2021): 012017. http://dx.doi.org/10.1088/1755-1315/668/1/012017.

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10

Awad, Mohamad M. "HYPERSPECTRAL REMOTE SENSING ROLE IN ENHANCING CROP MAPPING: A COMPARISON BETWEEN DIFFERENT SUPERVISED SEGMENTATION ALGORITHMS." SWS Journal of EARTH AND PLANETARY SCIENCES 1, no. 1 (June 1, 2019): 25–37. http://dx.doi.org/10.35603/eps2019/issue1.03.

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In agriculture sector there is need for cheap, fast, and accurate data and technologies to help decision makers to find solutions for many agricultural problems. Many solutions depend significantly on the accuracy and efficiency of the crop mapping and crop yield estimation processes. High resolution spectral remote sensing can improve substantially crop mapping by reducing similarities between different crop types which has similar ecological conditions. This paper presents a new approach of combining a new tool, hyperspectral images and technologies to enhance crop mapping. The tool includes spectral signatures database for the major crops in the Eastern Mediterranean Basin and other important metadata and processing functions. To prove the efficiency of the new approach, major crops such as “winter wheat” and “spring potato” are mapped using the spectral signatures database in the new tool, three different supervised algorithms, and CHRIS-Proba hyperspectral satellite images. The evaluation of the results showed that deploying different hyperspectral data and technologies can improve crop mapping. The improvements can be noticed with the increase of the accuracy to more than 86% with the use of the supervised algorithm Spectral Angle Mapper (SAM).

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11

Tirelli, C., C. Manzo, G. Curci, and C. Bassani. "EVALUATION OF THE AEROSOL TYPE EFFECT ON THE SURFACE REFLECTANCE RETRIEVAL USING CHRIS/PROBA IMAGES OVER LAND." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 30, 2015): 1311–16. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-1311-2015.

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Surface reflectance has a central role in the analysis of land surface for a broad variety of agricultural, geological and urban studies. An accurate atmospheric correction, obtained by an appropriate selection of aerosol type and loading, is the first requirement for a reliable surface reflectance estimation. The aerosol type is defined by its micro-physical properties, while the aerosol loading is described by optical thickness at 550 nm. The aim of this work is to evaluate the radiative impact of the aerosol model on the surface reflectance obtained from CHRIS (Compact High Resolution Imaging Spectrometer) hyperspectral data over land by using the specifically developed algorithm CHRIS@CRI (CHRIS Atmospherically Corrected Reflectance Imagery) based on the 6SV radiative transfer model. Five different aerosol models have been used: one provided by the AERONET inversion products (used as reference), three standard aerosol models in 6SV, and one obtained from the output of the GEOS-Chem global chemistry-transport model (CTM). As test case the urban site of Bruxelles and the suburban area of Rome Tor Vergata have been considered. The results obtained encourages the use of CTM in operational retrieval and provides an evaluation of the role of the aerosol model in the atmospheric correction process, considering the different microphysical properties impact.

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12

Sahithi, Veeramallu Satya, and Iyyanki V. Murali Krishna. "Investigating Image Fusion Techniques on CHRIS/Proba Space Borne Hyperspectral Data for Material Identification." International Journal of Advanced Remote Sensing and GIS 7, no. 1 (May 31, 2018): 2643–55. http://dx.doi.org/10.23953/cloud.ijarsg.359.

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13

Chan, Jonathan Cheung-Wai, Jianglin Ma, Tim Van de Voorde, and Frank Canters. "Preliminary Results of Superresolution-Enhanced Angular Hyperspectral (CHRIS/Proba) Images for Land-Cover Classification." IEEE Geoscience and Remote Sensing Letters 8, no. 6 (November 2011): 1011–15. http://dx.doi.org/10.1109/lgrs.2011.2147277.

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14

Garcia Millan, Virginia Elena, G. Arturo Sanchez-Azofeifa, and Gonzalo C. Malvarez. "Mapping Tropical Dry Forest Succession With CHRIS/PROBA Hyperspectral Images Using Nonparametric Decision Trees." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, no. 6 (June 2015): 3081–94. http://dx.doi.org/10.1109/jstars.2014.2365180.

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15

Wang, M. C., X. F. Niu, S. B. Chen, P. J. Guo, Q. Yang, and Z. J. Wang. "Inversion of chlorophyll contents by use of hyperspectral CHRIS data based on radiative transfer model." IOP Conference Series: Earth and Environmental Science 17 (March 18, 2014): 012073. http://dx.doi.org/10.1088/1755-1315/17/1/012073.

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16

Gómez-Chova, Luis, Luis Alonso, Luis Guanter, Gustavo Camps-Valls, Javier Calpe, and José Moreno. "Correction of systematic spatial noise in push-broom hyperspectral sensors: application to CHRIS/PROBA images." Applied Optics 47, no. 28 (July 22, 2008): F46. http://dx.doi.org/10.1364/ao.47.000f46.

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17

Guanter, L., L. Alonso, and J. Moreno. "First Results From the PROBA/CHRIS Hyperspectral/Multiangular Satellite System Over Land and Water Targets." IEEE Geoscience and Remote Sensing Letters 2, no. 3 (July 2005): 250–54. http://dx.doi.org/10.1109/lgrs.2005.851542.

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18

Chan, Jonathan Cheung-Wai, Jianglin Ma, Pieter Kempeneers, and Frank Canters. "Superresolution Enhancement of Hyperspectral CHRIS/Proba Images With a Thin-Plate Spline Nonrigid Transform Model." IEEE Transactions on Geoscience and Remote Sensing 48, no. 6 (June 2010): 2569–79. http://dx.doi.org/10.1109/tgrs.2009.2039797.

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19

Chen, Xiaoping, Lifu Zhang, Xia Zhang, and Bo Liu. "Comparison of the sensor dependence of vegetation indices based on Hyperion and CHRIS hyperspectral data." International Journal of Remote Sensing 34, no. 6 (November 27, 2012): 2200–2215. http://dx.doi.org/10.1080/01431161.2012.742216.

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20

Galvão, Lênio Soares, Flávio Jorge Ponzoni, Veraldo Liesenberg, and João Roberto dos Santos. "Possibilities of discriminating tropical secondary succession in Amazônia using hyperspectral and multiangular CHRIS/PROBA data." International Journal of Applied Earth Observation and Geoinformation 11, no. 1 (February 2009): 8–14. http://dx.doi.org/10.1016/j.jag.2008.04.001.

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21

Chen, Chen, Yi Ma, and Guangbo Ren. "A Convolutional Neural Network with Fletcher–Reeves Algorithm for Hyperspectral Image Classification." Remote Sensing 11, no. 11 (June 2, 2019): 1325. http://dx.doi.org/10.3390/rs11111325.

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Deep learning models, especially the convolutional neural networks (CNNs), are very active in hyperspectral remote sensing image classification. In order to better apply the CNN model to hyperspectral classification, we propose a CNN model based on Fletcher–Reeves algorithm (F–R CNN), which uses the Fletcher–Reeves (F–R) algorithm for gradient updating to optimize the convergence performance of the model in classification. In view of the fact that there are fewer optional training samples in practical applications, we further propose a method of increasing the number of samples by adding a certain degree of perturbed samples, which can also test the anti-interference ability of classification methods. Furthermore, we analyze the anti-interference and convergence performance of the proposed model in terms of different training sample data sets, different batch training sample numbers and iteration time. In this paper, we describe the experimental process in detail and comprehensively evaluate the proposed model based on the classification of CHRIS hyperspectral imagery covering coastal wetlands, and further evaluate it on a commonly used hyperspectral image benchmark dataset. The experimental results show that the accuracy of the two models after increasing training samples and adjusting the number of batch training samples is improved. When the number of batch training samples is continuously increased to 350, the classification accuracy of the proposed method can still be maintained above 80.7%, which is 2.9% higher than the traditional one. And its time consumption is less than that of the traditional one while ensuring classification accuracy. It can be concluded that the proposed method has anti-interference ability and outperforms the traditional CNN in terms of batch computing adaptability and convergence speed.

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22

Davies, W. H., and P. R. J. North. "Synergistic angular and spectral estimation of aerosol properties using CHRIS/PROBA-1 and simulated Sentinel-3 data." Atmospheric Measurement Techniques Discussions 7, no. 6 (June 3, 2014): 5381–422. http://dx.doi.org/10.5194/amtd-7-5381-2014.

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Abstract. A method has been developed to estimate Aerosol Optical Depth (AOD), Fine Mode Fraction (FMF) and Single Scattering Albedo (SSA) over land surfaces using simulated Sentinel-3 data. The method uses inversion of a coupled surface/atmosphere radiative transfer model, and includes a general physical model of angular surface reflectance. An iterative process is used to determine the optimum value of the aerosol properties providing the best fit of the corrected reflectance values for a number of view angles and wavelengths with those provided by the physical model. A method of estimating AOD using only angular retrieval has previously been demonstrated on data from the ENVISAT and PROBA-1 satellite instruments, and is extended here to the synergistic spectral and angular sampling of Sentinel-3 and the additional aerosol properties. The method is tested using hyperspectral, multi-angle Compact High Resolution Imaging Spectrometer (CHRIS) images. The values obtained from these CHRIS observations are validated using ground based sun-photometer measurements. Results from 22 image sets using the synergistic retrieval and improved aerosol models show an RMSE of 0.06 in AOD, reduced to 0.03 over vegetated targets.

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23

Delegido, J., G. Fernández, S. Gandía, and J. Moreno. "Retrieval of chlorophyll content and LAI of crops using hyperspectral techniques: application to PROBA/CHRIS data." International Journal of Remote Sensing 29, no. 24 (November 7, 2008): 7107–27. http://dx.doi.org/10.1080/01431160802238401.

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Davies, W. H., and P. R. J. North. "Synergistic angular and spectral estimation of aerosol properties using CHRIS/PROBA-1 and simulated Sentinel-3 data." Atmospheric Measurement Techniques 8, no. 4 (April 10, 2015): 1719–31. http://dx.doi.org/10.5194/amt-8-1719-2015.

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Abstract. We develop a method to derive aerosol properties over land surfaces using combined spectral and angular information, such as available from ESA Sentinel-3 mission, to be launched in 2015. A method of estimating aerosol optical depth (AOD) using only angular retrieval has previously been demonstrated on data from the ENVISAT and PROBA-1 satellite instruments, and is extended here to the synergistic spectral and angular sampling of Sentinel-3. The method aims to improve the estimation of AOD, and to explore the estimation of fine mode fraction (FMF) and single scattering albedo (SSA) over land surfaces by inversion of a coupled surface/atmosphere radiative transfer model. The surface model includes a general physical model of angular and spectral surface reflectance. An iterative process is used to determine the optimum value of the aerosol properties providing the best fit of the corrected reflectance values to the physical model. The method is tested using hyperspectral, multi-angle Compact High Resolution Imaging Spectrometer (CHRIS) images. The values obtained from these CHRIS observations are validated using ground-based sun photometer measurements. Results from 22 image sets using the synergistic retrieval and improved aerosol models show an RMSE of 0.06 in AOD, reduced to 0.03 over vegetated targets.

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Barnsley, M. J., J. J. Settle, M. A. Cutter, D. R. Lobb, and F. Teston. "The PROBA/CHRIS mission: a low-cost smallsat for hyperspectral multiangle observations of the Earth surface and atmosphere." IEEE Transactions on Geoscience and Remote Sensing 42, no. 7 (July 2004): 1512–20. http://dx.doi.org/10.1109/tgrs.2004.827260.

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Chan, Jonathan Cheung-Wai, Pieter Beckers, Toon Spanhove, and Jeroen Vanden Borre. "An evaluation of ensemble classifiers for mapping Natura 2000 heathland in Belgium using spaceborne angular hyperspectral (CHRIS/Proba) imagery." International Journal of Applied Earth Observation and Geoinformation 18 (August 2012): 13–22. http://dx.doi.org/10.1016/j.jag.2012.01.002.

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Hill, Michael J., Andrew Millington, Rebecca Lemons, and Cherie New. "Functional Phenology of a Texas Post Oak Savanna from a CHRIS PROBA Time Series." Remote Sensing 11, no. 20 (October 15, 2019): 2388. http://dx.doi.org/10.3390/rs11202388.

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Remnant midwestern oak savannas in the USA have been altered by fire suppression and the encroachment of woody evergreen trees and shrubs. The Gus Engeling Wildlife Management Area (GEWMA) near Palestine, Texas represents a relatively intact southern example of thickening and evergreen encroachment in oak savannas. In this study, 18 images from the CHRIS/PROBA (Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy) sensor were acquired between June 2009 and October 2010 and used to explore variation in canopy dynamics among deciduous and evergreen trees and shrubs, and savanna grassland in seasonal leaf-on and leaf-off conditions. Nadir CHRIS images from the 11 useable dates were processed to surface reflectance and a selection of vegetation indices (VIs) sensitive to pigments, photosynthetic efficiency, and canopy water content were calculated. An analysis of temporal VI phenology was undertaken using a fishnet polygon at 90 m resolution incorporating tree densities from a classified aerial photo and soil type polygons. The results showed that the major differences in spectral phenology were associated with deciduous tree density, the density of evergreen trees and shrubs—especially during deciduous leaf-off periods—broad vegetation types, and soil type interactions with elevation. The VIs were sensitive to high densities of evergreens during the leaf-off period and indicative of a photosynthetic advantage over deciduous trees. The largest differences in VI profiles were associated with high and low tree density, and soil types with the lowest and highest available soil water. The study showed how time series of hyperspectral data could be used to monitor the relative abundance and vigor of desirable and less desirable species in conservation lands.

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Awad, Mohamad, Ihab Jomaa, and Fatima Arab. "Improved Capability in Stone Pine Forest Mapping and Management in Lebanon Using Hyperspectral CHRIS-Proba Data Relative to Landsat ETM+." Photogrammetric Engineering & Remote Sensing 80, no. 8 (August 1, 2014): 725–31. http://dx.doi.org/10.14358/pers.80.8.725.

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Tirelli, Cecilia, Gabriele Curci, Ciro Manzo, Paolo Tuccella, and Cristiana Bassani. "Effect of the Aerosol Model Assumption on the Atmospheric Correction over Land: Case Studies with CHRIS/PROBA Hyperspectral Images over Benelux." Remote Sensing 7, no. 7 (June 26, 2015): 8391–415. http://dx.doi.org/10.3390/rs70708391.

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Kennedy, Blair E., Douglas J. King, and Jason Duffe. "Comparison of Empirical and Physical Modelling for Estimation of Biochemical and Biophysical Vegetation Properties: Field Scale Analysis across an Arctic Bioclimatic Gradient." Remote Sensing 12, no. 18 (September 19, 2020): 3073. http://dx.doi.org/10.3390/rs12183073.

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To evaluate the potential of multi-angle hyperspectral sensors for monitoring vegetation variables in Arctic environments, empirical and physical modelling using field data was implemented for the retrieval of leaf and canopy chlorophyll content (LCC, CCC) and plant area index (PAI) measured at four sites situated across a bioclimatic gradient in the Western Canadian Arctic. Field reflectance data were acquired with an ASD FieldSpec (305–1075 nm) and used to simulate CHRIS Mode1 spectra (411–997 nm). Multi-angle measurements were taken corresponding to CHRIS view zenith angles (VZA) (−55°, −36°, 0°, +36°, +55°). Empirical modelling compared parametric regression based on vegetation indices (VIs) to non-parametric Gaussian Processes Regression (GPR). In physical modelling, PROSAIL was inverted using numerical optimization and look-up table (LUT) approaches. Cross-validation of the empirical models ranked GPR as best, followed by simple ratio (SR) with optimally selected NIR and red wavelengths, and then ROSAVI using its published wavelengths (mean r2cv = 0.62, 0.58, and 0.54, respectively across all sites, variables, and VZAs). However, the best predictive performance was achieved by SR followed by GPR and ROSAVI (NRMSEcv = 0.12, 0.16, 0.16, respectively). PROSAIL simulated the multi-angle top-of-canopy reflectance well with numerical optimization (r2 = ~0.99, RMSE = 0.004 ± 0.002), but best performing LUT models of LCC, CCC and PAI were poorer than the empirical approaches (mean r2 = 0.48, mean NRMSE = 0.22). PROSAIL performed best at the high Arctic sparsely vegetated site (r2 = 0.57–0.86 for all parameters). Overall, the best performing VZA was −55° for empirical modelling and 0° and ±55° for physical modelling; however, these were not significantly better than the other VZAs. Overall, this study demonstrates that, for Arctic vegetation, nadir narrowband reflectance data used to derive simple empirical VIs with optimally selected bands is a more efficient approach for modelling chlorophyll and PAI than more complex empirical and physical approaches.

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Latorre-Carmona, Pedro, Yuri Knyazikhin, Luis Alonso, Jose F. Moreno, Filiberto Pla, and Yang Yan. "On Hyperspectral Remote Sensing of Leaf Biophysical Constituents: Decoupling Vegetation Structure and Leaf Optics Using CHRIS–PROBA Data Over Crops in Barrax." IEEE Geoscience and Remote Sensing Letters 11, no. 9 (September 2014): 1579–83. http://dx.doi.org/10.1109/lgrs.2014.2305168.

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Guzzi, Donatella, Vanni Nardino, Cinzia Lastri, and Valentina Raimondi. "A Fast Iterative Procedure for Adjacency Effects Correction on Remote Sensed Data." Remote Sensing 13, no. 9 (May 5, 2021): 1799. http://dx.doi.org/10.3390/rs13091799.

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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.

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Stagakis, Stavros, Nikos Markos, Olga Sykioti, and Aris Kyparissis. "Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: An application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations." Remote Sensing of Environment 114, no. 5 (May 2010): 977–94. http://dx.doi.org/10.1016/j.rse.2009.12.006.

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Alakian, Alexandre, and Véronique Achard. "Classification of Hyperspectral Reflectance Images With Physical and Statistical Criteria." Remote Sensing 12, no. 14 (July 21, 2020): 2335. http://dx.doi.org/10.3390/rs12142335.

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A classification method of hyperspectral reflectance images named CHRIPS (Classification of Hyperspectral Reflectance Images with Physical and Statistical criteria) is presented. This method aims at classifying each pixel from a given set of thirteen classes: unidentified dark surface, water, plastic matter, carbonate, clay, vegetation (dark green, dense green, sparse green, stressed), house roof/tile, asphalt, vehicle/paint/metal surface and non-carbonated gravel. Each class is characterized by physical criteria (detection of specific absorptions or shape features) or statistical criteria (use of dedicated spectral indices) over spectral reflectance. CHRIPS input is a hyperspectral reflectance image covering the spectral range [400–2500 nm]. The presented method has four advantages, namely: (i) is robust in transfer, class identification is based on criteria that are not very sensitive to sensor type; (ii) does not require training, criteria are pre-defined; (iii) includes a reject class, this class reduces misclassifications; (iv) high precision and recall, F 1 score is generally above 0.9 in our test. As the number of classes is limited, CHRIPS could be used in combination with other classification algorithms able to process the reject class in order to decrease the number of unclassified pixels.

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Bettina, Giuseppina Fiore, Belinda Giambra, Giuseppe Cavallaro, Giuseppe Lazzara, Bartolomeo Megna, Ramil Fakhrullin, Farida Akhatova, and Rawil Fakhrullin. "Restoration of a XVII Century’s predella reliquary: From Physico-Chemical Characterization to the Conservation Process." Forests 12, no. 3 (March 15, 2021): 345. http://dx.doi.org/10.3390/f12030345.

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We report on the restoration of a XVII century’s predella reliquary, which is a part of a larger setup that includes a wall reliquary and a wooden crucified Christ, both belonging to the church of “Madre Maria SS. Assunta”, in Polizzi Generosa, Sicily, Italy. The historical/artistic and paleographic research was flanked successfully by the scientific objective characterization of the materials. The scientific approach was relevant in the definition of the steps for the restoration of the artefact. The optical microscopy was used for the identification of the wood species. Electron microscopy and elemental mapping by energy-dispersive X-ray (EDX) was successful in the identification of the layered structure for the gilded surface. The hyperspectral imaging method was successfully employed for an objective chemical mapping of the surface composition. We proved that the scientific approach is necessary for a critical and objective evaluation of the conservation state and it is a necessary step toward awareness of the historical, liturgical, spiritual and artistic value. In the second part of this work, we briefly describe the conservation protocol and the use of a weak nanocomposite glue. In particular, a sustainable approach was considered and therefore mixtures of a biopolymer from natural resources, such as funori from algae, and naturally occurring halloysite nanotubes were considered. Tensile tests provided the best composition for this green nanocomposite glue.

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