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Ciraolo, Giuseppe, Mario Minacapilli, and Maurizio Sciortino. "STIMA DELL’EVAPOTRASPIRAZIONE EFFETTIVA MEDIANTE TELERILEVAMENTO AEREO IPERSPETTRALE." Journal of Agricultural Engineering 38, no. 2 (June 30, 2007): 49. http://dx.doi.org/10.4081/jae.2007.2.49.
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Pepe, Monica, Loredana Pompilio, Beniamino Gioli, Lorenzo Busetto, and Mirco Boschetti. "Detection and Classification of Non-Photosynthetic Vegetation from PRISMA Hyperspectral Data in Croplands." Remote Sensing 12, no. 23 (November 28, 2020): 3903. http://dx.doi.org/10.3390/rs12233903.
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This study introduces a first assessment of the capabilities of PRISMA (PRecursore IperSpettrale della Missione Applicativa)—the new hyperspectral satellite sensor of the Italian Space Agency (ASI)—for Non-Photosynthetic Vegetation (NPV) monitoring, a topic which is becoming very relevant in the field of sustainable agriculture, being an indicator of crop residue (CR) presence in the field. Data-sets collected during the mission validation phase in croplands are used for mapping the NPV presence and for modelling the diagnostic absorption band of cellulose around 2.1 μm with an Exponential Gaussian Optimization approach, in the perspective of the prediction of the abundance of crop residues. Results proved that PRISMA data are suitable for these tasks, and call for further investigation to achieve quantitative estimates of specific biophysical variables, also in the framework of other hyperspectral missions.
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Aneece, Itiya, and Prasad S. Thenkabail. "New Generation Hyperspectral Sensors DESIS and PRISMA Provide Improved Agricultural Crop Classifications." Photogrammetric Engineering & Remote Sensing 88, no. 11 (November 1, 2022): 715–29. http://dx.doi.org/10.14358/pers.22-00039r2.
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Using new remote sensing technology to study agricultural crops will support advances in food and water security. The recently launched, new generation spaceborne hyperspectral sensors, German DLR Earth Sensing Imaging Spectrometer (DESIS) and Italian PRecursore IperSpettrale della Missione Applicativa (PRISMA), provide unprecedented data in hundreds of narrow spectral bands for the study of the Earth. Therefore, our overarching goal in this study was to use these data to explore advances that can be made in agricultural research. We selected PRISMA and DESIS images during the 2020 growing season in California's Central Valley to study seven major crops. PRISMA and DESIS images were highly correlated (R 2of 0.9–0.95). Out of the 235 DESIS bands (400–1000 nm) and 238 PRISMA bands (400–2500 nm), 26 (11%) and 45 (19%) bands, respectively, were optimal to study agricultural crops. These optimal bands provided crop type classification accuracies of 83–90%. Hyperspectral vegetation indices to estimate plant pigment content, stress, biomass, moisture, and cellulose/lignin content were also identified.
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Hamzeh, S., M. Hajeb, S. K. Alavipanah, and J. Verrelst. "RETRIEVAL OF SUGARCANE LEAF AREA INDEX FROM PRISMA HYPERSPECTRAL DATA." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (January 13, 2023): 271–77. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-271-2023.
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Abstract. The PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite of the Italian Space Agency, lunched in 2019, has provided a new generation source of hyperspectral data showing to have high potential in vegetation variable retrieval. In this study, the newly available PRISMA spectra were exploited to retrieve Leaf Area Index (LAI) of sugarcane using a new kind of Artificial Neural Networks (ANN) so-called Bayesian Regularized Artificial Neural Network (BRANN). The suggested BRANN retrieval model was implemented over a dataset collected during a field campaign in Amir Kabir Sugarcane Agro-Industrial zone, Khuzestan, Iran, in 2020. Principle Component Analysis (PCA) was utilized to reduce the dimensionality of PRISMA data cube. An accuracy assessment based on the bootstrapping procedure indicated RMSE of 0.67 m2/m2 for the LAI retrieval by applying the BRANN model. This study is a confirmation of the high performance of the BRANN method and high potential of PRISMA images to retrieve sugarcane LAI.
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Rossini, M., C. Panigada, M. Meroni, L. Busetto, R. Castrovinci, and R. Colombo. "Pedunculate oak forests (Quercus robur L.) survey in the Ticino Regional Park (Italy) by remote sensing." Forest@ - Rivista di Selvicoltura ed Ecologia Forestale 4, no. 2 (June 19, 2007): 194–203. http://dx.doi.org/10.3832/efor0450-0040194.
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Shaik, Riyaaz Uddien, Aiswarya Unni, and Weiping Zeng. "Quantum Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers." Remote Sensing 14, no. 22 (November 16, 2022): 5774. http://dx.doi.org/10.3390/rs14225774.
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A quantum machine is a human-made device whose collective motion follows the laws of quantum mechanics. Quantum machine learning (QML) is machine learning for quantum computers. The availability of quantum processors has led to practical applications of QML algorithms in the remote sensing field. Quantum machines can learn from fewer data than non-quantum machines, but because of their low processing speed, quantum machines cannot be applied to an image that has hundreds of thousands of pixels. Researchers around the world are exploring applications for QML and in this work, it is applied for pseudo-labelling of samples. Here, a PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral dataset is prepared by quantum-based pseudo-labelling and 11 different machine learning algorithms viz., support vector machine (SVM), K-nearest neighbour (KNN), random forest (RF), light gradient boosting machine (LGBM), XGBoost, support vector classifier (SVC) + decision tree (DT), RF + SVC, RF + DT, XGBoost + SVC, XGBoost + DT, and XGBoost + RF with this dataset are evaluated. An accuracy of 86% was obtained for the classification of pine trees using the hybrid XGBoost + decision tree technique.
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Spiller, D., L. Ansalone, S. Amici, A. Piscini, and P. P. Mathieu. "ANALYSIS AND DETECTION OF WILDFIRES BY USING PRISMA HYPERSPECTRAL IMAGERY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 215–22. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-215-2021.
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Abstract. This paper deals with the analysis and detection of wildfires by using PRISMA imagery. Precursore IperSpettrale della Missione Applicativa (Hyperspectral Precursor of the Application Mission, PRISMA) is a new hyperspectral mission by ASI (Agenzia Spaziale Italiana, Italian Space Agency) launched in 2019. This mission provides hyperspectral images with a spectral range of 0.4–2.5 µm and an average spectral resolution less than 10 nm. In this work, we used the PRISMA hypercube acquired during the Australian bushfires of December 2019 in New South Wales. The analysis of the image is presented considering the unique amount of information contained in the continuous spectral signature of the hypercube. The Carbon dioxide Continuum-Interpolated Band Ratio (CO2 CIBR), Hyperspectral Fire Detection Index (HFDI), and Normalized Burn Index (NBR) will be used to analyze the informative content of the image, along with the analysis of some specific visible, near-infrared and shortwave-infrared bands. A multiclass classification is presented by using a I-dimensional convolutional neural network (CNN), and the results will be compared with the ones given by a support vector machine classifier reported in literature. Finally, some preliminary results related to wildfire temperature estimation are presented.
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Vangi, Elia, Giovanni D’Amico, Saverio Francini, Francesca Giannetti, Bruno Lasserre, Marco Marchetti, and Gherardo Chirici. "The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination." Sensors 21, no. 4 (February 8, 2021): 1182. http://dx.doi.org/10.3390/s21041182.
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Different forest types based on different tree species composition may have similar spectral signatures if observed with traditional multispectral satellite sensors. Hyperspectral imagery, with a more continuous representation of their spectral behavior may instead be used for their classification. The new hyperspectral Precursore IperSpettrale della Missione Applicativa (PRISMA) sensor, developed by the Italian Space Agency, is able to capture images in a continuum of 240 spectral bands ranging between 400 and 2500 nm, with a spectral resolution smaller than 12 nm. The new sensor can be employed for a large number of remote sensing applications, including forest types discrimination. In this study, we compared the capabilities of the new PRISMA sensor against the well-known Sentinel-2 Multi-Spectral Instrument (MSI) in recognition of different forest types through a pairwise separability analysis carried out in two study areas in Italy, using two different nomenclature systems and four separability metrics. The PRISMA hyperspectral sensor, compared to Sentinel-2 MSI, allowed for a better discrimination in all forest types, increasing the performance when the complexity of the nomenclature system also increased. PRISMA achieved an average improvement of 40% for the discrimination between two forest categories (coniferous vs. broadleaves) and of 102% in the discrimination between five forest types based on main tree species groups.
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Alicandro, Maria, Elena Candigliota, Donatella Dominici, Francesco Immordino, Fabrizio Masin, Nicole Pascucci, Raimondo Quaresima, and Sara Zollini. "Hyperspectral PRISMA and Sentinel-2 Preliminary Assessment Comparison in Alba Fucens and Sinuessa Archaeological Sites (Italy)." Land 11, no. 11 (November 17, 2022): 2070. http://dx.doi.org/10.3390/land11112070.
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Over the last decades, remote sensing techniques have contributed to supporting cultural heritage studies and management, including archaeological sites as well as their territorial context and geographical surroundings. This paper aims to investigate the capabilities and limitations of the new hyperspectral sensor PRISMA (Precursore IperSpettrale della Missione Applicativa) by the Italian Space Agency (ASI), still little applied to archaeological studies. The PRISMA sensor was tested on Italian terrestrial (Alba Fucens, Massa D’Albe, L’Aquila) and marine (Sinuessa, Mondragone, Caserta) archaeological sites. A comparison between PRISMA hyperspectral imagery and the well-known Sentinel-2 Multi-Spectral Instrument (MSI) was performed in order to better understand features and outputs useful to investigate the aforementioned areas. At first, bad bands analysis and noise removal were performed, in order to delete the numerically corrupted bands. Principal component analysis (PCA) was carried out to highlight invisible details in the original image; then, spectral signatures of representative areas were extracted and compared to Sentinel-2 data. At last, a classification analysis (ML and SAM) was performed both on PRISMA and Sentinel-2 imagery. The results showed a full agreement between Sentinel and PRISMA data, enhancing the capability of PRISMA in extrapolating more spectral information and providing a better reliability in the extraction of the features.
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Acito, Nicola, Marco Diani, Gregorio Procissi, and Giovanni Corsini. "Atmospheric Compensation of PRISMA Data by Means of a Learning Based Approach." Remote Sensing 13, no. 15 (July 28, 2021): 2967. http://dx.doi.org/10.3390/rs13152967.
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Atmospheric compensation (AC) allows the retrieval of the reflectance from the measured at-sensor radiance and is a fundamental and critical task for the quantitative exploitation of hyperspectral data. Recently, a learning-based (LB) approach, named LBAC, has been proposed for the AC of airborne hyperspectral data in the visible and near-infrared (VNIR) spectral range. LBAC makes use of a parametric regression function whose parameters are learned by a strategy based on synthetic data that accounts for (1) a physics-based model for the radiative transfer, (2) the variability of the surface reflectance spectra, and (3) the effects of random noise and spectral miscalibration errors. In this work we extend LBAC with respect to two different aspects: (1) the platform for data acquisition and (2) the spectral range covered by the sensor. Particularly, we propose the extension of LBAC to spaceborne hyperspectral sensors operating in the VNIR and short-wave infrared (SWIR) portion of the electromagnetic spectrum. We specifically refer to the sensor of the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission, and the recent Earth Observation mission of the Italian Space Agency that offers a great opportunity to improve the knowledge on the scientific and commercial applications of spaceborne hyperspectral data. In addition, we introduce a curve fitting-based procedure for the estimation of column water vapor content of the atmosphere that directly exploits the reflectance data provided by LBAC. Results obtained on four different PRISMA hyperspectral images are presented and discussed.
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Baiocchi, Valerio, Francesca Giannone, and Felicia Monti. "How to Orient and Orthorectify PRISMA Images and Related Issues." Remote Sensing 14, no. 9 (April 21, 2022): 1991. http://dx.doi.org/10.3390/rs14091991.
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The orientation of satellite images is a necessary operation for the correct geometric use of satellite images whether they are used individually to obtain an orthophoto or as stereocouples to extract three-dimensional information. The orientation allows us to reconstruct the correct position on the ground of the single pixels that form the image, which normally can be performed using certain functions of commercial software customised for each specific satellite. These functions read the metadata parameters provided by the satellite operator and use them to correctly orient the images. Unfortunately, these parameters have not been standardised and various satellites report them according to variable conventions, so new satellites or those that are not widely used cannot be oriented automatically. The PRISMA satellite launched by the Italian Space Agency (ASI) releases free hyperspectral and panchromatic images with metric resolution, but there is not yet a standardised procedure for orienting its images and this limits its usability. This paper reports on the first experimentation of orientation and orthorectification of PRISMA (PRecursore IperSpettrale della Missione Applicativa) images carried out using the three most widely used models, namely the rigorous, the Rational Polynomial Coefficients (RPC) and the Rational Polynomial Functions (RPF) tools. The results obtained by interpreting the parameters and making them suitable for use in standard procedures have made it possible to obtain results with an accuracy equal to the maximum resolution of panchromatic images (5 m), thus making it possible to achieve the highest level of geometric accuracy that can be extracted from the images themselves.
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Amici, Stefania, and Alessandro Piscini. "Exploring PRISMA Scene for Fire Detection: Case Study of 2019 Bushfires in Ben Halls Gap National Park, NSW, Australia." Remote Sensing 13, no. 8 (April 7, 2021): 1410. http://dx.doi.org/10.3390/rs13081410.
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Precursore IperSpettrale della Missione Applicativa (Hyperspectral Precursor of the Application Mission, PRISMA) is a new hyperspectral mission by the ASI (Agenzia Spaziale Italiana, Italian Space Agency) mission launched in 2019 to measure the unique spectral features of diverse materials including vegetation and forest disturbances. In this study, we explored the potential use of this new sensor PRISMA for active wildfire characterization. We used the PRISMA hypercube acquired during the Australian bushfires of 2019 in New South Wales to test three detection techniques that take advantage of the unique spectral features of biomass burning in the spectral range measured by PRISMA. The three methods—the CO2-CIBR (continuum interpolated band ratio), HFDI (hyperspectral fire detection index) and AKBD (advanced K band difference)—were adapted to the PRISMA sensor’s characteristics and evaluated in terms of performance. Classification techniques based on machine learning algorithms (support vector machine, SVM) were used in combination with the visual interpretation of a panchromatic sharpened PRISMA image for validation. Preliminary analysis showed a good overall performance of the instrument in terms of radiance. We observed that the presence of the striping effect in the data can influence the performance of the indices. Both the CIBR and HFDI adapted for PRISMA were able to produce a detection rate spanning between 0.13561 and 0.81598 for CO2-CIBR and that between 0.36171 and 0.88431 depending on the chosen band combination. The potassium emission index turned out to be inadequate for locating flaming in our data, possibly due to multiple factors such as striping noise and the spectral resolution (12 nm) of the PRISMA band centered at the potassium emission.
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Giardino, Claudia, Mariano Bresciani, Federica Braga, Alice Fabbretto, Nicola Ghirardi, Monica Pepe, Marco Gianinetto, et al. "First Evaluation of PRISMA Level 1 Data for Water Applications." Sensors 20, no. 16 (August 14, 2020): 4553. http://dx.doi.org/10.3390/s20164553.
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This study presents a first assessment of the Top-Of-Atmosphere (TOA) radiances measured in the visible and near-infrared (VNIR) wavelengths from PRISMA (PRecursore IperSpettrale della Missione Applicativa), the new hyperspectral satellite sensor of the Italian Space Agency in orbit since March 2019. In particular, the radiometrically calibrated PRISMA Level 1 TOA radiances were compared to the TOA radiances simulated with a radiative transfer code, starting from in situ measurements of water reflectance. In situ data were obtained from a set of fixed position autonomous radiometers covering a wide range of water types, encompassing coastal and inland waters. A total of nine match-ups between PRISMA and in situ measurements distributed from July 2019 to June 2020 were analysed. Recognising the role of Sentinel-2 for inland and coastal waters applications, the TOA radiances measured from concurrent Sentinel-2 observations were added to the comparison. The results overall demonstrated that PRISMA VNIR sensor is providing TOA radiances with the same magnitude and shape of those in situ simulated (spectral angle difference, SA, between 0.80 and 3.39; root mean square difference, RMSD, between 0.98 and 4.76 [mW m−2 sr−1 nm−1]), with slightly larger differences at shorter wavelengths. The PRISMA TOA radiances were also found very similar to Sentinel-2 data (RMSD < 3.78 [mW m−2 sr−1 nm−1]), and encourage a synergic use of both sensors for aquatic applications. Further analyses with a higher number of match-ups between PRISMA, in situ and Sentinel-2 data are however recommended to fully characterize the on-orbit calibration of PRISMA for its exploitation in aquatic ecosystem mapping.
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Romaniello, Vito, Malvina Silvestri, Maria Fabrizia Buongiorno, and Massimo Musacchio. "Comparison of PRISMA Data with Model Simulations, Hyperion Reflectance and Field Spectrometer Measurements on ‘Piano delle Concazze’ (Mt. Etna, Italy)." Sensors 20, no. 24 (December 17, 2020): 7224. http://dx.doi.org/10.3390/s20247224.
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In this work, we compare first acquisitions from the ASI-PRISMA (Agenzia Spaziale Italiana-PRecursore IperSpettrale della Missione Applicativa) space mission with model simulations, past data acquired by the Hyperion sensor and field spectrometer measurements. The test site is ‘Piano delle Concazze’ (Mt. Etna, Italy), suitable for calibration purposes due to its homogeneity characteristics. The area measures at about 0.2 km2 and is composed of very homogeneous trachybasalt rich in plagioclase and olivine. Three PRISMA acquisitions, achieved on 31 July and 8 and 17 August 2019, are analyzed. Firstly, spectral profiles of PRISMA top of atmosphere (TOA) radiance are compared with MODerate resolution atmospheric TRANsmission (MODTRAN) simulations. The Pearson correlation coefficient is equal to 0.998 and 0.994 for VNIR (Visible and Near InfraRed) and SWIR (Short-Wave InfraRed) spectral ranges, respectively. PRISMA radiance overestimates values simulated by MODTRAN for all considered days, showing a mean bias of +5.22 and of +0.91 Wm−2sr−1µm−1 for VNIR and SWIR, respectively. The relative mean difference between reflectance values estimated by PRISMA and Hyperion, on the test area, is around +19%, despite the great difference in time acquisition (up to 19 years); PRISMA slightly overestimates Hyperion reflectance with an absolute mean difference of about +0.0083, within the variability of Hyperion acquisitions of ±0.0250 (corresponding to ±2 standard deviation). Finally, FieldSpec measurements also confirm the great quality of PRISMA reflectance estimations. The absolute mean difference results are around +0.0089 (corresponding to a relative error of about +21%). In the study, we investigate only the lower values of reflectance characterizing the test site. A more complete evaluation of PRISMA performances needs to consider other test sites with different optical characteristics.
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Gancheva, Irina. "Analysis of hyperspectral and multispectral reflectance spectra in the Black Sea coastal area near the Danube delta: comparison of PRISMA and Sentinel-2 observations." Journal of Physics: Conference Series 2255, no. 1 (April 1, 2022): 012015. http://dx.doi.org/10.1088/1742-6596/2255/1/012015.
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Abstract In this study we investigate the possibility to distinguish between different water types in the Danube delta and the Black Sea coastal zone based on the reflectance spectra. For this we use hyperspectral satellite images from PRISMA (PRecursore IperSpettrale della Missione Applicativa) and multispectral images from Sentinel-2 MSI (MultiSpectral Instrument) in July 2020. The visual inspection of the available acquisitions differentiates between four aquatic types: lagoons; river and stream plumes mixing with marine waters; turbid and optically complex coastal waters; and optically clean waters away from the shore. For each of the four types we determine the characteristic averaged reflectance spectra from PRISMA and Sentinel-2 for the spectral range from 400 nm to 2500 nm. It is found that Sentinel-2 spectra are very similar for the 4 water types, in contrast to the PRISMA spectra which give substantial additional information. Further we analyse the gradual modification of the characteristic hyper- and multispectral spectra from the shoreline to the open sea (∼25 km distance) in 15 locations passing through areas of varying turbidity. We demonstrate that the intensity of surface reflectance from the hyperspectral instrument decreases gradually with distance from shoreline, clearly showing the transition zone between riverine and marine water. The multispectral reflectance spectra for the same study areas did not present such consistent behaviour. The presented results demonstrate the benefits of hyperspectral over multispectral images for turbid aquatic regions in the Black Sea coastal zone. They show that with little requirements regarding pre-processing and computational resources hyperspectral data can contribute greatly to classification of water types, in respect of their turbidity.
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Niroumand-Jadidi, Milad, Francesca Bovolo, and Lorenzo Bruzzone. "Water Quality Retrieval from PRISMA Hyperspectral Images: First Experience in a Turbid Lake and Comparison with Sentinel-2." Remote Sensing 12, no. 23 (December 6, 2020): 3984. http://dx.doi.org/10.3390/rs12233984.
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A new era of spaceborne hyperspectral imaging has just begun with the recent availability of data from PRISMA (PRecursore IperSpettrale della Missione Applicativa) launched by the Italian space agency (ASI). There has been pre-launch optimism that the wealth of spectral information offered by PRISMA can contribute to a variety of aquatic science and management applications. Here, we examine the potential of PRISMA level 2D images in retrieving standard water quality parameters, including total suspended matter (TSM), chlorophyll-a (Chl-a), and colored dissolved organic matter (CDOM) in a turbid lake (Lake Trasimeno, Italy). We perform consistency analyses among the aquatic products (remote sensing reflectance (Rrs) and constituents) derived from PRISMA and those from Sentinel-2. The consistency analyses are expanded to synthesized Sentinel-2 data as well. By spectral downsampling of the PRISMA images, we better isolate the impact of spectral resolution in retrieving the constituents. The retrieval of constituents from both PRISMA and Sentinel-2 images is built upon inverting the radiative transfer model implemented in the Water Color Simulator (WASI) processor. The inversion involves a parameter (gdd) to compensate for atmospheric and sun-glint artifacts. A strong agreement is indicated for the cross-sensor comparison of Rrs products at different wavelengths (average R ≈ 0.87). However, the Rrs of PRISMA at shorter wavelengths (<500 nm) is slightly overestimated with respect to Sentinel-2. This is in line with the estimates of gdd through the inversion that suggests an underestimated atmospheric path radiance of PRISMA level 2D products compared to the atmospherically corrected Sentinel-2 data. The results indicate the high potential of PRISMA level 2D imagery in mapping water quality parameters in Lake Trasimeno. The PRISMA-based retrievals agree well with those of Sentinel-2, particularly for TSM.
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Cavalli, Rosa Maria. "Local, Daily, and Total Bio-Optical Models of Coastal Waters of Manfredonia Gulf Applied to Simulated Data of CHRIS, Landsat TM, MIVIS, MODIS, and PRISMA Sensors for Evaluating the Error." Remote Sensing 12, no. 9 (May 1, 2020): 1428. http://dx.doi.org/10.3390/rs12091428.
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The spatial–temporal resolution of remote data covers coastal water variability, but this approach offers a lower accuracy than in situ observations. Two of the major error sources occur due to the parameterization of bio-optical models and spectral capability of the remote data. These errors were evaluated by exploiting data acquired in the coastal waters of Manfredonia Gulf. Chlorophyll-a concentrations, absorption of the colored dissolved organic material at 440 nm (aCDOM440nm), and tripton concentrations measured in situ varied between 0.09–1.76 mgm−3, 0.00–0.41 m−1, and 1.97–8.90 gm−3. In accordance with the position and time of in situ surveys, 36 local models, four daily models, and one total bio-optical model were parameterized and validated using in situ data before applying to Compact High-Resolution Imaging Spectrometer (CHRIS) mode 1, CHRIS mode 2, Landsat Thematic Mapper (TM), Multispectral Infrared and Visible Imaging Spectrometer (MIVIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Precursore Iperspettrale della Missione Applicativa (PRISMA) simulated data. Concentrations retrieved from PRISMA data using local models highlighted the smallest errors. Because tripton abundance is great and tripton absorptions were better resolved than those of chlorophyll-a and colored dissolved organic material (CDOM), tripton concentrations were adequately retrieved from all data using total models, while only local models adequately retrieved chlorophyll-a concentrations and aCDOM440nm from CHRIS mode 1, CHRIS mode 2, MIVIS, and MODIS data. Therefore, the application of local models shows smaller errors than those of daily and total models; however, the capability to resolve the absorption of water constituents and analyze their concentration range can dictate the model choice. Consequently, the integration of more models allows us to overcome the limitations of the data and sensors.
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Pascual-Venteo, Ana B., Enrique Portalés, Katja Berger, Giulia Tagliabue, Jose L. Garcia, Adrián Pérez-Suay, Juan Pablo Rivera-Caicedo, and Jochem Verrelst. "Prototyping Crop Traits Retrieval Models for CHIME: Dimensionality Reduction Strategies Applied to PRISMA Data." Remote Sensing 14, no. 10 (May 19, 2022): 2448. http://dx.doi.org/10.3390/rs14102448.
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In preparation for new-generation imaging spectrometer missions and the accompanying unprecedented inflow of hyperspectral data, optimized models are needed to generate vegetation traits routinely. Hybrid models, combining radiative transfer models with machine learning algorithms, are preferred, however, dealing with spectral collinearity imposes an additional challenge. In this study, we analyzed two spectral dimensionality reduction methods: principal component analysis (PCA) and band ranking (BR), embedded in a hybrid workflow for the retrieval of specific leaf area (SLA), leaf area index (LAI), canopy water content (CWC), canopy chlorophyll content (CCC), the fraction of absorbed photosynthetic active radiation (FAPAR), and fractional vegetation cover (FVC). The SCOPE model was used to simulate training data sets, which were optimized with active learning. Gaussian process regression (GPR) algorithms were trained over the simulations to obtain trait-specific models. The inclusion of PCA and BR with 20 features led to the so-called GPR-20PCA and GPR-20BR models. The 20PCA models encompassed over 99.95% cumulative variance of the full spectral data, while the GPR-20BR models were based on the 20 most sensitive bands. Validation against in situ data obtained moderate to optimal results with normalized root mean squared error (NRMSE) from 13.9% (CWC) to 22.3% (CCC) for GPR-20PCA models, and NRMSE from 19.6% (CWC) to 29.1% (SLA) for GPR-20BR models. Overall, the GPR-20PCA slightly outperformed the GPR-20BR models for all six variables. To demonstrate mapping capabilities, both models were tested on a PRecursore IperSpettrale della Missione Applicativa (PRISMA) scene, spectrally resampled to Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), over an agricultural test site (Jolanda di Savoia, Italy). The two strategies obtained plausible spatial patterns, and consistency between the two models was highest for FVC and LAI (R2=0.91, R2=0.86) and lowest for SLA mapping (R2=0.53). From these findings, we recommend implementing GPR-20PCA models as the most efficient strategy for the retrieval of multiple crop traits from hyperspectral data streams. Hence, this workflow will support and facilitate the preparations of traits retrieval models from the next-generation operational CHIME.
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van Gerrevink, Max J., and Sander Veraverbeke. "Evaluating the Hyperspectral Sensitivity of the Differenced Normalized Burn Ratio for Assessing Fire Severity." Remote Sensing 13, no. 22 (November 16, 2021): 4611. http://dx.doi.org/10.3390/rs13224611.
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Fire severity represents fire-induced environmental changes and is an important variable for modeling fire emissions and planning post-fire rehabilitation. Remotely sensed fire severity is traditionally evaluated using the differenced normalized burn ratio (dNBR) derived from multispectral imagery. This spectral index is based on bi-temporal differenced reflectance changes caused by fires in the near-infrared (NIR) and short-wave infrared (SWIR) spectral regions. Our study aims to evaluate the spectral sensitivity of the dNBR using hyperspectral imagery by identifying the optimal bi-spectral NIR SWIR combination. This assessment made use of a rare opportunity arising from the pre- and post-fire airborne image acquisitions over the 2013 Rim and 2014 King fires in California with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. The 224 contiguous bands of this sensor allow for 5760 unique combinations of the dNBR at a high spatial resolution of approximately 15 m. The performance of the hyperspectral dNBR was assessed by comparison against field data and the spectral optimality statistic. The field data is composed of 83 in situ measurements of fire severity using the Geometrically structured Composite Burn Index (GeoCBI) protocol. The optimality statistic ranges between zero and one, with one denoting an optimal measurement of the fire-induced spectral change. We also combined the field and optimality assessments into a combined score. The hyperspectral dNBR combinations demonstrated strong relationships with GeoCBI field data. The best performance of the dNBR combination was derived from bands 63, centered at 0.962 µm, and 218, centered at 2.382 µm. This bi-spectral combination yielded a strong relationship with GeoCBI field data of R2 = 0.70 based on a saturated growth model and a median spectral index optimality statistic of 0.31. Our hyperspectral sensitivity analysis revealed optimal NIR and SWIR bands for the composition of the dNBR that are outside the ranges of the NIR and SWIR bands of the Landsat 8 and Sentinel-2 sensors. With the launch of the Precursore Iperspettrale Della Missione Applicativa (PRISMA) in 2019 and several planned spaceborne hyperspectral missions, such as the Environmental Mapping and Analysis Program (EnMAP) and Surface Biology and Geology (SBG), our study provides a timely assessment of the potential and sensitivity of hyperspectral data for assessing fire severity.
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Gerhards, Max, Martin Schlerf, Kaniska Mallick, and Thomas Udelhoven. "Challenges and Future Perspectives of Multi-/Hyperspectral Thermal Infrared Remote Sensing for Crop Water-Stress Detection: A Review." Remote Sensing 11, no. 10 (May 24, 2019): 1240. http://dx.doi.org/10.3390/rs11101240.
Full textAbstract:
Thermal infrared (TIR) multi-/hyperspectral and sun-induced fluorescence (SIF) approaches together with classic solar-reflective (visible, near-, and shortwave infrared reflectance (VNIR)/SWIR) hyperspectral remote sensing form the latest state-of-the-art techniques for the detection of crop water stress. Each of these three domains requires dedicated sensor technology currently in place for ground and airborne applications and either have satellite concepts under development (e.g., HySPIRI/SBG (Surface Biology and Geology), Sentinel-8, HiTeSEM in the TIR) or are subject to satellite missions recently launched or scheduled within the next years (i.e., EnMAP and PRISMA (PRecursore IperSpettrale della Missione Applicativa, launched on March 2019) in the VNIR/SWIR, Fluorescence Explorer (FLEX) in the SIF). Identification of plant water stress or drought is of utmost importance to guarantee global water and food supply. Therefore, knowledge of crop water status over large farmland areas bears large potential for optimizing agricultural water use. As plant responses to water stress are numerous and complex, their physiological consequences affect the electromagnetic signal in different spectral domains. This review paper summarizes the importance of water stress-related applications and the plant responses to water stress, followed by a concise review of water-stress detection through remote sensing, focusing on TIR without neglecting the comparison to other spectral domains (i.e., VNIR/SWIR and SIF) and multi-sensor approaches. Current and planned sensors at ground, airborne, and satellite level for the TIR as well as a selection of commonly used indices and approaches for water-stress detection using the main multi-/hyperspectral remote sensing imaging techniques are reviewed. Several important challenges are discussed that occur when using spectral emissivity, temperature-based indices, and physically-based approaches for water-stress detection in the TIR spectral domain. Furthermore, challenges with data processing and the perspectives for future satellite missions in the TIR are critically examined. In conclusion, information from multi-/hyperspectral TIR together with those from VNIR/SWIR and SIF sensors within a multi-sensor approach can provide profound insights to actual plant (water) status and the rationale of physiological and biochemical changes. Synergistic sensor use will open new avenues for scientists to study plant functioning and the response to environmental stress in a wide range of ecosystems.
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TIRMANOĞLU, Buse, İrem İSMAİLOĞLU, Aylin TUZCU KOKAL, and Nebiye MUSAOĞLU. "Yeni Nesil Multispektral ve Hiperspektral Uydu Görüntülerinin Arazi Örtüsü / Arazi Kullanımı Sınıflandırma Performanslarının Karşılaştırılması: Sentinel-2 ve PRISMA Uydusu." Geomatik, November 21, 2022. http://dx.doi.org/10.29128/geomatik.1126685.
Full textAbstract:
Dünya gözlem uydularının gelişmesiyle Arazi Örtüsü/Arazi Kullanımı(AÖ/AK) sınıflandırması, ekosistemleri izlemede ve kaynak yönetiminde değerli bilgiler sağlayan önemli bir uygulama haline gelmiştir. Landsat ve Sentinel-2 gibi uydu görüntüleri ile AÖ/AK sınıfları belirli detayda çıkartılabilirken bazı uygulamalarda spektral çözünürlük nedeniyle sınıfların ayırt edilebilirliğinde problemler ortaya çıkabilmektedir. Günümüzde hiperspektral veri sağlayan uydulardan elde edilen görüntüler yüksek spektral çözünürlük sağladıklarından sınıfların ayırt edilebilirliğini arttırmaktadır. Farklı mekânsal çözünürlüklere sahip 13 spektral bandı bulunan Sentinel-2 uydusu farklı mekânsal çözünürlüğe sahip bantları ile detaylı AÖ/AK sınıflarının üretilmesine olanak sağlamaktadır. PRISMA (Precursore IperSpettrale della Missione Applicativa) uydusu ise 30 m mekânsal çözünürlük ve 240 spektral bant ile oldukça yüksek spektral çözünürlük sağlamaktadır. Bu çalışmada Marmara Denizi’ne önemli ölçüde deşarjı olan Susurluk Nehri ve çevresine ait 13.05.2021 tarihli PRISMA ve 14.05.2021 tarihli Sentinel-2 uydu görüntülerinden sınıflandırma ile ekili tarım alanı, boş arazi, orman, yerleşim, endüstri, yol, göl, akarsu, bataklık sınıfları belirlenmiş ve sonuçları karşılaştırılmıştır. Bu amaçla öncelikle PRISMA ve Sentinel-2 görüntülerine ana bileşenler dönüşümü uygulanmış ve oluşturulan veri setleri Maksimum Olabilirlik algoritması ile sınıflandırılmıştır. Tematik doğruluk analizi yapılarak sınıflandırma sonuçlarının doğrulukları belirlenmiş ve metrik sonuçları karşılaştırılarak her iki verinin sınıfları ayırt etmedeki performansları incelenmiştir. Yapılan değerlendirmede PRISMA uydu görüntüsünün sınıflandırma sonuçlarında spektral çözünürlüğün katkısı nedeniyle sınıfların büyük bölümünde Sentinel-2 uydusu sonuçlarına göre daha yüksek doğruluk elde edilmiştir.