PRecursore IperSpettrale Della Missione Applicativa Research Articles

Improving PRISMA hyperspectral spatial resolution and geolocation by using Sentinel-2: development and test of an operational procedure in urban and rural areas

Hyperspectral (HS) satellites like PRISMA (PRecursore IperSpettrale della Missione Applicativa) offer remarkable capabilities, yet they are constrained by a relatively coarse spatial resolution, curbing their efficacy in those applications that require pinpoint accuracy. Here we propose a fusion process, aimed at the enhancement of PRISMA HS spatial resolution by using the spatial and spectral information of Sentinel-2 multispectral (MS) data (HS-MS fusion process), validated against four airborne HS flights simultaneous to satellite overpasses on different land use distributions. Adopting the PRISMA panchromatic (PAN) image, the proposed solution was also compared with the results of a HS-PAN pansharpening process. A two-steps operational workflow is proposed, based on two state-of-the-art and open-source algorithms. The first step consisted of the geocoding of PRISMA L2 products using Senintel-2 as reference and was accomplished with the phase-based algorithm implemented in AROSICS (Automated and Robust Open-Source Image Co-registration Software). The geometric displacement in L2 data was found to be between 80 m and 250 m, irregularly spatially distributed throughout the same scene and among scenes, and it was corrected by means of thousands of regularly spatially distributed tie points. A second-order polynomial transformation function was integrated in the algorithm. The second step consisted of employing the HySure (HS Super resolution) fusion algorithm to perform both the HS-MS fusion and the HS-PAN pansharpening, returning a PRISMA HS improved dataset with a spatial resolution of 10 m and 5 m, respectively. Four different per-band accuracy metrics were used to evaluate the accuracy of both products against airborne data. Overall, HS-MS data achieved increased accuracy in all validation metrics, i.e. + 28 % (root mean square error, RMSE), +23 % (spectral angle mapper, SAM), +7% (peak signal-to-noise ratio, PSNR) and + 11 % (universal image quality index, UIQI), with respect of HS-PAN data. These outcomes showed that using the spectral information of Sentinel-2 both spectral and spatial patterns were reconstructed more consistently in three different urban and rural scenarios, avoiding the presence of blur and at-edge artefacts as opposed to HS-PAN pansharpening, therefore suggesting an optimal strategy for satellite HS data resolution enhancement.

Spaceborne imaging spectroscopy enables carbon trait estimation in cover crop and cash crop residues

Abstract Purpose Cover crops and reduced tillage are two key climate smart agricultural practices that can provide agroecosystem services including improved soil health, increased soil carbon sequestration, and reduced fertilizer needs. Crop residue carbon traits (i.e., lignin, holocellulose, non-structural carbohydrates) and nitrogen concentrations largely mediate decomposition rates and amount of plant-available nitrogen accessible to cash crops and determine soil carbon residence time. Non-destructive approaches to quantify these important traits are possible using spectroscopy. Methods The objective of this study was to evaluate the efficacy of spectroscopy instruments to quantify crop residue biochemical traits in cover crop agriculture systems using partial least squares regression models and a combination of (1) the band equivalent reflectance (BER) of the PRecursore IperSpettrale della Missione Applicativa (PRISMA) imaging spectroscopy sensor derived from laboratory collected Analytical Spectral Devices (ASD) spectra (n = 296) of 11 cover crop species and three cash crop species, and (2) spaceborne PRISMA imagery that coincided with destructive crop residue collections in the spring of 2022 (n = 65). Spectral range was constrained to 1200 to 2400 nm to reduce the likelihood of confounding relationships in wavelengths sensitive to plant pigments or those related to canopy structure for both analytical approaches. Results Models using laboratory BER of PRISMA all demonstrated high accuracies and low errors for estimation of nitrogen and carbon traits (adj. R2 = 0.86 − 0.98; RMSE = 0.24 − 4.25%) and results indicate that a single model may be used for a given trait across all species. Models using spaceborne imaging spectroscopy demonstrated that crop residue carbon traits can be successfully estimated using PRISMA imagery (adj. R2 = 0.65 − 0.75; RMSE = 2.71 − 4.16%). We found moderate relationships between nitrogen concentration and PRISMA imagery (adj. R2 = 0.52; RMSE = 0.25%), which is partly related to the range of nitrogen in these senesced crop residues (0.38–1.85%). PRISMA imagery models were also influenced by atmospheric absorption, variability in surface moisture content, and some presence of green vegetation. Conclusion As spaceborne imaging spectroscopy data become more widely available from upcoming missions, crop residue trait estimates could be regularly generated and integrated into decision support tools to calculate decomposition rates and associated nitrogen credits to inform precision field management, as well as to enable measurement, monitoring, reporting, and verification of net carbon benefits from climate smart agricultural practice adoption in an emerging carbon marketplace.

Hyperspectral Leaf Area Index and Chlorophyll Retrieval over Forest and Row-Structured Vineyard Canopies

As an unprecedented stream of decametric hyperspectral observations becomes available from recent and upcoming spaceborne missions, effective algorithms are required to retrieve vegetation biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC). In the context of missions such as the Environmental Mapping and Analysis Program (EnMAP), Precursore Iperspettrale della Missione Applicativa (PRISMA), Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and Surface Biology Geology (SBG), several retrieval algorithms have been developed based upon the turbid medium Scattering by Arbitrarily Inclined Leaves (SAIL) radiative transfer model. Whilst well suited to cereal crops, SAIL is known to perform comparatively poorly over more heterogeneous canopies (including forests and row-structured crops). In this paper, we investigate the application of hybrid radiative transfer models, including a modified version of SAIL (rowSAIL) and the Invertible Forest Reflectance Model (INFORM), to such canopies. Unlike SAIL, which assumes a horizontally homogeneous canopy, such models partition the canopy into geometric objects, which are themselves treated as turbid media. By enabling crown transmittance, foliage clumping, and shadowing to be represented, they provide a more realistic representation of heterogeneous vegetation. Using airborne hyperspectral data to simulate EnMAP observations over vineyard and deciduous broadleaf forest sites, we demonstrate that SAIL-based algorithms provide moderate retrieval accuracy for LAI (RMSD = 0.92–2.15, NRMSD = 40–67%, bias = −0.64–0.96) and CCC (RMSD = 0.27–1.27 g m−2, NRMSD = 64–84%, bias = −0.17–0.89 g m−2). The use of hybrid radiative transfer models (rowSAIL and INFORM) reduces bias in LAI (RMSD = 0.88–1.64, NRMSD = 27–64%, bias = −0.78–−0.13) and CCC (RMSD = 0.30–0.87 g m−2, NRMSD = 52–73%, bias = 0.03–0.42 g m−2) retrievals. Based on our results, at the canopy level, we recommend that hybrid radiative transfer models such as rowSAIL and INFORM are further adopted for hyperspectral biophysical and biochemical variable retrieval over heterogeneous vegetation.

Enhanced Detection of Artisanal Small-Scale Mining with Spectral and Textural Segmentation of Landsat Time Series

Artisanal small-scale mines (ASMs) in the Amazon Rainforest are an important cause of deforestation, forest degradation, biodiversity loss, sedimentation in rivers, and mercury emissions. Satellite image data are widely used in environmental decision-making to monitor changes in the land surface, but ASMs are difficult to map from space. ASMs are small, irregularly shaped, unevenly distributed, and confused (spectrally) with other land clearance types. To address this issue, we developed a reliable and efficient ASM detection method for the Tapajós River Basin of Brazil—an important gold mining region of the Amazon Rainforest. We enhanced detection in three key ways. First, we used the time-series segmentation (LandTrendr) Google Earth Engine (GEE) Application Programming Interface to map the pixel-wise trajectory of natural vegetation disturbance and recovery on an annual basis with a 2000 to 2019 Landsat image time series. Second, we segmented 26 textural features in addition to 5 spectral features to account for the high spatial heterogeneity in ASM pixels. Third, we trained and tested a Random Forest model to detect ASMs after eliminating irrelevant and redundant features with the Variable Selection Using Random Forests “ensemble of ensembles” technique. The out-of-bag error and overall accuracy of the final Random Forest was 3.73 and 92.6%, which are comparable to studies mapping large industrial mines with the normalized difference vegetation index (NDVI) and LandTrendr. The most important feature in our study was NDVI, followed by textural features in the near and shortwave infrared. Our work paves the way for future ASM regulation through large area monitoring from space with free and open-source GEE and operational satellites. Studies with sufficient computational resources can improve ASM monitoring with advanced sensors consisting of spectral narrow bands (Sentinel-2, Environmental Mapping and Analysis Program, PRecursore IperSpettrale della Missione Applicativa) and deep learning.

PRISMA Hyperspectral Remote Sensing Data for Mapping Alteration Minerals in Sar-e-Châh-e-Shur Region, Birjand, Iran

Remote sensing satellite imagery consistently provides valuable and frequent information, enabling the exploration of mineral resources across immense, remote and harsh domains. Recent developments in spaceborne hyperspectral remote sensing have opened avenues to support diverse remote sensing applications, particularly in the realm of mineral exploration. This study evaluates the capabilities of the PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral satellite data for mapping alteration minerals using the Matched Filtering Unmixing (MFU) approach in the Sar-e-châh-e-shur, Birjand, Iran. Minerals such as richterite, augite, psilomelane, ilmenite, kaolinite, smectite, mirabilite, muscovite, and chlorite were identified using the vertex component analysis (VCA) technique. Subsequently, alteration mineral maps of the study area were generated using a matched filtering technique. Additionally, through the integration of X-ray diffraction (XRD) analysis, thin section examination, geochemical study of stream sediments, and interpretation of geological maps, potential alteration mineralization zones were delineated in the study area. Ultimately, the validation process, which included comparing the maps with the findings derived from the PRISMA remote sensing study, was conducted using the normal score equation. Thus, our results yielded a normalized score of 3.42 out of 4, signifying an 85.71% agreement with the regional geological characteristics of the study area. The results of this investigation highlight the substantial potential of the PRISMA dataset for systematic alteration mineral mapping and consequent exploration of ore minerals, specifically in challenging and inaccessible terrains.

The application of PRISMA hyperspectral satellite imagery in the delineation of distinct hydrothermal alteration zones in the Chilean Andes: The Marimaca IOCG and the Río Blanco-Los Bronces Cu-Mo porphyry districts

This study provides an overview of the application of hyperspectral imagery acquired by the Italian satellite mission “PRecursore IperSpettrale della Missione Applicativa” (PRISMA), for mapping hydrothermal and supergene mineral alteration zones associated with different Cu deposits in the Chilean Andes. Study areas include the Marimaca Copper Project in the Naguayán district (Antofagasta Province), hosting Late Jurassic – Early Cretaceous iron oxide-copper–gold mineralizations and the Río Blanco-Los Bronces district (Santiago Region) known for its Late Miocene – Early Pliocene copper-molybdenum porphyry deposits. These mineral systems exhibit hydrothermal alteration haloes extending over several kilometers, with upward and outward mineral zonation.To characterize the surface-exposed alteration, the satellite imagery was processed using a multiple feature extraction workflow targeting the relative abundances and compositions of specific supergene and hydrothermal alteration minerals, including Fe-oxides and hydroxides (hematite-goethite), phyllosilicates (micas-kaolinite-chlorite), hydroxyl-bearing sulfates (alunite-natroalunite-jarosite) and epidote. The results reveal a zonation from proximal sulfates (natroalunite to alunite) and Al-rich white mica (ranging from 0.3 km-wide for the area identified to the east of the Marimaca deposit to 1 km-wide in correspondence of the Los Sulfatos deposit, respectively) to distal Al-poor white mica (0.2 to 0.5 km-wide, respectively) and an outer chlorite-epidote zone. In the Marimaca Project area, the structurally-controlled alteration evolves west to east from chlorite-rich to phengitic white mica (Al-poor). A NNE-SSW trending supergene leached cap covers a 6 km-long and up to 2 km-wide area from north to south. Our study documents how spaceborne hyperspectral imaging spectroscopy can support mineral exploration by enabling non-invasive reconnaissance mapping of the outcropping rocks, providing specific targeting information for follow-up field surveys and drilling campaigns.

Spectral imaging of grass species in arid ecosystems of Namibia

Grasslands across the African continent are under pressure from climate change and human activities, particularly in arid ecosystems. From a remote sensing perspective, these ecosystems have not received much scientific attention, especially in Namibia. To address this knowledge gap, various remote sensing methods were implemented using new generation spaceborne imaging spectrometers amongst others. Therefore, this research provides a first methodological approach aimed at mapping and evaluating the distribution of grasslands within two private nature reserves, namely, the NamibRand Nature Reserve (NRNR) and ProNamib Nature Reserve (PNNR) with surrounding farmlands on the edge of Namib Sand Sea. The multi-sensor approach utilizes Mixture Tuned Matched Filtering (MTMF) and incorporated spectral information collected in the field to analyze grasslands. The research involves a sensor comparison of multispectral Sentinel-2 and PlanetScope data, hyperspectral data from Environmental Mapping and Analysis Programme (EnMAP) and PRecursore IperSpettrale della Missione Applicativa (PRISMA) and an additional data fusion product derived from Sentinel-2 and EnMAP imagery based on a Smoothing Filter-based Intensity Modulation Hypersharpening method (SFIM-HS). Additionally, a unique spectral library of collected field spectra was established and inter-species spectral separability and intra-species spectral homogeneity was analyzed. This library presents newly published spectra of individual species. Due to dry initial conditions, the calculated spectral separability of individual grasses is limited, making only a mean endmember feasible for partial unmixing. The validation results of satellite comparison show that data fusion products (R2 = 0.51 with Normalized Difference Vegetation Index (NDVI); R2 = 0.66 with Soil Adjusted Vegetation Index (SAVI)) are more suitable for mapping arid grasslands than multispectral or hyperspectral data (all R2 < 0.35). More research is required and potential methodological adjustments are discussed to further investigate the spatio-temporal dynamics of arid grasslands and to aid conservation efforts in the Greater Sossusvlei-Namib Landscape in line with the United Nations Decade of Restoration.

Exploiting the entire near-infrared spectral range to improve the detection of methane plumes with high-resolution imaging spectrometers

Abstract. Remote sensing emerges as an important tool for the detection of methane plumes emitted by so-called point sources, which are common in the energy sector (e.g., oil and gas extraction and coal mining activities). In particular, satellite imaging spectroscopy missions covering the shortwave infrared part of the solar spectrum are very effective for this application. These instruments sample the methane absorption features at the spectral regions around 1700 and 2300 nm, which enables the retrieval of methane concentration enhancements per pixel. Data-driven retrieval methods, in particular those based on the matched filter concept, are widely used to produce maps of methane concentration enhancements from imaging spectroscopy data. Using these maps enables the detection of plumes and the subsequent identification of active sources. However, retrieval artifacts caused by particular surface components may sometimes appear as false plumes or disturbing elements in the methane maps, which complicates the identification of real plumes. In this work, we use a matched filter that exploits a wide spectral window (1000–2500 nm) instead of the usual 2100–2450 nm window with the aim of reducing the occurrence of retrieval artifacts and background noise. This enables a greater ability to discriminate between surface elements and methane. The improvement in plume detection is evaluated through an analysis derived from both simulated data and real data from areas including active point sources, such as the oil and gas (O&G) industry from San Joaquin Valley (US) and the coal mines from the Shanxi region (China). We use datasets from the Precursore IperSpettrale della Missione Applicativa (PRISMA) and the Environmental Mapping and Analysis Program (EnMAP) satellite imaging spectrometer missions and from the Airborne Visible/Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG) instrument. We find that the interference with atmospheric carbon dioxide and water vapor is generally almost negligible, while co-emission or overlapping of these trace gases with methane plumes leads to a reduction in the retrieved concentration values. Attenuation will also occur in the case of methane emissions situated above surface structures that are associated with retrieval artifacts. The results show that the new approach is an optimal trade-off between the reduction in background noise and retrieval artifacts. This is illustrated by a comprehensive analysis in a PRISMA dataset with 15 identified plumes, where the output mask from an automatic detection algorithm shows an important reduction in the number of clusters not related to CH4 emissions.

Exploring Urban XCO2 Patterns Using PRISMA Satellite: A Case Study in Shanghai

As global warming intensifies, monitoring carbon dioxide (CO2) has increasingly become a focal point of research. Investigating urban XCO2 emission systems holds paramount importance, given the pivotal role of cities as major contributors to carbon emissions. Consequently, this study centers on urban locales, employing Shanghai as a case study for a comprehensive evaluation of regional XCO2 levels. We utilized high spatial resolution imagery from the PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite to conduct an XCO2 assessment over the Baoshan District with a 30 m spatial resolution from April 2021 to October 2022. Our XCO2 analysis was conducted in two steps. Firstly, we conducted a sensitivity analysis on key parameters in the inversion process, where cloud cover severely interfered with inversion accuracy. Therefore, we developed the Fmask 4.0 cloud removal and iterative maximum a posteriori differential optical absorption spectroscopy (FIMAP-DOAS) algorithm. This novel integration eliminated cloud interference during the inversion process, achieving high-precision CO2 detection in the region. Secondly, we compared the XCO2 of the region with Level-2 data from carbon monitoring satellites such as OCO-2. The comparison results showed a strong consistency, with a root mean squared error (RMSE) of 0.75 ppm for Shanghai XCO2 data obtained from the PRISMA satellite compared to OCO-2 Level-2 data and an RMSE of 1.49 ppm compared to OCO-3. This study successfully established a high-accuracy and high-spatial-resolution XCO2 satellite monitoring system for the Shanghai area. The efficacy of the FIMAP-DOAS algorithm has been demonstrated in CO2 monitoring and inversion within urban environments, with potential applicability to other cities.

Quantifying particulate matter optical properties and flow rate in industrial stack plumes from the PRISMA hyperspectral imager

Abstract. Industrial activities such as metallurgy, coal and oil combustion, cement production, and petrochemistry release aerosol particles into the atmosphere. We propose analyzing the aerosol composition of plumes emitted by different industrial stacks using PRISMA (PRecursore IperSpettrale della Missione Applicativa) satellite hyperspectral observations. Three industrial sites have been observed: a coal-fired power plant in Matla, South Africa (imaged on 25 September 2021); a steel plant in Wuhan, China (24 March 2021); and gas flaring at an oil extraction site in Hassi Messaoud, Algeria (9 July 2021). Below-plume surface reflectances are constrained using a combination of PRISMA and Sentinel-2/MSI images. Radiative transfer simulations are performed for each scene including the surface, background atmosphere, and plume optical properties. The plume aerosol optical thickness (AOT), particle radius, volume of coarse-mode aerosol, and soot are then retrieved within the plumes following an optimal estimation framework. The mean plume retrieved AOT at 500 nm ranges between 0.27 and 1.27 and the median radius between 0.10 and 0.12 µm. We found a volume fraction of soot of 3.6 % and 10.4 % in the sinter plant and coal-fired plant plumes, respectively. The mass flow rate of particulate matter at a point source estimated by an integrated mass enhancement method varies from 840 ± 155 g s−1 for the flaring emission to 1348 ± 570 g s−1 at the coal-fired plant.

PRINCIPAL COMPONENTS VERSUS AUTOENCODERS FOR DIMENSIONALITY REDUCTION: A CASE OF SUPER-RESOLVED OUTPUTS FROM PRISMA HYPERSPECTRAL MISSION DATA

Abstract. This study attempts to solve these issues associated with hyperspectral (HS) data, i.e., coarse spatial resolution and high volume, by understanding the effect of deep learning and traditional dimensionality reduction on super-resolved products generated from the recently launched PRecursore IperSpettrale della Missione Applicativa (PRISMA) HS mission. Four single-frame super-resolution (SR) algorithms have been used to super-resolve a 30 m PRISMA scene of Ahmedabad, India and generate 15 m spatial resolution images with both spatial and spectral fidelity. Iterative back projection (IBP) and sparse representation (SIS) are the best and worst-performing SR algorithms following a comparative assessment and validation protocol. Next, denoising autoencoders and PCT computed using singular and eigenvalue decompositions have been executed on the original PRISMA, IBP and SIS-based super-resolved datasets. The resulting low-dimensional representations have been assessed to preserve the original dataset's topology using label-independent Lee and Verleysen's co-ranking matrix and loss of quality measure. Findings suggest that autoencoders are computationally expensive and require a higher neighbourhood size than PCT and its variants to produce a high-quality encoding. These insights remain significant for urban information extraction as there are few direct comparative assessments between machine learning-based linear and non-linear data compression methods in earlier studies.

Two years of satellite-based carbon dioxide emission quantification at the world's largest coal-fired power plants

Abstract. Carbon dioxide (CO2) emissions from combustion sources are uncertain in many places across the globe. Satellites have the ability to detect and quantify emissions from large CO2 point sources, including coal-fired power plants. In this study, we routinely made observations with the PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite imaging spectrometer and the Orbiting Carbon Observatory-3 (OCO-3) instrument aboard the International Space Station at over 30 coal-fired power plants between 2021 and 2022. CO2 plumes were detected in 50 % of the acquired PRISMA scenes, which is consistent with the combined influence of viewing parameters on detection (solar illumination and surface reflectance) and unknown factors (e.g., daily operational status). We compare satellite-derived emission rates to in situ stack emission observations and find average agreement to within 27 % for PRISMA and 30 % for OCO-3, although more observations are needed to robustly characterize the error. We highlight two examples of fusing PRISMA with OCO-2 and OCO-3 observations in South Africa and India. For India, we acquired PRISMA and OCO-3 observations on the same day and used the high-spatial-resolution capability of PRISMA (30 m spatial/pixel resolution) to partition relative contributions of two distinct emitting power plants to the net emission. Although an encouraging start, 2 years of observations from these satellites did not produce sufficient observations to estimate annual average emission rates within low (<15 %) uncertainties. However, as the constellation of CO2-observing satellites is poised to significantly improve in the coming decade, this study offers an approach to leverage multiple observation platforms to better quantify and characterize uncertainty for large anthropogenic emission sources.

Machine and Deep Learning Regression of Chlorophyll-a Concentrations in Lakes Using PRISMA Satellite Hyperspectral Imagery

The estimation of Chlorophyll-a concentration is crucial for monitoring freshwater ecosystem health, particularly in lakes, as it is closely linked to eutrophication processes. Satellite imagery enables synoptic and frequent evaluations of Chlorophyll-a in water bodies, providing essential insights into spatiotemporal eutrophication dynamics. Frontier applications in water remote sensing support the utilization of machine and deep learning models applied to hyperspectral satellite imagery. This paper presents a comparative analysis of conventional machine and deep learning models—namely, Random Forest Regressor, Support Vector Regressor, Long Short-Term Memory, and Gated Recurrent Unit networks—for estimating Chlorophyll-a concentrations. The analysis is based on data from the PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral mission, complemented by low-resolution Chlorophyll-a concentration maps. The analysis focuses on three sub-alpine lakes, spanning Northern Italy and Switzerland as testing areas. Through a series of modelling experiments, best-performing model configurations are pinpointed for both Chlorophyll-a concentration estimations and the improvement of spatial resolution in predictions. Support Vector Regressor demonstrated a superior performance in Chlorophyll-a concentration estimations, while Random Forest Regressor emerged as the most effective solution for refining the spatial resolution of predictions.

Evaluating changes with vegetation cover in PRISMA's spectral, spatial, and temporal attributes and their performance for classifying savannahs in Brazil

The recent advent of hyperspectral satellites with larger swath width than that of previous sampling missions brings new perspectives for mapping savannahs in Brazil. Here, we evaluated changes with vegetation cover in different spectral, spatial, and temporal attributes, derived from the PRecursore IperSpettrale della Missione Applicativa (PRISMA), and their performance for Random Forest (RF) classification of savannah physiognomies at the Brasília National Park (BNP). To obtain the spectral attributes, we selected a PRISMA image acquired during the local dry season (August 17, 2020). We evaluated the classification performance of the reflectance of 166 bands, 22 vegetation indices (VIs), and four endmember fractions derived from a linear spectral mixture model (SMA). In addition, 24 parameters describing the depth, area, width, and asymmetry of the absorption bands centred at 680 nm (chlorophyll), 980 nm and 1200 nm (leaf water), and 1750 nm, 2100 nm and 2300 nm (lignin-cellulose) were also considered in the analysis. For the spatial attributes, we tested the performance of 8 Gray Level Co-occurrence Matrix (GLCM) metrics of image texture associated with the 864-nm near-infrared (NIR) band. In order to determine the temporal attributes, we considered other three PRISMA images obtained in 2020 (11 May, 4 September, and 3 October). Using these images, we calculated the rate of changes for each of the 22 VIs in the browning and greening periods of the savannah environment. A feature selection procedure was applied to the datasets. The results showed that the vegetation gradient from savannah grassland to woodland areas controlled the behavior of most attributes. For instance, the reflectance of the PRISMA NIR bands and the depth of the chlorophyll (680 nm) and leaf water (980 nm and 1200 nm) absorption bands increased with increasing vegetation cover. On the other hand, the reflectance of the visible and shortwave infrared (SWIR) bands and the depth of spectral features associated with non-photosynthetic vegetation followed the opposite pattern. Except for the metrics of image texture, the other spectral (reflectance, VIs, endmember fractions, and absorption band parameters) and temporal (browning and greening rates of vegetation changes) attributes had close classification performance before or after feature selection. When combined into a single dataset, gains of 15% in overall classification accuracy were observed when compared to the individual use of reflectance data in the analysis. From the seven savannah classes tested for classification, areas of woodland savannah, savannah grassland, and riparian forest were adequately mapped using this approach (F1-scores between 0.72 and 0.91). In contrast, areas of wooded savannah, with and without Trembleias species, had low F1-scores (0.28 and 0.20, respectively). Our findings reinforce the need of considering different hyperspectral attributes in classification approaches of the savannahs in Brazil.

PRISMA hyperspectral data for lithological mapping in the Egyptian Eastern Desert: Evaluating the support vector machine, random forest, and XG boost machine learning algorithms

In essence, targeting mineralization necessitates exact structural delineation and thorough lithological mapping. The latter is still a challenge for geologists and its lack hinders meticulous exploration for various mineralizations. Here we show for the first time over a case study from Arabian Nubian Shield (ANS), the application of hyperspectral PRISMA (PRecursore IperSpettrale della Missione Applicativa) data for objective lithological mapping using the well-known Random Forest (RF), XGboost (XGB), and Support Vector Machine (SVM) algorithms. Our results manifested the worthiness of PRISMA data in further lithological mapping, especially with SVM with a resultant accuracy depending mainly on the input data combination. Upon field verification, the current research reveals the usefulness of PRISMA and its preceding four principal components in delivering a detailed lithological map for the study area. Additionally, the eligibility of RF, XGB, and SVM was confirmed in delivering acceptable results. SVM exceeds XGB and RF in their overall accuracy (95 %, 92 %, and 90 % for SVM, XGB, and RF respectively). Our research strongly recommends blending the vantages of Machine Learning Algorithms' (MLAs) objectivity and the wealth of PRISMA spectral coverage for further precise lithological mapping before applicable mineral exploration programs in similar terrains.

A hyperspectral inversion framework for estimating absorbing inherent optical properties and biogeochemical parameters in inland and coastal waters

The simultaneous remote estimation of biogeochemical parameters (BPs) and inherent optical properties (IOPs) from hyperspectral satellite imagery of globally distributed optically distinct inland and coastal waters is a complex, unsolved, non-unique inverse problem. To tackle this problem, we leverage a machine-learning model termed Mixture Density Networks (MDNs). MDNs outperform operational algorithms by calculating the covariance between the simultaneously estimated products. We train the MDNs on a large (N = 8237) dataset of co-aligned, in situ measured, hyperspectral remote sensing reflectance (Rrs), BPs, and absorbing IOPs from globally representative optically distinct inland and coastal waters. The estimated IOPs include absorption due to phytoplankton (aph), chromophoric dissolved organic matter (acdom), and non-algal particles (anap). The estimated BPs include chlorophyll-a, total suspended solids, and phycocyanin (PC). MDNs dramatically reduce uncertainty in the retrievals, relative to operational algorithms, when using a 50/50 dataset split, where the MDNs are trained on a randomly selected half of the in situ dataset and validated on the other half. Our model is shown to have higher, or equivalent, generalization performance than the calculated operational algorithms available for all BPs and IOPs (except PC) via a leave-one-out cross-validation assessment. The MDNs are sensitive to uncertainties in the hyperspectral satellite Rrs, resulting from instrument noise and atmospheric correction; there is a difference of ∼37.4–62.8% (using median symmetric accuracy) between the MDNs' estimates derived from co-located satellite-derived Rrs and in situ Rrs. Of the IOPs, acdom and anap are less sensitive to uncertainties in hyperspectral satellite imagery relative to aph, with remote estimates of aph exhibiting incorrect spectral shape and magnitude relative to in situ measured IOPs. Despite the uncertainties in satellite derived Rrs, the spatial distributions of BPs and IOPs in MDN-derived product maps of Lake Erie and the Curonian Lagoon, based on imagery taken with the Hyperspectral Imager for the Coastal Ocean (HICO) and PRecursore IperSpettrale della Missione Applicativa (PRISMA), are confirmed via co-aligned in situ measurements and agree with the literature's understanding of these well-studied regions. The consistency and accuracy of the model on HICO and PRISMA imagery, despite radiometric uncertainties, demonstrate its applicability to future hyperspectral missions, such as the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, where the simultaneous estimation model will serve as a key part of phytoplankton community composition analysis.

Using a deep neural network to detect methane point sources and quantify emissions from PRISMA hyperspectral satellite images

Abstract. Anthropogenic emissions of methane (CH4) have made a considerable contribution towards the Earth's changing radiative budget since pre-industrial times. This is because large amounts of methane are emitted from human activities, and the global warming potential of methane is high. The majority of anthropogenic fossil methane emissions to the atmosphere originate from a large number of small (point) sources. Thus, detection and accurate, rapid quantification of such emissions are vital to enable the reduction of emissions to help mitigate future climate change. There exist a number of instruments on satellites that measure radiation at methane-absorbing wavelengths, which have sufficiently high spatial resolution that can be used for detecting plumes of highly spatially localised methane “point sources” (areas on the order of m2 to km2). Searching for methane plumes in methane-sensitive satellite images using classical methods, such as thresholding and clustering, can be useful but is time-consuming and often involves empirical decisions. Here, we develop a deep neural network to identify and quantify methane point source emissions from hyperspectral imagery from the PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite with 30 m spatial resolution. The moderately high spectral and spatial resolution, as well as considerable global coverage and free access to data, makes PRISMA a good candidate for methane plume detection. The neural network was trained with simulated synthetic methane plumes generated with the large eddy simulation extension of the Weather Research and Forecasting model (WRF-LES), which we embedded into PRISMA images. The deep neural network was successful at locating plumes with a F1 score, precision, and recall of 0.95, 0.96, and 0.92, respectively, and was able to quantify emission rates with a mean error of 24 %. The neural network was furthermore able to locate several plumes in real-world images. We have thus demonstrated that our method can be effective in locating and quantifying methane point source emissions in near-real time from 30 m resolution satellite data, which can aid us in mitigating future climate change.

PRISMA and Sentinel-2 spectral response to the nutrient composition of grains

Micronutrient malnutrition is a global challenge affecting >2 billion people, in particular those with a crop-based diet and limited access to nutrient-rich food sources. Conventional methods for measuring the crop nutrients such as wet chemical analysis of grains are time-consuming and cost-prohibitive and, consequently, unsuitable for the consistent quantification of nutrients across space and time. In this study, we propose a new method that is using PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Sentinel-2 images to estimate the nutrient concentrations of crop grains before harvest.We collected grain samples for corn, rice, soybean, and wheat from a farm situated in Italy and measured their nutrient concentrations in the lab. These measurements together with the PRISMA and Sentinel-2 images acquired at the main phases of crop development (vegetative, reproductive, maturity) were used as input for two-band vegetation indices (TBVIs) and Partial Least Squares Regression (PLSR) to predict Calcium (Ca), Iron (Fe), Potassium (K), Magnesium (Mg), Nitrogen (N), Phosphorus (P), Sulphur (S) and Zinc (Zn). Models' performances were assessed using the coefficient of determination (R2) and Root Mean Square Error (RMSE). For PRISMA images, the best prediction results were obtained for P in soybean (R2 = 0.69), K in soybean (R2 = 0.66), Mg in soybean (R2 = 0.58), Fe in soybean (R2 = 0.57), K in wheat (R2 = 0.57), K in corn (R2 = 0.55), P in wheat (R2 = 0.51), S in rice (R2 = 0.58) using TBVIs. In contrast to PRISMA, PLSR outperformed TBVIs when Sentinel-2 images were used as input. For Sentinel-2, the best predictions were obtained for P in soybean (R2 = 0.73), K in wheat (R2 = 0.67), Mg in soybean (R2 = 0.62), Zn in wheat (R2 = 0.56), Fe in soybean (R2 = 0.52), P in wheat (R2 = 0.52). Our study showed that estimating the nutrient composition of crops using remote sensing images has the potential to change how we approach a cost-effective, timely, and spatially explicit representation of the crops' nutritional quality.

Potential of DESIS and PRISMA hyperspectral remote sensing data in rock classification and mineral identification:a case study for Banswara in Rajasthan, India.

Remote sensing datasets and methods are suitable for mapping and managing the natural resources like minerals, clean water, and energy and also govern their sustainability nowadays. Hyperspectral (HS) imaging has immense potential for rock type classification, mineral mapping, and identification. This work demonstrates the potential of feature extraction techniques and unsupervised machine learning methods for the space-borne hyperspectral remote sensing data in characterizing and identifying mineral and classifying rock type in Banswara, Rajasthan, India. Feature extraction techniques can reveal variations within the data, which can help identify geological areas, reduce noise, and check the dimensionality of the data. Singular value decomposition (SVD)-based principal component analysis (PCA), kernel PCA (KPCA), minimum noise fraction (MNF), and independent component analysis (ICA) were tested for lithological mapping using recently launched DLR Earth Sensing Imaging Spectrometer Hyperspectral (DESIS) and PRecursore IperSpettrale della Missione Applicativa (PRISMA) data in order to map geologically significant areas. Unsupervised machine learning methods, such as Iterative Self-Organizing Data Analysis Technique (ISODATA) and K-means, were also employed. Vertex component analysis (VCA) was utilized to check for similarity and identify various spectral features. Our work demonstrates the advantages of using feature extraction algorithms such as PCA and KPCA over MNF and ICA in geological mapping and interpretability. We recommend K-means as the preferred method for lithological classification of hyperspectral remote sensing data. Our work highlights the potential of advanced feature extraction algorithms for mineral mapping using hyperspectral data, providing different ways to identify minerals and ultimately leading to better mineral resource management.

Sensitivity of hyperspectral vegetation indices to rainfall seasonality in the Brazilian savannahs: an analysis using PRISMA data

ABSTRACT We evaluated the sensitivity of 14 narrowband vegetation indices (VIs) to rainfall seasonality over the Brazilian savannahs. Five images obtained in 2020 by the PRecursore IperSpettrale della Missione Applicativa (PRISMA) tracked the transition from the rainy (11 May) to the dry season (8 July, 17 August, and 4 September), and towards the beginning of a new seasonal cycle on 3 October. We considered two scenarios in the data analysis. First, we kept the PRISMA image from 11 May as a reference to evaluate the VI sensitivity with increasing water deficit from 8 July (49 days without any precipitation) to 4 September (102 days). Second, we changed the reference image to 4 September to evaluate the largest VI responses on 3 October after the first rainfall. The first three VIs (ranked by F-values) having significant changes with increasing water deficit over grasslands were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Moisture Stress Index (MSI). The Vogelmann red edge index (VOG) and Red-Edge Normalized Difference Vegetation Index (RENDVI) presented the largest F-values over woodlands. In the second scenario, Red-edge Vegetation Stress Index (RVSI) and RENDVI were among the first five ranked VIs by t-values over both areas. The largest changes in VIs were generally observed over savannah grassland, which is the most sensitive physiognomy to water deficit. The lowest modifications were noted over riparian forests, which have access to waters from rivers. The vegetation-type dependence of the VI changes was also observed after the occurrence of rainfall. Results suggest the potential use of different VIs to obtain phenological metrics for more accurate savannah mapping in Brazil.