Shortwave Infrared Bands Research Articles

Forest Conservation with Deep Learning: A Deeper Understanding of Human Geography around the Betampona Nature Reserve, Madagascar

Documenting the impacts of climate change and human activities on tropical rainforests is imperative for protecting tropical biodiversity and for better implementation of REDD+ and UN Sustainable Development Goals. Recent advances in very high-resolution satellite sensor systems (i.e., WorldView-3), computing power, and machine learning (ML) have provided improved mapping of fine-scale changes in the tropics. However, approaches so far focused on feature extraction or the extensive tuning of ML parameters, hindering the potential of ML in forest conservation mapping by not using textural information, which is found to be powerful for many applications. Additionally, the contribution of shortwave infrared (SWIR) bands in forest cover mapping is unknown. The objectives were to develop end-to-end mapping of the tropical forest using fully convolution neural networks (FCNNs) with WorldView-3 (WV-3) imagery and to evaluate human impact on the environment using the Betampona Nature Reserve (BNR) in Madagascar as the test site. FCNN (U-Net) using spatial/textural information was implemented and compared with feature-fed pixel-based methods including Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN). Results show that the FCNN model outperformed other models with an accuracy of 90.9%, while SVM, RF, and DNN provided accuracies of 88.6%, 84.8%, and 86.6%, respectively. When SWIR bands were excluded from the input data, FCNN provided superior performance over other methods with a 1.87% decrease in accuracy, while the accuracies of other models—SVM, RF, and DNN—decreased by 5.42%, 3.18%, and 8.55%, respectively. Spatial–temporal analysis showed a 0.7% increase in Evergreen Forest within the BNR and a 32% increase in tree cover within residential areas likely due to forest regeneration and conservation efforts. Other effects of conservation efforts are also discussed.

Relationship between TIR and NIR-SWIR as Indicator of Vegetation Water Availability

Water availability for vegetation use has been associated with the relative amount of water in the plant and is a key factor for modeling variables related to the soil-plant system (e.g., net primary production, drought effects on vegetation). To the best of our knowledge, the integration of spectral proxies of vegetation water content (near-infrared (NIR), shortwave-infrared (SWIR) bands) and land surface temperature (LST) for estimation, not only of vegetation water content but also soil water available for the evapotranspiration process requires more analysis. This study aims to assess the relationship between NIR, SWIR reflectance, and LST data as indicators of water availability for crop use. For this purpose, vegetation water content, LST, and spectral reflectance over soybean, corn, and barley were measured in the field and the laboratory. Based on the consistency of satellite data from Moderate-Resolution Imaging Spectroradiometer (MODIS/Aqua) in relation to such measurements, a model is proposed, which can be parameterized from remotely sensed NIR-SWIR/LST scatterplots. The obtained results were tested in the Argentine Pampas, showing coherence with surface processes at regional scale associated with soil water availability. The comparison with soil moisture at different depths (R2 > 0.7) showed that the method is sensitive to variations in root zone water availability. Given the reliance of the index on just satellite data, it can be pointed that the potential not only for vegetation water stress analyses but also in the context of hydrological modeling as an input of water availability.

Can Landsat 7 preserve its science capability with a drifting orbit?

Since 2017, the orbit of Landsat 7 has drifted outside its nominal mission requirement toward an earlier acquisition time because of limited onboard fuel resources. This makes quantitative analyses from Landsat 7 data potentially unreliable for many scientific studies. To comprehensively understand the effect of ongoing (2018–2020) orbit drift on Landsat 7 data, we compared surface reflectance and Top-Of-Atmosphere (TOA) reflectance of growing season observations (July 1 ± 30 days) from Landsat 7 with orbit drift and Landsat 8 with nominal orbit using a total of 10,000 randomly selected Northern Hemisphere (0–750 N) terrestrial pixels. To evaluate the future (2021–2023) effect of Landsat 7's orbit drift, we analyzed the historical Northern Hemisphere terrestrial growing season Earth Observing-1 (EO-1) TOA reflectance images, which shared a similar orbit drift as Landsat 7 but occurred much earlier. Results suggest that Landsat 7's orbit drift has already led to a general decrease in surface reflectance and TOA reflectance in 2019 and 2020, with a limited impact (overall reflectance changes less than 0.007). The influence of orbit drift is more substantial for the two shortwave infrared (SWIR) bands and the near infrared (NIR) band, but less for the three visible bands (i.e., Red, Green, and Blue). The Normalized Difference Vegetation Index (NDVI), derived from either surface reflectance or TOA reflectance, increased less than 0.003 in 2020. According to the historical EO-1 TOA reflectance data, we estimate that the effect of Landsat 7's orbit drift will be much more dramatic in the future (e.g., the NIR and SWIR bands will decrease more than 0.015 since July 1, 2021), and for different land cover types, the effects of orbit drift are also quite different. To reduce this influence, we examined the c-factor Bidirectional Reflectance Distribution Function (BRDF) normalization approach to correct the orbit drift impact for Landsat 7 surface reflectance data collected between 2019 and 2020. We found that the c-factor BRDF can reduce the data difference substantially, but how this approach works after Landsat 7's orbit drifts further still requires more investigation. Therefore, we determined that Landsat 7 can preserve its science capability until 2020, but will be less reliable for remote sensing applications that need accurate absolute radiometric values after 2020. Correction methods such as c-factor BRDF could be a potential viable approach to maintain its science capability going forward.

Optimal Bands Combination Selection for Extracting Garlic Planting Area with Multi-Temporal Sentinel-2 Imagery.

Garlic is one of the main economic crops in China. Accurate and timely extraction of the garlic planting area is critical for adjusting the agricultural planting structure and implementing rural policy actions. Crop extraction methods based on remote sensing usually use spectral–temporal features. Still, for garlic extraction, most methods simply combine all multi-temporal images. There has been a lack of research on each band’s function in each multi-temporal image and optimal bands combination. To systematically explore the potential of the multi-temporal method for garlic extraction, we obtained a series of Sentinel-2 images in the whole garlic growth cycle. The importance of each band in all these images was ranked by the random forest (RF) method. According to the importance score of each band, eight different multi-temporal combination schemes were designed. The RF classifier was employed to extract garlic planting area, and the accuracy of the eight schemes was compared. The results show that (1) the Scheme VI (the top 39 bands in importance score) achieved the best accuracy of 98.65%, which is 6% higher than the optimal mono-temporal (February, wintering period) result, and (2) the red-edge band and the shortwave-infrared band played an essential role in accurate garlic extraction. This study gives inspiration in selecting the remotely sensed data source, the band, and phenology for accurately extracting garlic planting area, which could be transferred to other sites with larger areas and similar agriculture structures.

A knowledge-based, validated classifier for the identification of aliphatic and aromatic plastics by WorldView-3 satellite data

Although the CH chains of petroleum derivatives display unique absorption features in the short-wave infrared (SWIR), it is a challenge to identify plastics on terrestrial surfaces. The diverse reflectance spectra caused by chemically varying polymer types and their different kinds of brightness and transparencies, which are, moreover, influenced further by the respective surface backgrounds. This paper investigates the capability of WorldView-3 (WV-3) satellite data, characterized by a high spatial resolution and equipped with eight distinct and relatively narrow SWIR bands suitable for global monitoring of different types of plastic materials. To meet the objective, hyperspectral measurements and simulations were conducted in the laboratory and by aircraft campaigns, based on the JPL-ECOSTRESS, USGS, and inhouse hyperspectral libraries, all of which are convolved to the spectral response functions of the WV-3 system. Experiments further supported the analyses wherein different plastic materials were placed on different backgrounds, and scaled percentages of plastics per pixel were modeled to determine the minimum detectable fractions. To determine the detectability of plastics with various chemical and physical properties and different fractions against diverse backgrounds, a knowledge-based classifier was developed, the routines of which are based on diagnostic spectral features in the SWIR range. The classifier shows outstanding results on various background scenarios for lab experimental imagery as well as for airborne data and it is further able to mask non-plastic materials. Three clusters of plastic materials can clearly be identified, based on spectra and imagery: The first cluster identifies aliphatic compounds, comprising polyethylene (PE), polyvinylchloride (PVC), ethylene vinyl acetate copolymer (EVAC), polypropylene (PP), polyoxymethylene (POM), polymethyl methacrylate (PMMA), and polyamide (PA). The second and third clusters are diagnostic for aromatic hydrocarbons, including polyethylene terephthalate (PET), polystyrene (PS), polycarbonate (PC), and styrene-acrylonitrile (SAN), respectively separated from polybutylene adipate terephthalate (PBAT), acrylonitrile butadiene styrene (ABS), and polyurethane (PU). The robustness of the classifier is examined on the basis of simulated spectra derived from our HySimCaR model, which has been developed in-house. The model simulates radiation transfer by using virtual 3D scenarios and ray tracing, hence, enables the analysis of the influence of various factors, such as material brightness, transparency, and fractional coverage as well as different background materials. We validated our results by laboratory and simulated datasets and by tests using airborne data recorded at four distinct sites with different surface characteristics. The results of the classifier were further compared to results produced by another signature-based method, the spectral angle mapper (SAM) and a commonly used technique, the maximum likelihood estimation (MLE). Finally, we applied and successfully tested the classifier on WV-3 imagery of sites known for a high abundance of plastics in Almeria (Spain), Cairo (Egypt), and Accra, (Ghana, West Africa). Both airborne and WV-3 data were atmospherically corrected and transferred to “at-surface reflectances”. The results prove the combination of WV-3 data and the newly designed classifier to be an efficient and reliable approach to globally monitor and identify three clusters of plastic materials at various fractions on different backgrounds.

CMOS-Compatible Bias-Tunable Dual-Band Detector Based on GeSn/Ge/Si Coupled Photodiodes

Infrared (IR) multispectral detection is attracting increasing interest with the rising demand for high spectral sensitivity, room temperature operation, CMOS-compatible devices. Here, we present a two-terminal dual-band detector, which provides a bias-switchable spectral response in two distinct IR bands. The device is obtained from a vertical GeSn/Ge/Si stack, forming a double junction n-i-p-i-n structure, epitaxially grown on a Si wafer. The photoresponse can be switched by inverting the bias polarity between the near and the short-wave IR bands, with specific detectivities of 1.9 × 1010 and 4.0 × 109 cm·(Hz)1/2/W, respectively. The possibility of detecting two spectral bands with the same pixel opens up interesting applications in the field of IR imaging and material recognition, as shown in a solvent detection test. The continuous voltage tuning, combined with the nonlinear photoresponse of the detector, enables a novel approach to spectral analysis, demonstrated by identifying the wavelength of a monochromatic beam.

ANALYSIS AND DETECTION OF WILDFIRES BY USING PRISMA HYPERSPECTRAL IMAGERY

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.

Evaluation of Object-Based Greenhouse Mapping Using WorldView-3 VNIR and SWIR Data: A Case Study from Almería (Spain)

Plastic covered greenhouse (PCG) mapping via remote sensing has received a great deal of attention over the past decades. The WorldView-3 (WV3) satellite is a very high resolution (VHR) sensor with eight multispectral bands in the visible and near-infrared (VNIR) spectral range, and eight additional bands in the short-wave infrared (SWIR) region. A few studies have already established the importance of indices based on some of these SWIR bands to detect urban plastic materials and hydrocarbons which are also related to plastics. This paper aims to investigate the capability of WV3 (VNIR and SWIR) for direct PCG detection following an object-based image analysis (OBIA) approach. Three strategies were carried out: (i) using object features only derived from VNIR bands (VNIR); (ii) object features only derived from SWIR bands (SWIR), and (iii) object features derived from both VNIR and SWIR bands (All Features). The results showed that the majority of predictive power was attributed to SWIR indices, especially to the Normalized Difference Plastic Index (NDPI). Overall, accuracy values of 90.85%, 96.79% and 97.38% were attained for VNIR, SWIR and All Features strategies, respectively. The main PCG misclassification problem was related to the agricultural practice of greenhouse whitewash (greenhouse shading) that temporally masked the spectral signature of the plastic film.

Detecting high-temperature anomalies from Sentinel-2 MSI images

High-temperature anomalies (HTAs) of the earth's surface, such as fires, volcanic activities, and industrial heat sources, have a profound impact on Earth's system. Sentinel-2 Multispectral Instrument (MSI) provides spatially-specific information for precisely measuring the location and extent of HTAs at a fine scale. However, detecting HTAs from MSI images remains challenging because the emitted radiance of an HTA in the short-wave infrared (SWIR) bands can be easily mixed with the reflected solar radiance background in the daytime; and an increasing number of atypical cases in MSI images need to be treated with the enhanced spatial resolution. A generic HTA detection approach that handles both anthropogenic and natural HTAs will broaden the scope of MSI applications. In this study, (i) we highlight two spectral characteristics of HTAs in the far-SWIR, near-SWIR, and NIR bands (i.e., (ρfar-SWIR - ρnear-SWIR)/ρNIR ≥ 0.45 and (ρfar-SWIR -ρnear-SWIR) ≥ ρnear-SWIR - ρNIR) that can effectively enhance HTAs from background geo-features, based on the reflectance spectra in airborne imaging spectrometer data. (ii) We propose a tri-spectral thermal anomaly index (TAI) that jointly uses the two high-temperature-sensitive SWIR bands and the high-temperature-insensitive NIR band to enhance HTAs, based on the above characteristics and a comprehensive sampling of different types of HTAs from 1,974 MSI images. (iii) We develop a TAI-based approach for MSI images to detect HTAs in general. The proposed approach was applied to detect different types of HTAs, including different biomass burnings, active volcanoes, and industrial HTAs, over a wide range of land-cover scenarios. Validations and comparisons demonstrate the proposed approach is reliable and performs better than the existing state-of-the-art HTA detection approaches. Evaluations on two types of small industrial HTAs, including operating kilns and enclosed landfill gas flares, show that the HTA detection probability of the TAI-based approach from time-series MSI images is ~ 84.91% and 88.23%, respectively. Further investigations show that the TAI-based approach also has good transferability in detecting HTAs from multispectral images acquired by Landsat-family satellites.

Mineral Detection Using Sharpened VNIR and SWIR Bands of Worldview-3 Satellite Imagery

Worldview-3 satellite imagery provides panchromatic images with a high spatial resolution and visible near infrared (VNIR) and shortwave infrared (SWIR) bands with a low spatial resolution. These images can be used for various applications such as environmental analysis, urban monitoring and surveying for sustainability. In this study, mineral detection was performed using Worldview-3 satellite imagery. A pansharpening technique was applied to the spatial resolution of the panchromatic image to effectively utilize the VNIR and SWIR bands of Worldview-3 satellite imagery. The following representative similarity analysis techniques were implemented for the mineral detection: the spectral angle mapper (SAM), spectral information divergence (SID) and the normalized spectral similarity score (NS3). In addition, pixels that could be estimated to indicate minerals were calculated by applying an empirical threshold to each similarity analysis result. A majority voting technique was applied to the results of each similarity analysis and pixels estimated to indicate minerals were finally selected. The results of each similarity analysis were compared to evaluate the accuracy of the proposed methods. From that comparison, it could be confirmed that false negative and false positive rates decreased when the methods proposed in the present study were applied.

Correction of Sunglint Effects in High Spatial Resolution Hyperspectral Imagery Using SWIR or NIR Bands and Taking Account of Spectral Variation of Refractive Index of Water

Passive high spatial resolution hyperspectral and multispectral imaging systems in the solar spectral region from aircraft and satellite platforms are being increasingly used for remote sensing of coastal waters and inland lakes. However, the remotely sensed data are often contaminated by the specular reflection of solar radiation at the air/water interface. Thus, the effects of sunglint need to be corrected. The purpose of sunglint correction is to remove the undesired specular reflected portion of radiance that does not carry any information about the water body and its bottom surface properties and to obtain the water-leaving radiance from the measured total radiance. In general, adequate treatment of the spectral variation of the sunglint effect with due consideration of the physics and the spectroscopy principles has not been done in previously reported hyperspectral sunglint removing techniques. In this study, a practical sunglint removing technique using either a shortwave infrared (SWIR) band or a near-infrared (NIR) band has been developed, where the liquid water absorption is sufficiently large, and the water-leaving radiance (in units of reflectance) is negligible. In the proposed method, proper consideration has been taken for the spectral Fresnel reflection in the wavelength range of 0.35–2.5 mm. Four cases of sunglint removal from hyperspectral imaging data acquired from two airborne imaging spectrometers have been presented.

An automatic classification algorithm for submerged aquatic vegetation in shallow lakes using Landsat imagery

Submerged aquatic vegetation (SAV) is one of the main producers in inland lakes. Tracking the temporal and spatial changes in SAV is crucial for the identification of state changes in lacustrine ecosystems, such as changes in light, nutrients, and temperature. However, the available SAV classification algorithms based on remote sensing are highly dependent on field survey data and/or human interventions, prohibiting the extraction of large-scale and/or long-term patterns. Here, we developed an automatic SAV classification algorithm using Landsat imagery, where the thresholds of two key parameters (the floating algae index (FAI) and reflectance in the shortwave-infrared (SWIR) band) are automatically determined. The algorithm was applied to eight Landsat images of four Yangtze Plain lakes and obtained a mean producer accuracy of 82.9% when gauged against field-surveyed datasets. The algorithm was further employed to obtain long-term SAV areal data from Changdang Lake on the Yangtze Plain from 1984 to 2018, and the result was highly consistent with lake transparency data. Numerical simulations indicated that our developed algorithm is insensitive to the Chl-a concentration of the water column. Yet, it has a detection limit of ~0.35 m below the water surface, and such a limit changes with different fractions of vegetation coverage within a pixel. The automatic classification algorithm proposed in this study has the potential to obtain the temporal and spatial distribution patterns of SAV in other shallow lakes where SAV grows in lakes sharing similar hydrological characteristics as the lakes in the Yangtze Plain.

Comparative assessment of drought monitoring index susceptibility using geospatial techniques.

There are two main categories of dryness monitoring indices based on spectral feature space. One category uses the vertical distance from any point to a line passing through the coordinate origin, which is perpendicular to a soil line, to monitor the dryness conditions. The most popular indices are the Perpendicular Dryness Index (PDI) and the modified perpendicular dryness index (MPDI). The other category uses the distance from any point in feature space to the coordinate origin to represent the dryness status, for instance, the soil moisture (SM) monitoring index (SMMI) and the modified soil moisture monitoring index (MSMMI). In this study, the performances and differences of these four indicators were evaluated using field-measured SM (FSM) data based on Gaofen-1 (GF-1) wide field of view (WFV), Landsat-8 Operational Land Imager (OLI), and Sentinel-2 Multi-Spectral Instrument (MSI) sensors. Performance evaluations were conducted in two study areas, namely an arid and semi-arid region of northwest China and a humid agricultural region of southwest Canada. We employed gradient-based structural similarity (GSSIM) to quantitatively assess the similarity of the structural information and structural characteristics among these four indicators. Monitoring SM in bare soil or low vegetation-covered areas in the semi-arid region, the SMMI, PDI, MSMMI, and MPDI from Near-Infrared (NIR)-Red had significantly negative linear correlations with the FSM at 0-5-cm depth (P < 0.01). However, SMMI was better than PDI in estimating SM in bare soil, which was better than MSMMI and MPDI for GF-1. Moreover, the PDI and SMMI had similar SM evaluation abilities, which were better than those of MPDI and MSMMI for Landsat-8. The GSSIM map of the SMMI/PDI and the MSMMI/MPDI showed that the low change areas accounted for 99.89% and 98.89% for GF-1, respectively, and 95.78% and 94.45% for Landsat-8, respectively. This result indicated that the SMMI, PDI, MSMMI, and MPDI values from NIR-Red in low vegetation cover were similar. In monitoring SM in agricultural vegetation areas, the accuracy of the four indices from Short-Wave Infrared (SWIR) feature space was higher than that from NIR-Red feature space for Sentinel-2. The SM monitoring effect of MSMMI and MPDI was better than that of SMMI and PDI. Due to the lack of SWIR band, GF-1 was limited in monitoring SM in vegetation-covered areas. The SMMI and MSMMI, which do not rely on the soil line, were more suitable than PDI and MPDI for retrieving SM in the complex surface environment depending on the soil line and the number of parameters. GF-1 with 16-m resolution had higher accuracy in SM assessment than Landsat-8 with 30-m resolution and had almost the same accuracy as Sentinel-2 with 20 m.

Global $9.5 bn refinery catalyst market to 2027 by material (zeolites, metallic, chemical compounds), &application (FCC, alkylation, hydrotreating, hydrocracking)

This study presents the air-borne geophysical Gamma Ray Spectrometry data have been used for detecting the alteration zones related to hydrothermal activity. Areas enriched in potassium may indicate the existence of potassic alteration zone that is often associated with hydrothermal alteration accompanied by economic mineral deposits. The radioelements (K (%), eTh (ppm), and eU (ppm)) concentration distribution and the ratios of (K/eTh and K/eU) over the study area were presented. A Potassium Point anomaly map was obtained from the statistical treatment of the K (%), K/eU, and K/eTh spectrometric data. Anomalously High (X+3σ or more) areas were delineated and considered the most preferred areas for potassic alteration. If the maximum values exceed the (X+3σ) threshold, then (X+2σ) or (X+σ) is applied instead. Sixteen areas were delineated and suggested for the presence of potassic alteration. The F-Parameter of Efimov (K*(eU/eTh)) was also implemented as proved approach for delineating the most preferred areas for hydrothermal alteration. The result of the Potassium point anomaly mapping was compared to that of the F-Parameter technique that was used as reference for validating the results. The sixteen anomalous areas revealed from the Potassium point anomaly technique showed perfect correlation with the areas of highly anomalous F-parameter.Remote sensing study supported by field data and laboratory spectral analyses was also implemented in this study as another useful powerful cost-effective approach for alteration mapping. A field study was conducted; samples from the different observed alterations were collected. The spectral signatures of these samples were measured using the “ASD TerraSpec Halo mineral identifier” hand-held spectroradiometer. Spectral analyses were performed to obtain the spectral characteristics of the alteration zones of the study are. The end-members were identified through comparing the measured spectra against the United States Geological Survey (USGS) spectral library in ENVI v.5.1. software. The identified end-members include; Kaolinite, Sericite, Montmorillonite, Chlorite, Epidote and iron oxides. Subpixel Mixture Tuned Matched Filtering (MTMF) mapping technique was finally used for mapping the distribution of these end-members on ASTER nine Visible Near Infra-Red (VNIR) and Short Wave Infra-Red (SWIR) bands. The remote sensing data were validated as Semna gold mine and the identified alterations during the field study were successfully mapped by the remote sensing technique. Finally; Most of the delineated areas using the proposed Potassium point anomaly technique were also evidenced by the approached remote sensing technique. Moreover remote sensing suggested more alteration areas than did the gamma ray spectrometry data.

BASF and Siemens Energy to cooperate in the field of carbon management

This study presents the air-borne geophysical Gamma Ray Spectrometry data have been used for detecting the alteration zones related to hydrothermal activity. Areas enriched in potassium may indicate the existence of potassic alteration zone that is often associated with hydrothermal alteration accompanied by economic mineral deposits. The radioelements (K (%), eTh (ppm), and eU (ppm)) concentration distribution and the ratios of (K/eTh and K/eU) over the study area were presented. A Potassium Point anomaly map was obtained from the statistical treatment of the K (%), K/eU, and K/eTh spectrometric data. Anomalously High (X+3σ or more) areas were delineated and considered the most preferred areas for potassic alteration. If the maximum values exceed the (X+3σ) threshold, then (X+2σ) or (X+σ) is applied instead. Sixteen areas were delineated and suggested for the presence of potassic alteration. The F-Parameter of Efimov (K*(eU/eTh)) was also implemented as proved approach for delineating the most preferred areas for hydrothermal alteration. The result of the Potassium point anomaly mapping was compared to that of the F-Parameter technique that was used as reference for validating the results. The sixteen anomalous areas revealed from the Potassium point anomaly technique showed perfect correlation with the areas of highly anomalous F-parameter.Remote sensing study supported by field data and laboratory spectral analyses was also implemented in this study as another useful powerful cost-effective approach for alteration mapping. A field study was conducted; samples from the different observed alterations were collected. The spectral signatures of these samples were measured using the “ASD TerraSpec Halo mineral identifier” hand-held spectroradiometer. Spectral analyses were performed to obtain the spectral characteristics of the alteration zones of the study are. The end-members were identified through comparing the measured spectra against the United States Geological Survey (USGS) spectral library in ENVI v.5.1. software. The identified end-members include; Kaolinite, Sericite, Montmorillonite, Chlorite, Epidote and iron oxides. Subpixel Mixture Tuned Matched Filtering (MTMF) mapping technique was finally used for mapping the distribution of these end-members on ASTER nine Visible Near Infra-Red (VNIR) and Short Wave Infra-Red (SWIR) bands. The remote sensing data were validated as Semna gold mine and the identified alterations during the field study were successfully mapped by the remote sensing technique. Finally; Most of the delineated areas using the proposed Potassium point anomaly technique were also evidenced by the approached remote sensing technique. Moreover remote sensing suggested more alteration areas than did the gamma ray spectrometry data.

Using air-borne gamma ray spectrometry and remote sensing data for detecting alteration zones around Wadi Saqia area, Central Eastern Desert, Egypt

This study presents the air-borne geophysical Gamma Ray Spectrometry data have been used for detecting the alteration zones related to hydrothermal activity. Areas enriched in potassium may indicate the existence of potassic alteration zone that is often associated with hydrothermal alteration accompanied by economic mineral deposits. The radioelements (K (%), eTh (ppm), and eU (ppm)) concentration distribution and the ratios of (K/eTh and K/eU) over the study area were presented. A Potassium Point anomaly map was obtained from the statistical treatment of the K (%), K/eU, and K/eTh spectrometric data. Anomalously High (X+3σ or more) areas were delineated and considered the most preferred areas for potassic alteration. If the maximum values exceed the (X+3σ) threshold, then (X+2σ) or (X+σ) is applied instead. Sixteen areas were delineated and suggested for the presence of potassic alteration. The F-Parameter of Efimov (K*(eU/eTh)) was also implemented as proved approach for delineating the most preferred areas for hydrothermal alteration. The result of the Potassium point anomaly mapping was compared to that of the F-Parameter technique that was used as reference for validating the results. The sixteen anomalous areas revealed from the Potassium point anomaly technique showed perfect correlation with the areas of highly anomalous F-parameter.Remote sensing study supported by field data and laboratory spectral analyses was also implemented in this study as another useful powerful cost-effective approach for alteration mapping. A field study was conducted; samples from the different observed alterations were collected. The spectral signatures of these samples were measured using the “ASD TerraSpec Halo mineral identifier” hand-held spectroradiometer. Spectral analyses were performed to obtain the spectral characteristics of the alteration zones of the study are. The end-members were identified through comparing the measured spectra against the United States Geological Survey (USGS) spectral library in ENVI v.5.1. software. The identified end-members include; Kaolinite, Sericite, Montmorillonite, Chlorite, Epidote and iron oxides. Subpixel Mixture Tuned Matched Filtering (MTMF) mapping technique was finally used for mapping the distribution of these end-members on ASTER nine Visible Near Infra-Red (VNIR) and Short Wave Infra-Red (SWIR) bands. The remote sensing data were validated as Semna gold mine and the identified alterations during the field study were successfully mapped by the remote sensing technique. Finally; Most of the delineated areas using the proposed Potassium point anomaly technique were also evidenced by the approached remote sensing technique. Moreover remote sensing suggested more alteration areas than did the gamma ray spectrometry data.

Co-seismic landslide mapping using Sentinel-2 10-m fused NIR narrow, red-edge, and SWIR bands

An accurate and timely inventory for major landslides triggered by seismic events is essential for hazard assessment and risk governance. Sentinel-2 MultiSpectral Instrument (MSI) data have been increasingly used for co-seismic landslide mapping in recent years. However, there have been few studies exploring the full use of Sentinel-2 near infrared (NIR) narrow, red-edge, and short-wave infrared (SWIR) bands for landslide mapping. In this study, Sentinel-2 10-m fused NIR narrow, red-edge-1, SWIR-1, and SWIR-2 bands were incorporated into the change detection-based Markov random field (CDMRF) that considers the spectral, spatial, and contextual information of landslides. The effectiveness of the developed method was proved in mapping co-seismic landslides triggered by two major earthquakes in the Yarlung Tsangpo Grand Canyon, China, in 2017 and Hokkaido, Japan, in 2018. The results demonstrated that the mapping performances with the fused Sentinel-2 bands were significantly improved for both seismic events. Such improvement is mainly attributed to the more abundant spectral information provided by the fused Sentinel-2 red-edge, NIR narrow, and SWIR bands. The novelties of this study are (1) the preliminary attempt to use the Sentinel-2 10-m fused bands for landslide mapping, and (2) the synergy of Sentinel-2 red-edge, NIR narrow, and SWIR bands for more reliable landslide mapping.

Assessing the Potential of Geostationary Himawari-8 for Mapping Surface Total Suspended Solids and Its Diurnal Changes

Ocean color sensors, typically installed on polar-orbiting satellites, have been used to monitor oceanic processes for last three decades. However, their temporal resolution is not considered to be adequate for monitoring highly dynamic oceanic processes, especially when considering data gaps due to cloud contamination. The Advanced Himawari Imager (AHI) onboard the Himawari-8, a geostationary satellite operated by the Japan Meteorological Agency (JMA), acquires imagery every 10 min at 500 m to 2000 m spatial resolution. The AHI sensor with three visible, one near-infrared (NIR), and two shortwave-infrared (SWIR) bands displays good potential in monitoring oceanic processes at high temporal resolution. This study investigated and identified an appropriate atmospheric correction method for AHI data; developed a model for Total Suspended Solids (TSS) concentrations estimation using hyperspectral data and in-situ measurements of TSS; validated the model; and assessed its potential to capture diurnal changes using AHI imagery. Two image-based atmospheric correction methods, the NIR-SWIR method and the SWIR method were tested for correcting the AHI data. Then, the new model was applied to the atmospherically corrected AHI data to map TSS and its diurnal changes in the Pearl River Estuary (PRE) and neighboring coastal areas. The results indicated that the SWIR method outperformed the NIR-SWIR method, when compared to in-situ water-leaving reflectance data. The results showed a good agreement between the AHI-derived TSS and in-situ measured data with a coefficient of determination (R²) of 0.85, mean absolute error (MAE) of 3.1 mg/L, a root mean square error (RMSE) of 3.9 mg/L, and average percentage difference (APD) of 30% (TSS range 1–40 mg/L). Moreover, the diurnal variation in the turbidity front, using the Normalized Suspended Material Index (NSMI), showed the capability of AHI data to track diurnal variation in turbidity fronts, due to high TSS concentrations at high temporal frequency. The present study indicates that AHI data with high image capturing frequency can be used to map surface TSS concentrations. These TSS measurements at high frequency are not only important for monitoring the sensitive coastal areas but also for scientific understanding of the spatial and temporal variation of TSS.

Deep Learning Based Thin Cloud Removal Fusing Vegetation Red Edge and Short Wave Infrared Spectral Information for Sentinel-2A Imagery

Thin clouds seriously affect the availability of optical remote sensing images, especially in visible bands. Short-wave infrared (SWIR) bands are less influenced by thin clouds, but usually have lower spatial resolution than visible (Vis) bands in high spatial resolution remote sensing images (e.g., in Sentinel-2A/B, CBERS04, ZY-1 02D and HJ-1B satellites). Most cloud removal methods do not take advantage of the spectral information available in SWIR bands, which are less affected by clouds, to restore the background information tainted by thin clouds in Vis bands. In this paper, we propose CR-MSS, a novel deep learning-based thin cloud removal method that takes the SWIR and vegetation red edge (VRE) bands as inputs in addition to visible/near infrared (Vis/NIR) bands, in order to improve cloud removal in Sentinel-2 visible bands. Contrary to some traditional and deep learning-based cloud removal methods, which use manually designed rescaling algorithm to handle bands at different resolutions, CR-MSS uses convolutional layers to automatically process bands at different resolution. CR-MSS has two input/output branches that are designed to process Vis/NIR and VRE/SWIR, respectively. Firstly, Vis/NIR cloudy bands are down-sampled by a convolutional layer to low spatial resolution features, which are then concatenated with the corresponding features extracted from VRE/SWIR bands. Secondly, the concatenated features are put into a fusion tunnel to down-sample and fuse the spectral information from Vis/NIR and VRE/SWIR bands. Third, a decomposition tunnel is designed to up-sample and decompose the fused features. Finally, a transpose convolutional layer is used to up-sample the feature maps to the resolution of input Vis/NIR bands. CR-MSS was trained on 28 real Sentinel-2A image pairs over the globe, and tested separately on eight real cloud image pairs and eight simulated cloud image pairs. The average SSIM values (Structural Similarity Index Measurement) for CR-MSS results on Vis/NIR bands over all testing images were 0.69, 0.71, 0.77, and 0.81, respectively, which was on average 1.74% higher than the best baseline method. The visual results on real Sentinel-2 images demonstrate that CR-MSS can produce more realistic cloud and cloud shadow removal results than baseline methods.

Comprehensive Evaluation of Sentinel-2 Red Edge and Shortwave-Infrared Bands to Estimate Soil Moisture

This article aims to explore the applicability of SMMI (soil moisture monitoring index), MSMMI (modified soil moisture monitoring index), PDI (perpendicular drought index), and MPDI (modified perpendicular drought index) in estimating soil moisture (SM) in farmland. The random forest classifier was used to obtain two-stage land cover types maps. The sensitivity of Sentinel-2 spectral bands to the measured SM at a depth of 0-5 cm was optimized by random forest regression. According to the sensitive bands, SMMI and PDI from different feature spaces were constructed to explore their feasibility for monitoring SM under different land cover types. Second, fractional vegetation cover (FVC) in the study area was estimated by nine kinds of FVC estimation models and compared with the measured FVC. The effects of different FVC methods on estimating SM by MSMMI and MPDI were evaluated. The results show that red edge and short-wave infrared (SWIR) bands of Sentinel-2 had irreplaceable effects on the land cover classification. In terms of monitoring SM in bare soil areas, the SM indices with SWIR bands had high correlations with measured SM. For vegetation-covered areas, MSMMI from the FVCgr model (dimidiate pixel model with red edge bands) and the Short wave infrared1-Short wave infrared2 feature space had the highest correlation with the measured 0-5 cm depth SM. Whether vegetation-covered areas or bare soil areas, the combination of red edge and SWIR bands can effectively improve the estimation accuracy of SM. MSMMI can be used as the best SMMI in the study area. Sentinel-2 images, with great potential, can effectively estimate SM at a depth of 0-5 cm in farmland with complex environments.