Shortwave Infrared Reflectance Research Articles

IDENTIFYING POTENTIAL PORPHYRY CU MINERALIZATION AT THE KIRAZLI DISTRICT IN BIGA PENINSULA (NW TURKEY): INSIGHTS FROM THE MAPPING HYDROTHERMAL ALTERATION BY USING SHORTWAVE INFRARED (SWIR) SPECTROMETRY

The Kirazlı mineral district is located at the center of the Biga peninsula metallogenic province, in a geological setting characterized by an extensional tectonic environment. The district hosts a high-sulfidation (HS) ore body with a total reserve of 33.86 Mt @ 0.69 g/t Au and 9.42 g/t Ag within a large-scale hydrothermal alteration. Although the ideal magmatic-hydrothermal models present the spatial and temporal association of HS-epithermal and porphyry Cu deposits, the porphyry Cu potential at the Kirazlı has not been evaluated, yet. Therefore, the mineral-based alteration mapping with the help of SWIR reflectance spectroscopy was carried out. Our study demonstrates that nine different mineral zone have been distinguished and mapped: (1) Silicification, (2) Alunite, (3) Kaolinite, (4) Dickite, (5) Illite, (6) Pyrophyllite, (7) Chlorite, (8) Sericite, and (9) Montmorillonite zones. Collectively, these alterations exhibit a zoned pattern, from central massive silicification to residual silica, NW-SE oriented alunite, and marginal montmorillonite-illite zones within widespread kaolinite – dickite dominated alteration in Kirazlı main zone. The occurrence of pyrophyllite and sericite dominates at the SE of the Kirazlı district, which refers to relative temperature increase. Subsequent drilling confirms the potential for deep prospecting of porphyry Cu mineralization in the region.

Assessment of the activity of secondary caries lesions with short-wavelength infrared, thermal, and optical coherence tomographic imaging.

Significance: Leakage in the interfaces between restorative materials and tooth structure allows for fluid and bacterial acid infiltration, causing restoration failure due to secondary caries. Dentists spend more time replacing composite restorations than placing new ones. Previous in vitro and in vivo studies on enamel and root surfaces using shortwave-infrared (SWIR) and thermal imaging during dehydration with forced air have been promising for assessing lesion activity. Aim: We hypothesized that SWIR reflectance and thermal imaging methods can be used to monitor the activity of secondary caries lesions around composite restorations. The objective of this study was to employ these methods to measure the rate of fluid loss from lesions during dehydration with forced air to assess lesion activity. Approach: Sixty-three extracted human teeth with total of 109 suspected secondary lesions were examined using SWIR and thermal imaging during dehydration. The thickness of the highly mineralized transparent surface layer (TSL) at lesion interfaces indicative of lesion activity was measured by optical coherence tomography (OCT). Micro-computed tomography (MicroCT) was used to further confirm lesion severity and structure. OCT and MicroCT measurements of lesion structure, depth, and severity were correlated with fluid loss rates measured with SWIR reflectance and thermal imaging. Results: TSL thickness measured with OCT correlated with both SWIR reflectance and thermal measurements of rates of fluid loss ( ). Increasing TSL thickness led to decreased permeability of lesions, potentially indicating full lesion arrest at . SWIR performed better than thermal imaging for secondary lesion activity assessment, although both methods performed best on smooth surface lesions. Conclusions: Nondestructive SWIR reflectance and OCT imaging methods are promising for clinically monitoring the activity of secondary caries lesions.

Logistic splicing correction for VNIR-SWIR reflectance imaging spectroscopy.

In the field of spectroscopy, a splicing correction is a process by which two spectra captured with different sensors in adjacent or overlapping electromagnetic spectrum ranges are smoothly connected. In our study, we extend this concept to the case of reflectance imaging spectroscopy in the visible-near-infrared (VNIR) and short-wave infrared (SWIR), accounting for additional sources of noise that arise at the pixel level. The proposed approach exploits the adaptive fitting of a logistic function to compute correcting coefficients that harmonize the two spectral sets. This short Letter addresses usage conditions and compares results against the existing state of the art.

Evaluating a new temperature-vegetation-shortwave infrared reflectance dryness index (TVSDI) in the continental United States

Accurate dryness monitoring is important for formulating reasonable response measures to reduce social and economic losses caused by drought. The land surface temperature (LST), shortwave infrared (SWIR) reflectance, and vegetation index (VI) are popular remote sensing (RS) indices that can be individually used to characterize surface dryness. Given the interactions of these factors, limitations are inevitably associated with using a single factor. Integrated dryness indices that combine LST or SWIR reflectance with the VI have thus been successively proposed and applied for dryness monitoring and soil moisture (SM) retrieval work. However, the advantages of these three indicators have not yet been combined to construct a more comprehensive dryness index. In this study, we integrated the LST, enhanced vegetation index (EVI), and SWIR reflectance and developed an integrated satellite-based dryness index with simple calculations, called the temperature vegetation shortwave infrared reflectance dryness index (TVSDI). The proposed TVSDI was thoroughly assessed in the continental United States (CONUS) using the following data: the soil moisture active passive (SMAP) SM; six commonly used dryness indices (i.e., temperature vegetation soil moisture dryness index (TVMDI), temperature vegetation dryness index (TVDI), modified perpendicular dryness index (MPDI), perpendicular dryness index (PDI), standardized precipitation evapotranspiration index (SPEI), and standardized precipitation index (SPI)); in situ SM data collected from 24 Cosmic-ray neutron probe (CRNP) sites covering different climates, soil types, and land cover types; and the United States Drought Monitor (USDM) maps. The results demonstrated that the TVSDI was significantly correlated with SMAP SM (R = − 0.75, p < 0.01) and exhibited better performance than the use of LST, EVI, and SWIR reflectance individually. Moreover, the TVSDI and the other six commonly used dryness indices exhibited good spatiotemporal consistency, all with consistency areas > 60%. The evaluation based on in situ SM from 24 CRNP sites indicated that the TVSDI exhibited more stability and accuracy than other satellite-based agricultural dryness indices (TVMDI, MPDI, PDI, and TVDI). Moreover, the spatial patterns of TVSDI maps were not only well-matched with SMAP SM maps but also provided more detailed spatial information. TVSDI maps could capture more dryness and drought variations in natural ecosystems and areas with less intensive human activities than USDM maps. Furthermore, the application of the TVSDI for dryness monitoring in the CONUS revealed that the dryness distributions differed greatly across different geographic regions at monthly and annual scales. In conclusion, the TVSDI was found to be a reliable and accurate satellite-based dryness index.

533 Human Case Characterizations of Skin Burn Using Novel Multi-Spectral Short Wave Infrared Imaging

IntroductionDetermining the depth of skin burns in patients is critical for surgical decision making, but currently lacks accuracy in clinical practice. Short-wave infrared (SWIR) light penetrates tissue more than visual or near-infrared light and is very sensitive to water content. We have shown in animal models that imaging of skin burns in the SWIR range distinguishes between superficial and deep tissue necrosis. Here we present the first 2 cases of multi-spectral SWIR imaging of human burn injury as a first step toward a non-invasive, label-free, technique for burn depth determination.MethodsTwo subjects admitted for mixed depth, thermal, 6% and 7% total body surface area (TBSA), burns were studied. Prior to burn excision, a novel system, based on a specialized camera, imaged the burn areas and normal skin at 4 different SWIR bands. Standard photographs from imaged areas were collected and presented for 5 independent, blinded, surgeons’ assessments. In SWIR images, 3-5 regions of interest (ROIs) were selected in burned and adjacent normal skin and the reflected light intensity in each ROI was averaged.ResultsVisual and SWIR images were collected for 9 burn areas in the hands, arms, and shoulder of 2 patients (Panel A). Fifty ROIs from the burn areas were assessed by the surgeons and 30 (60%) ROIs were agreed as being superficial or superficial partial thickness (n=5), deep partial thickness (n=11), or full thickness (n=14) burns by a majority (60% or above consensus together with a possible disagreement only between deep partial and full thickness burn). In Panel B the cumulative SWIR reflectance intensity at the 4 SWIR bands for the 3 burn groups, determined by expert surgeon evaluation, and normal skin are compared. The reflectance from superficial and superficial partial thickness burns (yellow) were 102.7±1.2%, 102.3±0.7% and 103.4±1.4% of the normal skin reflectance for 1200, 1650 and 1940 nm, respectively. On the other hand, the reflectance from deep partial thickness burns (grey) were 96.7±0.1% and 94.7±0.1%, and for full thickness burn (red) were 96.1±1.4% and 93.7±1.6% of normal skin reflectance for 1650 and 1940 nm, respectively.ConclusionsWe present the first human SWIR study demonstrating a distinct reflectance intensity of SWIR wavelengths for different burn depths based on surgeon assessments. The results motivate further studies of SWIR imaging of burns in the hope to non-invasively and accurately identify operative versus non-operative burns.

Evaluation of vegetation indices and imaging spectroscopy to estimate foliar nitrogen across disparate biomes

AbstractThe nitrogen content in plant foliar tissues (foliar N) regulates photosynthetic capacity and has a major impact on global biogeochemical cycles. Despite its importance, a robust, time, and cost‐effective methodology to estimate variation in foliar N concentration across globally represented terrestrial systems does not exist. Although advances in remote sensing data have enabled landscape‐scale foliar N predictions, improved accuracy is needed to effectively capture variation in foliar N across ecosystems. Airborne remote sensing imagery was analyzed in conjunction with ground‐sampled foliar chemistry data (n = 692), provided by the NEON, to predict foliar N at sites across the United States covering a variety of plant communities and climate types. We developed indices from novel two‐band combinations that predicted foliar N more accurately than existing indices (≈8% improvement across all sites and a 45% improvement in arid sites). Compared with two‐band indices, we increased accuracy and decreased bias of foliar N predictions by using full‐spectrum reflectance information and partial least squares regression (PLSR) models (R2 = 0.638; root mean square error = 0.440). Significant wavelengths included red edge (720–765 nm), near infrared (NIR) reflectance at 1125 nm, and shortwave infrared (SWIR) reflectance at 2050 and 2095 nm, which are regions indicative of foliar traits such as growth type (e.g., leaf area index with NIR) and photosynthetic parameters (e.g., chlorophyll and Rubisco with red and SWIR reflectance, respectively). With the confluence of rapid increases in computing power, several forthcoming or recently launched hyperspectral missions, and the development of large‐scale environmental research observatories worldwide, we have an exciting opportunity to estimate foliar N across larger spatial areas covering more diverse biomes than ever before. We anticipate that these predictions will prove to be invaluable in helping to constrain biogeochemical model uncertainties across a global range of terrestrial ecosystems.

Simulated Annealing-Based Hyperspectral Data Optimization for Fish Species Classification: Can the Number of Measured Wavelengths Be Reduced?

Relative to standard red/green/blue (RGB) imaging systems, hyperspectral imaging systems offer superior capabilities but tend to be expensive and complex, requiring either a mechanically complex push-broom line scanning method, a tunable filter, or a large set of light emitting diodes (LEDs) to collect images in multiple wavelengths. This paper proposes a new methodology to support the design of a hypothesized system that uses three imaging modes—fluorescence, visible/near-infrared (VNIR) reflectance, and shortwave infrared (SWIR) reflectance—to capture narrow-band spectral data at only three to seven narrow wavelengths. Simulated annealing is applied to identify the optimal wavelengths for sparse spectral measurement with a cost function based on the accuracy provided by a weighted k-nearest neighbors (WKNN) classifier, a common and relatively robust machine learning classifier. Two separate classification approaches are presented, the first using a multi-layer perceptron (MLP) artificial neural network trained on sparse data from the three individual spectra and the second using a fusion of the data from all three spectra. The results are compared with those from four alternative classifiers based on common machine learning algorithms. To validate the proposed methodology, reflectance and fluorescence spectra in these three spectroscopic modes were collected from fish fillets and used to classify the fillets by species. Accuracies determined from the two classification approaches are compared with benchmark values derived by training the classifiers with the full resolution spectral data. The results of the single-layer classification study show accuracies ranging from ~68% for SWIR reflectance to ~90% for fluorescence with just seven wavelengths. The results of the fusion classification study show accuracies of about 95% with seven wavelengths and more than 90% even with just three wavelengths. Reducing the number of required wavelengths facilitates the creation of rapid and cost-effective spectral imaging systems that can be used for widespread analysis in food monitoring/food fraud, agricultural, and biomedical applications.

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 &gt; 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.

Hyperspectral sensor: A handy tool to evaluate the efficacy of cleaning procedures

• ASD-FieldSpec® 3 operates VNIR and SWIR reflectance spectral range. • VNIR and SWIR provide ready-to-use information on the degree of sulphation of carbonate stone. • SWIR data allowed information on efficacy of three different cleaning techniques. • The method supports monitoring of stone alteration during restoration. In urban environments, degradation processes affecting both natural and artificial carbonate materials commonly result in the formation of sulphate-based deposits (i.e., black crusts). Hyperspectral techniques may provide ready-to-use information on the degree of sulphation of carbonate stone, hence helping to monitor the conservation state of carbonate stone monuments. In this study, the Short-Wave InfraRed (SWIR) hyperspectral technique was tested on the world-famous marble-made Loggia di Baccio d’Agnolo in Santa Maria del Fiore Cathedral of Florence (Italy), affected by extensive “black crust” formation. A SWIR method based on a portable spectroradiometer (ASD Fieldspec® 3) was applied to verify the efficacy of different cleaning methods (i.e., laser, chemical and microbial). The interpretation of the SWIR spectra was based on a full-profile approach, in which the calcite and gypsum contributions were evaluated by weighing the best fit simulated contributions of reference compounds. The technique was able to provide in real time semi-quantitative determinations of the amount of gypsum developed on the stone surface, demonstrating to be a fast and handy routine tool during restoration of artworks.

Laboratory shortwave infrared reflectance spectroscopy for estimating grain protein content in rice and wheat

ABSTRACT Grain protein content (GPC), as an important agronomic indicator of nutritional value, is commonly used for optimized cultivation, classified harvesting, and quality grading. Previous studies on the spectroscopic estimation of GPC were often conducted at the canopy level with vegetation indices (VIs) derived from the visible and near-infrared (VNIR) bands, which are apart from the absorption features of protein predominately located in the shortwave infrared (SWIR) region. Since those absorption features are mostly masked by water for living plants, the causal relationships between sensitive spectral bands and protein absorption features are poorly understood. Recently, continuous wavelet spectral analysis has been commonly applied to the spectroscopic estimation of vegetation parameters in the way of extracting the optimal wavelet feature (denoted as WF λ,S, λ is the shifting factor in wavelength and S is the scaling factor in the power number of two) or the red-edge position. This study proposed the wavelet-based SWIR edge position (SWEP) for estimating the GPC in cereal crops through extending the red edge position extraction technique to laboratory SWIR reflectance spectroscopy. The performance of SWEPs and WFs was evaluated in coefficient of determination (R 2), root mean squared error (RMSE) and relative RMSE (RRMSE) with experimental data over field trials. Our results demonstrated that the protein absorption features in the SWIR region could be enhanced using wavelet-based methods towards accurate estimation of the GPC across rice and wheat crops. The optimal features WF1510,4 (R 2 = 0.96) and WF1610,5 (R 2 = 0.93) exhibited similar performance for GPC estimation as compared to normalized difference protein indices (NDPIs) on calibration data, but the latter NDPI models had much poorer performance on independent validation data (NDPI1745,1780: R 2 = 0.35, RMSE = 1.83%, RRMSE = 21.70%; NDPI2080,2160: R 2 = 0.57, RMSE = 1.48%, RRMSE = 17.55%). The GPC could be estimated with high accuracies on both calibration (R 2 = 0.94) and validation (R 2 = 0.92, RMSE = 0.64%, RRMSE = 7.59%) using the SWEP extracted from the 1450–1650 nm range at Scale 5. Given their close predictive performance, the wavelet-based SWEP was less affected by the degradation of spectral resolution in the input data than the WF. The application of SWEPs could help us better understand the spectroscopy mechanism of GPC estimation from grain reflectance spectra. It also suggests that not just the commonly used red edges but also the insufficiently exploited SWIR edges are important in the entire spectral domain for quantifying vegetation properties.

Spectral and diurnal temporal suitability of GOES Advanced Baseline Imager (ABI) reflectance for burned area mapping

TheAdvanced Baseline Imager (ABI) onboard the new generation of National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite (GOES) series provides 10 min, multi-spectral, 500 m to 2 km observations,with significantly improved capabilities compared to the previous GOES sensors. For the first time GOES data are available with sufficient resolution to potentially enable burned area mapping using the reflective wavelength data. In this study GOES-16 ABI unburned and burned reflectance acquired over four extensive burns in the United States and South America were examined. Significant diurnal variations in the ABI reflectance due to surface reflectance anisotropy and changes in the position of the sun at each ABI observation time were observed, with unburned near-infrared (NIR) reflectance that varied by more than 50% over the day, and this variation was greater than the change in reflectance due to fire. The diurnal reflectance variation included locally increased backscatter reflectance acquired under near hot-spot conditions when the solar zenith was close to the view zenith. The spectral and diurnal temporal suitability of the ABI data for burned area mapping was assessed for the red, NIR and short wave infrared (SWIR) bands using the transformed divergence metric applied to the unburned and burned reflectance values for each ABI acquisition time. The separabilities varied diurnally but with no consistent view-solar geometry or time when the separabilities were consistently high among the four sites. The paper results demonstrate that use of ABI reflectance without consideration of, or correction, for surface anisotropy will be unreliable. The most suitable ABI bands to detect burned areas are the NIR, which provided high diurnal separabilities except in the early morning and late evening, and the normalized burn ratio (NBR), computed as the difference between the NIR and SWIR reflectance divided by their sum, that had more consistently high diurnal separability. Implications and recommendations for future ABI burned area mapping research are discussed.

Measuring land surface temperature, near-infrared and short-wave infrared reflectance for estimation of water availability in vegetation

The vegetation water status is a crucial variable for modelling of drought impact, vegetation productivity and water fluxes. Methods for spatial estimation of this variable still need to be improved. The integration of remotely sensed data of land surface temperature (LST) and water vegetation indices based on near-infrared (NIR) and short-wave infrared (SWIR) reflectance for estimation of vegetation water content and water available for evapotranspiration require more analysis. This study contains a detailed method and measurements of LST, NIR and SWIR reflectance of soybean, corn and barley taken in field campaigns in central Argentine Pampas and laboratory with a ST PRO Raytek (8–14 µm) and a spectrometer SVC HR-1024i (0.35 and 2.5 µm). Also, relative water content of leaves was measured in laboratory during the dehydration process. This method and dataset could be also used for researching other wavelengths between 0.35 and 2.5 µm as indicator of water vegetation status (e.g. solar-induced chlorophyll fluorescence, photosynthesis).•Procedures useful to measure field spectra of vegetation are presented.•Not only the traditional method to measure leaves spectra in laboratory, but also in field were applied.•The method allows the integration of spectra and thermal data as a proxy of vegetation water status.

A spatial downscaling method for SMAP soil moisture through visible and shortwave-infrared remote sensing data

Soil moisture (SM) plays an indispensable role in many practical applications, such as drought monitoring, hydrologic applications and agricultural management. Passive microwave remote sensing has proven capable of capturing changes in SM. However, the coarse spatial resolution (approximately 25–40 km) may greatly limit many regional hydrological and agricultural applications. In this study, we present a SM downscaling method based on the visible and shortwave-infrared (SWIR) remote sensing data toward improved spatial resolution of Soil Moisture Active Passive (SMAP) SM. In the proposed method, the Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), horizontally polarized Brightness Temperature (TBh), vertically polarized Brightness Temperature (TBv) and SWIR reflectance collected in Inner Mongolia from April to September 2018 and April to September 2019 are used as input data and combined with topographic information to downscale the SMAP SM L3 product from the original 36 km to 1 km. Random forest (RF) was used to link the input data and SMAP SM. Finally, 30 in situ station SM measurements distributed in Inner Mongolia and precipitation data were used to verify the downscaled SM. The results show that the correlation range of the downscaled SM and the in situ SM is 0.246 to 0.739, with an average of 0.535. The root mean square error (RMSE) range is 0.02 to 0.152 m3/m3, and the mean RMSE is 0.059 m3/m3. The ubRMSE has a range of 0.02 to 0.06 m3/m3, and the mean performance is 0.04 m3/m3. The bias ranges from −0.145 to 0.087 m3/m3, and the mean bias is 0.008 m3/m3. The downscaled SMAP SM can capture the spatial heterogeneity of the SMAP SM and the dynamic changes in measured soil moisture. The downscaled SMAP SM also has a high spatial correspondence with the original SMAP SM, providing more detailed soil water information than the original 36 km resolution.

Analysis of deep-ocean sediments from the TAG hydrothermal field (MAR, 26° N): application of short-wave infrared reflectance (SWIR) spectra for offshore geochemical exploration

PurposeThe cost-efficient methods of analysis, such as rapid short-wave infrared (SWIR) spectral analysis, have been applied for the efficient exploration of critical raw materials (CRM), including mineral components and rare earth elements (REE) from the deep-ocean sediments.MethodsGravity cored sediment samples were collected during an oceanographic mission to the Trans-Atlantic Geotraverse (TAG) hydrothermal field of the Mid-Atlantic Ridge (MAR, 26° N). SWIR reflectance spectra (dependent variable) of samples were mathematically tested against referent geochemical data (independent variable), obtained by conventional analysis (ICP/OES, ICP/MS), after applied full cross-validation multivariate partial least square regression (CVPLSR). Value of parameter-residual predictive deviation (RPD) was used for evaluation of CVPLSR modeling: RPD > 2.5 (satisfactory calibration model for the screening purposes) and RPD > 5.0 (model adequate for the quality control of the studied elements).ResultsThe CVPLSR modeling provided significant results for the determination of several mineral components: major elements (Fe and Si) had the values of RPD equal to 3.65 and 2.84, respectively, which indicated a viable potential for their routine analysis, whereas RPD for Ca was equal to 5.51, thus assuring its quality control by SWIR analysis, in sediment samples of the studied location. Among the REE, Ce (RPD = 2.55) and Er (RPD = 2.59) yielded the most satisfactory results.ConclusionsThe findings highlight the benefit of rapidly obtained empirical SWIR-reflectance data, which can be used for near-real-time exploration of geochemical deposits hosted in deep-ocean sediments.

A Universal Ratio Snow Index for Fractional Snow Cover Estimation

The moderate resolution imaging spectroradiometer (MODIS) snow algorithm has been used to generate global fractional snow cover (FSC) at a pixel size of 500 m using a linear regression relationship (called “FRA6T”) between FSC and the normalized difference snow index (NDSI). However, the linear relationship is problematic because of the considerable NDSI variation in nonsnow conditions. In this letter, we propose a universal ratio snow index (URSI), which is the ratio of the visible reflectance and the sum of the near infrared and shortwave infrared reflectances. It is called “universal” because it has weak sensitivity under snow-free ground conditions and, therefore, can improve the stability of the linear snow index methodology. A comparison between NDSI and URSI with regard to estimate FSC using the linear snow index methodology is carried out for the Tibetan Plateau. The scatter plots of MODIS NDSI/URSI and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) FSC indicate that a linear relationship can be assumed for both NDSI and URSI for barren land conditions and is more appropriate for URSI than it is for NDSI in forested areas. Validation efforts show that the linear relationship using URSI (designated “FracURSI”) achieves fewer errors in FSC estimation compared with the developed NDSI method (“FracNDSI”), particularly for forested areas and for moderate FSC values. Averaged over all comparisons, the root-mean-square error (RMSE) of FSC estimates for FRA6T is 0.13, and for FracNDSI is 0.12, whereas FracURSI RMSE is 0.11.

Detection of fish fillet substitution and mislabeling using multimode hyperspectral imaging techniques

Substitution of high-priced fish species with inexpensive alternatives and mislabeling frozen-thawed fish fillets as fresh are two important fraudulent practices of concern in the seafood industry. This study aimed to develop multimode hyperspectral imaging techniques to detect substitution and mislabeling of fish fillets. Line-scan hyperspectral images were acquired from fish fillets in four modes, including reflectance in visible and near-infrared (VNIR) region, fluorescence by 365 nm UV excitation, reflectance in short-wave infrared (SWIR) region, and Raman by 785 nm laser excitation. Fish fillets of six species (i.e., red snapper, vermilion snapper, Malabar snapper, summer flounder, white bass, and tilapia) were used for species differentiation and frozen-thawed red snapper fillets were used for freshness evaluation. All fillet samples were DNA tested to authenticate the species. A total of 24 machine learning classifiers in six categories (i.e., decision trees, discriminant analysis, Naive Bayes classifiers, support vector machines, k-nearest neighbor classifiers, and ensemble classifiers) were used for fish species and freshness classifications using four types of spectral data in three different datasets (i.e., full spectra, first ten components of principal component analysis, and bands selected by sequential feature selection method). The highest accuracies were achieved at 100% using full VNIR reflectance spectra for the species classification and 99.9% using full SWIR reflectance spectra for the freshness classification. The VNIR reflectance mode gave the overall best performance for both species and freshness inspection, and it will be further investigated as a rapid technique for detection of fish fillet substitution and mislabeling.

A robust visible near-infrared index for fire severity mapping in Arctic tundra ecosystems

Tundra fires are projected to increase with anthropogenic climate change, yet our ability to assess key wildfire metrics such as fire severity remains limited. The Normalized Burn Ratio (NBR) is the most commonly applied index for fire severity mapping. However, the computation of NBR depends on short-wave infrared (SWIR) data, which are not commonly available from historical and contemporary high-resolution (≤4 m) optical imagery. The increasing availability of visible near-infrared (VNIR) measurements from proximal to spaceborne sensors/platforms has the potential to advance our understanding of the spatiotemporal patterns of fire severity within tundra fires. Here we systematically assess the feasibility of using VNIR data for fire severity mapping in ten Alaskan tundra fires (cumulatively burned ~1700 km2). We compared the accuracy of 10 published VNIR-based fire indices using both uni-temporal (post-fire image) and bi-temporal (pre-fire and post-fire image difference) assessments against ground-based fire severity data (Composite Burn Index, CBI) at 109 tundra sites. The Global Environmental Monitoring Index (GEMI) had the highest correspondence with CBI (R2 = 0.77 uni-temporal; R2 = 0.85 bi-temporal), with similar performance to NBR (R2 = 0.77 uni-temporal; R2 = 0.83 bi-temporal). Tundra vegetation types affected NBR but not GEMI, as SWIR reflectance was influenced to a greater extent in shrub than graminoid tundra. We applied GEMI to contemporary high-resolution (i.e. Quickbird 2) and historical meso-resolution imagery (i.e. Landsat Multispectral Scanner) to demonstrate the capability of GEMI for resolving fine-scale patterns of fire severity and extending fire severity archives. Results suggest that GEMI accurately captured the heterogeneous patterns of tundra fire severity across fire seasons, ecoregions, and vegetation types.

Drought monitoring and assessment using Landsat TM/OLI data in the agricultural lands of Bandar-e-Turkmen and Gomishan cities, Iran

Investigating agricultural drought is very important in semiarid areas, and it has a significant impact on people’s livelihood, which depends on farming. This research tried to examine the capability of six Landsat surface reflectance bands, vegetation indices (VIs), and drought indices (DIs) for drought monitoring in the agricultural lands of Bandar-e-Turkmen and Gomishan cities in 1986 and 2015. Statistical analysis, Pearson correlation analysis, and correlation matrices between the VIs and DIs with land surface temperature (LST) were monitored separately for each date. In addition, the spatial and temporal drought extents using the Standardized Evapotranspiration Precipitation Index (SPEI) and the Vegetation Health Index (VHI) approaches were determined and compared for both study periods. The correlations between the LST and VIs were negative and significant in both study periods. The range of VI values decreased with increasing temperature in 2015 compared to 1986. The mean surface reflectance of the visible bands (blue, green, red), NIR (near-infrared), SWIR (shortwave infrared), and MWIR (mid-wave infrared) increased in 2015 compared to 1986. A higher spectral reflectance of the visible bands indicated a reduction in vegetation cover or increase in stress in 2015 compared to 1986. Meanwhile, the SWIR and MWIR bands showed that the average surface reflectance increased in 2015, while they showed a lower reflectance in 1986. The results of the Water Supplying Vegetation Index (WSVI) implied that the crop water stress in 2015 was more than that in 1986. The VHIs were separately compared with the NDVI and LST in each period of study. The maximum LST increased from 38 °C in 1986 to 44 °C in 2015. The moderate drought extent was 65% in 2015, while the study area did not experience any drought in 1986. The SPEI maps showed that the entire study area experienced moderate drought in 2015, while no drought was observed in 1986. The results showed that the DIs and VIs and the visible, SWIR and MWIR surface reflectance bands can be effectively used by sampling plots in future drought assessment studies. The present study also emphasizes that there is a similarity between traditional and remote sensing methods and indicates that remote sensing data can be effectively used instead of traditional approaches when there is no meteorological station. The results would be useful in food security and sustainable agriculture management plans.

New Proximal Sensing Approach to Measure Soil Aggregate Stability

New Proximal Sensing Approach to Measure Soil Aggregate Stability

Snow Cover Monitoring with Chinese Gaofen-4 PMS Imagery and the Restored Snow Index (RSI) Method: Case Studies

Snow cover is an essential climate variable of the Global Climate Observing System. Gaofen-4 (GF-4) is the first Chinese geostationary satellite to obtain optical imagery with high spatial and temporal resolution, which presents unique advantages in snow cover monitoring. However, the panchromatic and multispectral sensor (PMS) onboard GF-4 lacks the shortwave infrared (SWIR) band, which is crucial for snow cover detection. To reach the potential of GF-4 PMS in snow cover monitoring, this study developed a novel method termed the restored snow index (RSI). The SWIR reflectance of snow cover is restored firstly, and then the RSI is calculated with the restored reflectance. The distribution of snow cover can be mapped with a threshold, which should be adjusted according to actual situations. The RSI was validated using two pairs of GF-4 PMS and Landsat-8 Operational Land Imager images. The validation results show that the RSI can effectively map the distribution of snow cover in these cases, and all of the classification accuracies are above 95%. Signal saturation slightly affects PMS images, but cloud contamination is an important limiting factor. Therefore, we propose that the RSI is an efficient method for monitoring snow cover from GF-4 PMS imagery without requiring the SWIR reflectance.