Shortwave Infrared Bands Research Articles

Estimation of chlorophyll, macronutrients and water content in maize from hyperspectral data using machine learning and explainable artificial intelligence techniques

ABSTRACT We used the secondary hyperspectral data set (leaf reflectance of maize) taken in the spectral range from 350 to 2500 nm in a field (under low and high nitrogen conditions) and glasshouse experiment. Water, chlorophyll, nitrogen (N), phosphorus (P) and potassium (K) contents of maize leaves taken from these experiments were measured using the standard methods. Six machine learning regression algorithms (Random Forest, Support Vector Regression, k-Nearest Neighbours, Multilayer Perceptron, Gradient Boosting Regression and Partial Least Square Regression) were used for development of the models to predict these parameters from leaf reflectance data. Each plant parameter was estimated with a different machine learning algorithm. Explainable artificial intelligence methods were used to identify the optimum wavelengths for each parameter. The wavelengths in the short-wave infrared (SWIR) region were found optimum for estimating the water content across the three N regimes, and the red-edge band for chlorophyll. The optimum wavelengths for estimating N content in leaves were in the green spectral region under low N status, near-infrared (NIR) and SWIR regions under greenhouse and SWIR region under high N status. The important wavelengths for estimating P in maize leaves were 1088, 1262 and 1263 nm under low N status and the SWIR range under high N status and greenhouse conditions. The SWIR band was useful for estimating the K content in leaves.

Performance study of imageries from MODIS, Landsat 8 and Sentinel-2 on measuring shoreline change at a regional scale

In monitoring shoreline change, remote sensing data plays a pivotal role. Although different freely accessible satellite data are used in studies of shoreline change at various scales, it is imperative to identify their effectiveness at a regional scale. This study aims to determine how effectively MODIS, Landsat 8, and Sentinel-2 satellite images measure regional shoreline change. Digital Shoreline Analysis System (DSAS), an ArcGIS extension, was utilized to analyze the changes in the shoreline of Ganges deltaic coast in Bangladesh. The Shoreline Change Envelope (SCE) technique was applied to compare the shorelines extracted from MODIS, Landsat 8, and Sentinel-2 for the year 2016 with high-resolution Google Earth image. Next, using the End Point Rate (EPR) method, shoreline change rates between 2016 and 2019 were calculated separately from each satellite data. The study points out which satellite data was more accurate in delineating shoreline. Due to a higher spatial resolution, Sentinel-2 data were more effective than Landsat and MODIS data at a regional scale. In addition, MODIS, Landsat 8, and Sentinel-2 data indicate that the Ganges deltaic coast experiences landward movements of 54.65 m/yr, 12.56 m/yr, and 1.79 m/yr, respectively. Hence, it can be concluded that if the spectral resolution is coarse, shoreline change is overestimated. Therefore, in order to analyze or predict shoreline change (even at a regional scale), priority should be given to using images with fine spatial resolution. The spectral analysis of MODIS, Landsat and Sentinel showed a similar pattern of separability among the land cover classes observed along the Ganges deltaic coast. The separability is higher in NIR (Near Infrared) and SWIR (Short Wave Infrared) bands. This study will direct researchers in the selection of remote sensing data sources for shoreline change detection within a regional scale.

The Importance of Adding Short-Wave Infrared Bands for Forest Disturbance Monitoring in the Subtropical Region

Forest disturbance, such as harvest and fire, can cause a huge amount of carbon emission from soil to the atmosphere. Monitoring forest disturbance at a high spatial resolution is critical for soil carbon modeling. The short-wave infrared bands are important bands in monitoring forest disturbance. However, many high spatial resolution satellites do not contain the short-wave infrared bands in their band designs, and whether the lack of short-wave infrared (SWIR) bands will cause a large influence on forest disturbance monitoring remains unclear. This study aims to evaluate the values of adding SWIR bands in forest disturbance monitoring using the modified continuous monitoring of land disturbance (COLD) approach. Results showed that without the SWIR bands the accuracy of detecting forest disturbance will be reduced by 19–26%. The highest accuracy of modified COLD using the optimal band combination with SWIR bands was 76.3% for monitoring harvest and 86.6% for monitoring fire, while it decreased to 49.8% in detecting harvest and 67.6% in detecting fire without using any SWIR bands. The results demonstrated the importance of adding SWIR bands in forest disturbance monitoring and would guide users to select the satellite data with at least one SWIR band to monitor forest disturbance for improving the soil carbon modeling.

Evaluation of 10-Year NOAA/NASA Suomi NPP and NOAA-20 VIIRS Reflective Solar Band (RSB) Sensor Data Records (SDR) over Deep Convective Clouds

The Visible Infrared Imaging Radiometer Suite (VIIRS) is a key instrument onboard the Suomi NPP (S-NPP) and the NOAA-20 satellites that provides state-of-the-art Earth observations for ocean, land, aerosol, and cloud applications. VIIRS Reflective Solar Band (RSB) Sensor Data Records (SDR, or Level 1b products) are calibrated and produced independently by The National Oceanic and Atmospheric Administration (NOAA) and the National Aeronautics and Space Administration (NASA) VIIRS science teams. Multiple versions of S-NPP and NOAA-20 VIIRS SDRs are available to date. This study evaluates the long-term calibration stability, biases, and inter-channel consistency of S-NPP and NOAA-20 VIIRS SDRs generated by NOAA and NASA over Deep Convective Clouds (DCC) to support downstream applications, especially climate data record studies. Five VIIRS RSB SDRs were analyzed in this study: (1) NOAA version 2 S-NPP VIIRS reprocessed SDRs (NOAA-NPP-V2, 2012–2020), (2) NASA Collection 1 S-NPP VIIRS SDRs (NASA-NPP-C1, 2012–2021), (3) NASA Collection 2 S-NPP VIIRS SDRs (NASA-NPP-C2, 2012–2021), (4) NOAA constant F-factor calibrated NOAA-20 VIIRS SDRs (NOAA-N20-ConstF, 2018–2021), and (5) NASA Collection 2 NOAA-20 VIIRS SDRs (NASA-N20-C2, 2018–2021). The DCC time series analysis results indicate that the calibrations of the three S-NPP VIIRS RSB SDRs are generally stable, with trends within ±0.1%/year for all RSBs, except for M3–M4 (all three S-NPP SDRs) and I3 (NASA-NPP-C1 only). The calibration of NASA-NPP-C2 SDRs is more uniform at individual detector levels. NOAA-NPP-V2 and NASA-NPP-C1 SDRs exhibit non-negligible time-dependent detector level degradation in M1–M4 (up to 1.5% in 2020–2021), causing striping in the SDR imagery. The biases between NOAA and NASA S-NPP VIIRS RSB SDRs are from 0.1% to 2.4%. The calibrations of the two NOAA-20 VIIRS RSB SDRs are also generally stable, with trends within ±0.16%/year. Small downward trends were observed in the visible and near-infrared (VIS/NIR) bands, and small upward trends were observed in the shortwave infrared (SWIR) bands for both NOAA and NASA NOAA-20 SDRs. The biases between NOAA and NASA NOAA-20 VIIRS RSB SDRs are nearly constant over time and within ±0.2% for VIS/NIR bands and ±0.7% for SWIR bands. There exists large inter-satellite biases between S-NPP and NOAA-20 VIIRS SDRs, especially in the VIS/NIR bands (up to 4.5% for NOAA SDRs and up to 7% for NASA SDRs). In addition, the DCC reflectance of S-NPP VIIRS RSB spectral bands is more consistent with that of the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) than that of NOAA-20. Bands M4 and M9 seem out of family in all five S-NPP and NOAA-20 RSB SDRs evaluated.

Investigating the Impact of Using IR Bands on Early Fire Smoke Detection from Landsat Imagery with a Lightweight CNN Model

Smoke plumes are the first things seen from space when wildfires occur. Thus, fire smoke detection is important for early fire detection. Deep Learning (DL) models have been used to detect fire smoke in satellite imagery for fire detection. However, previous DL-based research only considered lower spatial resolution sensors (e.g., Moderate-Resolution Imaging Spectroradiometer (MODIS)) and only used the visible (i.e., red, green, blue (RGB)) bands. To contribute towards solutions for early fire smoke detection, we constructed a six-band imagery dataset from Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) with a 30-metre spatial resolution. The dataset consists of 1836 images in three classes, namely “Smoke”, “Clear”, and “Other_aerosol”. To prepare for potential on-board-of-small-satellite detection, we designed a lightweight Convolutional Neural Network (CNN) model named “Variant Input Bands for Smoke Detection (VIB_SD)”, which achieved competitive accuracy with the state-of-the-art model SAFA, with less than 2% of its number of parameters. We further investigated the impact of using additional Infra-Red (IR) bands on the accuracy of fire smoke detection with VIB_SD by training it with five different band combinations. The results demonstrated that adding the Near-Infra-Red (NIR) band improved prediction accuracy compared with only using the visible bands. Adding both Short-Wave Infra-Red (SWIR) bands can further improve the model performance compared with adding only one SWIR band. The case study showed that the model trained with multispectral bands could effectively detect fire smoke mixed with cloud over small geographic extents.

Characterization of the on-orbit response versus scan angle for Terra MODIS SWIR bands in Collection 7

The Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua platforms has 20 reflective solar bands (0.4 to 2.1 μm), for which the on-orbit detector gain changes are tracked using the on-board solar diffuser (SD) and SD stability monitor. SD and lunar observation data, supplemented by Earth-view (EV) observations for some bands, are used to characterize the scan-mirror response versus scan angle (RVS). The short-wave infrared (SWIR) bands (1.2 to 2.1 μm) have issues, such as electronic crosstalk and an out-of-band optical leak from the thermal bands, which complicate the use of the Moon for reliably characterizing the on-orbit RVS. Before Collection 7 (C7), the pre-launch RVS was deemed sufficient for both Aqua and Terra MODIS SWIR bands throughout the missions. However, recent studies have found that the RVS behavior does change with time in some SWIR bands, up to 2% and 1% for Terra MODIS bands 5 and 26, respectively. We assess the residual RVS effects present in the current MODIS calibration at desert sites and deep convective clouds (DCC). Due to the large uncertainties caused by the water vapor absorption effects from desert sites, especially for band 26 (1.375 μm), EV observations from DCCs are chosen to characterize the on-orbit RVS behavior for the SWIR bands. A time-dependent RVS characterization method for the SWIR bands is described and assessed by comparing the results with Collection 6.1 (C6.1) Level 1B (L1B) reflectance products. This new algorithm will be used in the upcoming C7 L1B reprocessing for Terra MODIS, providing more stable and accurate SWIR band reflectance data over time and across the scan range compared with the C6.1 results. For Aqua MODIS SWIR bands, performance evaluations with desert and DCC data indicate stable performance, so the pre-launch RVS will continue to be used in C7.

Enhanced mangrove vegetation index based on hyperspectral images for mapping mangrove

As a specific forest community in tropical and subtropical coastal zones, mangrove has unique ecological functions and great social and economic value. Accurate mangrove mapping is important to the protection and restoration of mangrove ecosystem. Traditional classification methods rely on a large number of samples and complex classifiers, which are unsuitable for the large-scale extraction of mangroves because of low computational efficiency and poor generalization ability. This study proposes an Enhanced Mangrove Vegetation Index (EMVI) based on hyperspectral images. This index enhances the difference in greenness and canopy moisture content between mangroves and other vegetation using a green band and two shortwave-infrared bands in the form (Green-SWIR2)/(SWIR1-Green). Six typical mangrove areas (i.e., Qinglan Harbor in Hainan, Zhenzhu Harbor–Fangcheng Harbor in Guangxi, Lianzhou Bay in Guangxi, Zhangjiang Estuary in Fujian, Quanzhou Bay in Fujian, and Oujiang Estuary in Zhejiang) were selected as the study areas, and sample datasets were produced by field surveys and Google Earth high-resolution images. Compared with other VIs, such as the Normalized Difference Vegetation Index, Enhanced Vegetation Index, Moisture Stress Index, Mangrove Vegetation Index, and Combined Mangrove Recognition Index, EMVI exhibited better ability to distinguish mangroves and other vegetation. EMVI was applied to mangrove extraction in the six study areas based on ZY1-02D images, and the extraction results were compared with existing mangrove maps (GMW_2016 and CAS_Mangrove 2015) and the results of SVM. Results showed that EMVI featured the better overall accuracy and the Kappa coefficient than existing mangrove maps and the performance was similar to SVM. Further tests showed that EMVI was also suitable to other hyperspectral remote sensing images (i.e., GF-5, Hyperion, and PRISMA), but not to Sentinel-2 images. These results indicate that EMVI can be applied to different hyperspectral remote sensing images and different types of mangrove extraction. This index also has excellent application potential in mangrove mapping.

Study of Statistical Narrow-Band Models for Infrared Signature of an Aeroengine Exhaust Plume in Mid-wave Infrared and Short-Wave Infrared Band

Study of Statistical Narrow-Band Models for Infrared Signature of an Aeroengine Exhaust Plume in Mid-wave Infrared and Short-Wave Infrared Band

Hyperspectral Imaging Analyses of Cleaning Tests on Edvard Munch's Monumental Aula Paintings

ABSTRACT The development of innovative applications of imaging technologies for monitoring change in cultural heritage objects is central to the CHANGE-ITN project. Within this framework, the authors’ ongoing work targets the harnessing of hyperspectral imaging (HSI) for the documentation of cleaning treatments on the monumental University of Oslo Aula unvarnished oil paintings on canvas (1909–1916) by Edvard Munch (1863–1944). This is applicable to unvarnished paint surfaces in general. Particularly, this paper evaluates HSI techniques for the investigation of cleaning tests carried out in 2008, which targeted the removal of visible, ingrained particulate matter from Munch's unvarnished oil paints and exposed grounds on Kjemi (1914–1916). An exploratory in situ HSI campaign using visible to near-infrared (VNIR) and short-wave infrared (SWIR) cameras was undertaken to capture the soiled or previously cleaned areas on the painting's surface, with comparisons to analogous mock-ups. Principal component analysis (PCA) was used to extract information about possible trends with respect to the soiling. Results indicate that in both VNIR and SWIR ranges, the intensity of reflectance can be used to discriminate statistically between soiled and unsoiled/cleaned areas, whereas in SWIR, combination and first overtone bands of oil peaks can also serve as markers.

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.

Recognition of aquatic vegetation above water using shortwave infrared baseline and phenological features

Accurate monitoring of emergent aquatic vegetation (EAV) and floating-leave aquatic vegetation (FAV), is vital because vegetation provides a critical habitat for aquatic animals and plays a critical role in protecting biodiversity. However, owing to the interference of water spectrum signals, it is difficult to extract the emerging aquatic vegetation information from remote sensing images, especially to distinguish between EAV and FAV. This study first proposed an enhanced aquatic vegetation index based on the difference in two short-wave infrared (SWIR) bands to extract the aquatic vegetation above the water surface, and then, the EAV and FAV were further distinguished coupled with the phenological characteristics, subsequently, a case study of Taihu Lake for accurately extracting seasonal and annual distributions. The results demonstrate that the proposed aquatic vegetation index is highly sensitive to EAV and FAV, and can further distinguish EAV from them coupled with phenological difference. Additionally, the dramatic changes in EAV and FAV in time and space indicated that human purse seine culture has had a great influence on the succession of aquatic vegetation, which may lead to the deterioration of the water ecology environment. Using the SWIR bands coupled with phenological difference is a promising method for recognizing aquatic vegetation above the water surface owing that it can weaken and eliminate the impact of algal bloom at the same time.

Atmospheric correction for HY-1C CZI images using neural network in western Pacific region

ABSTRACT With a spatial resolution of 50 m, a revisit time of three days, and a swath of 950 km, the coastal zone imager (CZI) offers great potential in monitoring coastal zone dynamics. Accurate atmospheric correction (AC) is needed to exploit the potential of quantitative ocean color inversion. However, due to the band setting of CZI, the AC over coastal waters in the western Pacific region with complex optical properties cannot be realized easily. This research introduces a novel neural network (NN) AC algorithm for CZI data over coastal waters. Total 100,000 match-ups of HY-1 C CZI-observed reflectance at the top-of-atmosphere and Operational Land Imager (OLI)-retrieved high-quality remote sensing reflectance (R rs) at the CZI bands are built to train the NN model. These reflectance data are obtained from the standard AC algorithm in the SeaDAS. Results indicate that the distributions of the CZI retrieved R rs were consistent with the quasi-synchronous OLI data, but the spatial information from the CZI is more detailed. Then, the accuracy of the CZI data for AC is evaluated using the multi-source in-situ data. Results further show that the NN-AC can successfully retrieve R rs for CZI and the coefficients of determination in the blue, green, red, and near-infrared bands were 0.70, 0.77, 0.76, and 0.67, respectively. The NN algorithm does not depend on shortwave-infrared bands and runs very fast once properly trained.

Analysis of modified water index (MWI) for extraction of water bodies in landsat-8 imagery

Remote sensing techniques play an important role in exploring and management of water resources on the surface of the earth. Water bodies' identification from multispectral imagery is mainly done using several water indices. Water indices evolved based on the reflectance variations in multispectral imagery from different water bodies. Normalized Difference Water Index (NDWI) is mostly popularly used water index for detection of water bodies. The NDWI water index is used to classify the clear water bodies from non-water bodies and mixed water bodies. Modified Normalized Difference Water Index (MNDWI) is used to classify the clear water bodies along with mixed water bodies from non-water bodies. The selection of appropriate indices significantly affects the performance accuracy in extraction of water bodies. This paper aims in proposing simple and effective water index based on multi bands, Blue, Green, NIR and SWIR2.The objective of Modified Water Index (MWI) is better handling classification of the mixed water pixels. The Blue, Green bands has high reflectance values and Near Infrared (NIR), Short Wave Infrared (SWIR2) bands has low reflectance from water bodies. The significant reflectance variance feature of SWIR2 from clear water to mixed water bodies is useful in the classification of the mixed water pixels in the MWI. The performance of the Modified Water Index is analyzed and compared with the existing several water indices. The performance metrics such as Water Spread Area, mean and standard deviation are used to compare the effectiveness of water body indices.

A Fusion of Feature-Oriented Principal Components of Multispectral Data to Map Granite Exposures of Pakistan

Despite low spatial resolutions, thermal infrared bands (TIRs) are generally more suitable for mineral mapping due to fundamental tones and high penetration in vegetated areas compared to shortwave infrared (SWIR) bands. However, the weak overtone combinations of SWIR bands for minerals can be compensated by fusing SWIR-bearing data (Sentinel-2 and Landsat-8) with other multispectral data containing fundamental tones from TIR bands. In this paper, marble in a granitic complex in Mardan District (Khyber Pakhtunkhwa) in Pakistan is discriminated by fusing feature-oriented principal component selection (FPCS) obtained from the ASTER, Landsat-8 Operational Land Imager (OLI), Thermal Infrared Sensor (TIRS) and Sentinel-2 MSI data. Cloud computing from Google Earth Engine (GEE) was used to apply FPCS before and after the decorrelation stretching of Landsat-8, ASTER, and Sentinel-2 MSI data containing five (5) bands in the Landsat-8 OLI and TIRS and six (6) bands each in the ASTER and Sentinel-2 MSI datasets, resulting in 34 components (i.e., 2 × 17 components). A weighted linear combination of selected three components was used to map granite and marble. The samples collected during field visits and petrographic analysis confirmed the remote sensing results by revealing the region’s precise contact and extent of marble and granite rock types. The experimental results reflected the theoretical advantages of the proposed approach compared with the conventional stacking of band data for PCA-based fusion. The proposed methodology was also applied to delineate granite deposits in Karoonjhar Mountains, Nagarparker (Sindh province) and the Kotah Dome, Malakand (Khyber Pakhtunkhwa Province) in Pakistan. The paper presents a cost-effective methodology by the fusion of FPCS components for granite/marble mapping during mineral resource estimation. The importance of SWIR-bearing components in fusion represents minor minerals present in granite that could be used to model the engineering properties of the rock mass.

INSAT-3D and -3DR relative radiometric performance over bright target

The Geostationary Earth Orbit (GEO) satellite INSAT-3D and -3DR derived data products plays a vital role in helping to develop long-term trends of many climate variables and to improve our understanding of earth's environmental processes and dynamics occurring on the land, in the oceans and in the atmosphere. In order to provide high quality, consistent and well calibrated measurements, the radiometric calibration of the sensor must be consistent, stable and traceable to its calibration standards. Due to absence of an onboard calibration mechanism for the visible (VIS) and Short Wave Infra-Red (SWIR) bands, accuracy of Top-of-atmospheric radiance of these bands data depend on vicarious calibration methods to provide post launch calibration coefficients to compensate for the degradation responsivity. In this study, a methodology has been implemented to perform degradation assessment and seasonal variation pattern for the visible and SWIR band of INSAT-3D and -3DR satellite based on clear-sky observations. The current methodology relies on clear sky apparent reflectance which are derived from level-1 images over pseudo-invariant desert target. In this study, authors are focused to investigate visible and SWIR band performance based on clear-sky observations over desert target and the instrument long-term drift traced by a polynomial fitting function. A trending algorithm is proposed here, which consists of a polynomial function (for the description of instrument degradation performance) with two sine terms for quashing the impact of the seasonal variations of the solar zenith angle and atmospheric constituents, is applied to fit the time series of apparent reflectance.

Evaluating the Hyperspectral Sensitivity of the Differenced Normalized Burn Ratio for Assessing Fire Severity

Fire severity represents fire-induced environmental changes and is an important variable for modeling fire emissions and planning post-fire rehabilitation. Remotely sensed fire severity is traditionally evaluated using the differenced normalized burn ratio (dNBR) derived from multispectral imagery. This spectral index is based on bi-temporal differenced reflectance changes caused by fires in the near-infrared (NIR) and short-wave infrared (SWIR) spectral regions. Our study aims to evaluate the spectral sensitivity of the dNBR using hyperspectral imagery by identifying the optimal bi-spectral NIR SWIR combination. This assessment made use of a rare opportunity arising from the pre- and post-fire airborne image acquisitions over the 2013 Rim and 2014 King fires in California with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. The 224 contiguous bands of this sensor allow for 5760 unique combinations of the dNBR at a high spatial resolution of approximately 15 m. The performance of the hyperspectral dNBR was assessed by comparison against field data and the spectral optimality statistic. The field data is composed of 83 in situ measurements of fire severity using the Geometrically structured Composite Burn Index (GeoCBI) protocol. The optimality statistic ranges between zero and one, with one denoting an optimal measurement of the fire-induced spectral change. We also combined the field and optimality assessments into a combined score. The hyperspectral dNBR combinations demonstrated strong relationships with GeoCBI field data. The best performance of the dNBR combination was derived from bands 63, centered at 0.962 µm, and 218, centered at 2.382 µm. This bi-spectral combination yielded a strong relationship with GeoCBI field data of R2 = 0.70 based on a saturated growth model and a median spectral index optimality statistic of 0.31. Our hyperspectral sensitivity analysis revealed optimal NIR and SWIR bands for the composition of the dNBR that are outside the ranges of the NIR and SWIR bands of the Landsat 8 and Sentinel-2 sensors. With the launch of the Precursore Iperspettrale Della Missione Applicativa (PRISMA) in 2019 and several planned spaceborne hyperspectral missions, such as the Environmental Mapping and Analysis Program (EnMAP) and Surface Biology and Geology (SBG), our study provides a timely assessment of the potential and sensitivity of hyperspectral data for assessing fire severity.

A geometric misregistration resistant data fusion approach for adding red-edge (RE) and short-wave infrared (SWIR) bands to high spatial resolution imagery

High spatial resolution images have been widely applied to the monitoring of land surfaces during the past decades. However, satellite and unmanned aerial vehicle (UAV) sensors (e.g., IKONOS, and QuickBird) usually only provide visible and near-infrared spectral bands with a high spatial resolution. Such limited spectral bands at a high spatial resolution are not adequate for crucial environmental and agricultural studies that require essential bands (e.g., the red-edge (RE) and the short-wave infrared (SWIR)), such as land cover classification and the estimation of plant physiological parameters. On the contrary, abundant spectral information is commonly available on sensors with a medium spatial resolution (e.g., Landsat, and Sentinel-2). Existing spatial-spectral fusion methods can leverage the unique advantages of these two types of sensors, and produce synthesized images with both spatial and spectral detail. However, most spatial-spectral methods ignore the inherent geometric registration errors when using multi-source data, inevitably introducing the errors into the fusion result. To address the limitation, this study presents a new spatial-spectral fusion method, the Misregistration-Resistant Data Fusion approach (MRDF), to add new spectral bands to the high spatial resolution images from medium resolution images. The proposed method is composed of a local-model-based similar pixel searching and a global partial least square (PLS) regression, and has the following advantages: (1) the local model utilizes neighborhood similar pixels to generate predictions robust against geometric misregistration; (2) the combined model integrates a global PLS regression to improve fusion accuracy for the SWIR bands; (3) the extended-box strategy and spatial filtering can relieve geometric errors when minimizing residuals for the SWIR bands. Satellite and airborne data from three sites were used to compare the performance of MRDF with seven other typical methods. Results showed that MRDF not only produced accurate fusion results with above-average efficiency but also improved the classification accuracy, which substantiated its great potential for various applications.

Evaluating the Persistence of Post-Wildfire Ash: A Multi-Platform Spatiotemporal Analysis

As wildland fires amplify in size in many regions in the western USA, land and water managers are increasingly concerned about the deleterious effects on drinking water supplies. Consequences of severe wildfires include disturbed soils and areas of thick ash cover, which raises the concern of the risk of water contamination via ash. The persistence of ash cover and depth were monitored for up to 90 days post-fire at nearly 100 plots distributed between two wildfires in Idaho and Washington, USA. Our goal was to determine the most ‘cost’ effective, operational method of mapping post-wildfire ash cover in terms of financial, data volume, time, and processing costs. Field measurements were coupled with multi-platform satellite and aerial imagery collected during the same time span. The image types spanned the spatial resolution of 30 m to sub-meter (Landsat-8, Sentinel-2, WorldView-2, and a drone), while the spectral resolution spanned visible through SWIR (short-wave infrared) bands, and they were all collected at various time scales. We that found several common vegetation and post-fire spectral indices were correlated with ash cover (r = 0.6–0.85); however, the blue normalized difference vegetation index (BNDVI) with monthly Sentinel-2 imagery was especially well-suited for monitoring the change in ash cover during its ephemeral period. A map of the ash cover can be used to estimate the ash load, which can then be used as an input into a hydrologic model predicting ash transport and fate, helping to ultimately improve our ability to predict impacts on downstream water resources.

Remote sensing satellite-based structural/alteration mapping for gold exploration in the Ketté goldfield, Eastern Cameroon

In-situ mineral prospecting studies in the tropics face challenging environmental conditions leading to paucity of data and structural mapping of new mineralizations. Here we present the case of the tropical mining goldfield of Ketté in Cameroon to demonstrate remote sensing techniques for mineral exploration purposes. In this investigation Visible Near Infra-Red (VNIR) and Short Wave Infra-Red (SWIR) bands of Landsat-8 (OLI) and Landsat-7 (ETM+) images were used with field data for lineaments and hydrothermal alterations mapping. Semi-automatic and automatic extraction methods were applied through sobel directional filters to detect the contours in the image. PCA, MNF transformation and a band ratio of 4/2, 6/5 and 6/7 were applied to map alteration minerals. The result revealed NE-SW to ENE-WSW main trends. Gold mineralization occurrences are spatially associated with WNW-ESE to NW-SE (110°–140°) ranging lineaments/faults and show a strong correlation with medium to high lineament density zones. Hydrothermal alteration minerals are spatially associated closely with gold occurrences and known mining sites that are structurally controlled by the NE-SW to ENE-WSW shear zone. The high-prospect zones for gold exploration are located along the Mama and Molé fault, Ngoubésseli, Boubara-Koumbé Tiko, Gwé, and Ndambi I-Tezoukpé.

Surface water estimation at regional scale using hybrid techniques in GEE environment-A case study on Punjab State of India

Monitoring surface water bodies accurately is one of the most crucial applications of remote sensing. It is a global challenge in the times of climate change, since surface water is the most important component of the hydrological cycle. Modified Normalized Difference Water Index (MNDWI) method based on spectral index is calculated from the Green and Shortwave-Infrared (SWIR) bands, for monitoring surface water. This index helps to map surface water accurately based on the threshold using Otsu method for achieving higher level of accuracy. Earth observing satellite by ESA, Sentinel-2 provides multispectral images at a fine spatial and spectral resolution. This dataset has high potentiality for significantly mapping regional surface water, due to its frequent revisiting capabilities and spectral capabilities. Google Earth Engine (GEE) platform can be used for processing the huge datasets easily in the most efficient way for fast computation, storing and displaying the huge datasets. A pilot study has been carried out in Northwest part of India to demonstrate the effectiveness of mapping mean surface water for the temporal window June 2016 to March 2020 from Sentinel-2 using GEE so as to understand the patterns of surface water inundation at a regional scale.