Multispectral Instrument Imagery Research Articles

Level0 to Level1B processor for MethaneAIR

Abstract. This work presents the development of the MethaneAIR Level0–Level1B processor, which converts raw L0 data to calibrated and georeferenced L1B data. MethaneAIR is the airborne simulator for MethaneSAT, a new satellite under development by MethaneSAT LLC, a subsidiary of the Environmental Defense Fund (EDF). MethaneSAT's goals are to precisely map over 80 % of the production sources of methane from oil and gas fields across the globe to an accuracy of 2–4 ppb on a 2 km2 scale. Efficient algorithms have been developed to perform dark corrections, estimate the noise, radiometrically calibrate data, and correct stray light. A forward model integrated into the L0–L1B processor is demonstrated to retrieve wavelength shifts during flight accurately. It is also shown to characterize the instrument spectral response function (ISRF) changes occurring at each sampled spatial footprint. We demonstrate fast and accurate orthorectification of MethaneAIR data in a three-step process: (i) initial orthorectification of all observations using aircraft avionics, a simple camera model, and a medium-resolution digital elevation map; (ii) registration of oxygen (O2) channel grayscale images to reference Multispectral Instrument (MSI) band 11 imagery via Accelerated-KAZE (A-KAZE) feature extraction and linear transformation, with similar co-registration of methane (CH4) channel grayscale images to the registered O2 channel images; and finally (iii) optimization of the aircraft position and attitude to the registered imagery and calculation of viewing geometry. This co-registration technique accurately orthorectifies each channel to the referenced MSI imagery. However, in the pixel domain, radiance data for each channel are offset by almost 150–200 across-track pixels (rows) and need to be aligned for the full-physics or proxy retrievals where both channels are simultaneously used. We leveraged our orthorectification tool to identify tie points with similar geographic locations in both CH4 and O2 images in order to produce shift parameters in the across-track and along-track dimensions. These algorithms described in this article will be implemented into the MethaneSAT L0–L1B processor.

Monitoring global cement plants from space

As an energy-intensive industry, cement production can emit significant air pollutants that negatively impact human health and the environment. Geospatial data on cement plants are crucial for managing the cement industry and designing effective policies to mitigate ever-increasing air pollution. However, the available fundamental asset-level datasets of global cement plants are generally incomplete and may not adequately satisfy the above requirements. To address this gap, (i) we proposed an innovative semi-automatic framework, which is based on the high-temperature signal from active kilns in time-series Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) imagery and the iconic characteristics of cement processing equipment in high-resolution imagery, to identify global cement plants, assess their operational status, and evaluate relevant activity levels. (ii) We compiled a state-of-the-art inventory of global cement plants, enriched with detailed attributes, using multi-source spaceborne observations/products. We identified 3075 cement plants with rotary/shaft kilns and pinpointed 1960 locations for cement grinding and storage facilities. (iii) We investigated the status of global cement plants using time-series Sentinel-2 MSI imagery. Our results show that from 2016 to date, 69.3% of cement plants are continuously operating, 4.3% were newly constructed, and 26.4% were decommissioned. (iv) We proposed two normalized metrics, namely the occurrence rate and the mean detected area, to assess the activity level of cement plants. The results showed that the rotary kilns in wet-process cement plants had the highest activity level due to the higher energy consumption. The activity level of global cement plants increased by ∼9% during 2016–2021 and then decreased by ∼4% in 2022, possibly due to the influence of COVID-19 and weak market demand. The proposed framework provides valuable insights for investigating the locations and dynamics of other industrial hot sources, such as those in steel mills, coke ovens, refineries, and oil/gas development areas, to improve the estimates of CO2 emissions and air pollutants and then to promote climate action.

Improving sea surface floating matter identification from Sentinel-2 MSI imagery using optical radiative simulation of neighborhood difference.

The reflectance difference (ΔR) between a floating matter pixel and a nearby water reference pixel is a method of atmospheric radiation unmixing. This technique unveils target signals by referencing the background within the horizontal neighborhood. ΔR is effective for removing the mixed-pixel effect and partial atmospheric path radiance. However, other atmospheric interference sources in the difference pixel, including atmospheric extinction and sunglint, need to be clarified. To address these challenges, we combined in situ floating matter endmember spectra for simulation and Sentinel-2 Multispectral Instrument (MSI) sensors for validation. We focused on radiative transfer simulation of horizontal neighborhood and vertical atmospheric column, investigating the bilateral conversion of ΔR between bottom-of-atmosphere (BOA) and top-of-atmosphere (TOA) signals, and clarifying how the atmosphere affects the difference pixel (ΔR) and floating matter identification. Results showed that direct use of TOA ΔR works in discriminating algae from non-algae floating matters under weak sunglint, and is a suitable candidate for no bother with atmospheric correction, least uncertain, and wider coverage. And then, sunglint interference is also inevitable, whether serious or not.

Performances of Atmospheric Correction Processors for Sentinel-2 MSI Imagery Over Typical Lakes Across China

The launched MultiSpectral Instrument (MSI) equipped on Sentinel-2 satellites offers a powerful tool for observing the biogeochemical parameters of inland waters on a large scale. The proper use of atmospheric correction processors is essential for acquiring accurate satellite remote-sensing reflectance and downstream products. Therefore, we compared the performances of typical atmospheric correction processors, such as Sen2Cor, C2RCC-nets, C2RCC-C2X, Acolite, iCOR, Polymer, SeaDas/l2gen, and 6S processors, for MSI imagery over lake groups ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> = 296) across China collected from 2016 to 2020. Linear fitting between corrected reflectance and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> spectral measurements was used to assess performance; for the single lake, we additionally evaluated the performance of atmospheric correction processors in typical Chagan Lake in 2021. For large-scale lake groups with different water quality backgrounds, the SeaDas/l2gen and C2RCC processors performed best for all band match-ups, and the C2RCC processor had the smallest errors. The SeaDas/l2gen processor works well for the signal bands (490, 560, 665, 704, and 740 nm), followed by the signal bands (560, 665, and 704 nm) of the C2RCC processor. For large-scale observations, this study revealed that Sentinel-2 satellite optical MSI imagery related to the C2RCC processor can be used to monitor aquatic systems with high-frequency investigations. For the signal band, the SeaDas/l2gen processor was used to select potential match-ups for the availability of MSI data related to the empirical models of processors. Our results may help satellite users select appropriate atmospheric correction processors for large-scale lake observations.

Region-Growing-Based Automatic Localized Adaptive Thresholding Algorithm for Water Extraction Using Sentinel-2 MSI Imagery

Water is a distinct land cover feature on the earth. Water can easily be extracted from satellite data under known/controlled conditions using simple thresholding techniques. However, these pixel thresholding techniques fail when applied over large regions because they cannot adapt the thresholds based on local variability. Although region-growing methods are available to overcome this issue, there is a limitation in using a global threshold value determined for a satellite scene. Such thresholds fail to adapt to the variations within the scene. Hence, this study proposed a new region-growing-based approach for improved classification of water features using Sentinel-2 Multi-Spectral Instrument (MSI) data. The significant difference between this approach and other region-growing methods is that this technique uses dynamic thresholding for localized adaption. The local image statistics of the Normalized Different Water Index (NDWI) layer are used for adding new pixels to the region, and the threshold is adjusted for every new pixel added. This technique also minimizes manual intervention in water classification, thus enabling total process automation in near-real-time. The thresholds are dynamically determined not only for each scene but also for each new pixel being classified for the accurate delineation of the water pixels. A rigorous quality evaluation was done on the resultant water layer. This technique achieved an overall accuracy of 84.04% in challenging conditions.

Spectral Discrimination of Pumice Rafts in Optical MSI Imagery

Pumice rafts are considered to be a long-range drifting agent that promotes material exchange and the dispersal of marine species. Large ones can also interfere with vessel navigation and have a negative impact on the social economy and marine ecosystems. Synoptic observations from the Multispectral Instrument (MSI) on-board Sentinel-2, with a spatial resolution of up to 10 m, provide an excellent means to monitor and track pumice rafts. In this study, the use of a Spectral-Feature-Based Extraction (SFBE) algorithm to automatically discriminate and extract pumice on the ocean surface from submarine volcano eruptions was proposed. Specifically, a Pumice Raft Index (PRI) was developed based on the spectral signatures of pumice in MSI imagery to identify potential pumice features. After pre-processing, the PRI image was then subjected to a series of per-pixel and object-based processes to rule out false-positive detections, including shallow water, striped edges, mudflats, and cloud edges. The SFBE algorithm showed excellent performance in extracting pumice rafts and was successfully applied to extract pumice rafts near the Fiji Yasawa islands in 2019 and Hunga Tonga island in 2022, with an overall pumice extraction accuracy of 95.5% and a proportion of pixels mis-extracted as pumice of &lt;3%. The robustness of the algorithm has also been tested and proved through applying it to data and comparing its output to results from previous studies. The timely and accurate detection of pumice using the algorithm proposed here is expected to provide important information to aid in response actions and ecological assessments, and will lead to a better understanding of the fate of pumice.

Capturing Secchi disk depth by using Sentinel-2 MSI imagery in Jiaozhou Bay, China from 2017 to 2021

Water clarity is a key parameter for assessing changes of aquatic environment. Coastal waters are complex and variable, remote sensing of water clarity for it is often limited by low spatial resolution. The Sentinel-2 Multi-Spectral Instrument (MSI) imagery with a resolution of up to 10 m are employed to solve the problem from 2017 to 2021. Distribution and characteristics of Secchi disk depth (SDD) in Jiaozhou Bay (JZB) are analyzed. Subtle changes in localized small areas are discovered, and main factors affecting the changes are explored. Among natural factors, precipitation and wind play dominant roles in variation in SDD. Human activities have a significant influence on transparency, among which fishery farming has the greatest impact. This is clearly evidenced by the significant improvement of SDD in JZB due to the sharp decrease in human activities caused by coronavirus disease 2019 (COVID-19).

Remote sensing identification of marine floating raft aquaculture area based on sentinel-2A and DEM data

Marine floating raft aquaculture forms an integral component of the monitoring of coastal marine environments. It is essential to accurately obtain the spatial distribution of marine floating raft aquaculture to gain the fullest understanding of the development of marine fishery production, optimization of the spatial layout of aquaculture, and protection of the marine environment. The Sentinel-2 Multispectral Instrument (MSI) is used to acquire optical imagery at a high spatial and temporal resolution, sampling 13 spectral bands in the visible, near-infrared, and short-wave infrared parts of the spectrum. This research reports how a decision-tree-based procedure was developed to map marine floating raft aquaculture using Sentinel-2A MSI imagery and DEM (Digital Elevation Model) data. Three indices and spectral features were used in this algorithm to differentiate marine floating rafts from other land-cover and land-use types in Fangchenggang City, China. These included the Differential Ratio Floating Raft Index (DRRI), newly proposed in the paper, the Normalized Difference Vegetation Index (NDVI), and visible reflectance. Additionally, a comparison was made between the decision tree classification method (DT) and the random forest (RF) and support vector machine (SVM) methods. The results demonstrate that these three methods can obtain raft information with high accuracy. Finally, the classification results were merged into aquaculture rafts and non-aquaculture rafts. The overall accuracy for DT was 98.20% and 1.28 and 4.99 percentage points higher than RF and SVM, respectively. The user accuracy for marine floating rafts for DT (98.25%) was also markedly higher than that of RF and SVM methods (93.97% and 86.50%, respectively). The producer accuracy for marine floating rafts through the DT method was 98.17%, 0.81 percent lower than that of RF, and 1.03 percent lower than that of SVM. The decision-tree method does not assume strict data distribution parameters, optimization of the application of multispectral imagery and elevation data becomes possible, and combing with the DRRI index, then results in higher classification accuracies of marine floating rafts. When using multi-source data of different types and distributions to map marine floating rafts, a decision-tree method, therefore, appears to be superior to RF and SVM classifiers.

A bankfull geometry dataset for major exorheic rivers on the Qinghai-Tibet Plateau

Bankfull river discharge shapes river morphology. The bankfull river surface planform and river width can be used to quantify river size. Regional studies of stream ecology, hydrologic modelling, river carbon emissions and geomorphology from the perspective of fluvial processes are hindered by the lack of a highly accurate spatially distributed river network that considers bankfull river geometry. Based on Sentinel-2 and Landsat 5/7/8 multispectral instrument imagery and in situ measured hydrological data, the river discharge and width of spatially distributed cross sections of six major exorheic rivers and their tributaries located on the Qinghai-Tibet Plateau (QTP) are calculated under bankfull conditions. Then, the bankfull river surface is extracted. Finally, a bankfull river width and surface area database is established. The provided planform river hydromorphology data can supplement global hydrography datasets and effectively represent the combined fluvial geomorphology and geological background in the study area.

A Four-Step Method for Estimating Suspended Particle Size Based on In Situ Comprehensive Observations in the Pearl River Estuary in China

The suspended particle size has great impacts on marine biology environments and biogeochemical processes, such as the settling rates of particles and sunlight transmission in marine water. However, the spatial–temporal variations in particle sizes in coastal waters are rarely reported due to the paucity of appropriate observations and the limitations of particle size retrieval methods, especially in areas with complex optical properties. This study proposed a remote sensing-based method for estimating the median particle size Dv50 (calculated with a size range of 2.05–297 μm) that correlates Dv50 with the inherent optical properties (IOPs) retrieved from in situ remote sensing reflectance above the water’s surface (Rrs(λ)) in the Pearl River estuary (PRE) in China. Rrs(λ) was resampled to simulate the Multispectral Instrument (MSI) onboard Sentinel-2A/B, and the wavebands in 490, 560, and 705 nm were utilized for the retrieval of the IOPs. The results of this method had a statistical performance of 0.86, 18.52, 21.28%, and −1.85 for the R2, RMSE, MAPE, and bias values, respectively, in validation, which indicated that Dv50 could be estimated by Rrs(λ) with the proposed four-step method. Then, the proposed method was applied to Sentinel-2 MSI imagery, and a clear difference in Dv50 distribution which was retrieved from a different time could be seen. The proposed method holds great potential for monitoring the suspended particle size of coastal waters.

Improving Urban Land Cover Classification with Combined Use of Sentinel-2 and Sentinel-1 Imagery

Accurate land cover mapping is important for urban planning and management. Remote sensing data have been widely applied for urban land cover mapping. However, obtaining land cover classification via optical remote sensing data alone is difficult due to spectral confusion. To reduce the confusion between dark impervious surface and water, the Sentinel-1A Synthetic Aperture Rader (SAR) data are synergistically combined with the Sentinel-2B Multispectral Instrument (MSI) data. The novel support vector machine with composite kernels (SVM-CK) approach, which can exploit the spatial information, is proposed to process the combination of Sentinel-2B MSI and Sentinel-1A SAR data. The classification based on the fusion of Sentinel-2B and Sentinel-1A data yields an overall accuracy (OA) of 92.12% with a kappa coefficient (KA) of 0.89, superior to the classification results using Sentinel-2B MSI imagery and Sentinel-1A SAR imagery separately. The results indicate that the inclusion of Sentinel-1A SAR data to Sentinel-2B MSI data can improve the classification performance by reducing the confusion between built-up area and water. This study shows that the land cover classification can be improved by fusing Sentinel-2B and Sentinel-1A imagery.

Use of optical absorption indices to assess seasonal variability of dissolved organic matter in Amazon floodplain lakes

Abstract. Given the importance of dissolved organic matter (DOM) in the carbon cycling of aquatic ecosystems, information on its seasonal variability is crucial. In this study we assess the use of optical absorption indices available in the literature based on in situ data to both characterize the seasonal variability of DOM in a highly complex environment and for application in large-scale studies using remote sensing data. The study area comprises four lakes located in the Mamirauá Sustainable Development Reserve (MSDR). Samples for the determination of colored dissolved organic matter (CDOM) and measurements of remote sensing reflectance (Rrs) were acquired in situ. The Rrs was used to simulate the response of the visible bands of the Sentinel-2 MultiSpectral Instrument (MSI), which was used in the proposed models. Differences between lakes were tested using the CDOM indices. The results highlight the role of the flood pulse in the DOM dynamics at the floodplain lakes. The validation results show that the use of the absorption coefficient of CDOM (aCDOM) as a proxy of the spectral slope between 275 and 295 nm (S275–295) during rising water is worthwhile, demonstrating its potential application to Sentinel-2 MSI imagery data for studying DOM dynamics on the large scale.

Advancing the Mapping of Mangrove Forests at National-Scale Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine: A Case Study in China

A high resolution mangrove map (e.g., 10-m), including mangrove patches with small size, is urgently needed for mangrove protection and ecosystem function estimation, because more small mangrove patches have disappeared with influence of human disturbance and sea-level rise. However, recent national-scale mangrove forest maps are mainly derived from 30-m Landsat imagery, and their spatial resolution is relatively coarse to accurately characterize the extent of mangroves, especially those with small size. Now, Sentinel imagery with 10-m resolution provides an opportunity for generating high-resolution mangrove maps containing these small mangrove patches. Here, we used spectral/backscatter-temporal variability metrics (quantiles) derived from Sentinel-1 SAR (Synthetic Aperture Radar) and/or Sentinel-2 MSI (Multispectral Instrument) time-series imagery as input features of random forest to classify mangroves in China. We found that Sentinel-2 (F1-Score of 0.895) is more effective than Sentinel-1 (F1-score of 0.88) in mangrove extraction, and a combination of SAR and MSI imagery can get the best accuracy (F1-score of 0.94). The 10-m mangrove map was derived by combining SAR and MSI data, which identified 20003 ha mangroves in China, and the area of small mangrove patches (&lt;1 ha) is 1741 ha, occupying 8.7% of the whole mangrove area. At the province level, Guangdong has the largest area (819 ha) of small mangrove patches, and in Fujian, the percentage of small mangrove patches is the highest (11.4%). A comparison with existing 30-m mangrove products showed noticeable disagreement, indicating the necessity for generating mangrove extent product with 10-m resolution. This study demonstrates the significant potential of using Sentinel-1 and Sentinel-2 images to produce an accurate and high-resolution mangrove forest map with Google Earth Engine (GEE). The mangrove forest map is expected to provide critical information to conservation managers, scientists, and other stakeholders in monitoring the dynamics of the mangrove forest.

EXPLORING THE CAPABILITIES OF COMBINING THE SENTINEL-2 MSI DATA AND HIGH RESOLUTION GOOGLE EARTH IMAGE FOR MAPPING MANGROVE SPECIES

Abstract. Knowledge gained about the mangrove species mapping is essential to understand mangrove species development and to better estimate their ecological service value. Spectral bands and spatial resolution of remote sensing data are two important factors for accurate discrimination of mangrove species. In this study, mangrove species classification in Shenzhen Bay, China was performed by using Sentinel 2 (S2) Multi Spectral Instrument (MSI) data and Google Earth (GE) high resolution imagery as data sources and their suitability in mapping mangrove forest at a species level was examined. In the classification feature groups, the spectral bands were from the S2 MSI data and the textural features were based on GE imagery. The SVM classifier was used in mangrove species classification processing with eight groups of features, which were based on different S2 spectral bands and different GE spatial resolution textural features. The highest overall accuracy of our mapping results was 78.57% and the Kappa coefficient was 0.74, which indicated great potential of using the combination of S2 MSI and GE imagery for distinguishing and mapping mangrove species.

Combination of Google Earth imagery and Sentinel-2 data for mangrove species mapping

Knowledge gained about mangrove species mapping is essential to understand mangrove species’ development and to better estimate their ecological service value. Spectral bands and spatial resolution of remote sensing data are two important factors for accurate discrimination of mangrove species. Mangrove species classification in Shenzhen Bay, China, was performed using Sentinel-2 (S2) multispectral instrument (MSI) data and Google Earth (GE) high-resolution imagery as data sources, and their suitability in mapping mangrove forest at a species level was examined. In the classification feature groups, the spectral bands were from the S2 MSI data and the textural features were based on GE imagery. The support vector machine classifier was used in mangrove species classification processing with eight groups of features, which were based on different S2 spectral bands and different GE spatial resolution textural features. The highest overall accuracy of our mapping results was 78.57% and the kappa coefficient was 0.74, which indicated great potential for using the combination of S2 MSI and GE imagery for distinguishing and mapping mangrove species.

Estimating the Urban Fractional Vegetation Cover Using an Object-Based Mixture Analysis Method and Sentinel-2 MSI Imagery

Accurate and efficient identification of the urban vegetation abundance is of great importance for urban planning and management. A lot of efforts have been made to estimate the urban fractional vegetation cover (FVC) using multispectral images by the pixel-based mixture analysis method. However, urban FVC maps comprising various meaningful landscapes have wider applications. Compared with other moderate spatial resolution multispectral imagery (e.g., SPOT, Landsat 8), the Sentinel-2 multispectral instrument (MSI) imagery has higher resolution, larger coverage, and shorter revisit time. So it may provide higher accuracy for urban FVC mapping. This article derives an accurate object-based urban FVC map for Changsha city, China, from the 10-m resolution Sentinel-2 data acquired in 2017. For producing the urban FVC maps, the mixture analysis methods were applied on segmental image objects instead of pixels. The results demonstrate that the object-based mixture analysis method achieved a higher FVC estimation accuracy than the pixel-based mixture analysis did, and it effectively removed the “salt and pepper” phenomena. The object-based linear model fully constrained least squares and achieved the best estimation accuracy ( R2 = 0.92, RMSE = 0.0956). The red-edge band reflectance information of the MSI imagery can improve the accuracy of the FVC maps, but not significantly. The object-based urban FVC maps would be a good alternative to the traditional pixel-based maps.

A comparison of Sentinel-3-OLCI and Sentinel-2-MSI-derived Chlorophyll-a maps for two large Italian lakes

ABSTRACTThe twin satellites Sentinel-3 (S3A/B) with Ocean and Land Colour Imager (OLCI) sensors, with a spatial resolution of 300 m, and a revisiting time of 1–2 days, and the twin satellites Sentinel-2 (S2A/B) with MultiSpectral Instrument (MSI) instrument, with a spatial resolution up to 10 m, offer a special opportunity to monitor at both small and large scale, phytoplankton phenology and variation at small time (OLCI) and at small spatial scale (MSI). The aim of this study was to contribute to further testing on MSI imagery, for retrieving chlorophyll-a (chl-a) concentration, proxy of phytoplankton abundance, in inland waters. Since OLCI has higher revisiting frequency and multiple matchups with MSI, it was also examined as a surrogate for MSI. A common image-processing scheme for both sensors was applied in the two large Italian lakes Garda (oligo-mesotrophic) and Trasimeno (eutrophic). Results showed good performances, both in retrieving atmospherically corrected Remote Sensing Reflectance, and, from this last derived, chl-a concentration maps. Very good accordance was assessed between simultaneous OLCI and MSI derived chl-a maps, both in terms of magnitude, and spatial distribution, despite the fact variations in concentration at small scale were better retrieved by MSI imagery.

Coastal Wetland Mapping with Sentinel-2 MSI Imagery Based on Gravitational Optimized Multilayer Perceptron and Morphological Attribute Profiles

Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery.

Combinational shadow index for building shadow extraction in urban areas from Sentinel-2A MSI imagery

Images from Multispectral Instrument (MSI) on Sentinel-2A are useful for studies of urban environments and their spatio-temporal changes. However, the frequent occurrence of building shadows in urban areas brings about great challenges in urban studies. Existing building shadow indices are not effective for Sentinel-2A MSI images due to that these indices do not make full use of the rich spectral information contained in Sentinel-2A. In this study, we propose a combinational shadow index (CSI) to address this challenge. In the formulation of CSI, three features, including the proposed shadow enhancement index (SEI), the normalized difference water index (NDWI) and the NIR band (B8), were combined to separate spectrally similar objects, such as building shadows, water and low albedo features at the Earth surface. The accuracy and robustness of CSI were tested by using six data sets from four cities, including Beijing, Shanghai, Guangzhou and Shenzhen in China. The performance of CSI was compared with three existing shadow indices, i.e., P algorithm, the normalized saturation-value difference index (NSVDI) and the shadow index (SI). Results show that CSI can detect building shadows with fine structures in both clear and cloudy images more effectively and worked well on large areas too. CSI can separate shadows from water and low albedo features as measured by a spectral discrimination index (SDI). Compared with the existing building shadow indices, CSI can improve the performance of building shadow detection by combining the feature information of SEI, NDWI and NIR band, and yielded satisfactory results for extracting building shadows from Sentinel-2A MSI imagery.

Shelterbelt Agroforestry Systems Inventory and Removal Analyzed by Object-based Classification of Satellite Data in Saskatchewan, Canada

Shelterbelt agroforestry systems inventory is challenging given their narrow linear feature and extensive distribution. The objective of this study was to evaluate the capability of Sentinel-2A Multispectral Instrument (MSI) and Sentinel-1B Synthetic Aperture Radar (SAR) imagery in (i) delineating shelterbelt tree rows on managed agricultural land in Saskatchewan, Canada, and (ii) detecting shelterbelt removal during the period 2008–2016. Contrast split segmentation for the normalized difference vegetation index and Gaussian filter (line filter) data sets from SAR were used to delineate feature borders. Several feature variables from the spectral bands of MSI were used as inputs for an object-based classification using the random forest classifier. A resulting land cover map, including the linear features of existing shelterbelts, was created with an overall accuracy of 80% and kappa value of 0.69. Shelterbelt change detection analysis using the land cover map (2016) and a legacy shelterbelt inventory map (2008) showed that 354 km of shelterbelts were removed within the study area (1,400 km2), accounting for 29.8% of the total shelterbelt length present in 2008. Our results demonstrated that the combination of Sentinel SAR and MSI imagery can provide sufficient information for mapping future shelterbelt planting, as well as allow the detection of shelterbelt removal.