MSI Data Research Articles

Fuzzy-based Integration of Sentinel-2 MSI Data for Locating Potential Hydrocarbon Microseepag

The vertical migration of hydrocarbons from oil and gas reservoirs to the surface and/or subsurface (i.e., microseepage) can induce mineral alteration that results in concentration of ferrous iron, clay, and carbonate minerals at the Earth’s surface. These altered zones may be detected in remote sensing imagery based on their specific reflectance spectral characteristics; zones where three types of mineral alteration occur simultaneously reflect possible hydrocarbon microseepage locations with the potential for underlying oil and gas reservoirs. In this study, a fuzzy set based approach was used to integrate the results of principal component analysis (PCA) and band ratios (BRs). First, each selected indicative principal component (PC) and BR was treated as a fuzzy set, and a corresponding fuzzy membership function defined. The membership degree of each pixel, which indicates the possibility of the presence of specific altered minerals, was then calculated. Subsequently, the Gamma operator was used to fuse all fuzzy sets to create a new fuzzy set, which was then defuzzied and used to indicate the possible locations of hydrocarbon microseepages. The Sentinel-2 MSI data were used to illustrate the approach and the zones of hydrocarbon microseepage mapped.

Assessing the influence of different validation protocols on Ocean Colour match-up analyses

Multiple approaches have been used by the Ocean Colour community for validating satellite-derived products using in situ data, with most of them derived from mainly two approaches, one suggested by Bailey and Werdell (2006) (BW06) and one suggested by Zibordi et al. (2009a) (Z09), each with a different set of quality checking and spatiotemporal collocation criteria. The question remains what sort of information is added or missed when choosing one over the other. In this work, the differences among validation approaches were determined by using a common dataset of in situ and satellite data. The match-up exercise was separated into two groups of datasets based on the spatial resolution of the sensors to be validated. Sentinel-3A/OLCI data were selected as a representation of medium spatial resolution sensors, and two validation approaches were selected to this match-up dataset. The high spatial resolution sensors were represented by Sentinel-2A/MSI data, and three validation approaches were tested. Data from the AERONET-OC network were chosen as the common in situ dataset. For Sentinel-3A/OLCI, the number of match-ups varies depending on the validation approach used. Bailey and Werdell (2006) produces 20% more match-ups for Sentinel-3A/OLCI due to its more relaxed filtering criteria compared to the criteria applied by Zibordi et al. (2009a). The validation metrics vary between different validation methods giving a different impression of accuracy of the satellite products. Also, the satellite data selected by BW06 have a statistical distribution with a higher median and standard deviation when compared to Z09. Similar findings are also confirmed for the match-up analysis conducted for Sentinel-2A/MSI. Therefore, although a common reference dataset was used, the validation statistical results were influenced by the validation approach selected. This does not suggest that one validation protocol is better than the other, but it implies that validation statistics reported in different studies may not always be directly comparable. Additionally, it was determined that BW06 could be a better fit when trying to obtain a sufficient number of match-ups for calibration purposes in the shortest time.

Transcriptomic, peptidomic, and mass spectrometry imaging analysis of the brain in the ant Cataglyphis nodus.

Behavioral flexibility is an important cornerstone for the ecological success of animals. Social Cataglyphis nodus ants with their age-related polyethism characterized by age-related behavioral phenotypes represent a prime example for behavioral flexibility. We propose neuropeptides as powerful candidates for the flexible modulation of age-related behavioral transitions in individual ants. As the neuropeptidome of C. nodus was unknown, we collected a comprehensive peptidomic data set obtained by transcriptome analysis of the ants' central nervous system combined with brain extract analysis by Q-Exactive Orbitrap mass spectrometry (MS) and direct tissue profiling of different regions of the brain by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS. In total, we identified 71 peptides with likely bioactive function, encoded on 49 neuropeptide-, neuropeptide-like, and protein hormone prepropeptide genes, including a novel neuropeptide-like gene (fliktin). We next characterized the spatial distribution of a subset of peptides encoded on 16 precursor proteins with high resolution by MALDI MS imaging (MALDI MSI) on 14µm brain sections. The accuracy of our MSI data were confirmed by matching the immunostaining patterns for tachykinins with MSI ion images from consecutive brain sections. Our data provide a solid framework for future research into spatially resolved qualitative and quantitative peptidomic changes associated with stage-specific behavioral transitions and the functional role of neuropeptides in Cataglyphis ants.

Evaluation of geomorphometric characteristics and soil properties after a wildfire using Sentinel-2 MSI imagery for future fire-safe forest

Understanding spatiotemporal geomorphological and pedological changes as a consequence of wildfires can allow stakeholders, land planners, and policymakers to design efficient fire safety-based afforestation and restoration programs of forest lands. The use of remote sensing techniques is a key tool to achieve this goal. The suitable combination of Sentinel-2 MSI data for mapping of different spectral indices related to burn severity and their relationship with other morphometric and soil properties can contribute to a better understanding of the impact of fire, and this is relevant in regions where is still scarce fire-related research such as Turkey. In this investigation, the use of NDVI (Normalized Difference Vegetation Index), dNDVI (Difference Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), NBR (Normalized Burn Ratio), dNBR (Difference Normalized Burn Ratio), RBR (Relativized Burn Ratio), SBI (Soil Bare Index), As (Upslope area), CTI (Compound Topographic Index), TCI (Terrain Characterization Index), SPI (Stream Power Index) and Curvature (Standard Curvature) were combined. As a study case, 47.43 ha in a burned area of Çınarpınar forest unit, Andırın, Kahramanmaraş in Turkey was selected. The results showed that dNDVI, dNBR, RBR, SBI contribute to relevant information about the effect of the wildfire. According to the dNBR fire severity classification, 75% of the total area has been exposed to high-severity fire. The relationship of Sentinel MSI satellite images with some soil and morphometric features have been found meaningful to understand the impact of forest fire in Mediterranean ecosystems. The information collected in the Turkish forest areas affected by wildfires should be relevant for planning and represent a key contribution to the selection of restoration programs and afforestation techniques for a future fire-safe forest.

Spectral Response of Winter Crops Using Multi-temporal Sentinel-2 MSI Data in Visnagar Taluka, Gujarat State

Developing Countries like India has more than half of the population engaged in fishery, agriculture etc. and agriculture drives the economy of the Nation. Traditional survey methods are tedious and time consuming, however, with advancement in Remote Sensing Technology data for Agriculture monitoring can be obtained at local and regional scale. The present study demonstrates the use of multi-year multi-date Sentinel-2 MSI data for winter crop monitoring in in Visnagar taluka Mahesansa district of Gujarat state. Multi-temporal Sentinel-2 data covering study area for the winter crop period from November-2018 to March-2019 was downloaded from https://earthexplorer.usgs.gov/ .The major objective of this study was to monitor site-specific crop growth in different villages of Visnagar Taluka by generating Normalized Difference Vegetation Index (NDVI) profiles of major winter crops. The spectral behavior of wheat, mustard, Fennel and castor crops during active growth stages was studied. The results indicated that different winter crops can be discriminated using the data during the early growth phases like flag-leaf emergence to flowering stages of these crops. Even it was observed that the spectral response of normal-sown and late-sown wheat crop have quite distinct spectral behavior as compared to other winter crops. In this study, the NDVI profiles of different winter crops indicated that Last week of January to first week of February, when all the winter crops were at grand growth stages of grain filling and grain-milk, pod formation (mustard & castor) stages have similar NDVI values, therefore during this period major winter crops in this region may not be possible to discriminate using single date satellite data.

A typical lake in the semi-arid area of northern China—The remote sensing retrieved of the transparency of the Lake Daihai (2013-2020)

水体的透明度是评价水质的重要指标，在水生态系统中起着重要的作用.借助遥感技术可以获得大范围、实时数据，并且有节省人力物力的优点.本文利用岱海的野外实测透明度数据和光谱数据，针对Sentinel-2 MSI和Landsat-8 OLI卫星数据波段设置，建立了岱海水体透明度反演模型.结果表明：1）本文建立的透明度反演模型中，蓝红波段比二次模型反演精度最好，决定系数R²=0.66，均方根误差（RMSE）为24.02 cm，平均绝对百分比误差（MAPE）为21.24%.2）将蓝红波段比二次模型应用于Landsat-8 OLI和Sentinel-2 MSI卫星数据，透明度反演精度较好，MAPE<28.82%，RMSE<23.26 cm，R²>0.60.3）此算法应用于时间序列MSI和OLI影像，得到了岱海水体透明度时空分布特征.结果表明，岱海水体透明度年平均变化范围在90.71~120.77 cm，2015年的平均透明度最高，2013年的平均透明度最低；月平均变化范围在90.68~122.53 cm，7月的平均透明度最高，5月的平均透明度最低.岱海透明度在空间上的分布趋势大致表现为西北高，东南低，中部高，四周低.4）影响岱海水体透明度变化的主要因素为风速和降水，透明度与风速和降水分别具有显著的负相关和正相关关系.;The transparency of water bodies is an important indicator for evaluating water quality and plays an important role in the water ecosystem. With the help of remote sensing technology, large-scale, real-time data can be obtained, and it has the advantage of saving manpower and material resources. According to the Sentinel-2 MSI and Landsat-8 OLI satellite data band settings, the Lake Daihai water body transparency retrieved model is established based on the measured transparency data and spectral data of Lake Daihai in this study. The results show that: 1) Among the transparency retrieved model established in this study, the blue-red band ratio quadratic model has the best retrieved accuracy than the quadratic model (R²=0.66, RMSE=24.02, MAPE=21.24%). 2) Applying the blue-red band ratio quadratic model to Landsat-8 OLI and Sentinel-2 MSI satellite data, the transparency retrieved accuracy is better (MAPE<28.82%, RMSE<23.26 cm, R²>0.60). 3) This algorithm is applied to MSI and OLI images, and the temporal and spatial distribution characteristics of the transparency of the Lake Daihai water body are obtained. The results showed that the average annual change range was 90.71-120.77 cm, the average transparency in 2015 was the highest, and the average transparency in 2013 was the lowest. The monthly range changed from 90.68 to 122.53 cm, the average transparency in July was the highest, and the average transparency in May was the lowest. The spatial distribution trend of Lake Daihai's water transparency is roughly high in the northwest, low in the southeast, high in the middle, and low in the central. 4) The main factors affecting the transparency of the Lake Daihai water body are wind speed and precipitation. The transparency and wind speed, precipitation have a significant negative correlation and positive correlation, respectively.

Matrix-Assisted Laser Desorption/Ionization-Mass Spectrometry Imaging of Lipids in the Ischemic Rat Brain Section: A Practical Approach.

Matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) of lipids is considered one of the shotgun lipidomic techniques that explores the in situ distribution of lipids in tissue sections. To successfully perform this task, knowledge and experience in the conventional cryosection, tissue section collection and handling, and mass spectrometry data analysis and signal processing are needed. A MALDI-MSI protocol covering from the fresh organ collection, cryosection and tissue processing, matrix application, MSI data acquisition, to the final MSI display of lipid distribution in the brain section of an ischemic stroke rat is described to exemplify this technique. Due to the multidisciplinary nature of this approach, plenty of preparation, practice, and familiarization to several critical steps ahead of the engagement with actual biological samples are needed to ensure a successful MALDI-MSI presentation of lipids in situ.

Machine learning method for quick identification of water quality index (WQI) based on Sentinel-2 MSI data: Ebinur Lake case study

Abstract Surface water quality is an important factor affecting the ecological environment and human living environment. The monitoring of surface water quality by remote sensing monitoring technology can provide important research significance for water resources protection and water quality evaluation. Finding the optimal spectral index sensitive to water quality for remote sensing monitoring of water quality is extremely important for surface water quality analysis and treatment in the Ebinur Lake Basin in arid areas. This study used Sentinel-2MSI data at 10 m resolution to quickly monitor the water quality of the watershed. Through laboratory experiments and measurement data from the Ebinur Lake Basin, 22 water quality parameters (WQPs) were obtained. Through Z-score and redundancy analysis, 9 WQPs with significant contributions were extracted. Based on the remote sensing spectral band, four water indexes (NDWI, NWI, EWI, AWEI-nsh) and 2D modeling spectral index (DI, RI, NDI), the correlation analysis between WQPs and two kinds of spectral band indexes is carried out, and it is concluded that the overall correlation between WQP and 2D spectral modeling is more relevant. This paper calculates the evaluation and models the 2D spectrum of the Water Quality Index (WQI). The WQI is predicted and modeled through four machine learning algorithms (RF, SVM, PLSR, PLSR-SVM).The results show that the inversion effect of the two-dimensional spectral modeling index on water quality parameters (WQPs) is superior to that of the water index, and the correlation coefficient of the DI (R12-R1) SWIR-2 and BLUE band interpolation index reaches 0.787. On this basis, three kinds of two-dimensional spectral modeling indexes are used to inversely synthesize the WQI, and the correlation coefficient of the ratio index of the RI (R11/R8) SWIR-1 and near-infrared (NIR) bands is preferably 0.69. In the WQI prediction, the partial least squares regression support vector machine (PLSR-SVM) model in machine learning algorithms has good modeling and prediction effects (R2c = 0.873, R2v = 0.87), which can provide a good basis. The research results provide references for remote monitoring of surface water in arid areas, and provide a basis for water quality prediction and safety evaluation.

Spectral Unmixing for Mapping a Hydrothermal Field in a Volcanic Environment Applied on ASTER, Landsat-8/OLI, and Sentinel-2 MSI Satellite Multispectral Data: The Nisyros (Greece) Case Study

The aim of this study was to propose a methodology that provides a detailed description of the argillic zone of a hydrothermal field, based on satellite multispectral data. More specifically, we developed a method based on spectral unmixing where hydroxyl-bearing alteration is represented by a single endmember (representing clays) and the three (nearly) non-altered primary volcanic lithologies, namely, two types of lava flows (basic and acidic compositions) and the loose materials (alluvial/beach deposits, scree, pyroclastic deposits, etc.), are represented by three endmembers. We also used one endmember representing elemental sulfur that is present in fumarolic vents hosted by active hydrothermal craters. The methodology was applied in the south part of Lakki plain inside the Nisyros volcano caldera (Greece), using Sentinel-2, Landsat-8/OLI, and ASTER satellite multispectral datasets. Specifically, it was applied separately to each one of the three datasets. The spectral unmixing results, combined with the relative geological map, provide quantitative estimations of the primary volcanic and loose material areas affected by alteration. In addition, pixels with high abundance values of hydroxyl-bearing alteration corresponded to mapped areas with strong hydrothermal alteration. The developed methodology is superior to conventional approaches (e.g., alteration spectral index) in terms of its ability to describe the overall pattern of the hydrothermal field. The most accurate results were taken when applied to ASTER or Sentinel-2 MSI data.

In Situ Metabolomics Expands the Spectrum of Renal Tumours Positive on 99mTc-sestamibi Single Photon Emission Computed Tomography/Computed Tomography Examination

BackgroundDefinite noninvasive characterisation of renal tumours positive on 99mTc-sestamibi single photon emission computed tomography/computed tomography (SPECT/CT) examination including renal oncocytomas (ROs), hybrid oncocytic chromophobe tumours (HOCTs), and chromophobe renal cell carcinoma (chRCC) is currently not feasible. ObjectiveTo investigate whether combined 99mTc-sestamibi SPECT/CT and in situ metabolomic profiling can accurately characterise renal tumours exhibiting 99mTc-sestamibi uptake. Design, setting, and participantsA tissue microarray analysis of 33 tumour samples from 28 patients was used to investigate whether their in situ metabolomic status correlates with their features on 99mTc-sestamibi SPECT/CT examination. In order to validate emerging data, an independent cohort comprising 117 tumours was subjected to matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI MSI). Outcome measurements and statistical analysisMALDI MSI data analysis and image generation were facilitated by FlexImaging v. 4.2, while k-means analysis by SCiLS Lab software followed by R-package CARRoT analysis was used for assessing the highest predictive power in the differential of RO versus chRCC. Heatmap-based clustering, sparse partial least-squares discriminant analysis, and volcano plots were created with MetaboAnalyst 3.0. Results and limitationsWe identified a discriminatory metabolomic signature for 99mTc-sestamibi SPECT/CT–positive Birt-Hogg-Dubè–associated HOCTs versus other renal oncocytic tumours. Metabolomic differences were also evident between 99mTc-sestamibi–positive and 99mTc-sestamibi–negative chRCCs, prompting additional expert review; two of three 99mTc-sestamibi–positive chRCCs were reclassified as low-grade oncocytic tumours (LOTs). Differences were identified between distal-derived tumours from those of proximal tubule origin, including differences between ROs and chRCCs. ConclusionsThe current study expands the spectrum of 99mTc-sestamibi SPECT/CT–positive renal tumours, encompassing ROs, HOCTs, LOTs, and chRCCs, and supports the feasibility of in situ metabolomic profiling in the diagnostics and classification of renal tumours. Patient summaryFor preoperative evaluation of solid renal tumours, 99mTc-sestamibi single photon emission computed tomography/computed tomography (SPECT/CT) is a novel examination method. To increase diagnostic accuracy, we propose that 99mTc-sestamibi–positive renal tumours should be biopsied and followed by a combined histometabolomic analysis.

Registration of Image Modalities for Analyses of Tissue Samples Using 3D Image Modelling.

Biopsies are a diagnostic tool for the diagnosis of histopathological, molecular biological, proteomic, and imaging data, to narrow down disease patterns or identify diseases. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) provides an emerging state-of-the-art technique for molecular imaging of biological tissue. The aim of this study is the registration of MALDI MSI data sets and data acquired from different histological stainings to create a 3D model of biopsies and whole organs. The registration of the image modalities is achieved by using a variant of the authors' global, deformable Schatten-q-Norm registration approach. Utilizing a connected-component segmentation for background removal followed by a principal-axis based linear pre-registration, the images are adjusted into a homogeneous alignment. This registration approach is accompanied by the 3D reconstruction of histological and MALDI MSI data. With this, a system of automatic registration for cross-process evaluation, as well as for creating 3D models, is developed and established. The registration of MALDI MSI data with different histological image data is evaluated by using the established global image registration system. In conclusion, this multimodal image approach offers the possibility of molecular analyses of tissue specimens in clinical research and diagnosis.

Updated inventory of glacier ice in New Zealand based on 2016 satellite imagery

AbstractThe only complete inventory of New Zealand glaciers was based on aerial photography starting in 1978. While there have been partial updates using 2002 and 2009 satellite data, most glaciers are still represented by the 1978 outlines in contemporary global glacier databases. The objective of this project is to establish an updated glacier inventory for New Zealand. We have used Landsat 8 OLI satellite imagery from February and March 2016 for delineating clean glaciers using a semi-automatic band ratio method and debris-covered glaciers using a maximum likelihood classification. The outlines have been checked against Sentinel-2 MSI data, which have a higher resolution. Manual post processing was necessary due to misclassifications (e.g. lakes, clouds), mapping in shadowed areas, and combining the clean and debris-covered parts into single glaciers. New Zealand glaciers cover an area of 794 ± 34 km2 in 2016 with a debris-covered area of 10%. Of the 2918 glaciers, seven glaciers are &gt;10 km2 while 71% is &lt;0.1 km2. The debris cover on those largest glaciers is &gt;40%. Only 15 glaciers are located on the North Island. For a selection of glaciers, we were able to calculate the area reduction between the 1978 and 2016 inventories.

Remote and Vis-NIR spectra sensing potential for soil salinization estimation in the eastern coast of Urmia hyper saline lake, Iran

Soil salinization is an important threat for agriculture and environment in the eastern coast of Urmia hyper saline Lake, a lake in the western part of Iran. Predicting soil salinization requires rapid and low-cost measurement tools of soil salinity. It is hypothesized that remote sensing and visible near-infrared spectroscopy may offer a feasible method for that purpose. To test this hypothesis, 96 soil samples were collected using Latin hypercube sampling method. Soil salinity (ECe) and spectral reflectance at various bands were measured in all samples in the laboratory. The spectral indices derived from Landsat OLI, Sentinel-2 MSI and visible near-infrared (Vis-NIR) spectroscopy data, as input variables, along with partial least squares regression (PLSR) method were used for predicting contents soil salt. The results revealed that the PLSR derived regression models of the spectral indices accounted for 42.7 and 45.3% of the soil salinity variation by Landsat OLI and Sentinel-2 MSI data, respectively. And PLSR derived model using Vis-NIR spectroscopy data explained 59.4% of the variation in soil salt contents. Compared to derived models using Vis-NIR spectroscopy, Landsat OLI and Sentinel-2 MSI data, the derived PLSR models (which were developed as a result of integrating Vis-NIR spectroscopy and remote sensing data) increased the power of soil salinity prediction Finally, the combined of Vis-NIR spectroscopy + Sentinel-2 MSI data was found to provide the best input data for the derived regression model to estimate soil salt contents (R2 = 0.713 and RMSE = 21.76 dS m−1).

Diffuse Attenuation of Clear Water Tropical Reservoir: A Remote Sensing Semi-Analytical Approach

The diffuse attenuation coefficient of downwelling irradiance (Kd) is an essential parameter for inland waters research by remotely sensing the water transparency. Lately, Kd semi-analytical algorithms substituted the empirical algorithms widely employed. The purpose of this research was to reparametrize a semi-analytical algorithm to estimate Kd and then apply it to a Sentinel-2 MSI time-series (2017–2019) for the Três Marias reservoir, Brazil. The results for the Kd semi-analytical reparametrization achieved good accuracies, reaching mean absolute percentage errors (MAPE) for bands B2, B3 and B4 (492, 560 and 665 nm), lower than 21% when derived from in-situ remote sensing reflectance (Rrs), while for MSI Data, a derived MAPE of 12% and 38% for B2 and B3, respectively. After the application of the algorithm to Sentinel-2 images time-series, seasonal patterns were observed in the results, showing high Kd values at 492 nm during the rainy periods, mainly in the tributary mouths, possibly due to an increase in the surface runoff and inflows and outflow rates in the reservoir watershed.

Validation of MALDI-MS imaging data of selected membrane lipids in murine brain with and without laser postionization by quantitative nano-HPLC-MS using laser microdissection

MALDI mass spectrometry imaging (MALDI-MSI) is a widely used technique to map the spatial distribution of molecules in sectioned tissue. The technique is based on the systematic generation and analysis of ions from small sample volumes, each representing a single pixel of the investigated sample surface. Subsequently, mass spectrometric images for any recorded ion species can be generated by displaying the signal intensity at the coordinate of origin for each of these pixels. Although easily equalized, these recorded signal intensities, however, are not necessarily a good measure for the underlying amount of analyte and care has to be taken in the interpretation of MALDI-MSI data. Physical and chemical properties that define the analyte molecules’ adjacencies in the tissue largely influence the local extraction and ionization efficiencies, possibly leading to strong variations in signal intensity response. Here, we inspect the validity of signal intensity distributions recorded from murine cerebellum as a measure for the underlying molar distributions. Based on segmentation derived from MALDI-MSI measurements, laser microdissection (LMD) was used to cut out regions of interest with a homogenous signal intensity. The molar concentration of six exemplary selected membrane lipids from different lipid classes in these tissue regions was determined using quantitative nano-HPLC-ESI-MS. Comparison of molar concentrations and signal intensity revealed strong deviations between underlying concentration and the distribution suggested by MSI data. Determined signal intensity response factors strongly depend on tissue type and lipid species.Graphical abstract

Ship-Iceberg Classification in SAR and Multispectral Satellite Images with Neural Networks

Classification of ships and icebergs in the Arctic in satellite images is an important problem. We study how to train deep neural networks for improving the discrimination of ships and icebergs in multispectral satellite images. We also analyze synthetic-aperture radar (SAR) images for comparison. The annotated datasets of ships and icebergs are collected from multispectral Sentinel-2 data and taken from the C-CORE dataset of Sentinel-1 SAR images. Convolutional Neural Networks with a range of hyperparameters are tested and optimized. Classification accuracies are considerably better for deep neural networks than for support vector machines. Deeper neural nets improve the accuracy per epoch but at the cost of longer processing time. Extending the datasets with semi-supervised data from Greenland improves the accuracy considerably whereas data augmentation by rotating and flipping the images has little effect. The resulting classification accuracies for ships and icebergs are 86% for the SAR data and 96% for the MSI data due to the better resolution and more multispectral bands. The size and quality of the datasets are essential for training the deep neural networks, and methods to improve them are discussed. The reduced false alarm rates and exploitation of multisensory data are important for Arctic search and rescue services.

Retrieval of Soil Moisture Content Based on a Modified Hapke Photometric Model: A Novel Method Applied to Laboratory Hyperspectral and Sentinel-2 MSI Data

Soil moisture is the crucial carrier of the global hydrologic cycle and the dynamic energy balance regulation process. Therefore, it is of great significance to monitor surface soil moisture content (SMC) accurately for the study of the natural ecological environment. The Hapke model is the most widely used photometric model in soil remote sensing research, but the development of this model is limited by the lack of valid multi–angular data. The main innovations of this paper have two aspects: (1) A novel soil moisture retrieval approach based on the Hapke (SMR–Hapke) model is derived by exploring the relationship between single scattering albedo (SSA) and SMC on the optical bands from 400 to 2400 nm. The performance of the proposed model was verified on a dataset consisting of four different soil samples, and the experimental results indicated that the inverted soil moisture from SMR–Hapke model coincided with the measurement values, with the R2 being generally more than 0.9 in the solar domain. (2) The SMR–Hapke model has been reduced to a linear form on the SWIR field and a physically-based normalized difference soil moisture index N D S M I H a p k e has been proposed. Based on the laboratory-based hyperspectral data, we compared the performance of N D S M I H a p k e with other traditional soil moisture indices using linear regression analysis, and the results demonstrate that the proposed N D S M I H a p k e had a great potential for estimating SMC with R2 values of 0.88. Finally, high–resolution SMC map was produced by combining the Sentinel–2 MSI data with N D S M I H a p k e . This study provides a novel extended Hapke model for the estimation of surface soil moisture content.

Mapping Winter Wheat with Combinations of Temporally Aggregated Sentinel-2 and Landsat-8 Data in Shandong Province, China

Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer’s and user’s accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 data were combined. As our results also revealed, it was always good to classify multi-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase’s and reviving phase’s data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical satellite data (Landsat-8 OLI and Sentinel-2 MSI) and prioritize key winter wheat development phases for accurate mapping winter wheat planting areas across China and elsewhere.

Burned Area Detection in the Brazilian Amazon using Spectral Indices and GEOBIA

Mapping refined burned areas (BA) in the Brazilian Amazon is still a challenge. The main difficulty of BA detection in large areas is the presence of cloud cover and water bodies. The use of different data sources of medium spatial resolution satellite images can provide a higher availability of cloud-free images. Besides that, it may decrease the uncertainties associated with coarse spatial resolution data (&gt;250m), which can under or overestimate BA and hinder the detection of small BA patches (&lt;0.1km²). In this study, we propose an innovative methodology based on spectral indices and geographic object-based image analysis (GEOBIA), using medium spatial resolution images to improve BA detection in the Brazilian Amazon region. Firstly, we assessed the performance of nine spectral indices in two study areas, derived from Landsat-8 OLI and Sentinel-2A MSI data to identify the most suitable index for BA detection in this region. Then, we refined this data through the GEOBIA-based model. The results showed that the Burned Area Index (BAI) was the most suitable index for BA mapping (M index &gt;1.5) for both sensors. Our model allowed detecting more than 80% of small BA and also presented high Dice coefficient values (~0.70) with low omission and commission errors (0.22 and 0.32, respectively). Such combined approach corresponds to a novel contribution to the BA detection in the Brazilian Amazon region and for enhancing the operational product generation.

Snow cover mapping, topographic controls and integration of meteorological data sets in Din-Gad Basin, Central Himalaya

Din-Gad basin, located in central Himalaya is occupied by the Dokriani glacier in upper reaches. Landsat TM, OLI and Sentinel MSI data sets of seven years from 2009 to 2011 and 2013 to 2016 are utilized to map the areal extent of snow-cover, its seasonal variability and topographical controls (elevation, aspect, and slope). Coefficient of Variation (CV) in Snow Cover Area (SCA) below the elevation of ~5000 m asl is high (~2.0), and is low at higher elevations. CV is also higher for southern as compared to northern aspects. Meteorological data (temperature, relative humidity, and solar radiation) from Automatic Weather Stations (AWSs) is utilized to compute isotherms (~0, 1, and 2 °C) by extrapolation method using Near-surface Temperature Lapse Rate (NSTLR). These data sets are integrated and presented by graphical as well as on Geographical Information System (GIS). Month-wise isotherms closely correspond with snow-cover extent during ablation season, suggesting that the extrapolated temperatures represent a significant proxy for depletion pattern, while month-wise and hourly isotherms deviate largely from the SCA during winter months due to snow events. Elevation zones above 5200 m asl retain snow throughout the year, indicating proximity with Equilibrium Line Altitude (ELA). The effects of Relative Humidity (RH) and solar radiation on melting history during different seasons have also emerged. The proposed methodology is efficient and reliable in terms of estimating the depletion patterns, and deriving the isotherms that can be applied to other comparable snow-covered areas.