Sentinel-2A Multispectral Instrument Research Articles

Mapping eruption affected area using Sentinel-2A imagery and machine learning techniques

Volcanic eruptions are natural disasters with significant environmental and societal impacts. Timely detection and monitoring of volcanic eruptions are crucial for effective hazard assessment, mitigation strategies, and emergency response planning. Remote sensing technology has emerged as a valuable tool for detecting and assessing the effects of volcanic eruptions. One of the challenges in remote sensing image processing is handling large data dimensions that are difficult to address using traditional methods. Machine learning approaches offer a suitable solution to tackle these challenges. Machine learning demonstrates increasing computational capabilities, the ability to handle big data and automation. This study aimed to compare different machine learning classification algorithms, including Random Forest (RF), Support Vector Machine (SVM), Gaussian Mixture Model (GMM), and K-Nearest Neighbors (KNN). The data utilized in this study was derived from Sentinel-2A Multi-Spectral Instrument (MSI) imagery, which was tested in areas affected by the eruption of Mount Agung, Bali Province, in 2017. The results indicated that the GMM algorithm performed the best among the machine learning classifiers, achieving an Overall Accuracy (OA) value of 82.04%. It was followed by RF (78.86%) and KNN (77.55%). The areas affected by volcanic eruptions were determined by overlaying disaster-prone regions with areas mapped using the machine learning approach. The total affected area was measured as 29.89 km<sup>2</sup>, with an additional 3.31 km<sup>2</sup> outside the designated zone. The findings of this study serve as a guideline for governmental entities, stakeholders, and communities to implement effective mitigation efforts for disaster risk reduction.

Automatic lineaments detection using radar and optical data with an emphasis on geologic and tectonic implications: a case study of Kabul Block, eastern Afghanistan

The earth’s surface linear features, expressing geological lineaments, play a key role in identifying hydrothermal alteration and mineralization zones, as well as in understanding tectonic settings of a region. The objective of this investigation is to utilize a method for extracting lineaments automatically, which will be integrated and applied to identify geological-based lineaments by making use of remote sensing data. The study will further examine the structural pattern and temporal-spatial evolution of the lineaments and establish their connection with the primary active faults present in the Kabul Block. Multi-sensor data from radar (DEM (Digital Elevation Model)-5m, Sentinel-1B GRD (Ground Range Detected)) and optical sensors (Sentinel-2 MSI (Multispectral Instrument) and ASTER) were processed using spatial and spectral filters before automatic lineament extraction. LINE-module algorithm was applied to various illuminated hill-shades of DEM-5m, filtered HH (Horizonal-Horizonal) and VH (Vertical-Horizontal) of Sentinel-1GRD and PC1 (Principle Component) of Sentinel-2A MSI, and ASTER data to detect linear surface features. Extensive testing was conducted to verify the accuracy of extracted lineaments and to exclude any artificial lineaments in the study area. The radar and optical data results were compared while taking into consideration the geological and tectonic settings of the study area to select the most appropriate extracted lineaments. The DEM-5m and Sentinel-1B GRD showed the best result for identifying lineaments, and these were found to be highly correlated with previously available data in the Kabul Block. The final results of DEM-5m and Sentinel-1B GRD were further analyzed. The extracted lineaments were found to trend predominantly in a NW-SE and NE-SW directions, which is consistent with the results of other data. Temporal evolution and spatial distribution reveal that a high density of the lineaments is associated with Paleogene and Quaternary formations, while a low density is observed in Proterozoic, Paleozoic, and Mesozoic formations. The west and southwest edges of the Kabul Block are controlled by compressive stress trending NNW-SSE, while the north and southeast margins are influenced by NE-SW and ∼ N-S trending compressive stress.

Vicarious Radiometric Calibration of the Multispectral Imager Onboard SDGSAT-1 over the Dunhuang Calibration Site, China

The multispectral imager (MII), onboard the Sustainable Development Science Satellite 1 (SDGSAT-1), performs detailed terrestrial change detection and coastal monitoring. SDGSAT-1 was launched at 2:19 UTC on 5 November 2021, as the world’s first Earth science satellite to serve the United Nations 2030 Sustainable Development Agenda. A vicarious radiometric calibration experiment was conducted at the Dunhuang calibration site (Gobi Desert, China) on 14 December 2021. In-situ measurements of ground reflectance, aerosol optical depth (AOD), total columnar water vapor, radiosonde data, and diffuse-to-global irradiance (DG) ratio were performed to predict the top-of-atmosphere radiance by the reflectance-, irradiance-, and improved irradiance-based methods using the moderate resolution atmospheric transmission model. The MII calibration coefficients were calculated by dividing the top-of-atmosphere radiance by the average digital number value of the image. The radiometric calibration coefficients calculated by the three calibration methods were reliable (average relative differences: 2.20% (reflectance-based vs. irradiance-based method) and 1.43% (reflectance-based vs. improved irradiance-based method)). The total calibration uncertainties of the reflectance-, irradiance-, and improved irradiance-based methods were 2.77–5.23%, 3.62–5.79%, and 3.50–5.23%, respectively. The extra DG ratio measurements in the latter two methods did not improve the calibration accuracy for AODs ≤ 0.1. The calibrated MII images were verified using Landsat-8 Operational Land Imager (OLI) and Sentinel-2A MultiSpectral Instrument (MSI) images. The retrieved ground reflectances of the MII over different surface types were cross-compared with those of OLI and MSI using the FAST Line-of-sight Atmospheric Analysis of Hypercubes software. The MII retrievals differed by <0.0075 (7.13%) from OLI retrievals and <0.0084 (7.47%) from MSI retrievals for calibration coefficients from the reflectance-based method; <0.0089 (7.57%) from OLI retrievals and <0.0111 (8.65%) from MSI retrievals for the irradiance-based method; and <0.0082 (7.33%) from OLI retrievals and <0.0101 (8.59%) from MSI retrievals for the improved irradiance-based method. Thus, our findings support the application of SDGSAT-1 data.

The Development of Dark Hyperspectral Absolute Calibration Model Using Extended Pseudo Invariant Calibration Sites at a Global Scale: Dark EPICS-Global

This research aimed to develop a novel dark hyperspectral absolute calibration (DAHAC) model using stable dark targets of “Global Cluster-36” (GC-36), one of the clusters from the “300 Class Global Classification”. The stable dark sites were identified from GC-36 called “Dark EPICS-Global” covering the surface types viz. dark rock, volcanic area, and dark sand. The Dark EPICS-Global shows a temporal variation of 0.02 unit reflectance. This work used the Landsat-8 (L8) Operational Land Imager (OLI), Sentinel-2A (S2A) Multispectral Instrument (MSI), and Earth Observing One (EO-1) Hyperion data for the DAHAC model development, where well-calibrated L8 and S2A were used as the reference sensors, while EO-1 Hyperion with a 10 nm spectral resolution was used as a hyperspectral library. The dark hyperspectral dataset (DaHD) was generated by combining the normalized hyperspectral profile of L8 and S2A for the DAHAC model development. The DAHAC model developed in this study takes into account the solar zenith and azimuth angles, as well as the view zenith and azimuth angles in Cartesian coordinates form. This model is capable of predicting TOA reflectance in all existing spectral bands of any sensor. The DAHAC model was then validated with the Landsat-7 (L7), Landsat-9 (L9), and Sentinel-2B (S2B) satellites from their launch dates to March 2022. These satellite sensors vary in terms of their spectral resolution, equatorial crossing time, spatial resolution, etc. The comparison between the DAHAC model and satellite measurements showed an accuracy within 0.01 unit reflectance across the overall spectral band. The proposed DAHAC model uncertainty level was determined using Monte Carlo simulation and found to be 0.04 and 0.05 unit reflectance for the VNIR and SWIR channels, respectively. The DAHAC model double ratio was used as a tool to perform the inter-comparison between two satellites. The sensor inter-comparison results for L8 and L9 showed a 2% difference and 1% for S2A and S2B across all spectral bands.

Fusion level of satellite and UAV image data for soil salinity inversion in the coastal area of the Yellow River Delta

ABSTRACT Rapid and accurate determination of soil salt content (SSC) and its spatial distribution are of great significance for the prevention and improvement of soil salinization. Satellite and unmanned aerial vehicle (UAV) remote sensing data have complementary advantages. The fusion of satellite and UAV multisource remote sensing data to improve the accuracy of SSC based on inversion methods has become a hot topic, and the appropriate fusion level of multisource remote sensing data needs to be explored and determined. The objective of this study was to determine the appropriate fusion level of Sentinel-2A Multispectral Instrument (Sentinel-MSI) and UAV image data for SSC inversion by comparing the fusion effect of three levels (spectral data, spectral index, and spectral model). A numerical regression method was employed to analyse the relationship between Sentinel-MSI and UAV image data (MSI-UAV), and MSI-UAV data were fused at different levels. Then, the appropriate fusion level and best inversion model were optimized to realize regional SSC inversion. The results indicate that spectral data fusion was better than spectral index fusion for enhancing the SSC spectral response, with the correlation between spectral indices and SSC increasing by 0.139–0.167 after fusion. After spectral data fusion, the model improved the SSC inversion accuracy most obviously, with a calibration R2 of 0.623, validation R2 of 0.571, and ratio of performance to deviation (RPD) of 1.821. Therefore, spectral data fusion was found to be superior in enhancing the spectral response of soil salinity and in improving the accuracy of the estimation model. This research optimized spectral data fusion as the appropriate fusion level of MSI-UAV for SSC inversion and formed a set of high-precision MSI-UAV multisource remote sensing fusion inversion approaches for SSC.

Transferability of Recursive Feature Elimination (RFE)-Derived Feature Sets for Support Vector Machine Land Cover Classification

Remote sensing analyses frequently use feature selection methods to remove non-beneficial feature variables from the input data, which often improve classification accuracy and reduce the computational complexity of the classification. Many remote sensing analyses report the results of the feature selection process to provide insights on important feature variable for future analyses. Are these feature selection results generalizable to other classification models, or are they specific to the input dataset and classification model they were derived from? To investigate this, a series of radial basis function (RBF) support vector machines (SVM) supervised machine learning land cover classifications of Sentinel-2A Multispectral Instrument (MSI) imagery were conducted to assess the transferability of recursive feature elimination (RFE)-derived feature sets between different classification models using different training sets acquired from the same remotely sensed image, and to classification models of other similar remotely sensed imagery. Feature selection results for various training sets acquired from the same image and different images widely varied on small training sets (n = 108). Variability in feature selection results between training sets acquired from different images was reduced as training set size increased; however, each RFE-derived feature set was unique, even when training sample size was increased over 10-fold (n = 1895). The transferability of an RFE-derived feature set from a high performing classification model was, on average, slightly more accurate in comparison to other classification models of the same image, but provided, on average, slightly lower accuracies when generalized to classification models of other, similar remotely sensed imagery. However, the effects of feature set transferability on classification accuracy were inconsistent and varied per classification model. Specific feature selection results in other classification models or remote sensing analyses, while useful for providing general insights on feature variables, may not always generalize to provide comparable accuracies for other classification models of the same dataset, or other, similar remotely sensed datasets. Thus, feature selection should be individually conducted for each training set within an analysis to determine the optimal feature set for the classification model.

Extraction of rocky desertification information in karst area by using different multispectral sensor data and multiple endmember spectral mixture analysis method

As a typical form of land degradation, karst rocky desertification seriously restricts the development of the regional social economy and seriously threatens the living environment of residents. Fractional vegetation cover (fVC) and bare rock fraction (fBR) are important indicators to identify and evaluate rocky desertification. However, it is a great challenge to obtain fVC and fBR due to the complex terrain and fragmentation of karst rocky desertification areas. In this study, comparisons between Sentinel-2A Multispectral Instrument (Sentinel-2), Landsat-8 Operational Land Imager (Landsat-8), and GF-6 Wide Field View (GF-6) sensors for retrieving fVC and fBR are presented. The multiple endmember spectral mixture analysis (MESMA) and measured spectral dates were used to overcome the limitations of Spectral mixture analysis (SMA). Subsequently, fVC and fBR were validated using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results showed that: 1) Sentinel-2 performed best in estimating fVC and obtained the highest accuracy (R2 = 0.6259, root mean square error = 0.1568, mean absolute error = 0.1215), followed by GF-6 and Landsat 8; in the extraction of the fBR, the performance was relatively the same, and the implementation of Sentinel-2 was also the best (R2 = 0.4911, root mean square error = 0.0714, mean absolute error = 0.0539), followed by GF-6 and Landsat 8. 2) Sentinel-2 images have higher resolution, the narrowest band range, and the most significant number of bands, which can better extract information about fVC and fBR in rocky desertification areas. 3) For the three optical sensors, the spatial resolution of the images is more important to extract the information of fVC and fBR in the rocky desertification areas. 4) In general, the extraction accuracy of fBR is not as good as that of fVC. The complicated ecological and geological environment of decertified areas has more influence on the effect of extraction of the fBR. 5) The Sentinel-2 achieves high accuracy for both fVC and fBR under different-level application scenarios. It thus has great potential for application in rocky desertification information extraction.

The possibility of spatial mapping of soil organic carbon content at three depths using easy-to-obtain ancillary data in a Mediterranean area

Background: Unlike most of Europe, Andalucía in southern Spain as a Mediterranean area still lacks digital maps of soil organic carbon (SOC) content at multiple depths, which can be generated by machine learning algorithms. The wide diversity of climate, geology, hydrology, landscape, topography, vegetation, and micro-relief data as easy-to-obtain covariates has facilitated the development of digital soil mapping (DSM). The purpose of this research is to model and map the spatial distribution of SOC at three depths, in an area of approximately 10000 km2 located in Seville and Cordoba Provinces, and to use R programming to compare two machine learning techniques (cubist and random forest) for developing SOC maps at multiple depths. Methods: Environmental covariates used in this research include nine derivatives from digital elevation models (DEM), three climatic variables, and 18 remotely-sensed spectral data (band ratios calculated by Landsat-8 Operational Land Imager ‘OLI’ and Sentinel-2A Multispectral Instrument ‘MSI’ in July 2019). In total, 300 soil samples from 100 points at three depths (0-25 cm, 25-50 cm, and 50-75 cm) were taken from existing literature. Both machine learning techniques were compared taking into account their accuracy using the goodness-of-fit criteria to predict SOC. Results: The findings showed that integrating the indices derived by Landsat-8 OLI and Sentinel-2A MSI satellite data had a better result than when satellite data was used separately. Conclusions: We obtained evidence that the resolution of satellite images is a key parameter in modelling and digital mapping.

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.

On the Retrieval of the Water Quality Parameters from Sentinel-3/2 and Landsat-8 OLI in the Nile Delta’s Coastal and Inland Waters

Reduced water quality due to the eutrophication process causes large economic losses worldwide. Multi-source remotely-sensed water quality monitoring can help provide effective water resource management. The research evaluates the retrieval of the water quality parameters: chlorophyll-a (Chl-a), total suspended matter (TSM), and chromophoric dissolved organic matter (CDOM), over optically different water types. Cross-sensor performance analysis of three satellite data sources: Sentinel-3 Ocean Land Color Imager (OLCI), Sentinel-2A Multi-Spectral Instrument (MSI), and Landsat-8 Operational Land Imager (OLI), acquired during a 45 min overpass on the Nile Delta coast on 22 March 2020 was performed. Atmospheric correction using the case 2 Regional Coast Color (C2RCC) was applied using local water temperature and salinity averages. Owing to the lack of ground-truth measurements in the coastal water, results were inter-compared with standard simultaneous color products of the Copernicus Marine Environment Monitoring Service (CMEMS), OLCI water full resolution (WFR), and the MODIS Aqua, in order to highlight the sensor data relative performance in the Nile Delta’s coastal and inland waters. Validation of estimates was carried out for the only cloud-free MSI data available in the 18–20 September 2020 period for the Burullus Lake nearly contemporaneous with in situ measurements in the 22–25 September 2020. Inter-comparison of the retrieved parameters showed good congruence and correlation among all data in the coastal water, while this comparison returned low positive or negative correlation in the inland lake waters. In the coastal water, all investigated sensors and reference data showed Chl-a content average of 3.14 mg m−3 with a range level of 0.39–4.81 mg m−3. TSM averaged 7.66 g m−3 in the range of 6.32–10.18 g m−3. CDOM clarified mean of 0.18 m−1 in the range level of 0.13–0.30 m−1. Analysis of the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE) clarified that the MSI sensor was ranked first achieving the smallest MAE and RMSE for the Chl-a contents, while the EFR proved superior for TSM and CDOM estimates. Validation of results in Burullus Lake indicated a clear underestimation on average of 35.35% for the Chl-a induced by the land adjacency effect, shallow bottom depths, and the optical dominance of the TSM and the CDOM absorption intermixed in turbid water loaded with abundant green algae species and counts. The underestimation error increased at larger estimates of the algal composition/abundance (total counts, Chlorophyacea, Euglenophycaea, and Bacillariophycaea) and the biological contents (carbohydrates, lipids, and proteins), arranged in decreasing order. The largest normalized RMSE estimates marked the downstream areas where the inflow of polluted water persistently brings nutrient loads of nitrogen and phosphorous compounds as well as substantial amounts of detrital particles and sediments discharged from the agricultural and industrial drains and the land use changes related to agricultural practices, resulting in the increase of water turbidity giving rise to inaccurate Chl-a estimates.

Comparison of sentinel 2A MSI and Landsat 8 OLI for soil organic matter inversion in southwestern Shandong province, China

It remains to be explored whether some new multispectral satellite images with more available bands and higher spatial resolution have better inversion capability for soil organic matter (SOM). Taking southwestern Shandong Province of China as the research area, this article compared the performance of Landsat 8 Operational Land Imager (OLI) and Sentinel 2A Multi-Spectral Instrument (MSI) data for SOM quantitative inversion. The results indicate that, the model including red-edge 1 band of Sentinel 2A MSI was determined as the best model, as the validation R 2 increased by 0.106, the RMSE decreased by 0.565 g/kg, and RPD increased by 0.163 compared with the model on Landsat 8 OLI image. The SOM distribution characteristics inversed on the best model were consistent with that on the measured samples. Therefore, the Sentinel 2A MSI can obtain better results than Landsat 8 OLI for the regional SOM inversion in the southwestern Shandong Province of China.

Decadal terminus position changes and ice thickness measurement of Menthosa Glacier in Lahaul region of North-Western Himalaya

Glacier ice-thickness measurement and distribution is one of the essential variables to assess present status of glacier-water equivalent and its volumetric reserve as well as to model the future glacier dynamics under the climate changing scenario. Yet, substantial gaps in ice thickness information exist for the Himalayan glaciers. The present study provides a long-term assessment (1965–2016) of recessional and area change patterns, as well as the detailed field-based (2016–2017) Ground Penetrating Radar(GPR), derived ice-thickness measurement of the Menthosa Glacier, Lahaul Himalaya. Additionally, the study examines whether the modelled ice thickness from remote sensing data is consistent with the field-based GPR measurement and how can it be improved. The extensive field surveys coupled with the multi-temporal high (Corona KH-4A) to medium resolution (Landsat Enhanced Thematic Mapper+ (ETM+)/Operational Land Imager (OLI), Sentinel 2A-Multispectral Instrument (MSI)) remote sensing data and cross-sectional GPR surveyed profile measurements have been used to examine past half a century (1965–2016) glacier fluctuation and the recent ice-thickness estimations, respectively. The results show that the Menthosa Glacier receded by 301.5 ± 19.2 m during the past half a century (1965–2016) with an average annual retreat of 5.9 ± 0.4 m a−1, whereas glacier lost 0.09 km2 ice in the frontal section. Field measurement over the past one decade (2006–2017) also conforms to a continuous recessional pattern and substantial glacier degeneration particularly the extensive surface lowering and significant appearance of ice-cliffs in the ablation and lateral zones over this period. The GPR measurements (2017) show the minimum glacier ice thickness of 24 meters at 4691 m a.s.l. (in the lower part of ablation area) and maximum glacier ice thickness of 55 meters measured at 4758 m a.s.l. (in the upper left-side tributary part of ablation area). Moreover, the modelled ice thickness derived from remotely sensed data is having Root Mean Square Error (RMSE) between 38 to 72 ± 10 m as compared with GPR measured ice thickness.

Soil salinity inversion based on differentiated fusion of satellite image and ground spectra

Due to the wide availability of remote sensing data from different sensors and platforms, soil salinity inversion based on the fusion of multisource remote sensing data is becoming a reality. However, existing fusion methods mainly use the average relationship from samples, which does not consider the differences in the relationships among samples. In this paper, a differentiated fusion method for determining satellite and ground spectral variables of soil salinity according to the differences among samples is proposed to increase the regional inversion precision. Nonnegative matrix factorization was employed to decompose soil salinity spectral variables from the Sentinel-2A Multi-Spectral Instrument (MSI) image and the simulated spectra on ground spectra. Then, the base spectra matrix of soil salinity was from the simulated data, and the weight coefficient matrix was obtained from the Sentinel-2A MSI data as the differentiated correction coefficients. By multiplying the base matrix and weight matrix, the spectral variables were reconstructed. The results indicate that this differentiated fusion method can not only enhance the correlation between soil salinity and Sentinel-2A MSI data but also improve the precision of regional soil salinity inversion models. For the differentiated fused model, the validation R2, RMSE, and RPD were 0.71, 7.02 g/kg, and 1.49, respectively; compared with the unfused model, the validation R2 increased by 0.09, the RMSE decreased by 0.80 g/kg, and the RPD increased by 0.18. Furthermore, the differentiated fused model performed better than the average-ratio adjusted model. These findings have practical implications for the use of multisource optical remote sensing data for regional soil salinity mapping and analysis.

Synergy of Sentinel-2A and Near-proximal sensor data for deriving biochemical parameters of paddy at different growth stages

The biochemical parameters (i.e. chlorophyll and nitrogen content) of crops are essential to evaluate the health status and to monitor crop growth. The incorporation of near-proximal sensor data with satellite data has offered a new angle for rapid, detailed, and reliable assessment of crop biochemical parameters. This study aimed to utilize the higher spatial and spectral resolution of Sentinel-2A multi-spectral instrument (MSI) satellite data in conjunction with near-proximal sensor data to derive the chlorophyll (Chl) content and nitrogen balance index (NBI) of paddy at different growth stages during the monsoon season (2017) in Ranchi district, Jharkhand. The key findings revealed that the satellite-derived Chl content and NBI of paddy were overestimated as compared to the field-based measurements. The correlations between satellite-derived and empirical model-based Chl content and NBI of paddy revealed a R2 of 0.92–0.99 (p < 0.001) at different growth stages. The Chl content was found higher (20–40 µg/cm2) during the peak growing stage of paddy and subsequently, it decreased (< 20 µg/cm2) by the start of the ripening stage. The root-mean-square error between the empirical model and satellite-based Chl content was from 7.7 to 8.5 µg/cm2. Thereby, it can be concluded that the MSI sensor with three red-edge bands along with visible and near-infrared has a virtuous capability to quantify the crop biochemical parameters. However, it needs a bias correction to derive Chl content/NBI accurately. The adopted methods of retrieving biochemical parameters at different growth stages would be beneficial for decision-makers in agriculture monitoring and can be incorporated in crop growth modelling and yield predictions.

Remote sensing of CDOM and DOC in alpine lakes across the Qinghai-Tibet Plateau using Sentinel-2A imagery data

As important components of dissolved organic matter (DOM) in an aquatic environment, colored DOM (CDOM) and dissolved organic carbon (DOC) play an essential role in the carbon cycle of an inland aquatic system. Traditionally, CDOM and DOC in inland waters have been primarily determined using in situ observations and laboratory measurements. Most of past lake investigations on CDOM and DOC focused on easily accessible regions and covered a small fraction of lakes worldwide. To our knowledge, little is known about lakes in less accessible areas like the Qinghai-Tibet Plateau (QTP). To address this challenge, optical satellite remote sensing might be useful for capturing a synoptic view of CDOM and DOC with high frequency at large scales, complementing in situ sampling methods for inland waters. In this study, 216 samples collected from 36 lakes across the QTP (2014–2017) were examined to determine the relationships between CDOM absorption coefficient at 350 nm (a350) and Sentinel-2A Multi Spectral Instrument (MSI) imagery reflectance data. A strong positive linear correlation with a350 was observed with B4/B2 (R2 = 0.78, p < 0.01) and with B4/B3 (R2 = 0.62). A multi-step regression model was established for estimating a350 with B4/B2 and B4/B3 as input variables (R2 = 0.81, p < 0.01). A scattered CDOM-DOC relationship was revealed (R2 = 0.34, p < 0.05) using a pooled dataset. By dividing the inland waters into four separate groups in accordance with their salinity gradients, we were able to develop much stronger relationships (R2 > 0.8, p < 0.01) for CDOM-DOC. Significant differences between fresh and saline waters were demonstrated using satellite-derived CDOM and DOC, where high CDOM (0.86 ± 0.67 m-1) and low DOC (3.76 ± 4.92 mg L−1) concentrations were observed for freshwaters, while inverse trends of CDOM (0.53 ± 0.72 m-1) and DOC (15.76 ± 17.07 mg L−1) were demonstrated for saline lakes in the Tibetan Plateau. This study confirmed that satellite optical imagery can be used for the monitoring of CDOM and DOC of the lakes of the Tibetan Plateau, which are sensitive to a changing climate and are infrequently investigated due to the harsh environment and poor accessibility. Moreover, it highlighted the importance of combining salinity and remote sensing data in the process of estimating lake DOC.

Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation

Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects.

Spatio-temporal analysis of glacier surface velocity in dhauliganga basin using geo-spatial techniques

Glacier velocity is one of the significant parameter for the assessment of the glacier dynamics, which directly signifies the response of the glacier health with the respect to climate change. The present work focuses on analysing the spatio-temporal characteristics of the surface velocity in the Dhauliganga basin for seven glaciers. The cross-correlation method was applied on the panchromatic scenes of Landsat-7 Enhanced Thematic Mapper Plus (ETM +), Landsat-8 Operational Land Imager (OLI) and Sentinel-2A Multispectral Instrument (MSI) sensors to estimate the velocity. The mean velocity of the seven glaciers along the central flow line ranges from 59.57 to 5.34 m/year while the average of mean velocity ranges from 30.82 to 11.04 m/year during 1993–2018. Whereas, across the flow line, mean velocity ranges from 63.58 to 4.74 m/year. The lowest velocity was observed in the T1–T2 locations near the snout, where the velocity highly dominated by mass loss and debris cover; the error in velocity estimation ranges from ± 3.67 to ± 4.57 m/year. Decreasing trend of surface velocity was observed for all the glaciers. The present work demonstrates the application of cross-correlation technique to monitor the dynamics of a group of glaciers on a basin scale having similar climatic setting. The velocity profiles of various glaciers can be used as input to methods estimating ice thickness and volume and to prioritize the glaciers in view of glacier-dynamics linked events such as surges and retreat induced to rise to pro-glacial lakes and glacier melt contribution to the river flows.

Remote Sensing of Burn Severity Using Coupled Radiative Transfer Model: A Case Study on Chinese Qinyuan Pine Fires

Burn severity mapping is critical to quantifying fire impact on key ecological processes and post-fire forest management. Satellite remote sensing has the advantages of high spatial-temporal resolution and large-scale monitoring and provides a more efficient way to evaluate forest fire burn severity than traditional field or aerial surveys. However, the proportion of tree canopy cover (TCC) affects the spectral signal received by remote sensing sensors from the background charcoal and ash. Consequently, not considering this factor normally leads a spectral confusion in burn severity retrieval. In this study, the burn severity of two Qinyuan forest fires was estimated using a coupled Radiative Transfer Model (RTM) and Sentinel-2A Multi-Spectral Instrument (MSI) reflectance data. A two-layer Canopy Reflectance Model (ACRM) RTM was coupled with the GeoSail RTM by replacing the spectra of the background input of GeoSail RTM to simulate the spectra of the three-layered forests for burn severity retrieval measured as the Composite Burn Index (CBI). The TCC data was then served to RTM parameterization and constrain the backward inversion procedure of the coupled RTM to alleviate spectral confusion. Finally, the inversion retrievals were evaluated using 163 field measured CBI. The coupled RTM can simulate the radiative transfer characteristics of three-layer vegetation and has greater potential to accurately estimate burn severity worldwide. To evaluate the merit of our proposed method, the CBI was estimated through coupled RTM inversion with TCC constraint (CP_RTM+TCC), coupled RTM inversion with global optimal search (CP-RTM+GOS), Forest Reflectance and Transmittance (FRT) RTM inversion with TCC constraint (FRT+TCC), and random forest (RF) algorithm. The results showed that the method proposed in this study (CP_RTM+TCC) yielded the highest estimation accuracy (R2 = 0.92, RMSE = 0.2) among the four methods used as benchmark, indicating its reasonable ability to assist forest managers to better understand post-fire vegetation regeneration and forest management.

Landsat-8 and Sentinel-2 Canopy Water Content Estimation in Croplands through Radiative Transfer Model Inversion

Despite the potential implications of a cropland canopy water content (CCWC) thematic product, no global remotely sensed CCWC product is currently generated. The successful launch of the Landsat-8 Operational Land Imager (OLI) in 2012, Sentinel-2A Multispectral Instrument (MSI) in 2015, followed by Sentinel-2B in 2017, make possible the opportunity for CCWC estimation at a spatial and temporal scale that can meet the demands of potential operational users. In this study, we designed and tested a novel radiative transfer model (RTM) inversion technique to combine multiple sources of a priori data in a look-up table (LUT) for inverting the NASA Harmonized Landsat Sentinel-2 (HLS) product for CCWC estimation. This study directly builds on previous research for testing the constraint of the leaf parameter (Ns) in PROSPECT, by applying those constraints in PRO4SAIL in an agricultural setting where the variability of canopy parameters are relatively minimal. In total, 225 independent leaf measurements were used to train the LUTs, and 102 field data points were collected over the 2015–2017 growing seasons for validating the inversions. The results confirm increasing a priori information and regularization yielded the best performance for CCWC estimation. Despite the relatively low variable canopy conditions, the inclusion of Ns constraints did not improve the LUT inversion. Finally, the inversion of Sentinel-2 data outperformed the inversion of Landsat-8 in the HLS product. The method demonstrated ability for HLS inversion for CCWC estimation, resulting in the first HLS-based CCWC product generated through RTM inversion.

Estimation and evaluation of high spatial resolution surface soil moisture using multi-sensor multi-resolution approach

Various soil moisture products (AMSR-E at 25 km, SMAP at 9 km, CCI at 25 km) available from different space agencies, derived using the active microwave remote sensing, may not be suitable for studies at a local level due to their coarse spatial resolution. Hence, an attempt has been made in this study to estimate the surface soil moisture at high spatial resolution using different high-resolution multispectral imageries from Landsat 8 OLI (Optical Land Imager) and Sentinel 2A MSI (Multi Spectral Instrument), of similar date. The thermal (10.9 µm) and short wave infra-red (SWIR) (2.2 µm) bands of the Landsat image and the SWIR band (2.19 µm) of Sentinel image were used to estimate the soil moisture at a spatial resolution of 30 m and 20 m respectively, whereas the red and near infrared (NIR) bands of both the images were used to estimate the dryness. As the thermal band at high spatial resolution (at 20 m or below) is not available, this study attempted to bridge the gap and integrate multi-sensor and multi-resolution feature space utilizing various scalable procedures so as to estimate soil moisture and dryness. Limitation due to unavailability of the thermal bands in Sentinel multispectral image was overcome through a bridging procedure using Landsat thermal bands derived LST and Sentinel derived NDVI to estimate Sentinel LST at 20 m. The distribution pattern of high-resolution estimated soil moisture in each coarse spatial resolution grid (1 km) was taken into consideration to downscale the available CCI and SMAP soil moisture data. The estimated soil moisture and dryness were upscaled to 1 km and then validated using the downscaled soil moisture products (SMAP and CCI), and a significant correlation having R2 greater than 0.8, 0.5 and 0.4 was observed for the thermal band derived soil moisture, short wave band derived soil moisture, and the red and NIR band derived dryness, respectively. To understand the temporal variability of the soil moisture, data from three different seasons representing extreme soil moisture conditions (winter, summer and monsoon) were analyzed and found that the independently estimated soil moisture had errors in temporal continuity and spatial spread. Hence, the estimated soil moisture was standardized using annual information which resulted in more accurate products, and the SWIR Transformed Reflectance (STR) derived soil moisture was more accurate than LST derived product.