Sentinel-2 Multispectral Imagery Research Articles

SEN2-AGRI – CROP TYPE MAPPING PILOT STUDY USING SENTINEL-2 SATELLITE IMAGERY IN INDIA

Abstract. Large-scale mapping and monitoring of agriculture land use are very important. It helps in forecast crop yields, assesses the factors influencing the crop stress and estimate the damage due to natural hazards. Also, more essentially, aids in calculating the irrigation water demand at the farm level and better water resource management. Recent developments in remote sensing satellite sensors spatial and temporal resolutions, global coverage and open access such as Sentinel-2, created new possibilities in mapping and monitoring land use/land cover features. The present study investigated the performance and applicability of Sen2-Agri system in the heterogeneous cropping system for operational crop type mapping at parcel resolution using time series Sentinel-2 multispectral satellite imagery. The parcel level crop type information was collected in the field by systematic sampling and used to train and validate the random forest (RF) classification in the system. The classification accuracy varied from 57% to 86% for different major crops. The overall classification accuracy was 70% with KAPPA index of 61%. The very small agriculture field size and persistent cloud cover are the major constraint to the improvement of classification accuracy. Combination of the time series imagery from multiple earth observation satellites for the monsoon cropping season and development of a robust system for in-situ data collection will further increase the mapping accuracy. Sen2-Agri system has the potential to handle a large amount of earth observation data and can be scaled up to the entire country, which will help in the efficient monitoring of crops.

Evaluation of Atmospheric Correction Algorithms over Spanish Inland Waters for Sentinel-2 Multi Spectral Imagery Data

The atmospheric contribution constitutes about 90 percent of the signal measured by satellite sensors over oceanic and inland waters. Over open ocean waters, the atmospheric contribution is relatively easy to correct as it can be assumed that water-leaving radiance in the near-infrared (NIR) is equal to zero and it can be performed by applying a relatively simple dark-pixel-correction-based type of algorithm. Over inland and coastal waters, this assumption cannot be made since the water-leaving radiance in the NIR is greater than zero due to the presence of water components like sediments and dissolved organic particles. The aim of this study is to determine the most appropriate atmospheric correction processor to be applied on Sentinel-2 MultiSpectral Imagery over several types of inland waters. Retrievals obtained from different atmospheric correction processors (i.e., Atmospheric correction for OLI ‘lite’ (ACOLITE), Case 2 Regional Coast Colour (here called C2RCC), Case 2 Regional Coast Colour for Complex waters (here called C2RCCCX), Image correction for atmospheric effects (iCOR), Polynomial-based algorithm applied to MERIS (Polymer) and Sen2Cor or Sentinel 2 Correction) are compared against in situ reflectance measured in lakes and reservoirs in the Valencia region (Spain). Polymer and C2RCC are the processors that give back the best statistics, with coefficients of determination higher than 0.83 and mean average errors less than 0.01. An evaluation of the performance based on water types and single bands–classification based on ranges of in situ chlorophyll-a concentration and Secchi disk depth values- showed that performance of these set of processors is better for relatively complex waters. ACOLITE, iCOR and Sen2Cor had a better performance when applied to meso- and hyper-eutrophic waters, compare with oligotrophic. However, other considerations should also be taken into account, like the elevation of the lakes above sea level, their distance from the sea and their morphology.

Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery

Accurate mapping the forest stand mean height (FSMH) and aboveground biomass (AGB) with a high spatial resolution are important for monitoring carbon stocks on Earth and the variability and trends of terrestrial carbon fluxes. The recently launched Sentinel-1 (SAR) and Sentinel-2 (multispectral) missions offers a new opportunity to map FSMH and AGB. Here we present a methodological framework to map the FSMH and AGB at a resolution of 10 m in Yichun, Northeast China, by integrating field plots, Sentinel imagery, topographic data, and national geographical conditions monitoring data. First, a spatial continuous FSMH product was retrieved using an empirical model, which adopts the backscattering of SAR Sentinel-1B and the fraction of vegetation cover (FVC) variable from multispectral Sentinel-2A imagery. Subsequently, three AGB estimation models were developed for different forest types to link the field measurements to the FSMH, biophysical variables, spectral vegetation index, and topographic variables using the random forest algorithm. The mapping results show that the FSMH estimated using SAR backscatter values from VH polarization is more robust and accurate than that based on VV polarization. Furthermore, the three AGB estimation models based on three different forest types perform better than the model built by grouping all forest types together. The determination coefficient (R2) and root-mean-squared error (RMSE) range from 0.69 to 0.74 and 23.38 Mg/ha to 24.21 Mg/ha, respectively. Overall, our study demonstrates that the proposed methodological framework can be used to map the FSMH and AGB products at a high spatial resolution utilizing freely accessible Sentinel-1 SAR and Sentinel-2 multispectral imagery.

МОНІТОРИНГ СІЛЬСЬКОГОСПОДАРСЬКИХ КУЛЬТУР ІЗ ЗАСТОСУВАННЯМ КОСМІЧНИХ ЗНІМКІВ SENTINEL-2

The subject of the study in the article is the growing need for the use of spatial information for efficient agricultural production, due to the growing tendency of Earth remote sensing data accessibility, which, due to the spatial and temporal resolution improvement, can be used in the land cover analysis and other related jobs. The goal is to review the obtaining process of satellite multispectral space imagery from Sentinel-2 and to consider the possibility of their use for monitoring crops during the entire vegetation phase. The tasks: to study the modern needs of agricultural producers in the field of analysis of land cover occupied by agricultural crops; the analysis of the European Space Agency programs and the global land program Copernicus, which uses spatial information from Sentinel-2 for use in the agricultural sector; estimation of the constellation characteristics of Sentinel-2, imaging equipment and remote sensing data processing results by ground services received from Internet services; the use of Sentinel-2 multispectral space imagery for monitoring crops during the entire vegetation phase. The following results were obtained. After analyzing agricultural producers needs and the European Space Agency program, the feasibility of using multispectral space images taken by the Multispectral Instrument installed on satellites Sentinel-2 was established. Free access to the space imagery database is provided through the Copernicus Open Access Hub Internet Service. For the researched territory, Poltava region, Chutov district, the village of Vilkhovatka, various time space images were obtained and the normalized difference vegetation index (NDVI) was calculated. Histogram analysis of the obtained vegetation index values distribution within a single field (corn to grain) allows to reveal a quantitative and qualitative change in biomass, indicating a change in the vegetative phase. Conclusions. The approach described in this paper allows to conduct monitoring of the cropping state during the vegetation phase using both qualitative – visual analysis and quantitative – NDVI index, criteria. The change in the values of the normalized difference vegetation index can reveal a change in the biomass state. However, for calculating the NDVI index, data from near-infrared and red channels is needed, which complicates the acquisition of the original image. Therefore, in order to obtain the quantitative criteria in subsequent jobs, it is expedient to consider the possibility of using fractal dimension, which will reduce the amount of input data required for calculations.

Comparing map-based and library-based training approaches for urban land-cover fraction mapping from Sentinel-2 imagery

An improved trade-off between resolution, coverage and revisit time, makes Sentinel-2 multispectral imagery an interesting data source for mapping the composition and spatial-temporal dynamics of urban land cover. To fully realize the potential of Sentinel-2′s high amount of available data, efficient urban mapping workflows are required. Machine learning regression is a powerful approach to produce subpixel land cover fractions from remote sensing imagery, yet it requires mixed spectra for model training for which the fractions of the land cover classes present in the pixel are known. Typically, this data is obtained by sampling spectra from the image to be unmixed, and corresponding land-cover fractions from higher-resolution land cover reference data, i.e. map-based training. We propose synthetic mixing of library spectra as an alternative for producing land cover fraction training data for regression modelling, i.e. library-based training. The approach is applied to a Sentinel-2 image of the city of Brussels (Belgium) and part of its urban fringe for mapping Vegetation, Impervious, and Soil (VIS) fractions at 20 m resolution. VIS fraction maps obtained with library-based training have mean absolute errors below 0.1 for all three surface types. The composition of these three key surface categories and their spatial distribution is well represented for the entire area in resulting maps. As a proof of concept, library-based training is compared with the map-based training approach. The more flexible library-based training not only achieves similar mapping accuracies, but in most cases, outperforms the map-based training approach in terms of bias and magnitude of error. The outcome of the research suggests that use of spectral libraries and synthetic mixing may provide an efficient modelling framework for regression-based mapping from Sentinel-2 imagery in operational contexts.

Recent glacier variations on Mount Melimoyu (44°50'S-72°51'W), Chilean Patagonia, using Sentinel-2 data

This work analyzed the application of Sentinel-2 multispectral imagery and GLIMS data for mapping glacier retreat and to estimate glacier area changes of Mount Melimoyu, located in northern Patagonia, Chile for the period between 1970 and 2017. The results showed a decrease of about 35.61% in the area for the period analyzed and there is a continuing retreating trend in the region. The decreasing trend in mean annual precipitation may explain the recent glacier changes, which indicates a large sensibility for meteorological variability of glaciers, which is influenced by geomorphometry and glacier area, in the region. Using Sentinel 2 imagery, we provided inventory of rock glaciers in the study area. Glacier outlines obtained through manual delineation showed comparable results with the glacier outlines using Sentinel-2 MSI data in the study area, which shows greater accuracy in glacier mapping using Sentinel-2 data.

Estimating Aboveground Biomass on Private Forest Using Sentinel-2 Imagery

Private forests have a crucial role in maintaining the functioning of the Indonesian forest ecosystem especially because of the continuous degradation of natural forests. Private forests are a part of social forestry which becomes a tool for the Indonesian government to reduce carbon dioxide (CO2) emission by 26% by 2030. The United Nations Programme on Reducing Emissions from Deforestation and Forest Degradation has encouraged the Indonesian government to establish a forest monitoring system by estimating forest carbon stock using a combination of forest inventory and remote sensing. This study is aimed at assessing the potential of vegetation indices derived from Sentinel-2 for estimating aboveground biomass (AGB) of private forests. We used 45 sample plots and 7 vegetation indices to evaluate the ability of Sentinel-2 in estimating AGB on private forests. Normalised difference index (NDI) 45 exhibited a strong correlation with AGB compared to other indices (r = 0.89;R2 = 0.79). Stepwise linear regression fitted for establishing the model between field AGB and vegetation indices (R2 = 0.81). We also found that AGB in the study area based on spatial analysis was 72.54 Mg/ha. A root mean square error (RMSE) value from predicted and observed AGB was 27 Mg/ha. The AGB value in the study area is higher than the AGB value from some of forest types, and it indicates that private forests are good for biomass storage. Overall, vegetation indices from Sentinel-2 multispectral imagery can provide a good result in terms of reporting the AGB on private forests.

Detecting urban ecological land-cover structure using remotely sensed imagery: A multi-area study focusing on metropolitan inner cities

With increasing attention being paid to sustainable urban development and human habitation improvement, urban ecological land cover (UELC), i.e., surface water and green space, has played an important role of the highly compact inner urban regions. In this study, we developed an efficient approach for UELC mapping by coupling Sentinel-2 multi-spectral imagery and Google Earth high-resolution imagery. In contrast with the conventional single-source and multi-source imagery-based classification methods, the proposed method respectively achieved the highest overall accuracies of 91.50% and 94.05% in the UELC mapping for two test sites (i.e. Shanghai and Seoul). The proposed method is used for urban surface mapping among six world-class cities. For an in-depth analysis of the landscape structures for inner urban regions, seven landscape metrics are introduced for the quantification of the UELC structure based on the obtained high-precision UELC maps. The result shows that London appears to have the best UELC-induced ecological quality, that is, with high percentage of landscape, area-weighted mean fractal dimension, edge density, Shannon’s evenness index values and a low contagion index value, while Tokyo is exactly the opposite. Several common characteristics found through the statistical analysis are: 1) all the inner-city regions have small UELC coverage (< 50%) and low shape complexity; 2) green space generally contributes more to urban eco-environment than the urban surface water; and 3) all cities show high landscape consistency in the inner urban region.

Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery

Water spectral indices can enhance the difference between water bodies and background features. Thus, they have been widely used to extract and map surface water bodies based on multispectral satellite imagery. The urban scene is very heterogeneous since the surface is composed of a vast diversity of man-made objects, often of mixed distribution. Urban surface water mapping faces an extreme overestimation phenomenon because certain types of objects such as shadow, dark roads and some artificial features may return similar values to water bodies after an index computation. This study proposes a noise-prediction strategy to eliminate such misclassified nonwater areas in an automated way. Constrained energy minimization (CEM), a typical sparse target detection algorithm that does not need any background information, is utilized to draw the possible distribution of noise based on prior noise samples. The initial noise samples are automatically extracted by calculating the difference between two water indices widely accepted in urban scenes, namely, the modified normalized difference water index (MNDWI) and the automated water extraction index (AWEI). Recently freely available Sentinel-2 multispectral satellite imagery, with high spatial resolution (up to 10 m) and high repeated global coverage (every 5 days), was adopted, considering its potential on urban land cover mapping. Compared with the AWEI based approach, the results show that the proposed noise-prediction approach obtained an improved overall accuracy (increased Kappa coefficient by 0.07 on average), dramatically enhanced user accuracy (by 12.47% on average) with reduced noise, and simultaneously slightly decreased producer accuracy (by −1.19% on average). That is, the proposed method possesses an improvement of the misclassification of nonwater bodies to water bodies and a suppression of the missing of water body extraction at the same time. Finally, the comparative results, with the varying water index segmentation thresholds (−0.2 to 0.3) and an automatic Otsu threshold, indicate the robustness to the threshold of the proposed approach.

Extraction of High-Precision Urban Impervious Surfaces from Sentinel-2 Multispectral Imagery via Modified Linear Spectral Mixture Analysis.

This study explores the performance of Sentinel-2A Multispectral Instrument (MSI) imagery for extracting urban impervious surface using a modified linear spectral mixture analysis (MLSMA) method. Sentinel-2A MSI provided 10 m red, green, blue, and near-infrared spectral bands, and 20 m shortwave infrared spectral bands, which were used to extract impervious surfaces. We aimed to extract urban impervious surfaces at a spatial resolution of 10 m in the main urban area of Guangzhou, China. In MLSMA, a built-up image was first extracted from the normalized difference built-up index (NDBI) using the Otsu’s method; the high-albedo, low-albedo, vegetation, and soil fractions were then estimated using conventional linear spectral mixture analysis (LSMA). The LSMA results were post-processed to extract high-precision impervious surface, vegetation, and soil fractions by integrating the built-up image and the normalized difference vegetation index (NDVI). The performance of MLSMA was evaluated using Landsat 8 Operational Land Imager (OLI) imagery. Experimental results revealed that MLSMA can extract the high-precision impervious surface fraction at 10 m with Sentinel-2A imagery. The 10 m impervious surface map of Sentinel-2A is capable of recovering more detail than the 30 m map of Landsat 8. In the Sentinel-2A impervious surface map, continuous roads and the boundaries of buildings in urban environments were clearly identified.

Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification

The European Space Agency Sentinel-2 satellites provide multispectral images with pixel sizes down to 10 m. This high resolution allows for fast and frequent detection, classification and discrimination of various objects in the sea, which is relevant in general and specifically for the vast Arctic environment. We analyze several sets of multispectral image data from Denmark and Greenland fall and winter, and describe a supervised search and classification algorithm based on physical parameters that successfully finds and classifies all objects in the sea with reflectance above a threshold. It discriminates between objects like ships, islands, wakes, and icebergs, ice floes, and clouds with accuracy better than 90%. Pan-sharpening the infrared bands leads to classification and discrimination of ice floes and clouds better than 95%. For complex images with abundant ice floes or clouds, however, the false alarm rate dominates for small non-sailing boats.

Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery

The recent launch of the Sentinel-1 (SAR) and Sentinel-2 (multispectral) missions offers a new opportunity for land-based biomass mapping and monitoring especially in the tropics where deforestation is highest. Yet, unlike in agriculture and inland land uses, the use of Sentinel imagery has not been evaluated for biomass retrieval in mangrove forest and the non-forest land uses that replaced mangroves. In this study, we evaluated the ability of Sentinel imagery for the retrieval and predictive mapping of above-ground biomass of mangroves and their replacement land uses. We used Sentinel SAR and multispectral imagery to develop biomass prediction models through the conventional linear regression and novel Machine Learning algorithms. We developed models each from SAR raw polarisation backscatter data, multispectral bands, vegetation indices, and canopy biophysical variables. The results show that the model based on biophysical variable Leaf Area Index (LAI) derived from Sentinel-2 was more accurate in predicting the overall above-ground biomass. In contrast, the model which utilised optical bands had the lowest accuracy. However, the SAR-based model was more accurate in predicting the biomass in the usually deficient to low vegetation cover non-forest replacement land uses such as abandoned aquaculture pond, cleared mangrove and abandoned salt pond. These models had 0.82–0.83 correlation/agreement of observed and predicted value, and root mean square error of 27.8–28.5 Mg ha−1. Among the Sentinel-2 multispectral bands, the red and red edge bands (bands 4, 5 and 7), combined with elevation data, were the best variable set combination for biomass prediction. The red edge-based Inverted Red-Edge Chlorophyll Index had the highest prediction accuracy among the vegetation indices. Overall, Sentinel-1 SAR and Sentinel-2 multispectral imagery can provide satisfactory results in the retrieval and predictive mapping of the above-ground biomass of mangroves and the replacement non-forest land uses, especially with the inclusion of elevation data. The study demonstrates encouraging results in biomass mapping of mangroves and other coastal land uses in the tropics using the freely accessible and relatively high-resolution Sentinel imagery.

A Direct and Fast Methodology for Ship Recognition in Sentinel-2 Multispectral Imagery

The European Space Agency satellite Sentinel-2 provides multispectral images with pixel sizes down to 10 m. This high resolution allows for ship detection and recognition by determining a number of important ship parameters. We are able to show how a ship position, its heading, length and breadth can be determined down to a subpixel resolution. If the ship is moving, its velocity can also be determined from its Kelvin waves. The 13 spectrally different visual and infrared images taken using multispectral imagery (MSI) are “fingerprints” that allow for the recognition and identification of ships. Furthermore, the multispectral image profiles along the ship allow for discrimination between the ship, its turbulent wakes, and the Kelvin waves, such that the ship’s length and breadth can be determined more accurately even when sailing. The ship’s parameters are determined by using satellite imagery taken from several ships, which are then compared to known values from the automatic identification system. The agreement is on the order of the pixel resolution or better.

An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery

Mapping forest structure variables provides important information for the estimation of forest biomass, carbon stocks, pasture suitability or for wildfire risk prevention and control. The optimization of the prediction models of these variables requires an adequate stratification of the forest landscape in order to create specific models for each structural type or strata. This paper aims to propose and validate the use of an object-oriented classification methodology based on low-density LiDAR data (0.5 m−2) available at national level, WorldView-2 and Sentinel-2 multispectral imagery to categorize Mediterranean forests in generic structural types. After preprocessing the data sets, the area was segmented using a multiresolution algorithm, features describing 3D vertical structure were extracted from LiDAR data and spectral and texture features from satellite images. Objects were classified after feature selection in the following structural classes: grasslands, shrubs, forest (without shrubs), mixed forest (trees and shrubs) and dense young forest. Four classification algorithms (C4.5 decision trees, random forest, k-nearest neighbour and support vector machine) were evaluated using cross-validation techniques. The results show that the integration of low-density LiDAR and multispectral imagery provide a set of complementary features that improve the results (90.75% overall accuracy), and the object-oriented classification techniques are efficient for stratification of Mediterranean forest areas in structural- and fuel-related categories. Further work will be focused on the creation and validation of a different prediction model adapted to the various strata.