scholarly journals Quantifying DOC and Its Controlling Factors in Major Arctic Rivers during Ice-Free Conditions using Sentinel-2 Data

2019 ◽  
Vol 11 (24) ◽  
pp. 2904
Author(s):  
Jue Huang ◽  
Ming Wu ◽  
Tingwei Cui ◽  
Fanlin Yang
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Carbon Cycle ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Strong Relationship ◽  
Retrieval Algorithm ◽  
Band Placement ◽  
Arctic Rivers ◽  
Sentinel 2

The six largest Arctic rivers (Yenisey, Lena, Ob’, Kolyma, Yukon, and Mackenzie) drain the organic-rich Arctic watersheds and serve as important pools in the global carbon cycle. Satellite remote sensing data are considered to be a necessary supplement to the ground-based monitoring of riverine organic matter circulation, especially for the ice-free periods in high-latitudes. In this study, we propose a remote sensing retrieval algorithm to obtain the chromophoric dissolved organic matter (CDOM) levels of the six largest Arctic rivers using Sentinel-2 images from 2016 to 2018. These CDOM results are converted to dissolved organic carbon (DOC) concentrations using the strong relationship (R2 = 0.89) between the field measurements of these two water constituents. The temporal-spatial distributions of the DOC in the six largest Arctic rivers during ice-free conditions are depicted. The performance of the retrieval algorithm verifies the capacity of using Sentinel-2 data to monitor riverine DOC variations due to its improved spatial resolution, better band placement, and increased observation frequency. River discharge, watershed slopes, human activities, and land use/land cover change drove much of the variation in the satellite-derived DOC. The seasonality, geography, and scale would affect the correlation between DOC concentration and these influence factors. Our results could improve the ability to monitor DOC fluxes in Arctic rivers and advance our understanding of the Earth’s carbon cycle.

scholarly journals Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2313
Author(s):  
Elena Prudnikova ◽  
Igor Savin
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Reflectance ◽  
Remote Sensing Data ◽  
Soil Surface ◽  
Optical Remote Sensing ◽  
Test Field ◽  
Sensing Data ◽  
Sentinel 2

Optical remote sensing only provides information about the very thin surface layer of soil. Rainfall splash alters soil surface properties and its spectral reflectance. We analyzed the impact of rainfall on the success of soil organic matter (SOM) content (% by mass) detection and mapping based on optical remote sensing data. The subject of the study was the arable soils of a test field located in the Tula region (Russia), their spectral reflectance, and Sentinel-2 data. Our research demonstrated that rainfall negatively affects the accuracy of SOM predictions based on Sentinel-2 data. Depending on the average precipitation per day, the R2cv of models varied from 0.67 to 0.72, RMSEcv from 0.64 to 1.1% and RPIQ from 1.4 to 2.3. The incorporation of information on the soil surface state in the model resulted in an increase in accuracy of SOM content detection based on Sentinel-2 data: the R2cv of the models increased up to 0.78 to 0.84, the RMSEcv decreased to 0.61 to 0.71%, and the RPIQ increased to 2.1 to 2.4. Further studies are necessary to identify how the SOM content and composition of the soil surface change under the influence of rainfall for other soils, and to determine the relationships between rainfall-induced SOM changes and soil surface spectral reflectance.

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

2020 ◽  
Vol 17 (21) ◽  
pp. 5355-5364
Author(s):  
Maria Paula da Silva ◽  
Lino A. Sander de Carvalho ◽  
Evlyn Novo ◽  
Daniel S. F. Jorge ◽  
Claudio C. F. Barbosa
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Optical Absorption ◽  
Dissolved Organic Matter ◽  
Seasonal Variability ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Floodplain Lakes ◽  
Sentinel 2

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

Modelling of soil organic matter of arable soils with optical remote sensing data: the impact of soil surface state

2020 ◽  
Author(s):  
Elena Prudnikova ◽  
Igor Savin
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Surface State ◽  
Organic Matter Content ◽  
Remote Sensing Data ◽  
Matter Content ◽  
Optical Remote Sensing ◽  
Arable Soils ◽  
Sensing Data ◽  
Sentinel 2

<p>The study presents the analysis of effect of changes of the open surface of arable soils occuring due to the influence of agricultural practices or natural factors (mainly, precipitation) on the possibility of assessment of organic matter content in the arable layer with optical remote sensing data.</p><p>The object of the research was gray forest arable soil of a test field located in the Yasnogorsky district of the Tula region. In 2019, the field was complete fallow.</p><p>During field work conducted on the test field on 15.08.2019, the spectral reflectance of the surface of arable soils and a wetter subsurface horizon was measured at 30 points. At the same points, 30 mixed samples of the arable horizon were collected for laboratory estimation of organic matter content.</p><p>Spectral reflectance was measured using a HandHeld-2 field spectroradiometer, which operates in the range 325–1050 nm with a step of 1 nm.</p><p>Proximal sensing data were smoothed with Savitzky-Golley function and recalculated into Sentinel-2 bands using Gaussian function.</p><p>We also chose seven Sentinel-2 scenes for 2019 for the studied region: 2.04.2019, 17.04.2019, 20.04.2019, 5.05.2019; 6.06.2019, 19.06.2019, 28.08.2019. Atmospheric correction for chosen scenes was performed with Sen2Cor model in SNAP. Aftewords we extracted reflectance values at points, where we collected spectral data and soil samples in the field.</p><p>Then we calculated a number of spectral indices and ratios for both proximal and Sentinel-2 data which were further used in regression modelling. Models were cross-validated by bootstrapping.</p><p>At field scale, difference in moisture content did not significantly affect the accuracy and quality of the models. R<sup>2</sup>adjcv of model for dry surface layer was a bit higher than in case of model for wet subsurface layer (0.77 vs. 0.72). RMSEPcv and RPIQ for both cases were very close (0.71 and 0.71; 2.09 and 2.12).</p><p>When we used models developed based on proximal sensing data to calculate OM content with Sentinel-2 data at different acquisition dates, we found that the accuracy of OM prediction varied. In some cases RMSE was higher than 7 % and predicted OM content was two times higher than actual.</p><p>Models developed based only on Sentinel-2 data for different acquisition dates, varied in accuracy, quality and informative bands. R<sup>2</sup>adjcv of most models was about 0.72-0.83, RPIQ was 2.09-2.07, and RMSEPcv was in the range of 0.56-0.77 %.</p><p>Therefore changes in surface state of arable soils result in a situation when for each state we have different model. That imposes restrictions on further use of such models for remote evaluation and monitoring of organic matter content in arable soils. To deal with this problem, it is necessary to account for soil surface state when developing models for properties of arable soils based on optical remote sensing data.</p><p>The research was funded by the Ministry of Science and Higher Education of Russia (contract № 05.607.21.0302). </p>

Characterizing sown biodiverse pastures using remote sensing data with machine learning

2020 ◽  
Author(s):  
Tiago G. Morais ◽  
Pedro Vilar ◽  
Marjan Jongen ◽  
Nuno R. Rodrigues ◽  
Ivo Gama ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Organic Matter ◽  
Estimation Error ◽  
Organic Matter Content ◽  
Remote Sensing Data ◽  
Matter Content ◽  
Nitrogen And Phosphorus ◽  
Soil Organic Matter Content ◽  
Sentinel 2

<p>In Portugal, beef cattle are commonly fed with a mixture of grazing and forages/concentrate feed. Sown biodiverse permanent pastures rich in legumes (SBP) were introduced to provide quality animal feed and offset concentrate consumption. SBP also sequester large amounts of carbon in soils. They use biodiversity to promote pasture productivity, supporting a more than doubling in sustainable stocking rate, with several potential environmental co-benefits besides carbon sequestration in soils.<br>Here, we develop and test the combination of remote sensing and machine learning approaches to predict the most relevant production parameters of plant and soil. For the plants, we included pasture yield, nitrogen and phosphorus content, and species composition (legumes, grasses and forbs). In the soil, we included soil organic matter content, as well as nitrogen and phosphorus content. For soils, hyperspectral data were obtained in the laboratory using previously collected soil samples (in near-infrared wavelengths). Remotely sensed multispectral data was acquired from the Sentinel-2 satellite. We also calculated several vegetation indexes. The machine learning algorithms used were artificial neural networks and random forests regressions. We used data collected in late winter/spring from 14 farms (more than 150 data samples) located in the Alentejo region, Portugal.<br>The models demonstrated a good prediction capacity with r-squared (r2) higher than in 0.70 for most of the variables and both spectral datasets. Estimation error decreases with proximity of the spectral data acquisition, i.e. error is lower using hyperspectral datasets than Sentinel-2 data. Further, results not shown systematic overestimation and/or underestimation. The fit is particularly accurate for yield and organic matter, higher than 0.80. Soil organic matter content has the lowest standard estimation error (3 g/kg soil – average SOM: 20 g/kg soil), while the legumes fraction has the highest estimation error (20% legumes fraction).<br>Results show that a move towards automated monitoring (combining proximal or remote sensing data and machine learning methods) can lead to expedited and low-cost methods for mapping and assessment of variables in sown biodiverse pastures.</p>

Monitoring plastic accumulation in water hyacinths using remote sensing

2021 ◽  
Author(s):  
Niels Janssens ◽  
Lauren Biermann ◽  
Louise Schreyers ◽  
Martin Herold ◽  
Tim van Emmerik
Keyword(s):  
Remote Sensing ◽  
Seasonal Dynamics ◽  
Water Hyacinth ◽  
Naive Bayes ◽  
Pearson Correlation ◽  
Field Measurements ◽  
Naïve Bayes ◽  
Plastic Waste ◽  
Water Hyacinths ◽  
Sentinel 2

<p>While efforts to quantify plastic waste accumulation in the marine environment are rapidly increasing, the data on plastic transport in rivers are relatively scarce. Rivers are a major source of plastic waste into the oceans and understanding seasonal dynamics of macroplastic transport is necessary to develop effective mitigation measures. Macroplastic transport in rivers varies significantly throughout the year. Research shows that in the case of the Saigon river, Vietnam, these plastic transport fluxes are mainly correlated to the amount of organic debris (mostly water hyacinths). Since large water hyacinths patches can be monitored from space, this gives the opportunity for large scale monitoring using freely available remote sensing products. Remote sensing products, such as Sentinel-2, can be applied to areas where water hyacinths occur and plastic emissions are estimated to be high. In this study, we present a first method to detect and monitor water hyacinths using optical remote sensing. This was done by developing an algorithm to automatically detect and quantify water hyacinth coverage for a large section of the Saigon river in Vietnam, for the year 2018. Spectral signatures of water,  infrastructure in the river, and water hyacinths were used to classify the water hyacinths coverage and dynamics using a Naive Bayes algorithm. Water hyacinths were promisingly identified with 95% accuracy by the Naive Bayes classifier. The comparison between the seasonal dynamics of classified water hyacinth and seasonal dynamics of the field measurements resulted in an overall Pearson correlation of 0.72. The comparison we attempted between seasonal dynamics of plastics from satellite and field measurements yielded a Pearson correlation of 0.48. With the next field campaign collecting in-situ data matched to satellite overpasses, we aim to improve this. In conclusion, we were able to successfully map seasonal dynamics of water hyacinth in an automated way using Sentinel-2 data. Our study provides the first step in exploring the possibilities of mapping water hyacinth from satellite as a proxy for river plastics.</p>

scholarly journals Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

2021 ◽  
Vol 13 (21) ◽  
pp. 4483
Author(s):  
W. Gareth Rees ◽  
Jack Tomaney ◽  
Olga Tutubalina ◽  
Vasily Zharko ◽  
Sergey Bartalev
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Boreal Forest ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Fundamental Parameter ◽  
Sensing Data ◽  
Growing Stock ◽  
Stock Volume ◽  
Sentinel 2

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data.

scholarly journals ESTIMATION OF SURFACE SNOW WETNESS USING SENTINEL-2 MULTISPECTRAL DATA

2018 ◽  
Vol IV-5 ◽  
pp. 223-228 ◽  
Author(s):  
D. Varade ◽  
O. Dikshit
Keyword(s):  
Remote Sensing ◽  
Water Resource Management ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Physical Relationship ◽  
Sensing Data ◽  
Surface Snow ◽  
Sentinel 2

<p><strong>Abstract.</strong> Snow cover characterization and estimation of snow geophysical parameters is a significant area of research in water resource management and surface hydrological processes. With advances in spaceborne remote sensing, much progress has been achieved in the qualitative and quantitative characterization of snow geophysical parameters. However, most of the methods available in the literature are based on the microwave backscatter response of snow. These methods are mostly based on the remote sensing data available from active microwave sensors. Moreover, in alpine terrains, such as in the Himalayas, due to the geometrical distortions, the missing data is significant in the active microwave remote sensing data. In this paper, we present a methodology utilizing the multispectral observations of Sentinel-2 satellite for the estimation of surface snow wetness. The proposed approach is based on the popular triangle method which is significantly utilized for the assessment of soil moisture. In this case, we develop a triangular feature space using the near infrared (NIR) reflectance and the normalized differenced snow index (NDSI). Based on the assumption that the NIR reflectance is linearly related to the liquid water content in the snow, we derive a physical relationship for the estimation of snow wetness. The modeled estimates of snow wetness from the proposed approach were compared with in-situ measurements of surface snow wetness. A high correlation determined by the coefficient of determination of 0.94 and an error of 0.535 was observed between the proposed estimates of snow wetness and in-situ measurements.</p>

scholarly journals Possibilities of milpa identification in Yucatan through remote sensing techniques and Sentinel-2 data

10.29007/hbs2 ◽  
2019 ◽  
Author(s):  
Juan Carlos Valdiviezo-Navarro ◽  
Adan Salazar-Garibay ◽  
Karla Juliana Rodríguez-Robayo ◽  
Lilián Juárez ◽  
María Elena Méndez-López ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Food Sovereignty ◽  
Region Of Interest ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Multispectral Images ◽  
Crop Fields ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Maya milpa is one of the most important agrifood systems in Mesoamerica, not only because its ancient origin but also due to lead an increase in landscape diversity and to be a relevant source of families food security and food sovereignty. Nowadays, satellite remote sensing data, as the multispectral images of Sentinel-2 platforms, permit us the monitor- ing of different kinds of structures such as water bodies, urban areas, and particularly agricultural fields. Through its multispectral signatures, mono-crop fields or homogeneous vegetation zones like corn fields, barley fields, or other ones, have been successfully detected by using classification techniques with multispectral images. However, Maya milpa is a complex field which is conformed by different kinds of vegetables species and fragments of natural vegetation that in conjunction cannot be considered as a mono-crop field. In this work, we show some preliminary studies on the availability of monitoring this complex system in a region of interest in Yucatan, through a support vector machine (SVM) approach.

scholarly journals ANALYSIS OF THE IMPACT OF OIL PRODUCT SPILLS ON NATURAL OBJECTS BASED ON SENTINEL-2 REMOTE SENSING DATA IN EASTERN SIBERIA

2021 ◽  
Vol 6 ◽  
pp. 24-31
Author(s):  
Dmitry A. Baikin
Keyword(s):  
Remote Sensing ◽  
Oil Spills ◽  
Remote Sensing Data ◽  
Oil Products ◽  
Eastern Siberia ◽  
Sensing Data ◽  
Sensing System ◽  
Natural Objects ◽  
The Impact ◽  
Sentinel 2

The article analyzes the impact of oil spills on natural objects according to the remote sensing system Sentinel-2 in Eastern Siberia. Remote sensing data analysis is used to detect traces of oil products in the accident area. Conclusions about the usage of Sentinel-2 data for detecting traces of oil products were made.

scholarly journals Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data

2021 ◽  
Vol 252 ◽  
pp. 112122 ◽  
Author(s):  
Jesús Aguirre-Gutiérrez ◽  
Sami Rifai ◽  
Alexander Shenkin ◽  
Imma Oliveras ◽  
Lisa Patrick Bentley ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Functional Traits ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Sentinel 2
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