scholarly journals Spatiotemporal Dynamics of Submerged Aquatic Vegetation in a Deep Lake from Sentinel-2 Data

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 563 ◽  
Author(s):  
Nicola Ghirardi ◽  
Rossano Bolpagni ◽  
Mariano Bresciani ◽  
Giulia Valerio ◽  
Marco Pilotti ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
In Situ Measurements ◽  
Spatiotemporal Variability ◽  
Annual Variations ◽  
Bare Sediment ◽  
Sentinel 2

We mapped the extent of submerged aquatic vegetation (SAV) of Lake Iseo (Northern Italy, over the 2015–2017 period based on satellite data (Sentinel 2 A-B) and in-situ measurements; the objective was to investigate its spatiotemporal variability. We focused on the southern sector of the lake, the location of the shallowest littorals and the most developed macrophyte communities, mainly dominated by Vallisneria spiralis and Najas marina. The method made use of both in-situ measurements and satellite data (22 Sentinel 2 A-B images) that were atmospherically corrected with 6SV code and processed with the BOMBER (Bio-Optical Model-Based tool for Estimating water quality and bottom properties from Remote sensing images). This modeling system was used to estimate the different substrate coverage (bare sediment, dense stands of macrophytes with high albedo, and sparse stand of macrophytes with low albedo). The presented results substantiate the existence of striking inter- and intra-annual variations in the spatial-cover patterns of SAV. Intense uprooting phenomena were also detected, mainly affecting V. spiralis, a species generally considered a highly plastic pioneer taxon. In this context, remote sensing emerges as a very reliable tool for mapping SAV with satisfactory accuracy by offering new perspectives for expanding our comprehension of lacustrine macrophyte dynamics and overcoming some limitations associated with traditional field surveys.

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 Fractional Vegetation Cover Derived from UAV and Sentinel-2 Imagery as a Proxy for In Situ FAPAR in a Dense Mixed-Coniferous Forest?

2022 ◽  
Vol 14 (2) ◽  
pp. 380
Author(s):  
Birgitta Putzenlechner ◽  
Philip Marzahn ◽  
Philipp Koal ◽  
Arturo Sánchez-Azofeifa
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Vegetation Cover ◽  
Satellite Remote Sensing ◽  
Coniferous Forest ◽  
Spatiotemporal Variability ◽  
Fractional Vegetation Cover ◽  
Spatially Distributed ◽  
Sentinel 2

The fraction of absorbed photosynthetic active radiation (FAPAR) is an essential climate variable for assessing the productivity of ecosystems. Satellite remote sensing provides spatially distributed FAPAR products, but their accurate and efficient validation is challenging in forest environments. As the FAPAR is linked to the canopy structure, it may be approximated by the fractional vegetation cover (FCOVER) under the assumption that incoming radiation is either absorbed or passed through gaps in the canopy. With FCOVER being easier to retrieve, FAPAR validation activities could benefit from a priori information on FCOVER. Spatially distributed FCOVER is available from satellite remote sensing or can be retrieved from imagery of Unmanned Aerial Vehicles (UAVs) at a centimetric resolution. We investigated remote sensing-derived FCOVER as a proxy for in situ FAPAR in a dense mixed-coniferous forest, considering both absolute values and spatiotemporal variability. Therefore, direct FAPAR measurements, acquired with a Wireless Sensor Network, were related to FCOVER derived from UAV and Sentinel-2 (S2) imagery at different seasons. The results indicated that spatially aggregated UAV-derived FCOVER was close (RMSE = 0.02) to in situ FAPAR during the peak vegetation period when the canopy was almost closed. The S2 FCOVER product underestimated both the in situ FAPAR and UAV-derived FCOVER (RMSE > 0.3), which we attributed to the generic nature of the retrieval algorithm and the coarser resolution of the product. We concluded that UAV-derived FCOVER may be used as a proxy for direct FAPAR measurements in dense canopies. As another key finding, the spatial variability of the FCOVER consistently surpassed that of the in situ FAPAR, which was also well-reflected in the S2 FAPAR and FCOVER products. We recommend integrating this experimental finding as consistency criteria in the context of ECV quality assessments. To facilitate the FAPAR sampling activities, we further suggest assessing the spatial variability of UAV-derived FCOVER to benchmark sampling sizes for in situ FAPAR measurements. Finally, our study contributes to refining the FAPAR sampling protocols needed for the validation and improvement of FAPAR estimates in forest environments.

scholarly journals Mapping Chlorophyll-a Concentrations in the Kaštela Bay and Brač Channel Using Ridge Regression and Sentinel-2 Satellite Images

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3004
Author(s):  
Antonia Ivanda ◽  
Ljiljana Šerić ◽  
Marin Bugarić ◽  
Maja Braović
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Satellite Data ◽  
Coastal Waters ◽  
Optical Parameter ◽  
Data Set ◽  
Chl A ◽  
Sentinel 2

In this paper, we describe a method for the prediction of concentration of chlorophyll-a (Chl-a) from satellite data in the coastal waters of Kaštela Bay and the Brač Channel (our case study areas) in the Republic of Croatia. Chl-a is one of the parameters that indicates water quality and that can be measured by in situ measurements or approximated as an optical parameter with remote sensing. Remote sensing products for monitoring Chl-a are mostly based on the ocean and open sea monitoring and are not accurate for coastal waters. In this paper, we propose a method for remote sensing monitoring that is locally tailored to suit the focused area. This method is based on a data set constructed by merging Sentinel 2 Level-2A satellite data with in situ Chl-a measurements. We augmented the data set horizontally by transforming the original feature set, and vertically by adding synthesized zero measurements for locations without Chl-a. By transforming features, we were able to achieve a sophisticated model that predicts Chl-a from combinations of features representing transformed bands. Multiple Linear Regression equation was derived to calculate Chl-a concentration and evaluated quantitatively and qualitatively. Quantitative evaluation resulted in R2 scores 0.685 and 0.659 for train and test part of data set, respectively. A map of Chl-a of the case study area was generated with our model for the dates of the known incidents of algae blooms. The results that we obtained are discussed in this paper.

scholarly journals Evaluation of Surface Reflectance Products Based on Optimized 6S Model Using Synchronous In Situ Measurements

2021 ◽  
Vol 14 (1) ◽  
pp. 83
Author(s):  
Xiaocheng Zhou ◽  
Xueping Liu ◽  
Xiaoqin Wang ◽  
Guojin He ◽  
Youshui Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Atmospheric Correction ◽  
Solar Spectrum ◽  
In Situ Measurements ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Landsat 8 Oli ◽  
Auxiliary Data ◽  
Sentinel 2

Surface reflectance (SR) estimation is the most essential preprocessing step for multi-sensor remote sensing inversion of geophysical parameters. Therefore, accurate and stable atmospheric correction is particularly important, which is the premise and basis of the quantitative application of remote sensing. It can also be used to directly compare different images and sensors. The Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi-Spectral Instrument (MSI) surface reflectance products are publicly available and demonstrate high accuracy. However, there is not enough validation using synchronous spectral measurements over China’s land surface. In this study, we utilized Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric products reconstructed by Categorical Boosting (CatBoost) and 30 m ASTER Global Digital Elevation Model (ASTER GDEM) data to adjust the relevant parameters to optimize the Second Simulation of Satellite Signal in the Solar Spectrum (6S) model. The accuracy of surface reflectance products obtained from the optimized 6S model was compared with that of the original 6S model and the most commonly used Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model. Surface reflectance products were validated and evaluated with synchronous in situ measurements from 16 sites located in five provinces of China: Fujian, Gansu, Jiangxi, Hunan, and Guangdong. Through the indirect and direct validation across two sensors and three methods, it provides evidence that the synchronous measurements have the higher and more reliable validation accuracy. The results of the validation indicated that, for Landsat-8 OLI and Sentinel-2 MSI SR products, the overall root mean square error (RMSE) calculated results of optimized 6S, original 6S and FLAASH across all spectral bands were 0.0295, 0.0378, 0.0345, and 0.0313, 0.0450, 0.0380, respectively. R2 values reached 0.9513, 0.9254, 0.9316 and 0.9377, 0.8822, 0.9122 respectively. Compared with the original 6S model and FLAASH model, the mean percent absolute error (MPAE) of the optimized 6S model was reduced by 32.20% and 15.86% for Landsat-8 OLI, respectively. On the other, for the Sentinel-2 MSI SR product, the MPAE value was reduced by 33.56% and 33.32%. For the two kinds of data, the accuracy of each band was improved to varying extents by the optimized 6S model with the auxiliary data. These findings support the hypothesis that reliable auxiliary data are helpful in reducing the influence of the atmosphere on images and restoring reality as much as is feasible.

scholarly journals Regional Models for High-Resolution Retrieval of Chlorophyll a and TSM Concentrations in the Gorky Reservoir by Sentinel-2 Imagery

2019 ◽  
Vol 11 (10) ◽  
pp. 1215 ◽  
Author(s):  
Alexander A. Molkov ◽  
Sergei V. Fedorov ◽  
Vadim V. Pelevin ◽  
Elena N. Korchemkina
Keyword(s):  
Optical Properties ◽  
Chlorophyll A ◽  
Russian Federation ◽  
Freshwater Ecosystems ◽  
In Situ Measurements ◽  
Spatiotemporal Variability ◽  
Chl A ◽  
The Russian Federation ◽  
Sentinel 2

The possibilities of chlorophyll a (Chl a) and total suspended matter (TSM) retrieval using Sentinel-2/MSI imagery and in situ measurements in the Gorky Reservoir are investigated. This water body is an inland freshwater ecosystem within the territory of the Russian Federation. During the algal bloom period, the optical properties of water are extremely heterogeneous and vary on scales of tens of meters. Additionally, they vary in time under the influence of currents and wind forcing. In this case, the usage of the traditional station-based sampling to describe the state of the reservoir may be uninformative and not rational. Therefore, we proposed an original approach based on simultaneous in situ measurements of the remote sensing reflectance by a single radiometer and the concentration of water constituents by an ultraviolet fluorescence LiDAR from a high-speed gliding motorboat. This approach provided fast data collection including 4087 synchronized LiDAR and radiometric measurements with high spatial resolutions of 8 m for two hours. A part of the dataset was coincided with Sentinel-2 overpass and used for the development of regional algorithms for the retrieval of Chl a and TSM concentrations. For inland waters of the Russian Federation, such research was performed for the first time. The proposed algorithms can be used for regular environmental monitoring of the Gorky Reservoir using ship measurements or Sentinel-2 images. Additionally, they can be adapted for neighboring reservoirs, for example, for other seven reservoirs on the Volga River. Moreover, the proposed ship measurement approach can be useful in the practice of limnological monitoring of inland freshwater ecosystems with high spatiotemporal variability of the optical properties.

scholarly journals Mapping Submerged Aquatic Vegetation along the Central Vietnamese Coast Using Multi-Source Remote Sensing

2020 ◽  
Vol 9 (6) ◽  
pp. 395
Author(s):  
Tran Ngoc Khanh Ni ◽  
Hoang Cong Tin ◽  
Vo Trong Thach ◽  
Cédric Jamet ◽  
Izuru Saizen
Keyword(s):  
Remote Sensing ◽  
Coastal Area ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Marine Ecosystem ◽  
Atmospheric Effects ◽  
Landsat 8 ◽  
Distribution Maps ◽  
South Central

Submerged aquatic vegetation (SAV) in the Khanh Hoa (Vietnam) coastal area plays an important role in coastal communities and the marine ecosystem. However, SAV distribution varies widely, in terms of depth and substrate types, making it difficult to monitor using in-situ measurement. Remote sensing can help address this issue. High spatial resolution satellites, with more bands and higher radiometric sensitivity, have been launched recently, including the Vietnamese Natural Resources, Environment, and Disaster Monitoring Satellite (VNREDSat-1) (V1) sensor from Vietnam, launched in 2013. The objective of the study described here was to establish SAV distribution maps for South-Central Vietnam, particularly in the Khanh Hoa coastal area, using Sentinel-2 (S2), Landsat-8, and V1 imagery, and then to assess any changes to SAV over the last ten years, using selected historical data. The satellite top-of-atmosphere signals were initially converted to radiance, and then corrected for atmospheric effects. This treated signal was then used to classify Khanh Hoa coastal water substrates, and these classifications were evaluated using 101 in-situ measurements, collected in 2017 and 2018. The results showed that the three satellites could provide high accuracy, with Kappa coefficients above 0.84, with V1 achieving over 0.87. Our results showed that, from 2008 to 2018, SAV acreage in Khanh Hoa was reduced by 74.2%, while gains in new areas compensated for less than half of these losses. This is the first study to show the potential for using V1 and S2 data to assess the distribution status of SAV in Vietnam, and its outcomes will contribute to the conservation of SAV beds, and to the sustainable exploitation of aquatic resources in the Khanh Hoa coastal area.

Particulate matter variability in Kathmandu based on in-situ measurements, remote sensing, and reanalysis data

2021 ◽  
pp. 105623
Author(s):  
Stefan Becker ◽  
Ramesh Prasad Sapkota ◽  
Binod Pokharel ◽  
Loknath Adhikari ◽  
Rudra Prasad Pokhrel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Particulate Matter ◽  
Reanalysis Data ◽  
In Situ Measurements
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