scholarly journals A synthetic satellite dataset of <i>E. huxleyi</i> spatio-temporal distributions and their impacts on Arctic and Subarctic marine environments (1998–2016)

2018 ◽  
Author(s):  
Dmitry Kondrik ◽  
Eduard Kazakov ◽  
Dmitry Pozdnyakov
Keyword(s):  
Remote Sensing ◽  
Inorganic Carbon ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Marine Environments ◽  
Co2 Partial Pressure ◽  
In Situ Observations ◽  
Spatio Temporal ◽  
Particulate Inorganic Carbon

Abstract. A 19-year (1998–2016) continuous dataset of coccolithophore E. huxleyi distributions and activity in Arctic and Subarctic seas is presented. The dataset is based on optical remote sensing data (mostly OC CCI data) with assimilation of different relevant in-situ observations, preprocessed with authorial algorithms. Alongside with bloom locations, we also provide both detailed information on E. huxleyi impacts within the bloom area on marine environments and the subdatasets of quantified coccolith concentrations, particulate inorganic carbon content and CO2 partial pressure in water driven by coccolithophores. All data are presented on a regular 4×4 km grid at a temporal resolution of 8 days. The paper describes the theoretical and methodological basis for all processing and modeling steps. The data are available on Zenodo: https://doi.org/10.5281/zenodo.1402033.

scholarly journals A synthetic satellite dataset of the spatio-temporal distributions of <i>Emiliania huxleyi</i> blooms and their impacts on Arctic and sub-Arctic marine environments (1998–2016)

2019 ◽  
Vol 11 (1) ◽  
pp. 119-128 ◽  
Author(s):  
Dmitry Kondrik ◽  
Eduard Kazakov ◽  
Dmitry Pozdnyakov
Keyword(s):  
Carbon Balance ◽  
Remote Sensing Data ◽  
Emiliania Huxleyi ◽  
Optical Remote Sensing ◽  
Arctic Seas ◽  
Co2 Partial Pressure ◽  
In Situ Observations ◽  
Spatio Temporal ◽  
Particulate Inorganic Carbon

Abstract. A 19-year (1998–2016) continuous dataset is presented of coccolithophore Emiliania huxleyi distributions and activity, i.e. the release of CaCO3 in water and the decrease of uptake of dissolved CO2 by Emiliania huxleyi cells (e.g. Kondrik et al., 2018a), in Arctic and sub-Arctic seas. The dataset is based on optical remote-sensing data (mostly OC CCI data) with assimilation of different relevant in situ observations, preprocessed with authorial algorithms. Alongside bloom locations, we provide both detailed information on E. huxleyi impacts on carbon balance and the sub-datasets of quantified coccolith concentrations, particulate inorganic carbon content and CO2 partial pressure in water driven by coccolithophores. All data are presented on a regular 4×4 km grid at a temporal resolution of 8 days. The paper describes the theoretical and methodological basis for all processing and modelling steps. The data are available on Zenodo: https://doi.org/10.5281/zenodo.1402033.

scholarly journals Estudo do ciclo diário da Camada Limite Planetária durante a estação cChuvosa da Amazônia (GOAmazon 2014/15)

2018 ◽  
Vol 40 ◽  
pp. 63 ◽  
Author(s):  
Rayonil Gomes Carneiro ◽  
Alice Henkes ◽  
Gilberto Fisch ◽  
Camilla Kassar Borges
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Planetary Boundary Layer ◽  
Remote Sensing Data ◽  
Great Depth ◽  
Wet Season ◽  
Daily Cycle ◽  
Convective Boundary ◽  
In Situ Observations

In the present study, the evolution the diurnal cycle of planetary boundary layer in the wet season at Amazon region during a period of intense observations carried out in the GOAmazon Project 2014/2015 (Green Ocean Amazon).The analysis includes radiosonde and remote sensing data. In general case, the results of the daily cycle in the wet season indicate a Nocturnal boundary layer with a small oscillation in its depth and with a tardy erosion. The convective boundary layer did not present great depth, responding to the low values of sensible heat of the wet season. A comparison between the different techniques(in situ observations and remote sensing)  for estimating the planetary boundary layer is also presented.

Assessment of meteorological and agricultural droughts using in-situ observations and remote sensing data

2019 ◽  
Vol 222 ◽  
pp. 125-138 ◽  
Author(s):  
Depeng Zuo ◽  
Siyang Cai ◽  
Zongxue Xu ◽  
Dingzhi Peng ◽  
Guangyuan Kan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
In Situ Observations

scholarly journals Combined use of FORMOSAT-2 images with a crop model for biomass and water monitoring of permanent grassland in Mediterranean region

2010 ◽  
Vol 14 (9) ◽  
pp. 1731-1744 ◽  
Author(s):  
D. Courault ◽  
R. Hadria ◽  
F. Ruget ◽  
A. Olioso ◽  
B. Duchemin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Agricultural Practices ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Optical Remote Sensing ◽  
Agricultural Practice ◽  
Area Index ◽  
Combined Use ◽  
Spatio Temporal

Abstract. The aim of this study is to propose methods to improve crop and water management in Mediterranean regions. At landscape scale, there is a spatial variability of agricultural practices, particularly for grasslands irrigated by flooding. These grasslands are harvested three times per year and produce high quality hay, but their productions decreased significantly during the last few years because of the water scarcity. It is therefore important to assess the real water requirement for crops in order to predict productions in the case of agricultural practice modifications. Until now, the spatial variability of agricultural practices was obtained through surveys from farmers, but this method was tedious to describe an entire region. Thus, the specific aim of the study is to develop and assess a new method based on a crop model for estimating water balance and crop yield constrained by products derived from optical remote sensing data with high spatio-temporal resolution. A methodology, based on the combined use of FORMOSAT-2 images and the STICS crop model, was developed to estimate production, evapotranspiration and drainage of irrigated grasslands in "the Crau" region in the South Eastern France. Numerous surveys and ground measurements were performed during an experiment conducted in 2006. Simple algorithms were developed to retrieve the dynamic of Leaf Area Index (LAI) for each plot and the main agricultural practices such as mowing and irrigation dates. These variables computed from remote sensing were then used to parameterize STICS, applied at region scale to estimate the spatial variability of water budget associated with the biomass productions. Results are displayed at the farm scale. Satisfactory results were obtained when compared to ground measurements. The method for the extrapolation to other regions or crops is discussed as regard to data available.

scholarly journals Limitations in identifying the equilibrium-line altitude from the optical remote-sensing derived snowline in the Tien Shan, China

2014 ◽  
Vol 60 (224) ◽  
pp. 1093-1100 ◽  
Author(s):  
WU Yuwei ◽  
HE Jianqiao ◽  
GUO Zhongming ◽  
Chen Anan
Keyword(s):  
Remote Sensing ◽  
Warm Season ◽  
Northwest China ◽  
Tien Shan ◽  
Equilibrium Line ◽  
Optical Remote Sensing ◽  
Large Area ◽  
Equilibrium Line Altitude ◽  
Spatio Temporal

AbstractOptical remote-sensing derived end-of-summer snowline altitude (SLA) has long been employed on glaciers as an indicator of the equilibrium-line altitude (ELA). In the Tien Shan, northwest China, both accumulation and ablation of glaciers occur mainly in the warm season, making it difficult to obtain the representative snowline (highest snowline) in the area. The high spatio-temporal resolution of HJ-1 satellite images enables the highest snowline to be acquired. In this paper, we compare image-derived SLA and measured in situ ELA for two adjacent glaciers in the Tien Shan over the period 2009–10. Results indicate that (1) in 2009, there was a substantial difference between SLA and ELA on one glacier, suggesting inconsistent applicability in using SLA to identify ELA over a large area; and (2) in 2010, an intense ablation year, the field-data-derived ELA surpassed the glacier peak. In this situation, there is no theoretical relationship between SLA and ELA, and the image-derived snowline actually indicates the boundary between ice and firn from previous years. In summary, errors will arise from the discrepancies between individual glaciers and from intensive ablation when using SLA to identify ELA over a large area.

scholarly journals On the spatio-temporal representativeness of observations

2017 ◽  
Vol 17 (16) ◽  
pp. 9761-9780 ◽  
Author(s):  
Nick Schutgens ◽  
Svetlana Tsyro ◽  
Edward Gryspeerdt ◽  
Daisuke Goto ◽  
Natalie Weigum ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Measurement Errors ◽  
Remote Sensing Data ◽  
Temporal Sampling ◽  
Representation Error ◽  
Temporal Averaging ◽  
Carbon Mass ◽  
Spatio Temporal

Abstract. The discontinuous spatio-temporal sampling of observations has an impact when using them to construct climatologies or evaluate models. Here we provide estimates of this so-called representation error for a range of timescales and length scales (semi-annually down to sub-daily, 300 to 50 km) and show that even after substantial averaging of data significant representation errors may remain, larger than typical measurement errors. Our study considers a variety of observations: ground-site or in situ remote sensing (PM2. 5, black carbon mass or number concentrations), satellite remote sensing with imagers or lidar (extinction). We show that observational coverage (a measure of how dense the spatio-temporal sampling of the observations is) is not an effective metric to limit representation errors. Different strategies to construct monthly gridded satellite L3 data are assessed and temporal averaging of spatially aggregated observations (super-observations) is found to be the best, although it still allows for significant representation errors. However, temporal collocation of data (possible when observations are compared to model data or other observations), combined with temporal averaging, can be very effective at reducing representation errors. We also show that ground-based and wide-swath imager satellite remote sensing data give rise to similar representation errors, although their observational sampling is different. Finally, emission sources and orography can lead to representation errors that are very hard to reduce, even with substantial temporal averaging.

scholarly journals Monitoring military landscapes and detection of underground man-made critical infrastructures in Cyprus using Earth Observation

2018 ◽  
Vol 45 ◽  
pp. 335-342 ◽  
Author(s):  
George Melillos ◽  
Athos Agapiou ◽  
Silas Michaelides ◽  
Diofantos G. Hadjimitsis
Keyword(s):  
Remote Sensing ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Earth Observation ◽  
Underground Structures ◽  
Spectral Signatures ◽  
Sensing Data ◽  
Phenological Cycle ◽  
In Situ Observations

Abstract. This paper aims to explore the importance of monitoring military landscapes in Cyprus using Earth Observation. The rising availability of remote sensing data provides adequate opportunities for monitoring military landscapes and detecting underground military man-made structures. In order to study possible differences in the spectral signatures of vegetation so as to be used for the systematic monitoring of military landscapes that comprise underground military structures, field spectroscopy has been used. The detection of underground and ground military structures based on remote sensing data could make a significant contribution to defence and security science. In this paper, underground military structures over vegetated areas were monitored, using both ground and satellite remote sensing data. Several ground measurements have been carried out in military areas, throughout the phenological cycle of plant growth, during 2016–2017. The research was carried out using SVC-HR1024 ground spectroradiometers. Field spectroradiometric measurements were collected and analysed in an effort to identify underground military structures using the spectral profile of the vegetated surface overlying the underground target and the surrounding area, comprising the in situ observations. Multispectral vegetation indices were calculated in order to study their variations over the corresponding vegetation areas, in presence or absence of military underground structures. The results show that Vegetation Indices such as NDVI, SR, OSAVI, DVI and MSR are useful for determining areas where military underground structures are present.

scholarly journals Detecting the Vegetation Change Related to the Creep of 2018 Baige Landslide in Jinsha River, SE Tibet Using SPOT Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Xinyi Guo ◽  
Qing Guo ◽  
Zhongkui Feng
Keyword(s):  
Remote Sensing ◽  
Vegetation Change ◽  
Remote Sensing Data ◽  
Vegetation Coverage ◽  
High Mountain ◽  
Optical Remote Sensing ◽  
Subtle Change ◽  
Sensing Data ◽  
Significant Downward Trend ◽  
Spatio Temporal

It is vital to monitor the post-seismic landslides economically and effectively in high-mountain regions for the long term. The landslide creep could cause a subtle change of the overlying vegetation after the earthquake, which will lead to the change of vegetation spectral characteristics in optical remote sensing data. The optical remote sensing technique can be used to monitor the landslide creep areas with dense vegetation in a large range at a low cost because it is easy to obtain multi-temporal, multiple-scale, and multi-spectral information. We identified and extracted the vegetation change area before the 2018 Baige landslide by the high-resolution optical remote sensing data. Firstly, the image fusion method was used to improve the accuracy of change detection. Then, vegetation coverage before the landslide was calculated. The vegetation change was identified, and qualitative and quantitative methods were used to analyze the spatio-temporal changes of vegetation coverage. Our results indicate that the creep distance of the landslide is about 50 m and the vegetation in the back scarp area and the main sliding area display a significant downward trend with time closing to the landslide comparing with that in the reference area. The vegetation change in the remote sensing image has an excellent spatio-temporal correlation with the landslide creep. This study provides a possible way and perspective for monitoring post-seismic landslide disasters.

Assessment of meteorological and agricultural droughts using in-situ observations and remote sensing data

2020 ◽  
Author(s):  
Depeng Zuo ◽  
Siyang Cai ◽  
Zongxue Xu ◽  
Hong Yang
Keyword(s):  
Remote Sensing ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Northeast China ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Drought Assessment ◽  
Sensing Data ◽  
In Situ Observations

&lt;p&gt;Most research on drought assessment adopted historical in-situ observations, however, there has been increased data availability from remote sensing during the recent years. This study utilizes the two sources of data in drought assessment. Using the historical in-situ observations, the spatiotemporal variations of meteorological drought were firstly investigated by calculating the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) at 1, 3, 6-month time scales in Northeast China. Using remote sensing data, the combined deficit index (CDI) for agricultural drought assessment was computed based on tri-monthly sum of deficit in antecedent rainfall and deficit in monthly NDVI at land cover type and sub-type levels in the same region. In the end, the agricultural drought calculated by the CDI was evaluated against the deficit in crop yield, as well as deficit in Land Surface Temperature (LST) and Evapotranspiration (ET), in order to verify the applicability of the CDI for agricultural drought assessment in the study region. The results showed that the CDI has better correlations with the SPEI (R&lt;sup&gt;2&lt;/sup&gt;=0.48) than the SPI (R&lt;sup&gt;2&lt;/sup&gt;=0.05) at 3-month scales with weight factor a=0.5 in dry farming areas. The spatial pattern of the CDI showed that the area of agricultural drought increased from July to October. In addition, a significant linear correlation was found between the CDI and anomaly in annual agricultural yield (R&lt;sup&gt;2&lt;/sup&gt;=0.55), and anomaly in monthly land surface temperature (R&lt;sup&gt;2&lt;/sup&gt;=0.42). The results prove that the CDI calculated by remote sensing data is not only a reliable indicator for agricultural drought assessment in Northeast China, but also provides useful information for agricultural drought disaster prevention and mitigation, and water management improvement.&lt;/p&gt;

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