Time-series MODIS satellite and in-situ data for spatio-temporal distribution of aerosol pollution assessment over Bucharest metropolitan area

2015 ◽  
Author(s):  
Maria A. Zoran ◽  
Roxana S. Savastru ◽  
Dan M. Savastru
Keyword(s):  
Time Series ◽  
Metropolitan Area ◽  
Temporal Distribution ◽  
Pollution Assessment ◽  
In Situ Data ◽  
Aerosol Pollution ◽  
Spatio Temporal

Validation studies of EUMETSAT's active fire monitoring product over Turkey

2009 ◽  
Vol 18 (5) ◽  
pp. 517 ◽  
Author(s):  
Ahmet Emre Tekeli ◽  
İbrahim Sönmez ◽  
Erdem Erdi ◽  
Fatih Demir
Keyword(s):  
Temporal Distribution ◽  
Fire Detection ◽  
Burnt Area ◽  
Pixel Resolution ◽  
European Organization ◽  
Fire Monitoring ◽  
In Situ Data ◽  
Active Fire ◽  
Spatio Temporal

Fire detection and monitoring are challenging tasks that require continuous, early and quick responses that are as accurate as possible. Satellite-based systems are indispensable tools for operational and research agencies to accomplish such a demanding task. The frequent and continuous imagery capability of the geostationary satellites makes them the best candidate for early fire detection systems. The main purpose of the present paper is to analyze the spatio-temporal distribution of active fire monitoring (FIR) products of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT)’s Meteosat Second Generation (MSG) satellite with in situ data for the summer of 2006 over Turkey. In situ data were obtained from the fire reports of the Ministry of Environment and Forestry, Turkey. The main shortcomings of the MSG active fire monitoring product validation arise from the pixel resolution and fire coverage, which are examined on the basis of some recent examples. The diurnal cycle of active fires identified well with the product. The burnt area effects on the accuracy of hit ratios were also analyzed. It is seen that the possibility for the fire to be detected by MSG increases with increasing burnt area. Even with the present anomalies, remote sensing may provide a consistent systematic way of monitoring fires, removing human biases and enabling a long-term dataset, which has been a goal of Global Observation of Forest and Land Cover Dynamics (GOFC/GOLD).

scholarly journals CONSTRUCTION AND ANALYSIS OF LONG-TERM SURFACE TEMPERATURE DATASET IN FUJIAN PROVINCE

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1223-1227 ◽  
Author(s):  
W. E. Li ◽  
X. Q. Wang ◽  
H. Su
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface ◽  
Rapid Development ◽  
Temporal Distribution ◽  
Heat Fluxes ◽  
Urban Region ◽  
Fujian Province ◽  
Spatio Temporal

Land surface temperature (LST) is a key parameter of land surface physical processes on global and regional scales, linking the heat fluxes and interactions between the ground and atmosphere. Based on MODIS 8-day LST products (MOD11A2) from the split-window algorithms, we constructed and obtained the monthly and annual LST dataset of Fujian Province from 2000 to 2015. Then, we analyzed the monthly and yearly time series LST data and further investigated the LST distribution and its evolution features. The average LST of Fujian Province reached the highest in July, while the lowest in January. The monthly and annual LST time series present a significantly periodic features (annual and interannual) from 2000 to 2015. The spatial distribution showed that the LST in North and West was lower than South and East in Fujian Province. With the rapid development and urbanization of the coastal area in Fujian Province, the LST in coastal urban region was significantly higher than that in mountainous rural region. The LST distributions might affected by the climate, topography and land cover types. The spatio-temporal distribution characteristics of LST could provide good references for the agricultural layout and environment monitoring in Fujian Province.

The limitations of in situ data for validating satellite-derived spatio-temporal ocean products.

2021 ◽  
Author(s):  
Rory Scarrott ◽  
Fiona Cawkwell ◽  
Mark Jessopp ◽  
Caroline Cusack
Keyword(s):  
Surface Temperature ◽  
Surface Heterogeneity ◽  
Ocean Surface ◽  
Bias Reduction ◽  
Data Validation ◽  
Near Surface ◽  
In Situ Data ◽  
Spatio Temporal ◽  
Image Pixels

<p>The Ocean-surface Heterogeneity MApping (OHMA) algorithm is an objective, replicable approach to map the spatio-temporal heterogeneity of ocean surface waters. It is used to processes hypertemporal, satellite-derived data and produces a single-image surface heterogeneity (SH) dataset for the selected parameter of interest. The product separates regions of dissimilar temporal characteristics. Data validation is challenging because it requires In-situ observations at spatial and temporal resolutions comparable to the hyper-temporal inputs. Validating this spatio-temporal product highlighted the need to optimise existing vessel-based data collection efforts, to maximise exploitation of the rapidly-growing hyper-temporal data resource.</p><p>For this study, the SH was created using hyper-temporal 1km resolution satellite derived Sea Surface Temperature (SST) data acquired in 2011. Underway ship observations of near surface temperature collected on multiple scientific surveys off the Irish coast in 2011 were used to validate the results. The most suitable underway ship SST data were identified in ocean areas sampled multiple times and with representative measurements across all seasons.</p><p>A 3-stage bias reduction approach was applied to identify suitable ocean areas. The first bias reduction addressed temporal bias, i.e., the temporal spread of available In-situ ship transect data across each satellite image pixel. Only pixels for which In-situ data were obtained at least once in each season were selected; resulting in 14 SH image pixels deemed suitable out of a total of 3,677 SH image pixels with available In-situ data. The second bias reduction addressed spatial bias, to reduce the influence of over-sampled areas in an image pixel with a sub-pixel approach. Statistical dispersion measures and statistical shape measures were calculated for each of the sets of sub-pixel values. This gave heterogeneity estimates for each cruise transit of a pixel area. The third bias reduction addressed bias of temporally oversampled seasons. For each transit-derived heterogeneity measure, the values within each season were averaged, before the annual average value was derived across all four seasons in 2011.</p><p>Significant associations were identified between satellite SST-derived SH values, and In-situ heterogeneity measures related to shape; absolute skewness (Spearman’s Rank, n=14, ρ[12]= +0.5755, P<0.05), and kurtosis (Spearman’s Rank, n=14, ρ[12] = 0.5446, P < 0.05). These are a consequence of (i) locally-extreme measurements, and/or (ii) increased presence of sharp transitions detected spatially by In-situ data. However, the number and location of suitable In-situ validation sites precluded a robust validation of the SH dataset (14 pixels located in Irish waters, for a dataset spanning the North Atlantic). This requires more targeted data. The approach would have benefited from more samples over the winter season, which would have enabled more offshore validation sites to be incorporated into the analysis. This is a challenge that faces satellite product developers, who want to deliver spatio-temporal information to new markets. There is a significant opportunity for dedicated, transit-measured (e.g. Ferry box data), validation sites to be established. These could potentially synergise with key nodes in global shipping routes to maximise data collected by vessels of opportunity, and ensure consistent data are collected over the same area at regular intervals.</p>

scholarly journals Time Series MODIS and in Situ Data Analysis for Mongolia Drought

2016 ◽  
Vol 8 (6) ◽  
pp. 509 ◽  
Author(s):  
Munkhzul Dorjsuren ◽  
Yuei-An Liou ◽  
Chi-Han Cheng
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
In Situ Data

scholarly journals Prototype Spatio-temporal Predictive System of pest development of the codling moth, Cydia pomonella, in Kazakhstan

2020 ◽  
Vol 13 (1) ◽  
pp. 1-12
Author(s):  
A. Afonin ◽  
B. Kopzhassarov ◽  
E. Milyutina ◽  
E. Kazakov ◽  
A. Sarbassova ◽  
...  
Keyword(s):  
Land Surface ◽  
Cydia Pomonella ◽  
Codling Moth ◽  
Moth Population ◽  
Development Stages ◽  
In Situ Data ◽  
Weather Stations ◽  
Spatio Temporal ◽  
Accumulated Degree Days

SummaryA prototype for pest development stages forecasting is developed in Kazakhstan exploiting data from the geoinformation technologies and using codling moth as a model pest in apples. The basic methodology involved operational thermal map retrieving based on MODIS land surface temperature products and weather stations data, their recalculation into accumulated degree days maps and then into maps of the phases of the codling moth population dynamics. The validation of the predicted dates of the development stages according to the in-situ data gathered in the apple orchards showed a good predictivity of the forecast maps. Predictivity of the prototype can be improved by using daily satellite sensor datasets and their calibration with data received from a network of weather stations installed in the orchards.

scholarly journals An update to the traditional water mass (trans)formation framework.

2021 ◽  
Author(s):  
Aqeel Piracha ◽  
Antonio Turiel ◽  
Estrella Olmedo ◽  
Marcos Portabella
Keyword(s):  
Water Mass ◽  
Deep Convection ◽  
Sea Surface ◽  
Future Directions ◽  
In Situ Data ◽  
Water Mass Formation ◽  
Alternative Approach ◽  
Spatio Temporal ◽  
Ocean Convection

<p>Traditional estimates of convection/water mass formation at the sea surface rely on measurements of air-sea fluxes of heat and freshwater<br>(evaporation minus precipitation), that are estimated by combining in-situ data with meteorological modelisation. Satellite-based estimates of ocean convection are thus largely impacted by the relatively high uncertainties and low space-time resolution of those fluxes. However, direct satellite measurements of the ocean surface offer a unique opportunity to study convection (upwelling, downwelling) events with unprecedented spatio-temporal resolution compared to in-situ measurements. In this work, we propose an alternative approach to the traditional framework for estimating ocean convection using satellites. Instead of combining high-resolution ocean data of sea surface temperature and salinity with the much less precise, less resolved air-sea interaction data, we estimate the air-sea fluxes by computing the material derivatives (using satellite ocean currents) of the satellite sea surface variables. We therefore obtain estimates at the same resolution of the satellite products, and with much better accuracy than what was estimated before. We present some examples of application in the Atlantic ocean and in the Mediterranean sea. Future directions of this work is the study of the seasonal and interannual variability of ocean convection, and the potential changes on deep convection associated to climate variability at different time scales.</p>

Precipitation diurnal cycle and associated valley wind circulations over an Andean glacier region (Antizana, Ecuador)

2020 ◽  
Author(s):  
Clementine Junquas ◽  
Maria Belen Heredia ◽  
Thomas Condom ◽  
Jhan Carlo Espinoza ◽  
Jean Carlos Ruiz ◽  
...  
Keyword(s):  
Diurnal Cycle ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
Temporal Distribution ◽  
Tropical Andes ◽  
Time Step ◽  
Valley Wind ◽  
Spatio Temporal

<p>In the tropical Andes, the evolution of the mass balance of glaciers is strongly controlled by the variability of precipitation and humidity transport. It is therefore crucial to better understand the main patterns of precipitation in terms of spatio-temporal distribution at the local scale. In this study, we focus on the region of the Antizana ice cap, located in the Equatorial Andes about 50 km east of the city of Quito (Ecuador). In addition, the Antizana region is located in a very complex zonal climate gradient, with the Pacific Ocean to the west and the humid Amazonian plains to the east, including an area of ​​maximum precipitation on the Amazonian slope, also called "Precipitation hotspot".</p><p>In this study, we perform dynamical downscaling using a Regional Climate Model (RCM) to improve the understanding of the atmospheric processes controlling the spatio-temporal variability of precipitation. The WRF (Weather Research and Forecasting) model is used to perform a set of ten experiments with four one-way nesting domains (27km, 9km, 3km, 1km), with the highest resolution domain centered on the Antizana mountain, for the year 2005. For the model validation, we use the 3B42 satellite product of the Tropical Rainfall Measuring Mission (TRMM) at 3-hourly time step, the ORE Antizana meteorological station (SNO GLACIOCLIM, LMI GREATICE) at hourly time step, and 2 meteorological in-situ stations, installed by the Instituto Nacional de Metereología e Hidrologia (INAMHI) in the Antizana region, with a complete chronology of daily precipitation (mm/day) during the 2005 year.</p><p>We test different forcings of DEM (Digital Elevation Model), microphysic schemes, Cumulus schemes and convection-permitting simulation, and radiation/slope dependent options. The analysis focuses in particular on how the different representation of thermally driven valley wind circulation can affect the diurnal cycle of precipitation at the ORE Antizana in-situ station. The influence of the diurnal cycle of the regional humidity flux on the mountain precipitation is also analyzed.</p>

scholarly journals An improved perspective in the spatial representation of soil moisture: potential added value of SMOS disaggregated 1 km resolution “all weather” product

2019 ◽  
Vol 23 (1) ◽  
pp. 255-275 ◽  
Author(s):  
Samiro Khodayar ◽  
Amparo Coll ◽  
Ernesto Lopez-Baeza
Keyword(s):  
Soil Moisture ◽  
Initial Conditions ◽  
Temporal Distribution ◽  
Added Value ◽  
Point Scale ◽  
Spatio Temporal ◽  
Analysis System ◽  
June July ◽  
Svat Model

Abstract. This study uses the synergy of multi-resolution soil moisture (SM) satellite estimates from the Soil Moisture Ocean Salinity (SMOS) mission, a dense network of ground-based SM measurements, and a soil–vegetation–atmosphere transfer (SVAT) model, SURFEX (externalized surface), module ISBA (interactions between soil, biosphere and atmosphere), to examine the benefits of the SMOS level 4 (SMOS-L4) version 3.0, or “all weather” high-resolution soil moisture disaggregated product (SMOS-L43.0; ∼1 km). The added value compared to SMOS level 3 (SMOS-L3; ∼25 km) and SMOS level 2 (SMOS-L2; ∼15 km) is investigated. In situ SM observations over the Valencia anchor station (VAS; SMOS calibration and validation – Cal/Val – site in Europe) are used for comparison. The SURFEX (ISBA) model is used to simulate point-scale surface SM (SSM) and, in combination with high-quality atmospheric information data, namely from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Système d'analyse fournissant des renseignements atmosphériques à la neige (SAFRAN) meteorological analysis system, to obtain a representative SSM mapping over the VAS. The sensitivity to realistic initialization with SMOS-L43.0 is assessed to simulate the spatial and temporal distribution of SSM. Results demonstrate the following: (a) All SMOS products correctly capture the temporal patterns, but the spatial patterns are not accurately reproduced by the coarser resolutions, probably in relation to the contrast with point-scale in situ measurements. (b) The potential of the SMOS-L43.0 product is pointed out to adequately characterize SM spatio-temporal variability, reflecting patterns consistent with intensive point-scale SSM samples on a daily timescale. The restricted temporal availability of this product dictated by the revisit period of the SMOS satellite compromises the averaged SSM representation for longer periods than a day. (c) A seasonal analysis points out improved consistency during December–January–February and September–October–November, in contrast to significantly worse correlations in March–April–May (in relation to the growing vegetation) and June–July–August (in relation to low SSM values < 0.1 m3 m−3 and low spatial variability). (d) The combined use of the SURFEX (ISBA) SVAT model with the SAFRAN system, initialized with SMOS-L43.0 1 km disaggregated data, is proven to be a suitable tool for producing regional SM maps with high accuracy, which could be used as initial conditions for model simulations, flood forecasting, crop monitoring and crop development strategies, among others.

A semi-automated approach for the generation of a new land use and land cover product for Germany based on Landsat time-series and Lucas in-situ data

2016 ◽  
Vol 8 (3) ◽  
pp. 244-253 ◽  
Author(s):  
Benjamin Mack ◽  
Patrick Leinenkugel ◽  
Claudia Kuenzer ◽  
Stefan Dech
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
In Situ Data
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