Suchergebnisse

Eight months (June 2011 to January 2012) of aerosol property data were obtained at the remote site of Alborán Island (35.95°N, 3.03°W) in the western Mediterranean basin. The aim of this work is to assess the aerosol properties according to air mass origin and transport over this remote station with a special focus on air mass transport from North Africa. For air masses coming from North Africa, different aerosol properties showed strong contributions from mineral dust lifted from desert areas. Nevertheless, during these desert dust intrusions, some atmospheric aerosol properties are clearly different from pure mineral dust particles. Thus, Angström exponent α(440–870) presents larger values than those reported for pure desert dust measured close to dust source regions. These results combine with α(440, 670) − α(670, 870) ≥ 0.1 and low single scattering albedo (ω(λ)) values, especially at the largest wavelengths. Most of the desert dust intrusions over Alborán can be described as a mixture of dust and anthropogenic particles. The analyses support that our results apply to North Africa desert dust air masses transported from different source areas. Therefore, our results indicate a significant contribution of fine absorbing particles during desert dust intrusions over Alborán arriving from different source regions. The aerosol optical depth data retrieved from Sun photometer measurements have been used to check Moderate Resolution Imaging Spectroradiometer retrievals, and they show reasonable agreement, especially for North African air masses. ; This work was supported by the Andalusia Regional Government through projects P12-RNM-2409 and P10-RNM-6299, by the Spanish Ministry of Science and Technology through projects CGL2010-18782 and CGL2013-45410-R, by the EU through ACTRIS project (EU INFRA-2010-1.1.16-262254), and by the University of Granada through the contract "Plan Propio. Programa 9. Convocatoria 2013." CIMEL Calibration was performed at the AERONET-EUROPE calibration center (http://aeronet.gsfc.nasa.gov), supported by ACTRIS (European Union Seventh Framework Program (FP7/2007-2013) under grant agreement 262254). Granados-Muñoz was funded under grant AP2009-0552. ALFA database computation was partly supported by RES (Spanish Supercomputation Network) computing resources (projects AECT-2009-1-0012 and AECT-2011-3-0016).

Cambodia is one of the most flood-prone countries in Southeast Asia. It is geographically situated in the downstream region of the Mekong River with a lowland floodplain in the middle, surrounded by plateaus and high mountains. It usually experiences devastating floods induced by an overwhelming concentration of rainfall water over the Tonle Sap Lake's and Mekong River's banks during monsoon seasons. Flood damage assessment in the rice ecosystem plays an important role in this region as local residents rely heavily on agricultural production. This study introduced an object-based approach to flood mapping and affected rice field estimation in central Cambodia. In this approach, image segmentation processing was conducted with optimal scale parameter estimation based on the variation of objects' local variances. The inundated area was identified by using Landsat 8 images with an overall accuracy of higher than 95% compared to those derived from finer spatial resolution images. Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index products were utilized to identify the paddy rice field based on seasonal inter-variation between vegetation and water index during the transplanting stage. The rice classification result was well correlated with the statistical data at a commune level (R2 = 0.675). The flood mapping and affected rice estimation results are useful to provide local governments with valuable information for flooding mitigation and post-flooding compensation and restoration.

We examine satellite-derived aerosol optical depth (AOD) data during the period 2000&ndash ; 2018 over the Middle East to evaluate the contribution of anthropogenic pollution. We focus on Iraq, where US troops were present for nearly nine years. We begin with a plausibility argument linking anthropogenic influence and AOD signature. We then calculate the percent change in AOD every two years. To pinpoint the causes for changes in AOD on a spatial basis, we distinguish between synoptically &ldquo ; calm&rdquo ; periods and those with vigorous synoptic activity. This was done on high-resolution 10 km AOD retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (Terra satellite). We found spatiotemporal variability in the intensity of the AOD and its standard deviation along the dust-storm corridor during three studied periods: before Operation Iraqi Freedom (OIF) (1 March 2000&ndash ; 19 March 2003), during OIF (20 March 2003&ndash ; 1 September 2010), and Operation New Dawn (OND ; 1 September 2010&ndash ; 18 December 2011), and after the US troops&rsquo ; withdrawal (19 December 2011&ndash ; 31 December 2018). Pixels of military camps and bases, major roads and areas of conflict, and their corresponding AOD values, were selected to study possible effects. We found that winter, with its higher frequency of days with synoptically &ldquo ; calm&rdquo ; conditions compared to spring and summer, was the best season to quantitatively estimate the impact of these ground-based sources. Surprisingly, an anthropogenic impact on the AOD signature was also visible during vigorous synoptic activity. Meteorological conditions that favor detection of these effects using space imagery are discussed, where the effects are more salient than in surrounding regions with similar meteorological conditions. This exceeds expectations when considering synoptic variations alone.

Knowledge of the agricultural calendar of crops is essential to better estimate and forecast the cultivation of large-scale crops. The aim of this study was to estimate sowing date (SD), date of maximum vegetative development (DMVD), and harvest date (HD) of soybean and corn in the state of Paraná, Brazil. Dates from 120 farms and the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2011 to 2014 were used into a seasonal trend analysis to obtain soybean and corn seasonal patterns. The results indicate that the majority soybean is sown during October and the DMVD occurs between the second ten-day period of December and the first ten-day period of January. Owing to the spatial variability of the SD, the difference in the maturation cycles of the cultivars, and regional climatic variation, the HD of soybean varied greatly during the studied crop years, ranging from mid-February to late March. The SD of corn is before that of soybean, and mainly occurs in late September to mid-October. The DMVD mainly occurs during December, and the HD is distributed throughout January to March in Paraná. When comparing the estimated dates with observed dates the mean error (ME) varied from 0.2 days earlier to 3.3 days after the observed date for soybean with root mean square error (RMSE) from 1.93 to 14.73 days. For corn, the ME varied from 10.3 days to 18.5 days after the observed date with RMSE from 18.02 to 27.82 days.

We examine satellite-derived aerosol optical depth (AOD) data during the period 2000-2018 over the Middle East to evaluate the contribution of anthropogenic pollution. We focus on Iraq, where US troops were present for nearly nine years. We begin with a plausibility argument linking anthropogenic influence and AOD signature. We then calculate the percent change in AOD every two years. To pinpoint the causes for changes in AOD on a spatial basis, we distinguish between synoptically "calm" periods and those with vigorous synoptic activity. This was done on high-resolution 10 km AOD retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (Terra satellite). We found spatiotemporal variability in the intensity of the AOD and its standard deviation along the dust-storm corridor during three studied periods: before Operation Iraqi Freedom (OIF) (1 March 2000-19 March 2003), during OIF (20 March 2003-1 September 2010), and Operation New Dawn (OND; 1 September 2010-18 December 2011), and after the US troops' withdrawal (19 December 2011-31 December 2018). Pixels of military camps and bases, major roads and areas of conflict, and their corresponding AOD values, were selected to study possible effects. We found that winter, with its higher frequency of days with synoptically "calm" conditions compared to spring and summer, was the best season to quantitatively estimate the impact of these ground-based sources. Surprisingly, an anthropogenic impact on the AOD signature was also visible during vigorous synoptic activity. Meteorological conditions that favor detection of these effects using space imagery are discussed, where the effects are more salient than in surrounding regions with similar meteorological conditions. This exceeds expectations when considering synoptic variations alone.

In the last two decades, the international community has given great attention to the issues of deforestation and degradation. In Indonesia, these issues had been a very critical as they were related to the Indonesian government's commitment in reducing greenhouse gases by 2020 through the Reducing Emission from Deforestation and forest Degradation (REDD) mechanism. This paper describes the use of low resolution satellite imagery, i.e., MODIS (Moderate Resolution Imaging Spectroradiometer) for monitoring deforestation in Sumatra during the period of 2000-2012. The main objective of the study was to derive rapid forest and land cover change information from low resolution imageries in Sumatra between 2000 - 2012. This study used level 2 Terra MODIS (MOD13Q1) imageries, acquired in 2000, 2006 and 2012 as the main data source, where the 16-day composite imageries were derived from NASA

The detection and estimation of weathering conditions have become a very important daily necessity in our life. For this purpose, several satellites of low resolution imagery were launched by the weathering and environmental agencies. The important weather paremeters are temperuter, wind direction, velocity, clould and humidity, etc. The low resolution images often deal with large-scale phenomena and the interpretation and projection of the produced data requires continuous development of tools and criteria. In this paper, the low spatial resolution data generated by the moderate resolution imaging spectroradiometer (MODIS) were used to monitor the cloud density and direction above Iraq and its neighboring countries for the period from 14-12-2021 to 23-12-2021. The MODIS reflectance and surface temperature (HDF format) data were used and processed to achieve the purpose of this research. The reflectance data were projected according to a sinusoidal system due to the presence of three Earth zones distributions (37, 38, and 39, N). The cloud density was estimated from the digital values and the wind direction was estimated from the bands animation. The direction of wind for the cloudy condition was from the North-East. The digital data processing included data preparation and selection, georeference (adding a projection system), features extraction, and displaying the final results. All data were evaluated using ENVI (Environment for Visualizing Images, ver. 4.5.)

Rice is the most important food crop. With the largest rain-fed lowland area in the world, flooding is considered as the most important abiotic stress to rice production in India. With climate change, it is expected that the frequency and severity of the floods will increase over the years. These changes will have a severe impact on the rain-fed agriculture production and livelihoods of millions of farmers in the flood affected region. There are numerous flood risk adaptation and mitigation options available for rain-fed agriculture in India. Procuring, maintaining and distributing the newly developed submergence-tolerant rice variety called Swarna-Sub1 could play an important role in minimizing the effect of flood on rice production. This paper assesses the quantity and cost of a flood-tolerant rice seed variety- Swarna-Sub1, that would be required during the main cropping season of rice i.e., kharif at a district level for 17 major Indian states. The need for SS1 seeds for rice production was assessed by developing a geospatial framework using remote sensing to map the suitability of SS1, to help stakeholders prepare better in managing the flood risks. Results indicate that districts of Bihar, West Bengal and Uttar Pradesh will require the highest amount of SS1 seeds for flood adaptation strategies. The total estimated seed requirement for these 17 states would cost around 370 crores INR, less than 0.01 percent of Indian central government's budget allocation for agriculture sector.

This is the publisher's version, also available electronically from http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0037-86822014000100057&lng=en&nrm=iso&tlng=en ; Introduction: In past decades, leishmaniasis burden has been low across Egypt; however, changing environment and land use has placed several parts of the country at risk. As a consequence, leishmaniasis has become a particularly difficult health problem, both for local inhabitants and for multinational military personnel. Methods: To evaluate coarse-resolution aspects of the ecology of leishmaniasis transmission, collection records for sandflies and Leishmania species were obtained from diverse sources. To characterize environmental variation across the country, we used multitemporal Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) for 2005-2011. Ecological niche models were generated using MaxEnt, and results were analyzed using background similarity tests to assess whether associations among vectors and parasites (i.e., niche similarity) can be detected across broad geographic regions. Results: We found niche similarity only between one vector species and its corresponding parasite species (i.e., Phlebotomus papatasi with Leishmania major), suggesting that geographic ranges of zoonotic cutaneous leishmaniasis and its potential vector may overlap, but under distinct environmental associations. Other associations (e.g., P. sergenti with L. major) were not supported. Mapping suitable areas for each species suggested that northeastern Egypt is particularly at risk because both parasites have potential to circulate. Conclusions: Ecological niche modeling approaches can be used as a first-pass assessment of vector-parasite interactions, offering useful insights into constraints on the geography of transmission patterns of leishmaniasis

Irrigation expansion is a critical development intervention to address food security challenges in Ethiopia. However, only a fraction of the country's irrigation potential has been utilized so far. Information about the location and spatial extent of irrigated and rainfed areas is an important requirement for sustainable water resources development and agricultural planning. Currently, considerable variations exist in the irrigated area estimates made by different government agencies. In addition, irrigated area maps created as part of global mapping efforts have a spatial resolution of anywhere between 10 kilometers and 250 meters, making them too coarse for planning and management at a subnational scale. This study aims to develop an irrigated area map of Ethiopia using satellite images to support agricultural water management practices in the country, using multi-temporal, multi-resolution data sets from 2015 to 2016 with a spatial resolution of 30 m. The total area of croplands was estimated as 21.8 million hectares (Mha), of which only 1.11 Mha were mapped as the irrigated area. This is only around 5% of the estimated total agricultural area. The accuracy of the results was evaluated using geographic coordinates of irrigated areas provided by the Ethiopian Ministry of Agriculture. The results confirmed that irrigated areas can be identified reasonably well by analyzing seasonal trends in vegetation and moisture levels.

O pantanal brasileiro está presente nos estados de Mato Grosso e Mato Grosso do Sul que possui diversidade florística e animal, é a maior planície alagada do mundo. A preservação no bioma é de suma importância para manter e progredir a diversidade do local, perante isso, a Reserva Particular do Patrimônio Natural (RPPN) no bioma pantanal tem o objetivo de preservar localidades que a evapotranspiração está sendo mais intensa com diferente espaço de tempo. A presente pesquisa apresenta por meio de resultados captados pelo sensoriamento remoto a respeito pelo índice de vegetação da diferença normalizada, chamado de NDVI, esse índice quantifica e qualifica o resultado da vegetação na região. Para o método de mapeamento da área foi utilizado o sensoriamento remoto, o satélite utilizado foi o TERRA/AQUA, e para determinação dos cálculos foi usada softwares de GIS (Sistema de Informação Geográfica) onde abrange sensores orbitais necessárias para a pesquisa. O sensor do satélite MOD16 contribuí com algoritmo de entrada de Índice de Área Foliar (LAI), fração de Radiação Fotossinteticamente (fPAR) e abrangendo albedo de classificação de uso da terra oriundos do sensor MODIS coleção 5 sendo séries temporais com intervalo de 8 e 16 dias do MODIS (Moderate Resolution Imaging Spectroradiometer).

Palavras-chave: Pantanal. NDVI. MOD16. MODIS. RPPN.

Abstract
The wetland in Brazilian is present in the states of Mato Grosso and Mato Grosso do Sul that has floristic and animal diversity, it is the largest flooded plain in the world. Preservation in the biome is of paramount importance to maintain and progress the diversity of the place, in view of this, the Private Natural Heritage Reserve (RPPN) in the wetland biome that aims to preserve locations where evapotranspiration is being more intense with different spaces. of time. The present research presents, through results captured by remote sensing, regarding the normalized difference vegetation index, called NDVI, this index quantifies and qualifies the result of vegetation in the region. Remote sensing was used for the area mapping method, the satellite used was TERRA/AQUA, and GIS software (Geographic Information System) was used to determine the calculations, covering orbital sensors necessary for the research. The MOD16 satellite sensor contributes with an input algorithm of Leaf Area Index (LAI), Photosynthetically Radiation Fraction (fPAR) and covering land use classification albedo from the MODIS sensor collection 5 being time series with interval of 8 and 16 days of the MODIS (Moderate Resolution Imaging Spectroradiometer).

Keywords: Pantanal. NDVI. MOD16. MODIS. RPPN.

One of the challenges in studying desert dust aerosol along with its numerous interactions and impacts is the paucity of direct in-situ measurements, particularly in the areas most affected by dust storms. Satellites typically provide columnintegrated aerosol measurements, but observationally-constrained continuous 3D dust fields are needed to assess dust variability, climate effects and impacts upon a variety of socio-economic sectors. Here, we present a high resolution regional reanalysis data set of desert dust aerosols that covers Northern Africa, the Middle East and Europe along with the Mediterranean sea and parts of Central Asia, and the Atlantic and Indian Oceans between 2007 and 2016. The horizontal resolution is 0.1° latitude × 0.1° longitude, and the temporal resolution is 3 hours. The reanalysis was produced using Local Ensemble Transform Kalman Filter (LETKF) data assimilation in the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH) developed at the Barcelona Supercomputing Center (BSC). The assimilated data are coarse-mode dust optical depth retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue Level 2 products. The reanalysis data set consists of upper air (dust mass concentrations and extinction coefficient), surface (dust deposition and solar irradiance fields, among them) and total column (e.g., dust optical depth and load) variables. Some dust variables, such as concentrations and wet and dry deposition, are expressed for a binned size distribution that ranges from 0.2 to 20 μm in particle diameter. Both analysis and first-guess (analysis-initialized simulation) fields are available for the variables that are diagnosed from the state vector. ; We acknowledge the DustClim project which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union's Horizon 2020 research and innovation programme (Grant n. 690462).