Spatio-temporal Variations in Drought with Remote Sensing from the Mongolian Plateau During 1982–2018

2020 ◽  
Vol 30 (6) ◽  
pp. 1081-1094
Author(s):  
Xiaoming Cao ◽  
Yiming Feng ◽  
Zhongjie Shi
Keyword(s):  
Remote Sensing ◽  
Temporal Variations ◽  
Mongolian Plateau ◽  
Spatio Temporal

scholarly journals Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2699 ◽  
Author(s):  
Jian Li ◽  
Liqiao Tian ◽  
Qingjun Song ◽  
Zhaohua Sun ◽  
Hongjing Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Lake Area ◽  
Short Term ◽  
Chl A ◽  
Spatio Temporal

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

Investigating of River-Aquifer Interactions using Sub-Surface Hydrological Model and Remote Sensing Inputs in an agriculturally Dominated Kosi River Basin, India.

2021 ◽  
Author(s):  
Naga Venkata Satish Laveti ◽  
Suresh A. Kartha ◽  
Subashisa Dutta
Keyword(s):  
Remote Sensing ◽  
Groundwater Recharge ◽  
River Basin ◽  
Hydrological Model ◽  
Temporal Variations ◽  
Interaction Process ◽  
Groundwater Levels ◽  
Physically Based ◽  
Spatio Temporal ◽  
Exchange Flux

<p>River-Aquifer Interaction is a natural and complex phenomenon for understanding its physical dynamic processes. These interactions highly vary with time and space and are to be investigated at river reach scale. The present study aims to understand and quantify the spatio-temporal variations of river-aquifer interaction process in Kosi river basin, India. This basin is majorly dominated with agricultural lands and irrigation requirement of the crops are mostly met by groundwater. In order to quantify the river-aquifer exchange flux at reach scale, a physically based sub-surface hydrological model has been carried for the study area. For this purpose, high resolution remotely sensed evapotranspiration data and groundwater recharge (estimated using soil water budget method method) along with other aquifer parameters were utilized for simulating the monthly groundwater levels as well as exchange flux between river and aquifer. The model results showed that simulated groundwater levels were well calibrated and validated with measured groundwater levels. Further, this calibrated groundwater flow model has been used to quantify the river-aquifer exchange flux. Based on the obtained exchange flux values, three different interaction zones were identified from upstream (Kosi barrage) to downstream (confluence point with Ganga river) in the study reach. It is observed that the river mostly loses water to the aquifer (as influent) in Zone I (80km from upstream) and the river mostly gains water from the aquifer (as effluent) in Zone III (40 km above downstream to confluence point). Whereas, the river has a combination of both losing and gaining natures in Zone II (between Zone I and III). From this study, it can be concluded that use of satellite remote sensing inputs (groundwater recharge and evapotranspiration) in the sub-surface hydrological model, facilitated to improve the assessment and understanding river-aquifer interaction process in an alluvial River basin.</p>

scholarly journals Remote Sensing-Based Biomass Estimation and Its Spatio-Temporal Variations in Temperate Grassland, Northern China

2014 ◽  
Vol 6 (2) ◽  
pp. 1496-1513 ◽  
Author(s):  
Yunxiang Jin ◽  
Xiuchun Yang ◽  
Jianjun Qiu ◽  
Jinya Li ◽  
Tian Gao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Northern China ◽  
Temporal Variations ◽  
Temperate Grassland ◽  
Biomass Estimation ◽  
Spatio Temporal

scholarly journals Identifying the spatio-temporal variations of Ulva prolifera disasters in all life cycle

2021 ◽  
Author(s):  
Baowei Zhang ◽  
Jianzhong Guo ◽  
Ziwei Li ◽  
Yi Cheng ◽  
Yao Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Life Cycle ◽  
Yellow Sea ◽  
Remote Sensing Data ◽  
Temporal Variations ◽  
Ulva Prolifera ◽  
The Yellow Sea ◽  
Sensing Data ◽  
Spatio Temporal ◽  
Sea Area

Abstract Since 2007, Ulva prolifera disasters have occurred every year in the South Yellow Sea of China, the largest green tide disaster in the world. The inter-annual differences make such disasters monitoring and early warning difficult. This study used remote sensing data (2015–2019) to determine its spatio-temporal variations in all life cycle. The results showed a lay effect between the NDVI-mean and the coverage area of U. prolifera. The spatio-temporal distribution of U. prolifera showed stages and regional differences. From late April to early May, U. prolifera first emerged near the Subei Shoal. After development in the middle of the Yellow Sea, U. prolifera outbroke in the eastern sea area of Shandong and Jiangsu, declined in the Shandong sea area, and disappeared near Qingdao. The cycle lasted for approximately 90 days. The sea surface temperature was the necessary condition for the disaster, and the sea wind field was the main driving force for its horizontal drift. This study overcomes the poor timing and continuity of remote sensing data in the monitoring of U. prolifera. It provides a theoretical reference for forecasting the outbreak period of U. prolifera and can aid policy-makers to avert such disasters in advance.

Evaluation of spatio-temporal variations in chlorophyll-a in Lake Naivasha, Kenya: remote-sensing approach

2013 ◽  
Vol 34 (22) ◽  
pp. 8142-8155 ◽  
Author(s):  
Jane Ndungu ◽  
Bruce C. Monger ◽  
Denie C.M. Augustijn ◽  
Suzanne J.M.H. Hulscher ◽  
Nzula Kitaka ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Lake Naivasha ◽  
Spatio Temporal

Spatio-Temporal Variations of Tropospheric Nitrogen Dioxide in South Mato Grosso Based on Remote Sensing by Satellite

2021 ◽  
Author(s):  
Amaury de Souza ◽  
Flavio Aristone ◽  
Marcel Carvalho Abreu ◽  
José Francisco de Oliveira-Júnior ◽  
Widinei Alves Fernandes ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Nitrogen Dioxide ◽  
Temporal Variations ◽  
Mato Grosso ◽  
Spatio Temporal
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