Characteristics of Plateau-Scale Precipitation in Tibet Estimated by Satellite Data during 1993 Monsoon Season

AbstractChlorophyll-a (Chl-a) concentration is an important issue in ocean ecosystem management and research. This study investigates seasonal and annual variability in Chl-a and its relationship with sea surface temperature (SST) and river discharge in the shelf region of the Northern Bay of Bengal (BoB), as well as validates satellite data against in-situ data. Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite data on Chl-a concentration and SST from 2002–2018 were used in this study. River discharge data were obtained from the Bangladesh Water Development Board (BWDB). The annual Chl-a concentration ranged from 2.08 to 2.94 mg m−3, with an average of 2.43 ± 0.24 mg m−3. The Chl-a concentration was found higher (2.21 ± 0.56 mg m−3) during the northeast monsoon (October–February) and lower (1.81 ± 1.14 mg m−3) during the pre-monsoon season (March–May). The study revealed a declining trend in Chl-a concentration from 2002 to 2018, and the rate of change was −0.0183 mg m−3 year−1. Chl-a concentration showed a weak inverse relationship with SST, both annually and seasonally, especially in the pre-monsoon season. River discharge masked the effect of SST on Chl-a variability during the southwest and northeast monsoon. A reasonable correlation (r = 0.78) was found between the MODIS-Aqua data and in-situ Chl-a observations.

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UP-SCALING PADDY YIELD AT SATELLITE-FOOTPRINT SCALE USING SATELLITE DATA IN CONJUNCTION WITH CCE DATA IN SAHIBGANJ DISTRICT, JHARKHAND

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w6-235-2019 ◽

2019 ◽

Vol XLII-3/W6 ◽

pp. 235-238 ◽

Cited By ~ 1

Author(s):

B. R. Parida ◽

A. K. Ranjan

Keyword(s):

Satellite Data ◽

Vegetation Index ◽

Monsoon Season ◽

Survey Method ◽

Yield Model ◽

Yield Data ◽

Time Saving ◽

Aquacrop Model ◽

Paddy Yield ◽

Up Scaling

<p><strong>Abstract.</strong> Agriculture plays a vital role in the economy of India as almost half of the workforce dependent on agriculture and allied activities. Rice is an important staple food and provides nutritious need for the billions of population. Mapping the spatial distribution of paddy and predicting yields at district level aggregation are crucial for food security measures. This study has utilized the time-series MODIS-based Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data in conjunction with CCE data to derive a statistical model for up-scaling paddy yield at satellite-footprint scale over Sahibganj district in Kharif (monsoon) season 2017. The CCE data were collected from ten random paddy plots. In addition, Area, Production, and Yield (APY) data were collected during harvesting period by interacting with eighty farmers belong to eight villages. The AquaCrop model was also used to simulate the paddy yield for Kharif season. The key results showed that based on the farmers-based yield data, paddy yield was observed as ~3200&thinsp;kg/hectare, whereas, NDVI and EVI-based yield models based on satellite data showed about 2,960 and 3,530 kg/hectare, respectively. Moreover, multi-regression-based yield model showed the mean yield of 3,070&thinsp;kg/hectare. With respect to farmers-level yield data, the relative deviation (RD) of yield based on NDVI data was &minus;7.5% (underestimation), while EVI was 10.31% (overestimation). The multi-regression-based yield model and AquaCrop model were underestimated by &minus;4.06 and &minus;10.16%, respectively. Thus, it can be inferred that the multi-regression-based yield was close to farmers-based survey yields. It can be concluded that the satellite databased yield prediction can be reliable with &plusmn;&thinsp;10% of RD. Nevertheless, remote sensing technology can be beneficial over traditional survey method as the satellite-based methods are cost-effective, robust, reliable, and time-saving than the traditional methods.</p>

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Thunderstorm Characteristics Over the Northeastern Region (NER) of India During the Pre-monsoon Season, 2011 Using Geosynchronous Satellite Data

Advances in Intelligent Systems and Computing - Emerging Technologies in Data Mining and Information Security ◽

10.1007/978-981-13-1498-8_26 ◽

2018 ◽

pp. 289-299

Author(s):

Sandeep Thakur ◽

Ismail Mondal ◽

Phani Bhushan Ghosh ◽

Tarun Kumar De

Keyword(s):

Satellite Data ◽

Monsoon Season ◽

Geosynchronous Satellite ◽

Northeastern Region

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Lightning in the North American Monsoon: An Exploratory Climatology

Monthly Weather Review ◽

10.1175/mwr-d-14-00363.1 ◽

2015 ◽

Vol 143 (5) ◽

pp. 1970-1977 ◽

Cited By ~ 9

Author(s):

Ronald L. Holle ◽

Martin J. Murphy

Keyword(s):

Satellite Data ◽

North American ◽

Gulf Of California ◽

Detection Efficiency ◽

Monsoon Season ◽

North American Monsoon ◽

Lightning Stroke ◽

The North ◽

Spatial Coverage ◽

Northwestern Mexico

Abstract Temporal and spatial distributions of the North American monsoon have been studied previously with rainfall and satellite data. In the current study, the monsoon is examined with lightning data from Vaisala’s Global Lightning Dataset (GLD360). GLD360 has been operating for over three years and provides sufficient data to develop an exploratory climatology with minimal spatial variation in detection efficiency and location accuracy across the North American monsoon region. About 80% of strokes detected by GLD360 are cloud to ground. This paper focuses on seasonal, monthly, and diurnal features of lightning occurrence during the monsoon season from Mazatlán north-northwest to northern Arizona and New Mexico. The goal is to describe thunderstorm frequency with a dataset that provides uniform spatial coverage at a resolution of 2–5 km and uniform temporal coverage with individual lightning events resolved to the millisecond, compared with prior studies that used hourly point rainfall or satellite data with a resolution of several kilometers. The monthly lightning stroke density over northwestern Mexico increases between May and June, as thunderstorms begin over the high terrain east of the Gulf of California. The monthly lightning stroke density over the entire region increases dramatically to a maximum in July and August. The highest stroke densities observed in Mexico approach those observed by GLD360 in subtropical and tropical regions in Africa, Central and South America, and Southeast Asia. The diurnal cycle of lightning exhibits a maximum over the highest terrain near noon, associated with daytime solar heating, a maximum near midnight along the southern coast of the Gulf, and a gradual decay toward sunrise.

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Entropy Analysis of Seasonal Groundwater Level Fluctuations for Delhi and Haridwar RegionsofIndia UsingGrace Satellite Data

Journal of Water Engineering and Management ◽

10.47884/jweam.v1i3pp26-32 ◽

2020 ◽

Vol 1 (3) ◽

Author(s):

Shekhar Singh ◽

Deepak Kumar

Keyword(s):

Water Level ◽

Satellite Data ◽

Large Scale ◽

Monsoon Season ◽

Groundwater Resources ◽

Winter Season ◽

Seasonal Fluctuation ◽

Remote Sensing Data ◽

Groundwater Level Fluctuations ◽

Groundwater Fluctuations

Groundwater monitoring and its spatio-temporal study require installation and management of ground-based observation wells on a large scale. The cost associated with such a study is generally high. An alternative to it is to use remote sensing data to manage groundwater resources in the least cost. There are only a few satellites which can provide gravity-based groundwater data. Gravity Recovery and Climate Experiment (GRACE) is a satellite which measures the change in gravity and is further used to study groundwater fluctuations. In the present study, groundwater fluctuations data (Product of GRACE satellite data) for Haridwar and Delhi region of India has been used to study the temporal and spatial variability using entropy theory. The temporal data from 2003 to 2016 has been used for both regions. The results suggested that the groundwater fluctuations are increasing in both regions of the study area. Results suggested that fluctuation of groundwater was high for the winter season of all years, but in the post-monsoon season, the fluctuation in between Delhi and Hardwar has been detected just about same Seasonal fluctuation in water level for both regions showed a maximum rise of 60 cm in water level and also maximum fall in the same range

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PREVALENCE OF PSEUDOINVERTA ORAIENSIS IN FRESH WATER FISH, CLARIAS BATRACHUS

FLORA AND FAUNA ◽

10.33451/florafauna.v25i1pp66-68 ◽

2019 ◽

Vol 25 (1) ◽

Author(s):

ADITYA NARAYAN

Keyword(s):

Fresh Water ◽

Monsoon Season ◽

Winter Season ◽

Uttar Pradesh ◽

Summer Season ◽

Clarias Batrachus ◽

Fresh Water Fish ◽

Water Fish

The present investigation deals with the prevalence of infection of cestode, Pseudoinverta oraiensis19 parasitizing Clarias batrachus from Bundelkhand Region (U.P.) India. The studies were recorded from different sampling stations of Bundelkhand region of Uttar Pradesh. For this study 360 fresh water fish, Clarias batrachus were examined. The incidence of infection, monsoon season (17.50%) followed by winter season (20.00%) whereas high in summer season (30.00%).

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