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

During the afternoon of 5th April, 2010, a thunderstorm swept across Guwahati Airport (Lat. 26º26´, Long 91º35´) and neighborhood from northwest direction. Strong squally winds (reaching up to 49 knots) and high intensity rain (11mm in 15 minutes) were registered accompanying the storm. One person was killed by the falling tree due to squally winds and several others were injured by the event. The observed evolution of temperature, humidity, wind and pressure at Guwahati Airport, as well as the sequence of satellite and radar images, revealed the presence and movement of convective cells. An observational analysis of the event has been given in this paper. The aim of the study is to contribute to the characterization of these events by analyzing the observational information available. The diagnosis is aimed at helping forecasters to identify this kind of organized deep convective events and being able to issue timely warnings. The synoptic scenario shows warm and moist advection from the Bay of Bengal in low levels over Northeastern region of India and an upper-level north-south trough running from Sub-Himalayan West Bengal to north Orissa. This situation is known to be favorable for development of severe convection over Northeastern region of India during pre-monsoon season.

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Seasonal and annual variability in chlorophyll-a in the shelf region of the Northern Bay of Bengal using MODIS-Aqua data

Oceanological and Hydrobiological Studies ◽

10.1515/ohs-2020-0035 ◽

2020 ◽

Vol 49 (4) ◽

pp. 398-407

Author(s):

Muhammad Abdur Rouf ◽

Al-Hasan Antu ◽

Imran Noor

Keyword(s):

Satellite Data ◽

Bay Of Bengal ◽

River Discharge ◽

Monsoon Season ◽

Northeast Monsoon ◽

Discharge Data ◽

Annual Variability ◽

Northern Bay Of Bengal ◽

Shelf Region

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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