Dr. Vinay K. Dadhwal Profile

Dr. Vinay K. Dadhwal

Director at National Remote Sensing Ctr

SPIE Involvement:

Author

SPIE Journal Paper | 6 March 2023

Spectrum recovery from multispectral ocean color data using system spectral shape factor

Desu Rama Mohana Samudraiah, Prakash Chauhan, Arumugam Senthil Kumar, Krishnamurthy V. N. Yelisety, Sandip Paul, Alur Seelin Kiran Kumar

JARS, Vol. 17, Issue 01, 017501, (March 2023) https://doi.org/10.1117/12.10.1117/1.JRS.17.017501

KEYWORDS: Sensors, Environmental monitoring, Remote sensing, Convolution, Data modeling, Water, Spectroscopy, Spectral response, Reflectivity, Interpolation

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Proceedings Article | 7 May 2016 Paper

High-resolution shipboard measurements of phytoplankton: a way forward for enhancing the utility of satellite SST and chlorophyll for mapping microscale features and frontal zones in coastal waters

Christy Jenkins, Joaquim Goes, Kali McKee, Helga do R. Gomes, Robert Arnone, Menghua Wang, Michael Ondrusek, P. Nagamani, T. Preethi Latha, K. Rao, V. Dadhwal

Proceedings Volume 9878, 98780U (2016) https://doi.org/10.1117/12.2225875

KEYWORDS: Satellites, Pulmonary function tests, Carbon, Remote sensing, Algorithm development, Data modeling, Luminescence, Ecosystems, Particles, Satellite communications

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Coastal eddies, frontal zones and microscale oceanographic features are now easily observable from satellite measurements of SST and Chl a. Enhancing the utility of these space-borne measurements for biological productivity, biogeochemical cycling and fisheries investigations will require novel bio-optical methods capable of providing information on the community structure, biomass and photo-physiology of phytoplankton associated on spatial scales that match these features. This study showcases high-resolution in-situ measurements of sea water hydrography (SeaBird CTD®), CDOM (WetLabs ALF®), phytoplankton functional types (PFTs, FlowCAM®), biomass (bbe Moldaenke AlgaeOnlineAnalyzer® and WetLabs ALF®) and phytoplankton photosynthetic competency (mini-FIRe) across microscale features encountered during a recent (Nov. 2014) cruise in support of NOAA's VIIRS ocean color satellite calibration and validation activities. When mapped against binned daily, Level 2 satellite images of Chl a, K_d490 and SST over the cruise period, these high-resolution in-situ data showed great correspondence with the satellite data, but more importantly allowed for identification of PFTs and water types associated with microscale features. Large assemblages of phytoplankton communities comprising of diatoms and diatom-diazotroph associations (DDAs), were found in mesohaline frontal zones. Despite their high biomass, these populations were characterized by low photosynthetic competency, indicative of a bloom at the end of its active growth possibly due to nitrogen depletion in the water. Other prominent PFTs such as Trichodesmium spp., Synechococcus spp. and cryptophytes, were also associated with specific water masses offering the promise and potential that ocean remote sensing reflectance bands when examined in the context of water types also measurable from space, could greatly enhance the utility of satellite measurements for biological oceanographic, carbon cycling and fisheries studies.

Proceedings Article | 7 May 2016 Paper

Optimization of spectral bands for ocean colour remote sensing of aquatic environments

P. V. Nagamani, Aneesh Lotliker, R. Navalgund, V. Dadhwal, K. Rao, T. Srinivasa Kumar, T. Preethi Latha

Proceedings Volume 9878, 987808 (2016) https://doi.org/10.1117/12.2225870

KEYWORDS: Sensors, Remote sensing, Signal to noise ratio, Atmospheric corrections, Remote sensing, Absorption, Luminescence, Near infrared, Aerosols, Short wave infrared radiation, MODIS

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Selection of central wavelengths, bandwidths and the number of spectral bands of any sensor to be flown on a remote sensing satellite is important to ensure discriminability of targets and adequate signal-to-noise ratio for the retrieval of parameters. In recent years, a large number of spectral measurements over a wide variety of water types in the Arabian Sea and the Bay of Bengal have been carried out through various ship cruises. It was felt pertinent to use this precious data set to arrive at meaningful selection of spectral bands and their bandwidths of the ocean colour sensor to be flown on the forthcoming Oceansat-3 of ISRO. According to IOOCG reports and studies by Lee and Carder (2002) it is better for a sensor to have ~15 bands in the 400-800 nm range for adequate derivation of major properties (phytoplankton biomass, colored dissolved organic matter, suspended sediments, and bottom properties) in both oceanic and coastal environments from observation of water color.

In this study, ~417 hyper-spectral remote-sensing reflectance spectra (spectral range varies from ~380-800 nm) covering different water types like open, coastal, mid coastal and near coastal waters have been used to identify the suitable spectral bands for OCM-3. Central wavelengths were identified based on the results obtained from hyper-spectral underwater radiometer measurements of Rrs, HPLC pigments and spectrometer analyzed absorption spectra for all the above water types. Derivative analysis has been carried out from 1st to 5th order to identify the inflection and null points for better discrimination / identification of spectral peaks from the in situ R_rs spectra. The results showed that open ocean and coastal ocean waters has spectra peaks mostly in the blue, green region; turbid coastal waters has maximum spectral peaks in the red region. Apart from this, the spectral peaks were identified in the red region for the chlorophyll fluorescence in the open ocean and coastal waters. Based on these results 13 spectral bands were proposed in the VNIR region for the upcoming OCM-3 sensor. In order to obtain water leaving radiances from the measurements at spacecraft platform, it is necessary to do atmospheric correction we need to have spectral bands in the NIR and above regions. Hence, a set of bands 3 bands in the NIR and SWIR region were proposed for OCM-3 to address the atmospheric correction related issues.

Proceedings Article | 7 May 2016 Paper

Estimation of latent heat flux using satellite based observations over the North Indian Ocean

Purnachand Ch., V. Rao M., Prasad K. V. S. R., K. H. Rao, V. K. Dadhwal

Proceedings Volume 9878, 98780O (2016) https://doi.org/10.1117/12.2223670

KEYWORDS: Heat flux, Satellites, Earth observing sensors, Aerodynamics, Microwave radiation, Imaging systems, Directed energy weapons, Remote sensing, Environmental sensing, Data analysis

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Proceedings Article | 5 May 2016 Paper

Spatio-temporal variability of atmospheric CO2 over India and its surroundings based on satellite measurements and numerical modeling

A. Bhuvan Chandra, M. Krishnapriya, R. K. Nayak, V. Dadhwal

Proceedings Volume 9876, 98761K (2016) https://doi.org/10.1117/12.2223842

KEYWORDS: Carbon dioxide, Atmospheric modeling, Data modeling, Satellites, Combustion, Earth observing sensors, Climatology, Biofuels, Spatial resolution, Atmospheric sensing

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Proceedings Article | 3 May 2016 Paper

Seasonal dynamics of circulation in Hooghly Estuary and its adjacent coastal oceans

Shashank Kr. Mishra, Gourav Nayak, R. Nayak, V. Dadhwal

Proceedings Volume 9882, 988220 (2016) https://doi.org/10.1117/12.2223882

KEYWORDS: Earth observing sensors, Coastal modeling, Satellites, 3D modeling, Mouth, Head, Floods, Computer simulations, Magnesium, Remote sensing

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Proceedings Article | 30 April 2016 Paper

Study of land cover classes and retrieval of leaf area index using Landsat 8 OLI data

Amit Verma, P. K. Garg, K. S. Hari Prasad, V. Dadhwal

Proceedings Volume 9880, 988025 (2016) https://doi.org/10.1117/12.2224430

KEYWORDS: Image classification, Landsat, Earth observing sensors, Remote sensing, Environmental sensing, Spectral data processing, Vegetation, Satellites, Global Positioning System, Atmospheric corrections, Reflectivity, Sensors

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SPIE Journal Paper | 7 May 2014

Estimating aboveground biomass in Avicennia marina plantation in Indian Sundarbans using high-resolution satellite data

Sudip Manna, Subrata Nandy, Abhra Chanda, Anirban Akhand, Sugata Hazra, Vinay Kumar Dadhwal

JARS, Vol. 8, Issue 01, 083638, (May 2014) https://doi.org/10.1117/12.10.1117/1.JRS.8.083638

KEYWORDS: Vegetation, Biological research, Satellites, Carbon, Reflectivity, Remote sensing, Statistical analysis, Ecosystems, Carbon sequestration, Near infrared

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Proceedings Article | 9 November 2012 Paper

Using CEOS reference standard targets to assess on-orbit performance of Resourcesat-2 AWiFS in comparison with Terra MODIS-preliminary results

A. Angal, A. Senthil Kumar, X. Xiong, A. S. Kiran Kumar, V. Dadhwal, T. Choi

Proceedings Volume 8528, 85280K (2012) https://doi.org/10.1117/12.977549

KEYWORDS: MODIS, Sensors, Reflectivity, Calibration, Molybdenum, Spatial resolution, Image resolution, Neodymium, Satellites, Visible radiation

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SPIE Journal Paper | 1 April 2009

Potential of IRS P-6 LISS IV for agriculture field boundary delineation

Poonam Tiwari, Hina Pande, Minakshi Kumar, Vinay Dadhwal

JARS, Vol. 3, Issue 01, 033528, (April 2009) https://doi.org/10.1117/12.10.1117/1.3133306

KEYWORDS: Agriculture, Image segmentation, Satellites, Image analysis, Infrared imaging, Fuzzy logic, Image classification, Remote sensing, Image processing, Feature extraction

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SPIE Journal Paper | 1 June 2007

Subpixel classifiers: fuzzy theory versus statistical learning algorithm

Anil Kumar, S. K. Ghosh, Vinay Dadhwal

JARS, Vol. 1, Issue 01, 013517, (June 2007) https://doi.org/10.1117/12.10.1117/1.2759178

KEYWORDS: Fuzzy logic, Image classification, Agriculture, Algorithms, Accuracy assessment, Error analysis, Scene classification, Remote sensing, Statistical analysis, Satellites

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Proceedings Article | 11 December 2006 Paper

Methodology for national wheat yield forecast using wheat growth model, WTGROWS, and remote sensing inputs

Naveen Kalra, P. Aggarwal, A. Singh, V. Dadhwal, V. Sehgal, R. Harith, S. Sharma

Proceedings Volume 6411, 641106 (2006) https://doi.org/10.1117/12.697698

KEYWORDS: Data modeling, Solar radiation models, Remote sensing, Meteorology, Process modeling, Databases, Atmospheric modeling, Agriculture, Satellites, Calibration

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Wheat is an important food crop of the country. Its productivity lies in a very wide range due to diverse bio-physical and socio-economic conditions in the growing regions. Crop cutting and sample surveys are time consuming as well tedious, and procedure of forecast is delayed. CAPE methodology, which uses remote sensing, ground truth and prevailing weather, has been very successful, but empirical in nature. In a joint IARI-SAC Research Programme, possibility of linking the dynamic wheat growth model with the remote sensing input and other relational database layers was tried. Use of WTGROWS, a wheat growth model developed at IARI, with the remote sensing and relational databases is dynamic and can be updated whenever weather, acreage and fertilizer and other inputs are received. National wheat yield forecast was done for three seasons on meteorological sub-division scale by using WTGROWS, relational database layers and satellite image. WTGROWS was run for historic weather dataset (last 25 years), with the relational database inputs through their associated growth rates and compared with the productivity trends of the met-subdivision. Calibration factor, for each met-subdivision, were obtained to capture the other biotic and abiotic stresses and subsequently used to bring down the yields at each sub-division to realistic scale. The satellite image was used to compute the acreage with wheat in each sub-division. Meteorological data for each-subdivision was obtained from IMD (weekly basis). WTGROWS was run with actual weather data obtained upto a given time, and weather normals use for subsequent period, and the forecast was prepared. This was updated on weekly basis, and the methodology could forecast the wheat yield well in advance with a great accuracy. This procedure shows the pathway for Crop Growth Monitoring System (CGMS) for the country, to be used for land use planning and agri-production estimates, which although looks difficult for diverse agro-ecologies and wide range of bio-physical and socio-economic characters contributing to differential productivity trends.

Proceedings Article | 11 December 2006 Paper

Yield estimation in farmers' fields at Alipur Block of Delhi by using crop model and satellite data

V. Sehgal, Naveen Kalra, A. Singh, V. Dadhwal, R. Harith, R. Sahoo, S. Sharma

Proceedings Volume 6411, 641107 (2006) https://doi.org/10.1117/12.697705

KEYWORDS: Vegetation, Satellites, Remote sensing, Data modeling, Infrared imaging, Agriculture, Soil science, Near infrared, Iron, Earth observing sensors

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Proceedings Article | 11 December 2006 Paper

Remote sensing and GIS based information system for sustainable resources planning at Panchayat level

A. Velmurugan, S. Bhatt, V. Dadhwal

Proceedings Volume 6411, 64110Q (2006) https://doi.org/10.1117/12.694814

KEYWORDS: Soil science, Decision support systems, Geographic information systems, Remote sensing, Data modeling, Carbon, Databases, Human-machine interfaces, Climatology, Water

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Showing 5 of 14 publications

Conference Committee Involvement (2)

SPIE Asia-Pacific Remote Sensing

4 April 2016 | New Delhi, India

Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications

13 November 2006 | Goa, India

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