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Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/34449
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kishan Singh Rawat | en_US |
dc.contributor.author | Vinay Kumar Sehgal | en_US |
dc.contributor.author | Shibendu S. Ray | en_US |
dc.date.accessioned | 2020-04-03T01:01:32Z | - |
dc.date.available | 2020-04-03T01:01:32Z | - |
dc.date.issued | 2018-12-28 | - |
dc.identifier.citation | Not Available | en_US |
dc.identifier.issn | Not Available | - |
dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/34449 | - |
dc.description | Not Available | en_US |
dc.description.abstract | In this paper, integration of two models TsHARP (Tsharp) and Thin plate spline (TPS) has been performed for spatial sharpening of 1 km (coarse) resolution of MODIS thermal imagery to 250 m resolution. Afterwards it was validated with LANDSAT-7 thermal data (after resampled to 250 m pixel). The results showed that LST based on integration of two (TsHARP and TPS) models is consistent with true data (LANDSAT-7 ETM+, thermal data). We have observed R2 at pure cropped area, cropped area with low settlement and cropped area with high settlement is showing, 0.74 (Multi R = 0.80, Adju R = 0.75 and p = .001), 0.72 (Multi R = 0.78, Adju R = 72 and p = .001) and 0.71 (Multi R = 0.78, Adju R = 0.71 and p = .001) respectively. While overall R2 of 0.69 (Multi R = 0.76, Adju R = 0.71 and p = .000) for all categories of classes (cropped area + cropped area with low settlement + cropped area with high settlement). LST shows root mean square error (RMSE) = 0.307 °C, Relative-RMSE (R-RMSE) = 0.167 °C, mean absolute error (MAE) = 0.033 °C, normalized RMSE (NRMSE) = 0.018 °C, index of agreement (d) = 0.99, RMSE-observations standard deviation ratio (RSR) = 0.39 and RMSE% = 0.02 for merging process based LST. We conclude that combination of TsHARP and TPS model has a great potential to estimate LST at 250 m with high temporal resolution. This LST can be used as an input in various models to estimate other components which are LST dependent. | en_US |
dc.description.sponsorship | Not Available | en_US |
dc.language.iso | English | en_US |
dc.publisher | Elsevier B.V. | en_US |
dc.relation.ispartofseries | Not Available; | - |
dc.subject | Remote Sensing | en_US |
dc.subject | Thermal | en_US |
dc.subject | MODIS | en_US |
dc.subject | Landsat | en_US |
dc.subject | Temperature | en_US |
dc.title | Downscaling of MODIS thermal imagery | en_US |
dc.title.alternative | Not Available | en_US |
dc.type | Research Paper | en_US |
dc.publication.projectcode | Not Available | en_US |
dc.publication.journalname | The Egyptian Journal of Remote Sensing and Space Science | en_US |
dc.publication.volumeno | 22(1) | en_US |
dc.publication.pagenumber | 49-58 | en_US |
dc.publication.divisionUnit | Division of Agricultural Physics | en_US |
dc.publication.sourceUrl | https://doi.org/10.1016/j.ejrs.2018.01.001 | en_US |
dc.publication.authorAffiliation | ICAR::Indian Agricultural Research Institute | en_US |
dc.publication.authorAffiliation | Sathyabama Institute of Science and Technology, Chennai | en_US |
dc.publication.authorAffiliation | Mahalanobis National Crop Forecast Centre | en_US |
dc.ICARdataUseLicence | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf | en_US |
dc.publication.naasrating | Not Available | en_US |
Appears in Collections: | CS-IARI-Publication |
Files in This Item:
File | Description | Size | Format | |
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Kishan_MODIS_2019.pdf | 3.77 MB | Adobe PDF | View/Open |
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