Evaluation of Spatio-Temporal Evapotranspiration Using Satellite-Based Approach and Lysimeter in the Agriculture Dominated Catchment

Kumar, U; Srivastava, A; Kumari, N; Rashmi,; Sahoo, B; Chatterjee, C; Raghuwanshi, NS

Evaluation of Spatio-Temporal Evapotranspiration Using Satellite-Based Approach and Lysimeter in the Agriculture Dominated Catchment

Kumar, U Srivastava, A

Kumari, N Rashmi, Sahoo, B Chatterjee, C Raghuwanshi, NS

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Journal of the Indian Society of Remote Sensing, 2021, 49, (8), pp. 1939-1950
Issue Date:: 2021-08-01

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Field	Value	Language
dc.contributor.author	Kumar, U
dc.contributor.author	Srivastava, A https://orcid.org/0000-0002-3963-265X
dc.contributor.author	Kumari, N
dc.contributor.author	Rashmi,
dc.contributor.author	Sahoo, B
dc.contributor.author	Chatterjee, C
dc.contributor.author	Raghuwanshi, NS
dc.date.accessioned	2022-03-27T23:21:00Z
dc.date.available	2022-03-27T23:21:00Z
dc.date.issued	2021-08-01
dc.identifier.citation	Journal of the Indian Society of Remote Sensing, 2021, 49, (8), pp. 1939-1950
dc.identifier.issn	0255-660X
dc.identifier.issn	0974-3006
dc.identifier.uri	http://hdl.handle.net/10453/155618
dc.description.abstract	Crop coefficient (Kc) represents the actual crop growth of the crop. It plays an important role in estimating water requirements at the different growth stages of the crop. However, FAO 56 Penman–Monteith Kc method does not account for spatial heterogeneity and uncertainty for regional climatic conditions significantly. Therefore, this study aims to develop the relation between Kc and normalized difference vegetation index (NDVI) using a linear regression and back calculations. These relationships were adjusted to local conditions using information from survey data obtained during Rabi season (2014–2015). The NDVI–Kc model (r2 = 0.86) has developed using NDVI–Kc from a fine resolution Landsat 8 remote sensing data. NDVI–Kc regression equation was utilized for generating crop coefficient for different month of season. The Vegetation Index-based AET estimated was evaluated with lysimeter data for different crop growth stage across the season. The results have shown that NDVI–Kc estimated AET has been better correlated with NDVI–Kc remote sensing model. Thus, the output of this research can help to calculate actual water demand in a command area and be helpful in allocating water from less demand area toward more demand area.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Journal of the Indian Society of Remote Sensing
dc.relation.isbasedon	10.1007/s12524-021-01367-w
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0406 Physical Geography and Environmental Geoscience, 0801 Artificial Intelligence and Image Processing, 0909 Geomatic Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.title	Evaluation of Spatio-Temporal Evapotranspiration Using Satellite-Based Approach and Lysimeter in the Agriculture Dominated Catchment
dc.type	Journal Article
utslib.citation.volume	49
utslib.for	0406 Physical Geography and Environmental Geoscience
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
utslib.copyright.status	closed_access	*
dc.date.updated	2022-03-27T23:20:56Z
pubs.issue	8
pubs.publication-status	Published
pubs.volume	49
utslib.citation.issue	8

Abstract:

Crop coefficient (Kc) represents the actual crop growth of the crop. It plays an important role in estimating water requirements at the different growth stages of the crop. However, FAO 56 Penman–Monteith Kc method does not account for spatial heterogeneity and uncertainty for regional climatic conditions significantly. Therefore, this study aims to develop the relation between Kc and normalized difference vegetation index (NDVI) using a linear regression and back calculations. These relationships were adjusted to local conditions using information from survey data obtained during Rabi season (2014–2015). The NDVI–Kc model (r2 = 0.86) has developed using NDVI–Kc from a fine resolution Landsat 8 remote sensing data. NDVI–Kc regression equation was utilized for generating crop coefficient for different month of season. The Vegetation Index-based AET estimated was evaluated with lysimeter data for different crop growth stage across the season. The results have shown that NDVI–Kc estimated AET has been better correlated with NDVI–Kc remote sensing model. Thus, the output of this research can help to calculate actual water demand in a command area and be helpful in allocating water from less demand area toward more demand area.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/155618