Monitoring of agricultural drought in semi-arid ecosystem of Peninsular India through indices derived from time-series CHIRPS and MODIS datasets

The reliable and consistent remote sensing-based drought indices along with Geographic Information System (GIS) play a significant role in the mapping, and monitoring of agricultural drought. The core objective of the present study is to monitor agricultural drought dynamics over the semi-arid Rayal...

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Autores principales:	P. Sandeep, G.P. Obi Reddy, R. Jegankumar, K.C. Arun Kumar
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	CHIRPS Agricultural drought NVSWI SPI NDVI LST Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/fc25c7964afc460ca5feb2f8db0c48d8
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spelling	oai:doaj.org-article:fc25c7964afc460ca5feb2f8db0c48d82021-12-01T04:32:58ZMonitoring of agricultural drought in semi-arid ecosystem of Peninsular India through indices derived from time-series CHIRPS and MODIS datasets1470-160X10.1016/j.ecolind.2020.107033https://doaj.org/article/fc25c7964afc460ca5feb2f8db0c48d82021-02-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X20309729https://doaj.org/toc/1470-160XThe reliable and consistent remote sensing-based drought indices along with Geographic Information System (GIS) play a significant role in the mapping, and monitoring of agricultural drought. The core objective of the present study is to monitor agricultural drought dynamics over the semi-arid Rayalaseema region of Peninsular India during the year 2000 to 2018 by using the Normalized Vegetation Supply Water Index (NVSWI) derived from time-series remote sensing data products. NVSWI is the normalization of Vegetation Supply Water Index (VSWI), which was computed by integrating the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). The analysis of Standardized Precipitation Index (SPI) shows that in dry year (2001), majority of the study area experienced extreme dry conditions (SPI < −2.0) due to the deficit rainfall during the growing season. The spatio-temporal analysis of NVSWI reveals that the western, central, and northern parts of the study area were prone to frequent droughts of moderate to severe intensity. The high temperatures and below normal rainfall are found to be the major triggering factors for the droughts in the region. During the years 2001, 2002, 2003, 2006, 2009, 2014, 2017, and 2018 worst droughts were observed in the study region. The Pearson correlation analysis between monthly NVSWI with 1-month SPI and Vegetation Health Index (VHI) for the growing season of dry (2001) and wet (2007) years clearly show a significant positive correlation. The study clearly demonstrates the potential of NVSWI as one of the robust indices in the assessment and monitoring of agricultural droughts.P. SandeepG.P. Obi ReddyR. JegankumarK.C. Arun KumarElsevierarticleCHIRPSAgricultural droughtNVSWISPINDVILSTEcologyQH540-549.5ENEcological Indicators, Vol 121, Iss , Pp 107033- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	CHIRPS Agricultural drought NVSWI SPI NDVI LST Ecology QH540-549.5
spellingShingle	CHIRPS Agricultural drought NVSWI SPI NDVI LST Ecology QH540-549.5 P. Sandeep G.P. Obi Reddy R. Jegankumar K.C. Arun Kumar Monitoring of agricultural drought in semi-arid ecosystem of Peninsular India through indices derived from time-series CHIRPS and MODIS datasets
description	The reliable and consistent remote sensing-based drought indices along with Geographic Information System (GIS) play a significant role in the mapping, and monitoring of agricultural drought. The core objective of the present study is to monitor agricultural drought dynamics over the semi-arid Rayalaseema region of Peninsular India during the year 2000 to 2018 by using the Normalized Vegetation Supply Water Index (NVSWI) derived from time-series remote sensing data products. NVSWI is the normalization of Vegetation Supply Water Index (VSWI), which was computed by integrating the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). The analysis of Standardized Precipitation Index (SPI) shows that in dry year (2001), majority of the study area experienced extreme dry conditions (SPI < −2.0) due to the deficit rainfall during the growing season. The spatio-temporal analysis of NVSWI reveals that the western, central, and northern parts of the study area were prone to frequent droughts of moderate to severe intensity. The high temperatures and below normal rainfall are found to be the major triggering factors for the droughts in the region. During the years 2001, 2002, 2003, 2006, 2009, 2014, 2017, and 2018 worst droughts were observed in the study region. The Pearson correlation analysis between monthly NVSWI with 1-month SPI and Vegetation Health Index (VHI) for the growing season of dry (2001) and wet (2007) years clearly show a significant positive correlation. The study clearly demonstrates the potential of NVSWI as one of the robust indices in the assessment and monitoring of agricultural droughts.
format	article
author	P. Sandeep G.P. Obi Reddy R. Jegankumar K.C. Arun Kumar
author_facet	P. Sandeep G.P. Obi Reddy R. Jegankumar K.C. Arun Kumar
author_sort	P. Sandeep
title	Monitoring of agricultural drought in semi-arid ecosystem of Peninsular India through indices derived from time-series CHIRPS and MODIS datasets
title_short	Monitoring of agricultural drought in semi-arid ecosystem of Peninsular India through indices derived from time-series CHIRPS and MODIS datasets
title_full	Monitoring of agricultural drought in semi-arid ecosystem of Peninsular India through indices derived from time-series CHIRPS and MODIS datasets
title_fullStr	Monitoring of agricultural drought in semi-arid ecosystem of Peninsular India through indices derived from time-series CHIRPS and MODIS datasets
title_full_unstemmed	Monitoring of agricultural drought in semi-arid ecosystem of Peninsular India through indices derived from time-series CHIRPS and MODIS datasets
title_sort	monitoring of agricultural drought in semi-arid ecosystem of peninsular india through indices derived from time-series chirps and modis datasets
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/fc25c7964afc460ca5feb2f8db0c48d8
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_version_	1718405880618156032

Monitoring of agricultural drought in semi-arid ecosystem of Peninsular India through indices derived from time-series CHIRPS and MODIS datasets

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