Understanding community perspectives for estimation of crop water stress in an agriculture-dominated CZO in the Ganga plains, India
Abstract
Agricultural water management is likely the most important parameter for crop health assessment and also to encourage the farmers for best agricultural practices. This study pertains to a critical zone observatory (CZO) in the Ganga plains where a large population depends on agriculture for their living. A close interaction with the local farmers reveals significant variation in cropping pattern, crop yield, irrigation water source and their current water management. Areas irrigated by canal have higher crop yield than those by tube wells but good aquifers are quite deep in this region where the top 50-60 m of lithology is dominated by muddy sediments. So, the groundwater is not easily accessible, and canal water is available only for a small population due to inadequate network and poor maintenance. This is posing serious difficulty in sustaining the agriculture in this region and this necessitates a proper agriculture water scheduling for optimizing the water resource usage. This study has used drone-based imaging and field data to determine the crop water stress index (CWSI). Thermal and multispectral image acquisition was synchronized with the Sentinel 3A passing record and midday was selected as an optimal time of acquisition. Concurrent in-situ land surface temperature (LST) and meteorological data is obtained from the on-site radiometer and automatic weather station respectively. Thermal mages were processed first to create an orthorectified map of the surface temperature and compared to land surface temperature derived from the Sea and Land Surface Temperature Radiometer (SLSTR). The CWSI is evaluated using an empirical model which requires a non-water stressed baseline being derived here from the AWS and radiometer LST by establishing the relationship between temperature and vapor pressure deficit. The near infrared and red band from multispectral imaging is formulated with the thermal geo-rectified image to produce a spatiotemporal soil moisture (SM) map and this is used further for the CWSI validation. Both the CWSI and SM values were also compared with the in-situ soil moisture and surface temperature measured during or just after the aerial acquisition. All data and information generated from this research are being shared with the farmers for their benefit through continued interaction with them.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMPA43A..15S
- Keywords:
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- 0485 Science policy;
- BIOGEOSCIENCES;
- 1632 Land cover change;
- GLOBAL CHANGE;
- 1865 Soils;
- HYDROLOGY;
- 6309 Decision making under uncertainty;
- POLICY SCIENCES & PUBLIC ISSUES