Search

Accurate and timely information on evapotranspiration (ET0) is essential for multiple agricultural applications, including irrigation scheduling, studying crop-specific water loss at different growth stages, predicting crop yields, and forecasting drought conditions. This study aims to examine the spatiotemporal patterns of ET0 and facilitate the monitoring of crop water demand, optimize irrigation water usage, and enhance agricultural advisory services. This paper estimates regional-level daily ET0 gridded data with a spatial resolution of 12.5 km by integrating observed weather data, IMD GFS-T1534 reanalysis data, and INSAT-3D satellite-based insolation product using the standard FAO56 Penman–Monteith empirical equation. The estimated monthly mean of ET0 across India ranged from 10 to 400 mm. ET0 exhibited an increasing trend from January to May, reaching its maximum values in May. In June, ET0 significantly decreased as the monsoon arrived, coinciding with the movement of rainfall patterns. The month of December exhibited the lowest ET0 values. The estimated daily gridded ET0 was compared with station-based ET0, resulting in daily correlation coefficient R2 and daily maximum absolute percentage errors ranging from 0.34 to 0.90 and 10% to 27% respectively. However, these errors decreased to a large extent when considering multiday accumulated values. A comparison was conducted between the GLDAS model ET0 and the station-estimated values, revealing an overestimation of ET0 by the GLDAS model. Additionally, significant variations were observed among the meteorological subdivisions. This highlights the necessity for proper calibration of the GLDAS model ET0 or its effective agricultural application. [ABSTRACT FROM AUTHOR]

Accurate information of crop water requirements is essential for optimal crop growth and yield. Assessing this information at the appropriate time, particularly during the vegetative and reproductive stages when water demand is highest, is crucial for successful crop production. Our study centered on the drought-prone Marathwada region, specifically targeting the years 2015 to 2020, encompassing the challenging drought year of 2015 and the favourable year of 2020. The crop water stress was detected using crop water stress (CWSI) index and compared with normalized difference vegetation index (NDVI) and normalized difference wetness index (NDWI) derived from satellite data. Our findings reveal a negative correlation between the CWSI and satellite derived vegetation indices NDVI and NDWI. Notably, the NDWI index exhibits stronger alignment with CWSI compared to NDVI. The correlation demonstrates particular robustness during drought or deficient rainfall years such as 2015, 2017, and 2019, while weaker correlations are observed in 2016, 2018, and 2020. Moreover, these correlations display variations across different areas within distinct rainfall zones. [ABSTRACT FROM AUTHOR]

The untimely onset and uneven distribution of south-west monsoon rainfall lead to agricultural drought causing reduction in food-grain production with high vulnerability over semi-arid tract (SAT) of India. A combined deficit index (CDI) has been developed from tri-monthly sum of deficit in antecedent rainfall and deficit in monthly vegetation vigor with a lag period of one month between the two. The formulation of CDI used a core biophysical ( e.g. , NDVI) and a hydro-meteorological ( e.g. , rainfall) variables derived using observation from Indian geostationary satellites. The CDI was tested and evaluated in two drought years (2009 and 2012) within a span of five years (2009–2013) over SAT. The index was found to have good correlation (0.49–0.68) with standardized precipitation index (SPI) computed from rain-gauge measurements but showed lower correlation with anomaly in monthly land surface temperature (LST). Significant correlations were found between CDI and reduction in agricultural carbon productivity (0.67–0.83), evapotranspiration (0.64–0.73), agricultural grain yield (0.70–0.85). Inconsistent correlation between CDI and ET reduction was noticed in 2012 in contrast to consistent correlation between CDI and reduction in carbon productivity both in 2009 and 2012. The comparison of CDI-based drought-affected area with those from existing operational approach showed 75% overlapping regions though class-to-class matching was only 40–45%. The results demonstrated that CDI is a potential indicator for assessment of late-season regional agricultural drought based on lag-response between water supply and crop vigor. [ABSTRACT FROM AUTHOR]