Critical information on a flood-affected area is needed in a short time frame to initiate rapid response operations and develop long-term flood management strategies. This study combined rainfall trend analysis using Asian Precipitation—Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE) gridded rainfall data with flood maps derived from Synthetic Aperture Radar (SAR) and multispectral satellite to arrive at holistic spatio-temporal patterns of floods in Sri Lanka. Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data were used to map flood extents for emergency relief operations while eight-day Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data for the time period from 2001 to 2016 were used to map long term flood-affected areas. The inundation maps produced for rapid response were published within three hours upon the availability of satellite imagery in web platforms, with the aim of supporting a wide range of stakeholders in emergency response and flood relief operations. The aggregated time series of flood extents mapped using MODIS data were used to develop a flood occurrence map (2001–2016) for Sri Lanka. Flood hotpots identified using both optical and synthetic aperture average of 325 km² for the years 2006–2015 and exceptional flooding in 2016 with inundation extent of approximately 1400 km². The time series rainfall data explains increasing trend in the extreme rainfall indices with similar observation derived from satellite imagery. The results demonstrate the feasibility of using multi-sensor flood mapping approaches, which will aid Disaster Management Center (DMC) and other multi-lateral agencies involved in managing rapid response operations and preparing mitigation measures.

This paper proposes a new approach to estimate and map separately gross and net water requirements and actual crop water use by applying multi–spectral satellite data. Remote sensing information is witnessing a boom in the availability of high-resolution multi-spectral data with frequent revisit time, paving the path for improved assessment of precision agriculture and minimizing the wastage of irrigation water. In this study, we have tried to integrate multi-source remote sensing information with farmer’s irrigation practices to evaluate the water use and losses at farm-scale for center pivot irrigation systems (CPIS) in Inner Mongolia autonomous region of China. The region is practicing modernized irrigation methods to efficiently use groundwater. Crop gross water requirements are estimated by evaluating separately the net crop water requirements (CWR) and the water losses inherently from a CPIS, i.e. droplet evaporation to the air directly before they fell on the crop canopy during irrigation application (EA) and canopy interception loss (Ic). The crop water requirement is estimated according to the FAO-56 method based on the Penman-Monteith equation. Actual crop water use is evaluated by estimating separately soil evaporation (ES) and plant transpiration (ET) by applying the ETMonitor model. High-resolution multi–spectral data acquired by Sentinel-2 MSI and Landsat-8 OLI together with meteorological forcing data and soil moisture retrievals were used to construct daily estimates of crop water requirements and actual use. Finally, the performance of irrigation scenarios was assessed by applying a performance indicator (IP), as the ratio between gross water requirement and the volume of irrigation applied, where values closer to unity referring to optimum utilization and minimum loss. Measurements of actual evapotranspiration by eddy covariance system were applied to evaluate the actual evapotranspiration estimates by the ETMonitor. Field experiments were also carried out to validate the estimated irrigation losses, i.e. EA and IC. The estimates were in good agreement with the ground observations, i.e. an R2 of 0.64 – 0.80 for actual water use and 0.66 – 0.97 for water losses. The RMSE was 0.6 – 1.2 mm/day for actual daily water use and 0.64 – 1.55 mm water losses for each irrigation, respectively. The IP was estimated as 1.6 for the performance of CPIS as per the above definition. Overall, the study shows that CPIS has under-performed in minimizing water losses in the study area with losses of 25.4% per season of the total volume of water applied for wheat, and 23.7% per season for potato. This implies that the amount of water applied was largely insufficient to meet the gross water requirements, i.e. including losses.