Remote Sensing has made substantial contribution in flood monitoring and damage assessment that leads the disaster management authorities to contribute significantly. In this paper, techniques for mapping flood extent and assessing flood damages have been developed which can be served as a guideline for Remote Sensing (RS) and Geographical Information System (GIS) operations to improve the efficiency of flood disaster monitoring and management.High temporal resolution played a major role in Remote Sensing data for flood monitoring to encounter the cloud cover. In this regard, MODIS Aqua and Terra images of Sindh province in Pakistan were acquired during the flood events and used as the main input to assess the damages with the help of GIS analysis tools. The information derived was very essential and valuable for immediate response and rehabilitation

In August 2022, one of the most severe floods in the history of Pakistan was triggered due to the exceptionally high monsoon rainfall. It has affected ~ 33 million people across the country. The agricultural losses in the most productive Indus plains aggravated the risk of food insecurity in the country. As part of the loss and damage (L&D) assessment methodologies, we developed an approach for evaluating crop-specific post-disaster production losses based on multi-sensor satellite data. An integrated assessment was performed using various indicators derived from pre- and post-flood images of Sentinel-1 (flood extent mapping), Sentinel-2 (crop cover), and GPM (rainfall intensity measurements) to evaluate crop-specific losses. The results showed that 2.5 million ha (18% of Sindh’s total area) was inundated out of which 1.1 million ha was cropland. The remainder of crop damage came from the extreme rainfall downpour, flash floods and management deficiencies. Thus approximately 57% (2.8 million ha) of the cropland was affected out of the 4.9 million ha of agricultural area in Sindh. The analysis indicated expected production losses of 88% (3.1 million bales), 80% (1.8 million tons), and 61% (10.5 million tons) for cotton, rice, and sugarcane. This assessment provided useful tools to evaluate the L&D of agricultural production and to develop evidence-based policies enabling post-flood recovery, rehabilitation of people and restoration of livelihood.

This paper uses Normalized Difference Water Index (NDWI) of McFeeters (1996), Water Index (WI) introduced by Rogers and Kearney (2004), referred to as Red and Short Wave Infra-Red (RSWIR) and WI suggested as the best by Ji et al. (2009), referred to as Green and Short Wave Infra-Red (GSWIR) for delineating and mapping of surface water using MODIS (Terra) near real time images during 2012 floods in Pakistan. The results from above indices have been compared with Landsat ETM+ classified images aiming to assess the accuracy of the indices. Accuracy assessment has been performed using spatial statistical techniques and found NDWI, RSWIR and GSWIR with kappa coefficient (κ) of 46.66%, 70.80% and 60.61% respectively. It has been observed using statistical analysis and visual interpretation (expert knowledge gained by past experience) that the NDWI and GSWIR have tendencies to underestimate and overestimate respectively the inundated area. Keeping in view the above facts, RSWIR has proved to be the best of the three indices. In addition, assessment of the damages has been carried out considering accumulated flood extent obtained from RSWIR. The information derived proved to be essential and valuable for disaster management plan and rehabilitation

Snow is a significant component of the ecosystem and water resources in high-mountain Asia (HMA). Therefore, accurate, continuous, and long-term snow monitoring is indispensable for the water resources management and economic development. The present study improves the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites 8 d (“d” denotes “day”) composite snow cover Collection 6 (C6) products, named MOD10A2.006 (Terra) and MYD10A2.006 (Aqua), for HMA with a multistep approach. The primary purpose of this study was to reduce uncertainty in the Terra–Aqua MODIS snow cover products and generate a combined snow cover product. For reducing underestimation mainly caused by cloud cover, we used seasonal, temporal, and spatial filters. For reducing overestimation caused by MODIS sensors, we combined Terra and Aqua MODIS snow cover products, considering snow only if a pixel represents snow in both the products; otherwise it is classified as no snow, unlike some previous studies which consider snow if any of the Terra or Aqua product identifies snow. Our methodology generates a new product which removes a significant amount of uncertainty in Terra and Aqua MODIS 8 d composite C6 products comprising 46 % overestimation and 3.66 % underestimation, mainly caused by sensor limitations and cloud cover, respectively. The results were validated using Landsat 8 data, both for winter and summer at 20 well-distributed sites in the study area. Our validated adopted methodology improved accuracy by 10 % on average, compared to Landsat data. The final product covers the period from 2002 to 2018, comprising a combination of snow and glaciers created by merging Randolph Glacier Inventory version 6.0 (RGI 6.0) separated as debris-covered and debris-free with the final snow product MOYDGL06*. We have processed approximately 746 images of both Terra and Aqua MODIS snow containing approximately 100 000 satellite individual images. Furthermore, this product can serve as a valuable input dataset for hydrological and glaciological modelling to assess the melt contribution of snow-covered areas. The data, which can be used in various climatological and water-related studies, are available for end users at https://doi.org/10.1594/PANGAEA.901821 (Muhammad and Thapa, 2019).