Seaweed is a valuable coastal resource for its use in food, cosmetics, and other items. This study proposed new remote sensing based seaweed enhancing index (SEI) using spectral bands of near-infrared (NIR) and shortwave-infrared (SWIR) of Landsat 8 satellite data. Nine Landsat 8 satellite images of years 2014, 2016, and 2018 for the January, February, and March months were utilized to test the performance of SEI. The seaweed patches in the coastal waters of Karachi, Pakistan were mapped using the SEI, normalized difference vegetation index (NDVI), and floating algae index (FAI). Seaweed locations recorded during a field survey on February 26, 2014, were used to determine threshold values for all three indices. The accuracy of SEI was compared with NDVI while placing FAI as the reference index. The accuracy of NDVI and SEI were assessed by matching their spatial extent of seaweed cover with FAI enhanced seaweed area. SEI images of January 2016, February 2018, and March 2018 enhanced less than 50 percent of the corresponding FAI total seaweed areas. However, on these dates the NDVI performed very well, matching more than 95 percent of FAI seaweed coverage. Except for these three times, the performance of SEI in the remaining six images was either similar to NDVI or even better than NDVI. SEI enhanced 99 percent of FAI seaweed cover on January 2018 image. Overall, seaweed area not covered by FAI was greater in SEI than NDVI in almost all images, which needs to be further explored in future studies by collecting extensive field information to validate SEI mapped additional area beyond the extent of FAI seaweed cover. Based on these results, in the majority of the satellite temporal images selected for this study, the performance of the newly proposed index—SEI, was found either better than or similar to NDVI.

The global demand for food is growing with the population and urbanization, which puts pressure on water resources, which need assessing and quantifying water requirements. Adopting efficient irrigation methods to optimize water use is essential in this situation. In this study, crop water productivity (CWP) of major crops in the Rohri canal command area was estimated by the ratio of yield and actual evapotranspiration (ET_a). Analyzing the CWP of major crops, water scarcity challenges can be tackled by selecting the most feasible irrigation methods. However, ET_a was calculated and aggregated for all four stages of the crop growth period: initial, crop development, flowering stage, and maturity seasons. The crop yield data were obtained from the districts’ agricultural statistics. For this purpose, evapotranspiration products of Landsat 5 and 8 were downloaded from Earth Engine Evapotranspiration Flux (EEFlux). Landsat images were processed in a GIS environment to calculate ET_a. The approach suggests developing a CWP database for major crops like wheat, cotton, and rice to improve irrigation water management. The objectives of this study are to estimate and analyze the difference in the CWP and evapotranspiration of major crops for the Rabi and Kharif seasons with high and moderate flows during 1998–2019. It comprises nine districts of Sindh that come under the Rohri Canal command area. To analyze the difference in CWP between the Rabi and Kharif seasons for all study crops of the seasons of Rabi (2014–2015 and 2016–2017) and Kharif (1998 and 2017). The growing periods for wheat, cotton, and rice in the Rohri Canal command area are 160, 195, and 180 days, respectively. The estimated ET_a of the Rohri canal command area and CWP were in good agreement with the literature-reported values. Hence, enhanced agricultural productivity can be achieved by making considerable investments to improve agricultural research and extension systems.

Satellite Radar altimetry has emerged as a powerful tool for monitoring inland water bodies including rivers, lakes, and coastal regions. However, challenges persist regarding its accuracy, particularly over complex terrains. This study addresses these challenges by enhancing water level estimation accuracy in complex environments. Although the scope of our study may appear limited in terms of temporal and spatial scales, it aims to contribute additional validation results for a comprehensive water-level database utilizing satellite radar altimetry data. In this paper, we applied two pulse filter criteria on Sentinel-3A radar data—Pulse Peakiness (PP) and Misfit (Mf)—to enhance the selection of altimetry waveforms. After applying the filters, the estimated water levels show improved accuracy when validated against in-situ observations. Pulses from non-water surfaces were excluded using riverbank boundaries to ensure higher-quality pulses. A total of six scenarios were considered where the PP and Mf criteria were used to optimize water level estimates. The results indicate that Mf < 1.5 performs well (RMSE = 0.27 m, R = 0.93), and PP > 0.3 yields even better outcomes (RMSE = 0.28 m, R = 0.95). These findings underscore the significance and efficacy of filtering in enhancing the accuracy of water level estimation. The dataset correlation (R) increased substantially, from 0.088 to 0.935–0.953, with the implementation of filters. Additionally, the RMSE significantly improved, reducing from 4.2 m to 0.26–0.27 m with filter parameters. As anticipated, filters removed data points but vastly improved results. Integrating multi-mission satellite data can compensate for data loss and augment overall quantity and quality.

This study aims to assess the impact of the 2010–2011 flash floods on the crop production of Sanghar—an eastern district of Sindh. The population of almost two million is mainly dependent on agriculture. Cotton, sugarcane, and wheat are the main cash crops of the district. In 2010–2011 Sanghar was hit by flash floods after heavy rainfall. These floods adversely affected the crops standing at that time. However, on the other hand, they increased the fertility of agricultural lands. Satellite images, precipitation data, and geographical information system (GIS) tools were used to quantify floods’ effects by mapping pre- and post-flood vegetation cover. Supervised classification was applied on Landsat images from 2009 to 2013 to extract vegetation area. The temporal analysis unveils a significant increase in the vegetation cover in the post-flood years. Furthermore, the comparison between pre-and post-flood crop production data collected from the Pakistan Bureau of Statistics also validated the study results by revealing an increase in crop production.

Karachi, the biggest city of Pakistan, has undergone rapid urbanization since the creation of Pakistan in 1947. The city is expanding with an alarming rate placing pressure on the available natural resources and services with rising number of natural hazards. Urbanization brings not only multiple environmental challenges but also causes socioeconomic issues. One of the current environmental problems due to urbanization of Karachi is blockages in its natural drainage system causing localized floods after heavy rainfalls. In this paper, landuse/cover (LULC) changes of the Karachi city and their impacts on natural drains of one of its three major watersheds, Malir River Basin, over the last 23 years (1993–2015) have been studied. Historical satellite data and remote sensing tools were utilized to examine LULC changes of the Malir River Basin. The U.S.-based first earth observing satellite series of Landsat were selected for this purpose. Shuttle Radar Topographic Mission’s Digital Elevation Model (DEM) along with the topographic sheets of 1975 was utilized to delineate the streams of the Malir Basin using geographical information system (GIS) tools. GIS overlay analyses were done to identify the blockages in the riverbed and flood damages. For flood simulation, historical (1985 to 2014) rainfall gauge observations of Pakistan Meteorological Department (PMD) were acquired. The results of the study showed that a significant portion of the riverbed, delineated from 1975 topographical sheets, has currently been occupied by urban and agricultural lands which have severely affected the natural drainage of stormwater causing localized floods in the basin.