Estimation of Canal Water Deficit Using Satellite Remote Sensing and GIS: A Case Study in Lower Chenab Canal System

Land Use Land Cover Classification and Wheat Yield Prediction in the Lower Chenab Canal System Using Remote Sensing and Gis

“…The area comprises of three different agro-climatic zones (Table 1), i.e. the cottonwheat zone, rice-wheat zone and a mix sugarcane-cotton-wheat zone (Waqas et al 2019). The LCC takes off water from Khanki head works located on Chenab River.…”

“…These estimates are basis for management of agricultural lands, fixing prices of food and trade policies. Land use is the utilization of the land by humans for economic activities like agriculture, forests, construction, and farming (Waqas et al 2019). Agriculture land areas are adapted and managed intensively through a variety of human activities, which can have a wide effect on hydrologic cycles, ecosystem functioning, patterns of land use and changes in environmental system.…”

“…Various methods like agro-climate models, remote sensing techniques, statistical method and field sampling are being used for LULC and crop yield modelling(M J M Cheema and Bastiaanssen 2010;Fahad et al 2019;Waqas et al 2019). These methods, however, have many limitations like field data collection is resources intensive and personal biases often subjective to chance of errors.…”

“…Remote sensing methods are the best among all due to their accuracy, less time consumption and better synoptic coverage for mapping the agriculture and water balance components (Awan et al 2011;Awan, Tischbein, and Martius 2013;M J M Cheema and Bastiaanssen 2010;M J M Cheema, Immerzeel, and Bastiaanssen 2014;Muhammad Jehanzeb Masud Cheema et al 2016;Karimi et al 2013;Usman et al 2015;Waqas et al 2019;Waqas et al 2020). Satellite data also offers the direct calculation of Normalized Difference Vegetation Index (NDVI) values that monitor the biomass development and represents variation due to the changes in meteorological parameters (M J M Cheema and Bastiaanssen 2010; Waqas et al 2019). NDVI values approaching to -1 refers to any water body, approaching to zero generally refers to barren land or snow and high values up to 1 refers to dense vegetation like tropical rainforests.…”

“…Remote sensing and GIS have diverse application in the crop production management components. Remote sensing data-based study for irrigated agriculture shows that freshwater have becomes an increasingly scarce resource, so there is need of serious concern about the water management (M J M Cheema, Immerzeel, and Bastiaanssen 2014;Waqas et al 2019). (Awan et al 2016) analyzed the equity of the water distribution in the irrigated area of the Indus basin using remote sensing.…”