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The, data of. remote sensing has been used in all the, fields of. natural sciences and became an important tool in many research areas. Using remote sensing for identifying pollutants and polluted objects or materials is a new field of. remote sensing applications.
The, current study aims to assess pollution of. heavy metals for two important fruits for local consumption and exportation as well based on remote sensing technology. Six heavy metals were determined in the, lab and measured in the, field, the,y are nickel, cadmium, chromium, lead, zinc and mercury .The, citrus and mango farms in the, study area are closing to the, industrial area in South Giza governorate. This area is suffering from many sources of. pollution including factories, highways and bad drainage system in some areas.
The, first step of. the, process was to identify the, areas in which citrus and mango are cultivated and to create modified maps of land cover and crop production. The study area was revised using the Food and Agriculture Organization’s (FAO) global land cover classification scheme (FAO-LCCS). Using combined multispectral bands of sentinel-2 data acquired in 2018, land cover classification and crop pattern were performed. spatial resolution of ten metres. Geographic information system (GIS) was used to edit the, classification result in order to reach the, maximum possible accuracy. Based on the,se maps, the, study area was divided into six zones and the, locations of. the, observed sites were identified and covering variations within mango and citrus farms. from each location, spectral reflectances was measured and leave plant samples were collected from the, same trees for laboratory determination of. heavy metals. The, last step of. the, methodology was to statically correlate spectral reflectance characteristics in forms of. spectral vegetation indices and laboratory analysis of. heavy metals to produced models to estimate heavy metals using spectral data with adequate accuracy.
Irrigated herbaceous crops, irrigated tree crops, bare land, water sources, and artificial surfaces make up the total study area of 514425 feddans. GIS was used to provide the area of each land cover as well as geospatial data. Among predicted modelled heavy metals, the models were evaluated using coefficient of determination (R2) and root mean square (RMSE) error. The findings revealed that, correlation coefficient of. the, generated models, NDVI, Red, IR, Lic1, VOG, RVI, SAVI, SIPI, and DWSI for predicting heavy metals in the, leaves of. mango and citrus. The, models could be applied in othe,r study area with similar conditions.