Ammar S. Ismaeal; Mohammed Jarullah Farhan farhan; Ayad Abdullah Khalaf
Volume 24, Issue 2 , June 2024, , Pages 131-160
Abstract
The study used remote sensing to manage and monitor wheat crop health in some gypsiferous soil units. Five sites cultivated with wheat and irrigated by a central pivot irrigation system were selected within gypsiferous soil units in some agricultural lands. Soil and plant samples were collected at the ...
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The study used remote sensing to manage and monitor wheat crop health in some gypsiferous soil units. Five sites cultivated with wheat and irrigated by a central pivot irrigation system were selected within gypsiferous soil units in some agricultural lands. Soil and plant samples were collected at the best spectral and vegetative growth stage (grain filling stage) from each site. Three samples of the plant and soil were collected with three replicates, resulting in a total of (5 × 3 × 3 = 45 samples for both soil and plant). Samples were prepared for conducting laboratory analyses Satellite imagery of the OLI type from the Landsat 8 satellite, acquired on 24/2/2020, was used to calculate the following spectral indices: NDVI, SAVI, OSAVI, GOSAVI, GDVI, NDMI, CMFI, LAI. The results showed a variation in the concentration of fertile elements in the soil and plants between the study sites, with the third site relatively outperforming the other sites. On the other hand, we observed a variation in the values of spectral indices between the study sites for all the spectral indices and an increase in values with the progress and increase in the size of the canopy cover to reach the best spectral growth stage at the stage of ear fullness, which is the stage of spectral stability and appropriate for monitoring crop productivity and assessing plant health. The results also concluded that NDVI and LAI are among the most important pieces of evidence that have a strong relationship with plant density and estimation of its general condition, as the relationship was strong logarithmic and linear with nitrogen, as the values of R2 reached 0.91 and 0.88, respectively, and the value of the coefficient of determination R2 reached with The phosphorus concentration was 0.67 and 0.67, respectively. Also, the use of spectral indices depends on the spectral bands that fall at the wavelength of 0.6-0.7 micrometers, which is the region where a high absorption process occurs if the vegetation cover is intact and healthy, especially the NDVI index.
Ali H. Hummadi; Ayad A.Khalaf
Volume 24, Issue 1 , March 2024, , Pages 206-222
Abstract
The aim of study to time series analysis of agricultural drought and desertification using Spectral Indices and Landsat Images. A time series of satellite images (TM and OLI) were coducted for the period 1990 to 2022. The located at coordinate 34°52'29.386"N and 43°26'15.703" E and the area study is ...
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The aim of study to time series analysis of agricultural drought and desertification using Spectral Indices and Landsat Images. A time series of satellite images (TM and OLI) were coducted for the period 1990 to 2022. The located at coordinate 34°52'29.386"N and 43°26'15.703" E and the area study is (33.98) km2. The (18) eighteen of satellite images were selected and then image processing was carried out using ERDAS imagen Ver 15 and ArcGIS 10.6. The spectral indices (Normalized Difference Vegetation Index (NDVI), Land Surface Temprature (LST), Vegetation Condition Index (VCI), and Vegetation Health Idex (VHI) were calculated. The regression and correlation coefficient between rainfall and spectral indices were determined using SPSS programm. The result show that VHI at 1990, 2000 and 2010 are sever drought class and its area 63.66, 57.63 and 63.85% respectively. In addition, the simple linear regression and correlation coefficient were positive between a rainfall and spectral indices reach ≥ 0.70. The years 1998, 2008, 2013 and 2022 were suffering from sever drought and desertification compared with 2006, 2016 and 2019, respectively.
Ahmed Musrhed; Ayad Khalaf; Mohamed Ferhan; Ibrahim Ortas
Volume 23, Issue 2 , June 2023, , Pages 224-234
Abstract
This study aimed assessing the land degradation status of some soil series of the North Tikrit Agricultural Project using remote sensing data. Part of the project area (246,555) km2 was selected based on variations in soil characteristics and agricultural crops. The study area is located between the ...
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This study aimed assessing the land degradation status of some soil series of the North Tikrit Agricultural Project using remote sensing data. Part of the project area (246,555) km2 was selected based on variations in soil characteristics and agricultural crops. The study area is located between the longitudes of 43˚ 12′ 30ʹʹ and 43˚ 27′ 30ʹʹ E and the latitudes of 35˚ 15′ 00ʹʹ and 35˚ 0′ 00ʹʹ N. This project area includes five soil series: Hatra, Jareesh, Safa, Seneyah, and Shurqat. Forty-four soil samples covering these five soil series were collected from the surface layer (0-30 cm). The chemical properties of these samples were determined, including pH, Electric Conductivity (EC), calcium carbonate content, gypsum content, cation exchange capacity, and organic matter content. Two Landsat satellite images were employed for calculation of soil and vegetation spectral indices. One of these two images was acquired on 15 June, 2002 while the other was acquired on 25 June, 2022. The spectral indices of concern encircled three vegetation indices (Advanced Vegetation Index (AVI), Specific Leaf Area Vegetation Index (SLAVI), and Structure Insensitive Pigment Index (SIPI)) and three soil spectral indices (Bare Soil Index (BSI), Modified Bare Soil Index (MBSI), and Normalized Difference Bare Soil Index (NDBSI)). The results show that there are variations in the values of the various computed soil and vegetation spectral indices during the two study periods and that the values were, in general, lower in 2022 than in 2002. It was found that the soils were degraded and that, consequently, the plant density declined during the study period. This had negative impacts on the fertility and productivity of the soils in the study area. The results also showed differences in the soil spectral reflectivity curves, especially at the wavelengths of water absorption; 1.4, 1.9, and 2.2 μm, due to the presence of gypsum at high concentrations in the soils.
