Basic Informations

C.V

Mohamed Mortada Ragab El Sharkawy

Master Title

Precision Farming Using Remote Sensing and GIS to Improve Soil and Crop Management

Master Abstract

Recently, the Egyptian Government has tried to increase the crop production in agricultural areas by establishing new efficient management systems; however, its economic and environmental benefits have always not been proved. Therefore, new techniques should be followed to solve this problem such as precision agriculture using satellite remote sensing imagery. Modifying the management within fields by using information about soil and crop variability can be useful to farmers. Since the cost of obtaining soil samples to characterize field variability is a key problem in precision agriculture, satellite remote sensing imagery is particularly advantageous. Also, multiple-variable regression analysis is beyond the reach of commercial growers due to the high cost of the specialized labor, equipment and analytical services that are required. Remote sensing can be used as a useful, quick and cost-effective tool in precision farming and regional analysis giving timely information about crops in specific areas and thereby providing valuable data for decision makers. Remote sensing are perfect tools to assess crop production situation and nutrients status as well as their changes. GIS for precision farming management stores data, such as land use and land cover map, soil type, nutrient levels, etc… in layers and assigns that information to the particular field location. GIS used to analyze characteristics between layers to develop application maps. GPS for precision farming stores locations such as yield areas, boundary of crop type, soil sample, etc. A GPS system has the ability to return to a particular location repeatedly. Storing this information in a GIS makes it possible to develop site-specific spatial yield variability models. Crop growth models (statistics and spatial models) are excellent tools for evaluating these complex interactions and provide insight into the causes of spatial variability Soil sample analysis is an important step in generating site specific information on which to base gypsum or lime and fertilizer decisions and in monitoring soil nutrient states over time. Budget is one of the significant factors in balancing available resources, date quality and effective sample design. Therefore, the best sampling design should be objective and technically defensible (must follow principles of unbiased sampling), cost effective and practical to implement. In the current study, we applied remote sensing and GIS techniques to identify management zones for potato crop. Remote sensing are perfect tools to assess crop production and nutrient status as well as their changes. GIS for precision farming management stores data, such as soil type and nutrient levels in layers and assigns that information to the particular field location. This study aims to predicting and increase potato yield production by using precision farming management practices. Work Plan Date collection and information 1-Remote sensing data - Landsat ETM 7+ Multi-Spectral with 8 bands. - Egyptsat1 for study area with high spatial resolution (7.5m( 2-Maps -Topographic maps -Soil maps -Geological and Geomorphological maps 3-Lectures Reviews Collect geological, geomorphological, soil, land use and land cover, crop types and nature vegetation, Climatic data which it were done by other researcher and Universities to use it as guide for the current study. Image Processing: -Geometric, radiometric and atmospheric correction for images. -Image transformations {principale component analyses (PCA), NDVI and SAVI} -Image classification Field Investigation: Design of efficient soil sampling scheme and computation of weighting for sampling on the basis of EC and historical soil data with two sampling approaches: "smooth fractionator" and comparison with traditional "grid sampling". Collect and lab analysis of new soil samples (pH, NPK, etc.) and collect soil penetrometer strength profiles for investigated fields Geo-Statistical Analysis and mapping using cumulative data Statistical comparison of relationship between soil and yield and crop quality data for the different sampling schemes. GIS technology: -Produce spatial modeling for generation management zones using Model Builder Module. -Generation digital soil, water, and crop growth data bases in the study area. -Geo-Statistical Analysis for relationships between soil variability and wheat yield. -Layout for all the maps Maps Production: Produce geomorphological, soil, soil variability, land use and land cover, yield and management zones (application map) maps for the investigation area. The results obtained: 1. Sampling method. 1.1. The results showed that the use of geographic information systems (GIS) and ground truth points determined by GPS has effectively contributed to a more efficient way asymmetric random sampling in the study area. 2. The results of water chemical analysis. 2.1. Total dissolved salts EC was 0.85 dS m-1 = 554 ppm which has not any side effects on potato growth. 2.2. SAR ratio was 4, which means no problems in the soil, especially in sandy soils, also no amount of carbonate in the sample that means the water soluble Salts located in the low range. 3. Digital soil maps. 3.1. Using geographic information systems (GIS) and predictive statistical models we produced various soil maps, which included maps of salinity, the proportion of calcium carbonate, texture, pH, Digital Elevation Model, Slope, soil saturation percentage, ESP. 3.2. Texture is sandy soil 3.3. small percentage of calcium carbonate was found in some areas and moderate in other areas so this land are not considered calcareous soil. 3.4. pH ranged from 7.2: 8 and the soil tend to be alkaline. 4. Soil suitability and productivity model using digital soil maps. 4.1. The results obtained from this study indicate that the integration of RS-GIS and application of Spatial Multi-Criteria Evaluation (SMCE) could provide a good database and production guide map for decision makers considering crop substitution in order to achieve better agricultural production. After analyzing the output soil maps we can conclude that these maps are useful as first step to build the soil database it need to be updated. 4.2. SMCE criteria estimated based on salinity elements effect on the production, but this data need to be updated annually, because soil parameters change continuously by the changes on CEC and organic matter percent. Organic matter has a major role in improving soil characteristics where it increase soil fertility and modify pH range accompanied with decrease soil salinity and increase cation exchange capacity. On the other hand, by the time the salinity of water increase and the only solution of that adding water availability to the final suitable crop distribution map. SMCE could be done to other crops, also it’s recommended to combine these criteria with water availability, Crop economics, and agriculture machine availability. 5. Mapping of Vegetation Indices (of NDVI and SAVI ) 5.1. The highest values for evidence of vegetation derived from remote sensing data at 60 days of plant age while she was with low values at 90 days from sowing and 30 days from sowing, which is due to the overlapping of reflections spectral soil with reflections spectral plant. 5.2. Methodology for predicting nutrients status from satellite data, based on the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI) have been successfully tested with the measured and predicted data of yield. 6. predict productivity using vegetation indices 6.1. Statistical analysis was applied multiple linear regression using the stepwise selection method for development of empirical equations to estimate crop yield by vegetation indices. 6.2. The results of empirical equations showed high coefficient of determination (R2) values = 0.90, 0.95 and 0.92 as well as the adjusted coefficient of determination (Adjusted R2) were 0.89, 0.94 and 0.91 for the initial, middle and the last stage of the season, respectively. 6.3. In addition to this the validation results of empirical equations showed a significant correlation between the crop co-efficient calculated and expected values of 0.84 and 0.91 and 0.87 for the initial, middle and the last stage of the season, respectively. 6.4. The results of the analysis also showed the calculated values and projected production for both expected and calculated for the potato crop that the mid-season stage were higher accuracy than other stages of growth. 6.5. Compared with the crop growth models, the soil suitability model provided better detection of small areas referred to soil properties, such as Calcareous areas and saline soils. The results showed that yield estimations have a significant correlation coefficient with field measurements. Also both soil suitability and potato growth models were successfully employed to simulate plant indices effect on canopy structure and final yield In the range of 350–1000 nm, the red-edge (705-750 nm) is the most sensitive spectral region for assessing LAI, for potato spectral. However, the degree of importance is determined by the specific band formation of the hyperspectral sensor as well as the crop. The results of Tukey’s HSD showed that blue, green and NIR spectral zones are more sufficient in the discrimination between potato growth stages than red, SWIR-1 and SWIR-2 spectral zones.

PHD Title

Precision Agriculture Using Advanced Remote Sensing Techniques in Arid Lands

PHD Abstract

Soil characteristics play a major role in determining the cause and effects of crop selection and yield production. The soil morphology shows potential worrisome soil properties. Precision farming aim to manage fields according to topography, water consumption and soil types in different areas and its effect on crop yield. The current study aimed to use advanced techniques of remote sensing as a tools to solve the challenges facing the new reclaimed areas, especially in arid lands such as water scarcity and soil problems. Precision agriculture aims to reach the highest output and most appropriate production using lowest inputs while maintaining the safety of the surrounding environment and managing fields based on soil types in different areas. Moreover, the study discusses the impact of precision farming techniques on crop productivity and highlights the application of GPS techniques to adjust fertilizer nutrition according to Available Phosphorus, Available Potassium and soil Micro nutrients distribution and yield goals set by decision makers. The integrated management achieved using remote sensing and GIS techniques by producing of soil topography, various soil maps such as soil physical characteristics, EC, pH, CaCO3, Available phosphorus, Available potassium and Micro nutrients (Fe, Mn and Zn) linked to productivity crop of the study soil locations. Furthermore, study the relationship of plant spectral characteristics and yield response and to use the variable irrigation rate in irrigation scheduling precisely 5*5 meters. To achieve the main goal of the current study, Landsat satellite data were selected. The imagery information of the Landsat OLI provide visible re?ective bands, shortwave infrared bands at 30 meter and thermal infrared bands resampled to 30-meter resolution, also the revisit time every eight days, allowing continuous monitoring of crop growth and the amount of water consumption by the presence of thermal bands. The advanced resolution merge techniques were used to increase high spatial resolution from 30 meter in Landsat sensors to 5 meters using Rapideye imagery which specially designed for precision agriculture service where it can be daily acquired and at a reasonable price and accurately spatial five meters. In this study we applied image fusion using Principal Component Spectral Sharpening (PCSS) method to integrate NDVI and plant water consumption calculated from Landsat satellite data. Furthermore, the ultra-multi-spectral devices has been used as a kind of new remote sensing modern techniques to study the vegetation characteristics and monitor vegetation healthy using narrow bands vegetation indices which easily can be linked to crop productivity. The global GPS system had a major role in locating training samples location, spectral measurements locations and revisiting the same places to take spectral measurements during different stages of crop growth. Collecting information on soil analyzes from previous studies allow identifying different soil units of the study area. Furthermore, the analysis of the soil gives a reasonable idea of the level of productivity in different soil units and also helps in developing new strategies to resolve the problems of soil to reach the highest productivity. The GIS techniques and geo-statistical models helped in the production of various soil maps for the study area, identifying the degree of soil fertility and adding the optimal fertilizer units, also GIS helps in producing yield map, soil samples grid system. The results showed that the use of Geographic Information Systems (GIS) and ground truth points determined by GPS has effectively contributed to a more efficient way a symmetric random sampling in the study area. Also, the Electrical Conductivity of water (ECw) was 0.85 dS m-1 which has not any side effects on peanut and olive growth. Using GIS and predictive statistical models we produced various soil maps, which included maps of salinity, the proportion of calcium carbonate, texture, pH, DEM, Slope, soil saturation percentage, ESP. Texture was sandy clay and sandy clay loam soils in salhiya pivot; also texture generally was loamy sand in olive field at east of Beni-Suef site. In salhiya site a small percentage of calcium carbonate was found in some areas and moderate in other areas so this land are not considered calcareous soil; however in Beni-Suef site calcium carbonate was found with high percent. The pH value ranged from 7.1: 8.2 and the soil tend to be alkaline in both sites before and after cultivation. The results obtained from this study indicate that the integration of RS-GIS and application of Spatial Multi-Criteria Evaluation (SMCE) could provide a good database and production guide map for decision makers considering crop substitution in order to achieve better agricultural production. After analyzing soil maps, we can conclude that these maps are useful as first step to build the soil database it need to be updated. SMCE criteria estimated based on salinity elements effect on the production, but this data needs to be updated annually, because soil parameters change continuously by mineral fertilizers and organic matter percent. Organic matter has a major role in improving soil characteristics where it increases soil fertility and modify pH range accompanied with decrease soil salinity and increase cation exchange capacity. On the other hand, by the time the salinity of water increases and the only solution of that adding water availability to the final suitable crop distribution map. SMCE could be done to other crops, also it’s recommended to combine these criteria with water availability, Crop economics, and agriculture machine availability. The results of Mapping of Vegetation Indices (of EVI, NDVI and SAVI) showed that the highest values for evidence of vegetation derived from remote sensing data at 60 days of plant age while she was with low values at 90 days from sowing and 30 days from sowing, which is due to the overlapping of reflections spectral soil with reflections spectral plant. Methodology for predicting nutrients status from satellite data, based on vegetation indices has been successfully tested with the measured and predicted data of yield. The results showed that the use of Field Spectroradiometer device as one of the advanced remote sensing techniques which give one nanometer spectral resolution and very accurate data about vegetation healthy, estimated yield and linked to soil productivity. The study indicated that in case of the use of this device, the manager can obtain precise information with nanometer accuracy about the healthy status of the plants in various stages of growth and give an idea of the quality and quantity of the final production. Moreover, in the range of 350–1000 nm, the red-edge (705-750 nm) is the most sensitive spectral region for assessing LAI, for peanut spectral. However, the degree of importance is determined by the specific band formation of the hyperspectral sensor as well as the crop. The results of Tukey’s HSD showed that blue, green and NIR spectral zones are more sufficient in the discrimination between peanut growth stages than red, SWIR-1 and SWIR-2 spectral zones. The results showed that yield estimations have a significant correlation coefficient with field measurements. Also, both soil suitability and peanut growth models were successfully employed to simulate plant indices effect on canopy structure and final yield The results of empirical equations showed high coefficient of determination (R2) value = 0.90 as well as the adjusted coefficient of determination (Adjusted R2) were 0.85 for the soil properties and yield. The results of the analysis also showed the calculated values and projected production for both expected and calculated for the peanut crop that the mid-season stage was higher accuracy than other stages of growth. Compared with the crop growth models, the soil suitability model provided better detection of small areas referred to soil properties, such as Calcareous areas and saline soils. The study indicated those modern techniques of remote sensing and its promising opportunity to achieve integrated management and sustainable development of the agriculture sector in Egypt, especially the newly reclaimed areas, or arid lands where a drop of water equal to wealth treasure. Moreover, this study faced many difficulties and challenges; such as clouds in some Landsat 8 imagery so we recommend the use of radar data because it is not affected by clouds. Moreover, Rapideye data is available daily however you cannot acquire area less than one thousand square kilometers in a time which represents a high cost, non-economic in Egypt so we recommend working on launching an Egyptian satellite with suitable spatial and spectral accuracy to help in the development of the agricultural strategies. Furthermore, the ASD field spectroradiometer although nanometer accuracy, but it too expensive and does not have devices available for commercial use or logistical. Fifthly field work always going to be very expensive, so the study recommends using farm records to record seasonal activities, productivity and the results of soil analyses and nutrients and infected areas using GPS and GIS, a relatively low-cost and easy-to-learn techniques. Study recommends applying the new models and new techniques used during the study for managing the new reclaimed areas and for other economic crops also, preparing training workshops for methods and modern techniques used during the study where the future of agriculture in Egypt is the integrated management of precision agriculture using advanced remote sensing techniques for arid areas.