Irrigation
S.F. Mousavizadeh; H. Ansari; A. R. Faridhoseini
Abstract
Introduction: In the last decade, satellite-based methods, including remote sensing and microwave methods, have been used in many studies to detect soil surface moisture regionally. Thermal remote sensing method is quite effective for checking moisture for bare soil but shows poor correlation for vegetated ...
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Introduction: In the last decade, satellite-based methods, including remote sensing and microwave methods, have been used in many studies to detect soil surface moisture regionally. Thermal remote sensing method is quite effective for checking moisture for bare soil but shows poor correlation for vegetated surfaces. In addition, there is a widespread use of this method in the presence of temperature differences during the day. Satellite imagery enables the ability to measure humidity according to the environmental conditions at the surface. Thus, compared to field measurements, remote sensing techniques are promising because they are capable of spatial measurements at a relatively low cost. Water supply is one of the main causes of evapotranspiration, which can affect it. Soil moisture can be considered as the most direct and important variable describing drought and is the main parameter describing water circulation and energy exchange between the surface and the atmosphere. Scale reduction methods for soil moisture can be divided into three main groups including satellite-based method, GIS data and model-based methods. The same methods have been used extensively in monitoring soil moisture for different spectral patterns at different wavelengths, from visible to microwave remote sensing data. Spectral reflectance decreases with increasing soil moisture in the visible and near-infrared (NIR) range. Therefore, these methods can be used to estimate soil moisture using satellite data for water budgeting and other meteorological and agricultural applications.Materials and Methods: In this study, using the information provided by Zaki (2013), the measured humidity by the sensor was compared with the humidity obtained from the satellite. The soil moisture were measured in 16 points from an area of 13 hectares from Neyshabour plain of Khorasan Razavi province. The novelty of this study is to provide a simple method for using Landsat 7 satellite imagery to estimate the surface moisture of areas of the Earth to eliminate field sampling and optimal use for agriculture. One of the advantages of this method is the reduction of information obtained from the field as input values for crop modeling that can be used to estimate crop yield, so the moisture measured during the winter wheat crop period from November 2012 to March 2013 was used.Results and Discussion: The placement of band numbers 3 and 4 opposite each other to calculate M, the line equation was fitted. Since satellite imagery is not performed daily by satellite, six images were extracted during the growing season. On November 12, which is actually 12 days after planting, the plant is entering the germination stage and the soil is mostly bare. Because the satellite does not receive enough reflected green light, the accuracy of the image in measuring soil moisture decreases, but after the plant grows, the green light is reflected and the amount of digital digit of band 4 is affected, as a result, the amount of moisture in the plant leaves and stem is involved in measuring soil moisture, which is consistent with the results obtained by Petropoulos et al.Conclusion: In general, the results of this study showed that the simple and efficient Red-NIR spatial geometry model has a great ability to estimate soil surface moisture in favorable weather conditions and this method can be used for plant modeling as input data.
Yaser Ostovari; shoja ghorbani; Hosseinali Bahrami; Mahdi Naderi; mozhgan abasi
Abstract
Introduction: Soil erodibility (K factor) is generally considered as soil sensitivity to erosion and is highly affected by different climatic, physical, hydrological, chemical, mineralogical and biological properties. This factor can be directly determined as the mean rate of soil loss from standard ...
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Introduction: Soil erodibility (K factor) is generally considered as soil sensitivity to erosion and is highly affected by different climatic, physical, hydrological, chemical, mineralogical and biological properties. This factor can be directly determined as the mean rate of soil loss from standard plots divided by erosivity factor. Since measuring the erodibility factor in the field especially watershed scale is time-consuming and costly, this factor is commonly estimated by pedotransfer functions (PTFs) using readily available soil properties. Wischmeier and Smith (1978) developed an equation using multiple linear regressions (MLR) to estimate erodibility factor of the USA using some readily available soil properties. This equation has been used to estimate K based on soil properties in many studies. As using PTFs in large sales is limited due to cost and time of collecting samples, recently soil spectroscopy technique has been widely used to predict certain soil properties using Point SpectroTransfer Functions (PSTFs). PSTFs use the correlation between soil spectra in Vis-NIR (350-2500 nm) and certain soil properties. The objective of this study was to develop PSTFs and PTFs for soil erodibility factor prediction in the Simakan watershed Fars, Iran.
Materials and Methods: The Semikan watershed, which mainly has calcareous soil with more than 40% lime (total carbonates), is located in the central of Fars province, between 30°06'-30°18'N and 53°05'-53°18'E (WGS′ 1984, zone 39°N) with an area of about 350 km2. For this study, 40 standard plots, which are 22.1×1.83 m with a uniform ploughed slope of 9% in the upslope/downslope direction, were installed in the slopes of 8-10% and the deposit of each plot was collected after rainfall. From each plot three samples were sampled and some physicochemical properties including soil texture, organic matter, water aggregate stability, soil permeability, pH, EC were analyzed Spectra of the air-dried and sieved soil samples were recorded in the Vis-NIR-SWIR (350 to 2500 nm) range at 1.4- to 2-nm sampling intervals in a standard and controlled dark laboratory environment using a portable spectroradiometer apparatus (FieldSpec 3, Analytical Spectral Device, ASD Inc.). Some bands which had the highest correlation with K factor were chosen as input parameter for developing PSTFs. A stepwise multiple linear regression method was used for developing PTFs and SPTFs. R2, RMSE and ME were used for comparing PTFs and SPTFs.
Results and Discussion: The K values varied from 0.005 to 0.023 t h MJ−1 mm−1 with an average standard deviation of 0.014 and of 0.003 t h MJ−1 mm−1, respectively. The K estimated by Wischmeier and Smith (1978) equation varied from 0.015 to 0.045 t h MJ−1 mm−1 with an average of 0.030 t h MJ−1 mm−1. There was a significant difference (p
