peyman madanchi; mahmod habibnejadroshan; kaka shahedi; karim soleymani; Ahmad Fatehi
Abstract
Abstract:At present, there are a variety of reliable and practical methods for measuring soil moisture from point to world scale. Recently, remarkable progress in remote sensing techniques has allowed the scientific community to accurate and repeatedly map soil moisture anywhere in the world. The above ...
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Abstract:At present, there are a variety of reliable and practical methods for measuring soil moisture from point to world scale. Recently, remarkable progress in remote sensing techniques has allowed the scientific community to accurate and repeatedly map soil moisture anywhere in the world. The above points out the need for this research. In this research, this is the hypothesis, Do MERRA-LAND remote sensing data have an acceptable accuracy in determining the soil moisture content of the baft watershed? To answer this question, After downloading the data and reading them, Pearson correlation method was used to validate the data between monthly average remote sensing data and monthly average precipitation of baft synoptic and Kiskan Rainfall stations measurement in 2009-2013. The results showed 99% confidence in the Kiskan station and 95% confidently at the baft station There is a high correlation between monthly average soil moisture content downloaded with average monthly rainfall, Then, to compare the mean of MERRA-LAND data from the Goddard earth science(GES DISC) As predicted data at the specified date and time and the percentage moisture content obtained from 14 sampling points from the soil surface and the root zone area in the baft basin, the same time and date was used as in-situ data. Mean comparison of T-pair method was performed in SPSS software for each sample. The results showed that due to the higher T calculated from the table T with a degree of freedom 13 compared to the moisture content of the surface area and the root area there is no significant difference with surface download data and root area with 99% confidence. And can be recommended to the executive department Instead of spending a lot of time and cost Use the percentage moisture content provided by this site to predict and monitoring agricultural drought.
