Zahra Rezghi; Mehdi Homaee; Aliakbar Noroozi
Abstract
Knowledge about soil texture is very important in agricultural studies due to its direct impact on other soil properties. However, determining the soil texture in vast areas requires a lot of time and money. For this reason, researchers are looking for ways to determine this important feature of the ...
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Knowledge about soil texture is very important in agricultural studies due to its direct impact on other soil properties. However, determining the soil texture in vast areas requires a lot of time and money. For this reason, researchers are looking for ways to determine this important feature of the soil on a large scale. One of these methods is the use of surface soil spectrometry. In this method, the choice of calibration method significantly affects the accuracy of measuring the characteristics of the surface. In this study, the performance of two regression techniques, namely, partial least-squares regression (PLSR), principal component regression (PCR) were compared to identify the best method to assess sand, silt and clay. For this purpose, 50 soil samples from Tehran province were collected and used as a data set for Calibration and Validation. Soil samples with different moisture levels (oven dry, 5, 10, 15 and 20 w/w) were scanned using a FieldSpec Pro Spectroradiometer with a measurement range of 350–2500 nm. The spectra were subjected to three pre-processed techniques, e.g., Savitzky–Golay (SG) smoothing, first derivative with SG smoothing (FD-SG), Normalization with SG smoothing (Normal-SG). The R2 results from cross-validation indicated that the PLSR model had a better performance than PCR. Normal + SG pre-processing method for clay loam texture and SG method for sandy clay loam texture showed better estimation of measured properties. The amount of R2 for clay was 0.74, 0.81, 0.97 and 0.87, respectively, in moisture content of oven dry, 5, 15 and 20% in clay loam texture And 0.95 and 0.61 at oven dry and 5% levels in sandy clay loam. Silt was better predicted by R2 0.67 in moisture content of 5% in clay loam texture and R2 0.97 in moisture content of 20% at sandy clay loam texture. Sand was also predicted (R2= 0.86 and 0.72) in moisture content of 5 and 10% in clay loam texture.
Milad Nouri; Mehdi Homaee; Mohammad Bannayan
Abstract
In this study, changes of aridity index (AI), reference evapotranspiration (ET0) and precipitation were investigated in six stations located in the west and northwest of Iran over 1966-2010, 2011-2040, 2041-2070 and 2071-2100 periods. The outputs of HadCM3 under A2 and B2 emission scenarios were downscaled ...
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In this study, changes of aridity index (AI), reference evapotranspiration (ET0) and precipitation were investigated in six stations located in the west and northwest of Iran over 1966-2010, 2011-2040, 2041-2070 and 2071-2100 periods. The outputs of HadCM3 under A2 and B2 emission scenarios were downscaled using statistical downscaling approach by Statistical downscaling model (SDSM). Mann-Kendall trend test was applied to assess the significance of trends of aridity index, reference evapotranspiration and precipitation in 1966-2010. The results of Mann-Kendall test revealed that there was a significant decreasing trend in AI and precipitation at the level of 95% over 1966-2010 in most of the surveyed stations. The negative trends of AI during winter, spring, summer and autumn were significant at five, four, zero and two of six surveyed stations, respectively. This indicates that reduced wintertime aridity index plays an important role in downward trend of annual aridity index in the studied area. The results also showed that AI, averaged across all stations, would decline by 8.0, 14.7 and 34.3% under A2 and 12.6, 12.5 and 20.1% under B2 over the early, middle and late 21st century relative to the baseline period (1966-2010), respectively, indicating a drier climate in northwest and west of country over the 21st century. On seasonal scale, the greatest decrease of AI is expected in summertime under A2 and springtime under B2 over the 21st century. AI, averaged over all stations, will most likely approach 0.2 indicating a severe reduction of aridity index in the studied area under A2. In some regions such as Tabriz, increased ET0 and decline of precipitation will cause a shift from semi-arid to arid climatic condition over the 21st century.
