Mohammad Khosravi Aqdam’s research while affiliated with Ministry of Education Iran and other places

Today, remote sensing is widely used to estimate soil properties. Because it is an easy and accessible way to estimate soil properties that are difficult to estimate in the field. Based on this, to evaluate the soil fertility (SF), soil sampling was performed irregularly from the surface depth of 0–30 cm in 216 points, 11 soil properties were measured, and the soil fertility index (SFI) was calculated by soil properties. Simultaneously, we combined satellite images of Landsat 8 and Sentinel-2 using the Gram-Schmidt algorithm. Finally, multiple linear regression SFI was calculated using satellite data, as well as the spatial distribution of SFI was obtained in very low, low, moderate, high, and very high classes. Our findings showed that the combination of Landsat 8 and Sentinel-2 data using the Gram-Schmidt algorithm has a higher correlation with SFI than when these data are individually. Therefore, combined Landsat 8 and Sentinel 2 data were used for SFI modeling. Using model selection procedure indices (including Cp, AIC, and ρc criteria), the visible range bands, notably blue (r = 0.65), green (r = 0.63), and red (r = 0.61), provide the best model for estimating SFI (R² = 0.43, Cp = 3.34, AIC = -277.4, and ρc = 0.44). Therefore, these bands were used to estimate the SFI index. Also, the spatial distribution of the SIF index showed that the most significant area was related to the low class, and the lowest area belonged to the high and very high fertility classes. According to these results, it can be concluded that using the combination of Landsat 8 and Sentinel 2 bands to estimate soil fertility index in agricultural lands can increase the accuracy of soil fertility estimation.

Texture is one of the most important soil properties that knowledge of the spatial distribution is essential for land-use planning and other activities related to agriculture and environment protection. So, this study was performed to supply the soil texture spatial distribution using standardized spectral reflectance (ZPC1) index of Landsat 8 satellite images in the northwest of Iran. The soil sampling was performed using a random method in 145 points. Mineral soil particles including clay, silt, and sand were determined, and soil texture was calculated. In this study, Landsat 8 satellite images were used to interpolate the soil texture spatial distribution. In the first step, the principal component analysis (PCA) was obtained. Then, PCA1 was standardized using a z-score (ZPC1), and regression techniques were used to create proper relationships between ZPC1 and the primary soil particles. Then, spatial distribution of soil particles was used to create a spatially distributed map of the soil textural classes. The results showed that the standardization of the first component reduced the standard deviation of PCA1 from 23.6 to 10.8. The results of comparing ZPC1 with soil mineral components showed that with increasing the amounts of soil clay and sand, the ZPC1 value decreases and increases, respectively. The results showed that the ranges of the spatial distribution of clay and sand were similar to the laboratory-measured amounts. The results of texture class prediction using the soil texture triangle showed that the amount of similarity between the measured and predicted classes was 53.79%.