Assessment of soil fertility and nutrient management strategies in calcareous soils of Khuzestan province: a case study using the Nutrient Index Value method.

Soil fertility (SF) is a crucial factor that directly impacts the performance and quality of crop production. To investigate the SF status in agricultural lands of winter wheat in Khuzestan province, 811 samples were collected from the soil surface (0-25 cm). Eleven soil properties, i.e., electrical conductivity (EC), soil organic carbon (SOC), total nitrogen (TN), calcium carbonate equivalent (CCE), available phosphorus (Pav), exchangeable potassium (Kex), iron (Fe), copper (Cu), zinc (Zn), manganese (Mn), and soil pH, were measured in the samples. The Nutrient Index Value (NIV) was calculated based on wheat nutritional requirements. The results indicated that 100%, 93%, and 74% of the study areas for CCE, pH, and EC fell into the low, moderate, and moderate to high NIV classes, respectively. Also, 25% of the area is classified as low fertility (NIV < 1.67), 75% falls under medium fertility (1.67 < NIV value < 2.33), and none in high fertility (NIV value > 2.33). Assessment of the mean wheat yield (AWY) and its comparison with NIV showed that the highest yield was in the Ramhormoz region (5200 kg.ha-1), while the lowest yield was in the Hendijan region (3000 kg.ha-1) with the lowest EC rate in the study area. Elevated levels of salinity and CCE in soils had the most negative impact on irrigated WY, while Pav, TN, and Mn availability showed significant effects on crop production. Therefore, implementing SF management practices is essential for both quantitative and qualitative improvement in irrigated wheat production in Khuzestan province.

Supervised cross-fusion method: a new triplet approach to fuse thermal, radar, and optical satellite data for land use classification.

This study presents a new fusion method namely supervised cross-fusion method to improve the capability of fused thermal, radar, and optical images for classification. The proposed cross-fusion method is a combination of pixel-based and supervised feature-based fusion of thermal, radar, and optical data. The pixel-based fusion was applied to fuse optical data of Sentinel-2 and Landsat 8. According to correlation coefficient (CR) and signal to noise ratio (SNR), among the used pixel-based fusion methods, wavelet obtained the best results for fusion. Considering spectral and spatial information preservation, CR of the wavelet method is 0.97 and 0.96, respectively. The supervised feature-based fusion method is a fusion of best output of pixel-based fusion level, land surface temperature (LST) data, and Sentinel-1 radar image using a supervised approach. The supervised approach is a supervised feature selection and learning of the inputs based on linear discriminant analysis and sparse regularization (LDASR) algorithm. In the present study, the non-negative matrix factorization (NMF) was utilized for feature extraction. A comparison of the obtained results with state of the art fusion method indicated a higher accuracy of our proposed method of classification. The rotation forest (RoF) classification results improvement was 25% and the support vector machine (SVM) results improvement was 31%. The results showed that the proposed method is well classified and separated four main classes of settlements, barren land, river, river bank, and even the bridges over the river. Also, a number of unclassified pixels by SVM are very low compared to other classification methods and can be neglected. The study results showed that LST calculated using thermal data has had positive effects on improving the classification results. By comparing the results of supervised cross-fusion without using LST data to the proposed method results, SVM and RoF classifiers showed 38% and 7% of classification improvement, respectively.