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Due to the unique feature of the three -dimensional convolution neural network, it is used in image classification. For There are some problems such as noise, lack of labeled samples, the tendency to overfitting, a lack of extraction of spectral and spatial features, which has challenged the classification. Among the mentioned problems, the lack of experimental samples is the main problem that has been used to solve the methods in recent years. Among them, convolutional neural network-based algorithms have been proposed as a popular option for hyperspectral image analysis due to their ability to extract useful features and high performance. The traditional CNN-based methods mainly use the 2D-CNN for feature extraction, which makes the interband correlations of HS Is underutilized. The 3D-CNN extracts the joint spectral-spatial information representation, but it depends on a more complex model. To address these issues, the report uses a 3D fast learning block (depthwise separable convolution block and a fast convolution block) followed by a 2D convolutional neural network was introduced to extract spectral-spatial features. Using a hybrid CNN reduces the complexity of the model compared to using 3D-CNN alone and can also perform well against noise and a limited number of training samples. In addition, a series of optimization methods including batch normalization, dropout, exponential decay learning rate, and L2 regularization are adopted to alleviate the problem of overfitting and improve the classification results. To test the performance of this hybrid method, it is performed on the S alinas, University Pavia and Indian Pines datasets, and the results are compared with 2D-CNN and 3D-CNN deep learning models with the same number of layers.

BACKGROUNDBarley is one of the strategic agricultural products available in the world, and yield prediction is important for ensuring food security. One way of estimating a product is to use remote sensing data in conjunction with field data and meteorological data. One of the main issues surrounding this comprises the use of machine learning techniques to create a multi‐resource data‐based estimation model. Many studies have been conducted on barley yield prediction from planting to harvest. Still, the effect of different time intervals on yield prediction has not been investigated. Furthermore, the effect of different periods on yield prediction has not been investigated.RESULTSIn the present study, the whole growth period was divided into three parts. Using one of the major barley production areas in Iran, the performance of the proposed model was evaluated. In the first step, a model for integrating field data, remote sensing data and meteorological data was prepared. The results obtained show that, among the four machine learning methods implemented, the gaussian process regression algorithm performed best and estimated yield with r2 = 0.84, root mean square error = 737 kg ha−1 and mean absolute = 650 kg ha−1, 1 month before harvest.CONCLUSIONIt was found that the estimation results change depending on different agricultural zones and temporal training settings. The findings of the present study provide a powerful potential tool for the yield prediction of barley using multi‐source data and machine learning. © 2020 Society of Chemical Industry

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Land use and land cover change (LULCC) is a main driver of global environmental change and has destructive effects on the structure and function of the ecosystem. This study attempts to detect temporal and spatial changes in LULC patterns of the Chalus watershed during the last two decades using multitemporal Landsat images and predict the future LULC changes and patterns of the Chalus watershed for the year 2040. A hybrid method between segment-based and pixel-based classification was applied for each Landsat image 2001, 2014 and 2021 to produce LULC maps of the Chalus watershed. In this study, the transition potential maps and the transition probability matrices between LULC types were provided by the Support Vector Machine (SVM) algorithm and the Markov Chain model, respectively, to project the 2021 and 2040 LULC maps. The achieved K-index values that compared the simulated LULC map with the actual LULC map of the year 2021 resulted in a Kstandard = 0.9160, Kno = 0.9379, Klocation = 0.9318 and KlocationStrata = 0.9320, showing a good agreement between the actual and simulated LULC map. Analysis of the historical LULC changes depicted that during 2001-2021, the significant increase of Agricultural land (14317 ha) and Barren area (9063 ha), and the sharp decline of Grassland (26215 ha) and Forest cover (5989 ha) were the major LULC changes in the Chalus watershed. The model predicted that Forest cover will continue to decrease from 29.46% (50720.2667 ha) in 2021 to 25.67% of area (44207.78694 ha) in 2040, as well as, unceasing expansion of Barren area, Agricultural land and Built-up area will be expected by 2040. Therefore, understanding the spatiotemporal dynamics of LULC change is extremely important to implement essential measures and minimize the destructive consequences of these changes.