5/31/2023 0 Comments Data merge imagesadopted the vegetation indices (VIs) derived from three-channel UAV images of Red, Green, and Blue (RGB) bands to predict soybean plant density. In recent years, the rapid development of Unmanned Aerial Vehicles (UAV) has provided higher-resolution remote sensing imagery for soybean monitoring. The overall accuracies are generally lower than 90%. It can be found that multitemporal features of satellite imagery are mainly used to identify soybean information. The RF proved to be superior to a Back-Propagation Neural Network (BPNN) and Support Vector Machine (SVM). integrated multi-temporal Sentinel-1/2 microwave and optical multispectral data to map the spatial distribution of soybean through a stepwise hierarchical extraction strategy. Multiple sets of input variables and RF classifiers were jointly used to achieve accuracies higher than 88%. developed an innovative phenology-based classification method to map corn and soybean via over 100 Landsat TM and ETM+ images. identified the corn and soybean cropping areas using the random forest (RF) classifier and multi-temporal 16-meter-resolution GF-1 Wide Field of View (WFV) imagery. applied a 500-meter time-sequential composite MODIS to estimate corn and soybean areas for the dominant production areas of the USA by taking advantage of low spatial and high temporal resolution MODIS data. In most previous studies, single-source RS images have been adopted to extract soybean planting areas, mainly including the MODerate Resolution Imaging Spectroradiometer (MODIS) series of satellites of Gaofen, Landsat, and Sentinel. It is obvious that a precise understanding of soybean planting areas and their geographical distribution is the prerequisite for various applications. RS technology has been widely used with soybean crops including estimating the planting areas, yield estimation, growth monitoring, detection and classification of diseases and insect pests, etc. With the development of satellite RS technology, RS images have gradually become a main data source for extracting crop planting information. When RS technology is applied to the monitoring of soybean, the specific properties and features can be derived from various sensors. Remote sensing (RS) technology can provide spatial, spectral, and temporal information of soybean, with macroscopic and dynamic characteristics. The advancement of earth-observing techniques has greatly improved the monitoring and extraction of crop planting and growth information, especially at a large spatial scale. Traditional methods mainly rely on manual measurement and statistical sampling to achieve statistical data, which are time-consuming, susceptible to subjective judgment, labor-intensive, etc. To make a decision on the cultivation and trade of soybean, it is highly important to figure out the planting areas and spatial distribution. The largest production areas are in China’s three northeastern provinces. It is also one of China’s major food crops, which can be used to provide valuable oil and protein constituents for both humans and livestock. Soybean ( Glycine max (L.) Merr.) is one of the most important oil-bearing crops around the world. This study provides an effective and easily operated approach to accurately derive soybean planting areas from satellite images. ![]() ![]() ![]() In addition, the mIoU has been also improved by 8.89% compared with DeepLabv3+. The results show that U-Net achieves the highest Accuracy of 92.31% with a Mean Intersection over Union ( mIoU) of 81.35%, which is higher than SegNet with an improvement of nearly 4% in Accuracy and 10% on mIoU. To verify the extraction effect of the U-Net model, comparison experiments were also conducted based on the SegNet and DeepLabv3+. Specifically, three cropping sizes of 128 × 128, 256 × 256, and 512 × 512 px, and 20, 40, 60, 80, and 100 training epochs were compared to optimally determine the values of the two parameters. Two vital influencing factors on the accuracies of the U-Net model, including cropping size and training epoch, were compared and discussed. The deep learning U-Net model was then adopted to perform the accurate extraction of soybean planting areas. The 10 m multispectral and 2 m panchromatic Gaofen-1 (GF-1) image data were first fused to produce training, test, and validation data sets after the min–max standardization and data augmentation. Two typical planting areas of Linhu Town and Baili Town in Northern Anhui Province, China, were selected to explore the accurate extraction method. High-resolution satellite remotely sensed imagery has greatly facilitated the effective extraction of soybean planting areas but novel methods are required to further improve the identification accuracy. It is of great significance to accurately identify soybean planting areas for ensuring agricultural and industrial production.
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