Timely and accurate prediction of winter wheat yield is crucial for national food security. In the field of crop yield prediction, deep learning techniques are playing an increasingly important role. However, many existing methods mainly utilize convolutional neural network (CNN) or long short-term memory (LSTM) network, failing to fully exploit the spatiotemporal information in remote sensing data. To address this issue, a CNN–bidirectional long short-term memory (BiLSTM)–attention model for winter wheat yield prediction was proposed using time series Sentinel-1A synthetic aperture radar images. The histogram dimension reduction technique was employed to generate the samples. The CNN was used to extract the spatial–spectral features from the image samples, and the BiLSTM network was adopted to learn the temporal features of winter wheat growth stages from the time series samples. Furthermore, an attention mechanism was introduced to make the networks learn important features more efficiently to improve the accuracy of yield prediction. The time series Sentinel-1A synthetic aperture radar images covering Weishi County, Kaifeng city, Henan province, China, were used for model training and validation. The experimental results demonstrated that the proposed model exhibited good accuracy in yield prediction for the study area, with a coefficient of determination of 0.79, a root mean square error of 583.53 kg/ha, and a mean absolute error of 458.41 kg/ha. The proposed method has a promising application in crop yield prediction and provides a useful reference for similar crop yield prediction.
The precise detection of a surface water body using synthetic aperture radar (SAR) images is crucial for flood mitigation, disaster reduction, and water resource planning applications. Although SAR has been proven to have the ability to provide information for water-body detection, a single SAR feature is still insufficient to achieve high-precision classification. To fully leverage the backscatter intensity and polarimetric features of SAR images, we propose the dual-branch fusion network (DBFNet), an innovative semantic segmentation model that integrates the backscatter intensity and polarimetric features. Specifically, the DBFNet employs a distinctive dual-branch architecture that integrates the complementary information of both feature types using the layer feature fusion module and refines multiscale features at various levels through the intermediate feature refinement module. The performance of the proposed DBFNet is evaluated by conducting comparative experiments with five deep learning models: FCN, U-Net, DeepLabv3+, FWENet, and FFEDN. The experimental results demonstrate that the DBFNet achieves the highest accuracy in water-body detection, with an intersection over union of 89.28% and an F1-score of 94.34%.
Crop monitoring and phenology estimation based on the satellite systems have become an important research area due to high demand on crops. Synthetic Aperture Radar (SAR) is a kind of microwave remote sensing equipment, which has the advantage of all-weather and all-day, and can realize large-scale and periodic crop phenological monitoring. Besides, thanks to the high temporal resolution of new generation space-based sensors, it has been possible to monitor growth cycle of crops by classification algorithms. A stacking ensemble learning algorithm using time series Sentinel-1A SAR images for winter wheat phenology classification was proposed in this paper based on multiple machine learning models, including Random Forest (RF), Support Vector Machine (SVM), K-nearest Neighbor(K-NN), Naive Bayes (NB) and BP Neural Network (BP) models. The experimental results showed that, comparing with each single model, the stacking ensemble learning algorithm proposed in this paper had the optimal performance, with the highest overall recognition accuracy of 81.40%, demonstrating its effectiveness and application potential for winter wheat phenology identification.
Spaceborne synthetic aperture radar (SAR) is an active radar system carried on a satellite, with the help of synthetic aperture technology to achieve all-day, all-weather, high-resolution imaging, it has been widely used in military reconnaissance, resource exploration, deep space exploration and other fields. As the electromagnetic environment becomes more and more complex, spaceborne SAR is more likely to receive radio frequency interference (RFI) from other radar equipment in the working frequency band, which will affect the interpretation of SAR images and lead to image quality degradation. Although there are endless methods for RFI detection and suppression, each method has its limitations and can only deal with specific interference scenarios. A unified, robust and high-precision SAR interference detection and suppression platform is urgently needed. Based on SAR signal processing technology, this paper uses multiple methods to jointly design a unified high-robust system for SAR data containing different types of RFI, and realizes efficient detection and suppression of spaceborne SAR RFI. The popularization and application of this system is of great significance to improve the anti-interference ability of SAR system, improve the processing efficiency of a large number of SAR data in complex scenes and ensure the accuracy of data.
KEYWORDS: Data modeling, Yield improvement, Remote sensing, Vegetation, Agriculture, Solar radiation models, Solar radiation, Atmospheric modeling, Process modeling, Meteorology
Accurately and timely grasping agricultural information at the regional scale helps to solve food security issues and formulate agricultural policies. Remote sensing images have the advantages of wide monitoring range and the ability to eliminate human interference. In recent years, they have been increasingly valued in crop yield estimation. The CASA model is used to estimate the net primary productivity (NPP) of crops and then combine it with the harvest index (HI) to estimate crop yields. However, most studies use a fixed HI for yield estimation, which can lead to low accuracy. In this context, the HI is studied and improved for winter wheat yield prediction in this research. The main contents are as follows: Firstly, time-series Sentinel-2 optical data and meteorological data were used as inputs of the CASA model to calculate the NPP of winter wheat. Then, HI was calculated using the fieldwork data and a method was proposed to improve it due to its geographic location-related characteristics. Finally, the calculated NPP and improved HI were used to estimate the winter wheat yield. The experimental results prove that the proposed method has higher accuracy than original methods, with determination coefficient(R2) of 0.595, root mean square error(RMSE) of 793.4 kg/ha, and mean absolute error(MAE) of 659.53 kg/ha.
Synthetic aperture radar (SAR), as an active microwave remote sensor with high-resolution earth observation, is easily affected by radio frequency interference (RFI). Pulse radio frequency interference (PRFI), as a typical form of RFI, pollutes SAR raw data with bright stripes, which increases the difficulty of SAR image interpretation. However, the conventional eigen subspace projection (ESP) method decomposes the whole pulse into the interferences and the useful signals, resulting in the loss of useful signals in the non-interference positions with the pulse. To solve this problem, this paper proposes an improved SAR PRFI suppression method based on ESP. Firstly, the receiving pulses containing PRFI are detected and the specific position of PRFI in the pulse are determined by eigenvalue decomposition and energy accumulation in time-frequency domain, respectively. Secondly, the PRFI signal is separated from the useful signal by applying the ESP method only to the data where the PRFI is located. Finally, the PRFI signal projected by ESP is eliminated from the raw data. Simulation results shown that the proposed method can better protected useful signals and improve SAR image quality compared with conventional methods.
Real-time and dynamic monitoring of soil moisture is critically essential for farming activities and crop yield estimation. This paper focuses on the study of surface soil moisture (SSM) in agricultural fields with different vegetation sparsity. Based on the improved water cloud model (WCM) and convolutional neural network (CNN), the SSM inversion of agricultural fields covered with winter wheat at different growth stages was achieved. Firstly, the vegetation cover factor was introduced into the WCM to separate the scattering contribution under crop cover from the direct scattering contribution from the bare ground. Then, the remote sensing data was used to derive numerous characteristic parameters to the SSM. Finally, the CNN model was established for the purpose of SSM inversion. The better correlation between the backscattering coefficients obtained by the improved WCM simulations and the measured SSM was shown. The coefficients of determination were 0.46 and 0.39 under VV and VH polarization, respectively. Better inversion accuracy can be obtained by combining the two parameters with other characteristic parameters as input data into the CNN model for SSM inversion. The coefficient of determination was 0.75, root mean square error was 2.51 vol.% and mean absolute error was 2.12 vol.%. Therefore, the method can effectively separate the effects of agricultural crops and bare ground on radar signals, which contributes a novel research approach and guidance for handling SSM inversion in agricultural fields covered by vegetation.
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