Soil moisture is an important parameter that directly impacts crop productivity. Microwave signals are highly sensitive to soil dielectric constant and so they are used to derive soil moisture. The potential of entropy and alpha (H / α) decomposition for moisture estimations at 0- to 5-cm soil depth was assessed. The H / α parameters were extracted from the dual-polarimetric Sentinel-1 dataset. Also, we used the gray level co-occurrence matrix (GLCM) texture parameters extracted from Sentinel-1 and tested them for soil moisture retrieval. The generalized regression neural network (GRNN), neural network (NN), and support vector regression (SVR) algorithms were trained and tested for soil moisture estimation. Multiple input features, including Sentinel-1 intensities, GLCM parameters, and H / α parameters, were used for training these algorithms. For NN, three activation functions of rectified linear unit (ReLU), tanh, and sigmoid and for SVR three kernel functions of radial basis function (RBF), polynomial, and linear kernel were tested. The ReLU outperformed the other two activation functions with root mean squared error (RMSE) of 0.042 m3 m − 3 and coefficient of determination (R2) of 0.72. For the SVR algorithm, the highest accuracies derived from the RBF kernel function with RMSE 0.053 m3 m − 3 and R2 of 0.51. Between all the three machine learning algorithms, the GRNN algorithm outperformed the other two algorithms with RMSE of 0.033 m3 m − 3 and R2 of 0.82. These results demonstrated the high potential of using polarimetric synthetic aperture radar data in combination with the machine learning algorithms for surface soil moisture monitoring.
Estimating volumetric soil moisture (Mv) and surface roughness (S) are the key parameters for numerous agricultural and hydrological applications. Although these two parameters can be effectively retrieved from synthetic aperture radar (SAR) data, the presence of vegetation can negatively affect the results. A method was proposed to accurately estimate Mv and S over vegetated agricultural areas. The method was based on applying the machine learning inversion approach along with SAR data to invert a combination of the parameterized water cloud model (PWCM) and the calibrated integral equation model (CIEM). The soil backscattered component in water cloud model (WCM) was generated by CIEM to be applied to the WCM parameterization and dataset simulation. Three machine learning algorithms, including the support vector regression (SVR), multi-output SVR (MSVR), and artificial neural network (ANN), were employed to model the relationship between the simulated dataset variables. The genetic algorithm was also applied to optimize the models’ parameters. The inversion technique results demonstrated that the MSVR and ANN had the highest accuracy in estimating Mv and S due to their better structures. The SMAPVEX-16 in situ dataset, along with three Sentinel-1 images, was applied to evaluate the accuracy of the WCM parameterization and the proposed method for Mv and S estimation. The accuracies of the PWCM in the VV and VH polarizations of Sentinel-1 C-band data were reasonable for VWC < 2.5 kg / m2 [root-mean-square error (RMSE) = 1.44 and 1.77 dB, respectively]. Additionally, it was observed that the trained SVR, MSVR, and ANN had similar results for different VWC values. In summary, the proposed method had high potential in vegetated agricultural areas with VWC < 2.5 kg / m2, for which the RMSEs were 4 to 7 vol. % and 0.35 to 0.46 cm depending on the VWC values in retrieving Mv and S, respectively.
Upwelling is an oceanographic process that transfers cool and nutrient-rich waters toward the sea surface. Due to the relation between low sea surface temperature (SST) and high nutrient-rich water, the upwelling regions can be easily recognized in satellite imagery. An optical flow (OF) method, Horn–Schunck, is used to discover the upwelled water motion (UWM) and its pattern using sequential (pair) SST imageries. The SST imageries of Aqua and Terra satellites between 2004 and 2012 are processed to extract the properties of upwelling in the Shevchenko area (Caspian Sea). Results show that the upwelling is periodic (with an ∼24 h period) and it matches with a Fourier model. In addition, the UWM has a specific direction from morning to night. It is also shown that the OF cannot extract correct UWM if the SST imageries are selected on different cycles. Level of chlorophyll_a in the same area is used to independently validate the existence of the upwelling.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.