Transparent hydrogels are key materials for many applications, such as contact lens, imperceptible soft robotics and invisible wearable devices. Introducing large and engineerable optical anisotropy offers great prospect for endowing them with extra birefringence-based functions and exploiting their applications in see-through flexible polarization optics. However, existing transparent hydrogels suffer from limitation of low and/or non-fine engineerable birefringence. Here, we invent a transparent magneto-birefringence hydrogel with large and finely engineerable optical anisotropy. The large optical anisotropy factor of the embedded magnetic two-dimensional material gives rise to the large magneto-birefringence of the hydrogel in the transparent condition of ultra-low concentration, which is several orders of magnitude larger than usual transparent magnetic hydrogels. High transparency, large and tunable optical anisotropy cooperatively permit the magnetic patterning of interference colours in the hydrogel. The hydrogel also shows mechanochromic and thermochromic property. Our finding provides an entry point for applying hydrogel in optical anisotropy and colour centred fields, with several proof-of-concept applications been demonstrated.

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Digital soil mapping has emerged as a new method to describe the spatial distribution of soils economically and efficiently. In this study, a lightweight soil organic matter (SOM) mapping method based on a deep residual network, which we call LSM-ResNet, is proposed to make accurate predictions with background covariates. ResNet not only integrates spatial background information around the observed environmental covariates, but also reduces problems such as information loss, which undermines the integrity of information and reduces prediction uncertainty. To train the model, rectified linear units, mean squared error, and adaptive momentum estimation were used as the activation function, loss/cost function, and optimizer, respectively. The method was tested with Landsat5, the meteorological data from WorldClim, and the 1602 sampling points set from Xinxiang, China. The performance of the proposed LSM-ResNet was compared to a traditional machine learning algorithm, the random forest (RF) algorithm, and a training set (80%) and a test set (20%) were created to test both models. The results showed that the LSM-ResNet (RMSE = 6.40, R2 = 0.51) model outperformed the RF model in both the roots mean square error (RMSE) and coefficient of determination (R2), and the training accuracy was significantly improved compared to RF (RMSE = 6.81, R2 = 0.46). The trained LSM-ResNet model was used for SOM prediction in Xinxiang, a district of plain terrain in China. The prediction maps can be deemed an accurate reflection of the spatial variability of the SOM distribution.

The microwave backscattering model is one of the most effective tools for surface soil moisture (SSM) inversion, which has strong theoretical support, but the inverse problem is difficult to solve. Advance in artificial intelligence offers possibilities to learn complex nonlinear relationships in a data-driven way, but it lacks physical mechanism. To combine the advantages of model-driven and data-driven methods, an SSM inversion approach that couples the AIEM-Oh model with deep neural networks (DNNs) was proposed in this study. DNNs with different inputs were trained with a large number of simulation data generated from the AIEM-Oh model, thus embedding physical mechanisms in the data-driven scheme. Two field experiments at different scales were carried out to evaluate the performances of the proposed approach over bare surfaces. The effects of polarization modes and prior knowledge of surface roughness on SSM inversion were explored, and the accuracy of the approach was compared with the existing methods. The results suggest that satisfactory accuracy was obtained by the proposed approach, the RMSE between the measured and estimated values of SSM was 0.03-0.04 cm 3 • cm −3 with prior knowledge of soil roughness, and the RMSE was 0.08-0.10 cm 3 • cm −3 without the prior soil roughness information. VV polarization was more sensitive to SSM over bare surfaces than VH polarization. Moreover, the approach showed stable performance in different experimental regions. The results demonstrate the capability and reliability of the coupled approach for SSM inversion over bare surfaces.