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Transition metal oxides have been regarded as the most potential anode material for lithium-ion batteries (LIBs), because of their high theoretical capacity, abundant resource, and low cost. Nevertheless, poor conductivity and large volumetric expansion during the charge–discharge processes make it difficult for LIBs application. In this work, we have adopted an in situ carbon coating method to synthesize 1D mesoporous MnO@C nanorods, which effectively work out the above problems of LIBs. Meanwhile, the MnO@C-3 (the mixture is 40 mL of H2O in solvothermal reaction) anode exhibits excellent rate capability with the capacity retention of 90.3% at 2.0 A g–1 and prominent cycling stability at 5.0 A g–1 with the discharge capacity of 1210.1 mAh g–1 after 400 cycles. When assembled to a full cell with commercial LiFePO4 as cathode, the full cell represents 360.7 mAh g–1 after 30 cycles (based on the anode), which displays a development potential for practical application.

Soil moisture is an important variable in ecological, hydrological, and meteorological studies. An effective method for improving the accuracy of soil moisture retrieval is the mutual supplementation of multi-source data. The sensor configuration and band settings of different optical sensors lead to differences in band reflectivity in the inter-data, further resulting in the differences between vegetation indices. The combination of synthetic aperture radar (SAR) data with multi-source optical data has been widely used for soil moisture retrieval. However, the influence of vegetation indices derived from different sources of optical data on retrieval accuracy has not been comparatively analyzed thus far. Therefore, the suitability of vegetation parameters derived from different sources of optical data for accurate soil moisture retrieval requires further investigation. In this study, vegetation indices derived from GF-1, Landsat-8, and Sentinel-2 were compared. Based on Sentinel-1 SAR and three optical data, combined with the water cloud model (WCM) and the advanced integral equation model (AIEM), the accuracy of soil moisture retrieval was investigated. The results indicate that, Sentinel-2 data were more sensitive to vegetation characteristics and had a stronger capability for vegetation signal detection. The ranking of normalized difference vegetation index (NDVI) values from the three sensors was as follows: the largest was in Sentinel-2, followed by Landsat-8, and the value of GF-1 was the smallest. The normalized difference water index (NDWI) value of Landsat-8 was larger than that of Sentinel-2. With reference to the relative components in the WCM model, the contribution of vegetation scattering exceeded that of soil scattering within a vegetation index range of approximately 0.55–0.6 in NDVI-based models and all ranges in NDWI1-based models. The threshold value of NDWI2 for calculating vegetation water content (VWC) was approximately an NDVI value of 0.4–0.55. In the soil moisture retrieval, Sentinel-2 data achieved higher accuracy than data from the other sources and thus was more suitable for the study for combination with SAR in soil moisture retrieval. Furthermore, compared with NDVI, higher accuracy of soil moisture could be retrieved by using NDWI1 (R2 = 0.623, RMSE = 4.73%). This study provides a reference for the selection of optical data for combination with SAR in soil moisture retrieval.

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