Estimation of Aboveground Vegetation Water Storage in Natural Forests in Jiuzhaigou National Nature Reserve of China Using Machine Learning and the Combination of Landsat 8 and Sentinel-2 Data

Aboveground vegetation water storage (AVWS) is a fundamental ecological parameter of terrestrial ecosystems which participates in plant metabolism, nutrient and sugar transport, and maintains the integrity of the hydraulic system of the plant. The Jiuzhaigou National Nature Reserve (JNNR) is located in the Eastern Tibet Plateau and it is very sensitive to climate change. However, a regional estimate of the AVWS based on observations is still lacking in the JNNR and improving the model accuracy in such mountainous areas is challenging. Therefore, in this study, we combined the Landsat 8 and Sentinel-2 data to estimate AVWS using multivariate adaptive regression splines (MARS), random forest (RF) and extreme gradient boosting (XGBoost) with the linkage of 54 field observations in the JNNR. The results showed that AVWS varied among different forest types. The coniferous forests had the highest AVWS (212.29 ± 84.43 Mg ha−1), followed by mixed forests (166.29 ± 72.73 Mg ha−1) and broadleaf forests (142.60 ± 46.36 Mg ha−1). The average AVWS was 171.2 Mg ha−1. Regardless of the modelling approaches, both Sentinel-2 and Landsat 8 successfully estimated AVWS separately. Prediction accuracy of AVWS was improved by combining Landsat 8 and Sentinel-2 images. Among the three machine learning approaches, the XGBoost model performed best with a model efficiency of 0.57 and root mean square error of 48 Mg ha−1. Predicted AVWS using XGBoost showed a strong spatial pattern of across the study area. The total AVWS was 5.24 × 106 Mg with 67.2% coming from conifer forests. The results highlight the potential of improving the accuracy of AVWS estimation by integrating different optical images and using machine learning approaches in mountainous areas

Improving the Arctic sea-ice numerical forecasts by assimilation using a local SEIK filter

Appropriate initial conditions are essential for accurate forecasts of sea ice conditions in the Arctic. We present a prototype of an assimilation and forecast system, where a new sea ice thickness data set based on the Soil Moisture and Ocean Salinity (SMOS) satellite data and sea ice concentration data (SSMIS) are assimilated with a local Singular Evolutive Interoplated Kalman (SEIK) [3] filter. The system is run for 3 months in the transition between autumn and winter 2011/2012. Forecasts of different length are evaluated and compared to independent in-situ data

Dynamic Power Sharing and Autonomous Voltage Regulation in Islanded DC Microgrids

This paper proposes a coordinated control strategy for islanded microgrids (MGs) including bus voltage compensation and dynamic power sharing. The droop control is used to realize power sharing among the distributed generations (DGs) in the primary control layer. On this basis, a distributed secondary control with voltage regulation control and current correction control is proposed. This control strategy compensates the DC bus voltage drop caused by the traditional droop control, thus restoring the bus voltage to the rated value. Moreover, the droop coefficient is designed to be adjusted constantly, which achieves high precision of power sharing. The simulations under different operation modes are conducted to validate the performance of the proposed method, where the results demonstrate its effectiveness

Assimilating SMOS sea ice thickness into a coupled ice-ocean model using a local SEIK filter

The impact of assimilating sea ice thickness data derived from ESA's Soil Moisture and Ocean Salinity (SMOS) satellite together with Special Sensor Microwave Imager/Sounder (SSMIS) sea ice concentration data of the National Snow and Ice Data Center (NSIDC) in a coupled sea ice-ocean model is examined. A period of 3 months from 1 November 2011 to 31 January 2012 is selected to assess the forecast skill of the assimilation system. The 24 h forecasts and longer forecasts are based on the Massachusetts Institute of Technology general circulation model (MITgcm), and the assimilation is performed by a localized Singular Evolutive Interpolated Kalman (LSEIK) filter. For comparison, the assimilation is repeated only with the SSMIS sea ice concentrations. By running two different assimilation experiments, and comparing with the unassimilated model, independent satellite-derived data, and in situ observation, it is shown that the SMOS ice thickness assimilation leads to improved thickness forecasts. With SMOS thickness data, the sea ice concentration forecasts also agree better with observations, although this improvement is smaller

Estimation of leaf water content from hyperspectral data of different plant species by using three new spectral absorption indices.

The leaf equivalent water thickness (EWT, g cm-2) and fuel moisture content (FMC, %) are key variables in ecological and environmental monitoring. Although a variety of hyperspectral vegetation indices have been developed to estimate the leaf EWT and FMC, most of these indices are defined considered two or three specific bands for a specific plant species, which limits their applicability. In this study, we proposed three new spectral absorption indices (SAI970, SAI1200, and SAI1660) for various plant types by considering the symmetry of the spectral absorption at 970 nm, 1200 nm and 1660 nm and spectral heterogeneity of different leaves. The indices were calculated considering the absorption peak and shoulder bands of each leaf instead of the same specific bands for all leaves. A pooled dataset of three tree species (camphor (VX), capricorn (VJ), and red-leaf plum (VL)) was used to test the performance of the SAIs in terms of the leaf EWT and FMC estimation. The results indicated that, first, SAI1200 was more suitable for estimating the EWT than FMC, whereas SAI970 and SAI1660 were more suitable for estimating the FMC. Second, SAI1200 achieved the most accurate estimation of the EWT with a cross-validation coefficient of determination (Rcv2) of 0.845 and relative cross-validation root mean square error (rRMSEcv) of 8.90%. Third, SAI1660 outperformed the other indices in estimating the FMC at the leaf level, with an Rcv2 of 0.637 and rRMSEcv of 8.56%. Fourth, SAI970 achieved a moderate accuracy in estimating the EWT (Rcv2 of 0.25 and rRMSEcv of 19.68%) and FMC (Rcv2 of 0.275 and rRMSEcv of 12.10%) at the leaf level. These results can enrich the application of the SAIs and demonstrate the potential of using SAI1200 to determine the leaf EWT and SAI1660 to obtain the leaf FMC among various plant types

Removal of Uranyl Ion from Wastewater by Magnetic Adsorption Material of Polyaniline Combined with CuFe2O4

The magnetic adsorption material of polyaniline (PANI) with amino functional group combined with CuFe2O4 (CuFe2O4/PANI nanocomposite) has been described in this work. It has been characterized by TEM, XRD, XPS, BET, FTIR, and VSM, respectively. Significantly, it exhibits extremely high maximum adsorption capacity (322.6 mg/g) for removal of uranyl ions from wastewater at a pH of 4. The adsorption process is consistent with the quasisecond-order kinetic equation, and the isotherm and kinetic data are accurately described by the Langmuir isothermal adsorption model. Furthermore, the magnetic CuFe2O4/PANI displays stable adsorption performance for uranyl ions after five cycles of recovery in acid medium, which indicates it possesses good recovery due to its magnetism and excellent regeneration ability for reusability

Sea Ice Thickness Retrieval Based on GOCI Remote Sensing Data: A Case Study

The accurate monitoring and measurement of sea ice thickness (SIT) is crucial for understanding climate change and preventing economic losses caused by sea ice disasters near coastal regions. In this study, a new method is developed to retrieve the SIT in Liaodong Bay (LDB) based on the Rayleigh-corrected reflectance from Geostationary Ocean Color Imager (GOCI) images in the winters of 2012 and 2013. Compared with previously developed SIT retrieval methods (e.g., the method based on the thermodynamic principle of sea ice) using remote sensing data, our method has significant advantages with respect to the inversion accuracy (achieving retrieval skill scores as high as 0.86) and spatiotemporal resolution. Moreover, there is no significant increase in the computational cost with this method, which makes the method suitable for operational SIT retrieval in the global ocean

Comparative Shock Tube and Kinetic Study on High-Temperature Ignition of 2,3-Dihydrofuran and 2,5-Dihydrofuran

The ignition delay times of 2,3-dihydrofuran (23DHF) and 2,5-dihydrofuran (25DHF) were investigated over the temperature range of 1100 to 1635 K with pressure of 1.2, 4, and 10 atm for lean (φ= 0.5), stoichiometric, and rich (φ= 2) fuel/O₂/Ar mixtures. 23DHF shows shorter ignition delay times than 25DHF under the above conditions. A modified model (M_Tran model) was presented to improve the prediction of DHF ignition. Kinetic analysis indicated that most 23DHF transforms to cyclopropane carboxaldehyde (CPCA) and further to croton aldehyde (CA) by isomerization while most 25DHF dehydrogenates to furan. Some reactions involving CA and propene show strong sensitivity for 23DHF ignition. Some reactions of furan present strong effect on 25DHF ignition. Ignition delay data between furan, 23DHF, and 25DHF were compared to reveal the effect of number and location of carbon double bonds on the ignition characteristics. The bond dissociation energies of DHF are not as strong as that of furan and are significantly influenced by the locations of carbon double bonds, causing the differences in structure stability. As a result, the ignition trends of furan and DHF in this research are furan < 25DHF < 23DHF

Structural Phase Transition and Compressibility of CaF2 Nanocrystals under High Pressure

The structural phase transition and compressibility of CaF2 nanocrystals with size of 23 nm under high pressure were investigated by synchrotron X-ray diffraction measurement. A pressure-induced fluorite to &alpha;-PbCl2-type phase transition starts at 9.5 GPa and completes at 20.2 GPa. The phase-transition pressure is lower than that of 8 nm CaF2 nanocrystals and closer to bulk CaF2. Upon decompression, the fluorite and &alpha;-PbCl2-type structure co-exist at the ambient pressure. The bulk modulus B0 of the 23 nm CaF2 nanocrystals for the fluorite and &alpha;-PbCl2-type phase are 103(2) and 78(2) GPa, which are both larger than those of the bulk CaF2. The CaF2 nanocrystals exhibit obviously higher incompressibility compare to bulk CaF2. Further analysis demonstrates that the defect effect in our CaF2 nanocrystals plays a dominant role in the structural stability