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UAV (unmanned aerial vehicle) remote sensing provides the feasibility of high-throughput phenotype nondestructive acquisition at the field scale. However, accurate remote sensing of crop physicochemical parameters from UAV optical measurements still needs to be further studied. For this purpose, we put forward a crop phenotype inversion framework based on the optimal estimation (OE) theory in this paper, originating from UAV low-altitude hyperspectral/multispectral data. The newly developed unified linearized vector radiative transfer model (UNL-VRTM), combined with the classical PROSAIL model, is used as the forward model, and the forward model was verified by the wheat canopy reflectance data, collected using the FieldSpec Handheld in Qi County, Henan Province. To test the self-consistency of the OE-based framework, we conducted forward simulations for the UAV multispectral sensors (DJI P4 Multispectral) with different observation geometries and aerosol loadings, and a total of 801 sets of validation data were obtained. In addition, parameter sensitivity analysis and information content analysis were performed to determine the contribution of crop parameters to the UAV measurements. Results showed that: (1) the forward model has a strong coupling between vegetation canopy and atmosphere environment, and the modeling process is reasonable. (2) The OE-based inversion framework can make full use of the available radiometric spectral information and had good convergence and self-consistency. (3) The UAV multispectral observations can support the synchronous retrieval of LAI (leaf area index) and Cab (chlorophyll a and b content) based on the proposed algorithm. The proposed inversion framework is expected to be a new way for phenotypic parameter extraction of crops in field environments and had some potential and feasibility for UAV remote sensing. [ABSTRACT FROM AUTHOR]

Aims: To screen heavy metal-tolerant strains from heavy metal-contaminated soil in mining areas and determine the tolerance of the strains to different heavy metals and their removal rates through experiments. Methods: Mercury-resistant strain LBA119 was isolated from mercury-contaminated soil samples in Luanchuan County, Henan Province, China. The strain was identified by Gram staining, physiological and biochemical tests, and 16S rDNA sequences. The LBA119 strain showed good resistance and removal rates to heavy metals such as Pb2+, Hg2+, Mn2+, Zn2+, and Cd2+ using tolerance tests under optimal growth conditions. The mercury-resistant strain LBA119 was applied to mercury-contaminated soil to determine the ability of the strain to remove mercury from the soil compared to mercury-contaminated soil without bacterial biomass. Results: Mercury-resistant strain LBA119 is a Gram-positive bacterium that appears as a short rod under scanning electron microscopy, with a single bacterium measuring approximately 0.8 × 1.3 μm. The strain was identified as a Bacillus by Gram staining, physiological and biochemical tests, and 16S rDNA sequence analysis. The strain was highly resistant to mercury, with a minimum inhibitory concentration (MIC) of 32 mg/L for mercury. Under a 10 mg/L mercury environment, the optimal inoculation amount, pH, temperature, and salt concentration of the LBA119 strain were 2%, 7, 30 °C, and 20 g/L, respectively. In the 10 mg/L Hg2+ LB medium, the total removal rate, volatilization rate, and adsorption rate at 36 h were 97.32%, 89.08%, and 8.24%, respectively. According to tolerance tests, the strain showed good resistance to Pb2+, Mn2+, Zn2+, Cd2+, and other heavy metals. When the initial mercury concentration was 50 mg/L and 100 mg/L, compared with the mercury-contaminated soil that contained an LB medium without bacterial biomass, LBA119 inoculation increased 15.54–37.67% after 30 days of culture. Conclusion: This strain shows high bioremediation potential for mercury-contaminated soil. [ABSTRACT FROM AUTHOR]