Improving the ability of straw biochar to remediate Cd contaminated soil: KOH enhanced the modification of K3PO4 and urea on biochar.

In recent years, the improvement of soil cadmium (Cd) contamination remediation effect of biochar by modification has received wide attention. However, the effect of combined modification on biochar performance in soil Cd contamination remediation and the mechanism are still unclear. In this study, cotton straw biochar and maize straw biochar were co-modified by KOH (0, 3, 5 mol L-1), K3PO4, and urea. Then, two modified biochars with high Cd adsorption capacity were selected to test the soil Cd contamination remediation effect through a pot experiment. The results showed that the combined modification by using KOH, K3PO4, and urea significantly increased the specific surface area and nitrogen (N) and phosphorus (P) contents of biochar, providing more adsorption sites for Cd. Among the modified biochar, the cotton straw biochar modified with KOH (3 mol L-1), K3PO4, and urea (m3-CSB) had the highest adsorption capacity (111.25 mg g-1), which was 7.86 times that of cotton straw biochar (CSB). The m3-CSB for adsorption isotherm and kinetics of Cd conformed to the Langmuir model and Pseudo-second-order kinetic equation, respectively. In the pot experiment, under different exogenous Cd levels (0 (Cd0), 4 (Cd4), and 8 (Cd8) mg kg-1), m3-CSB treatment decreased soil available Cd content the most (51.68%-63.4%) compared with other biochar treatments. Besides, m3-CSB treatment significantly promoted the transformation of acid-soluble Cd to reducible, oxidizable, and residual Cd, reducing the bioavailability of Cd. At the Cd4 level, the application of m3-CSB significantly reduced cotton Cd uptake compared to CK, and the maximum reduction of Cd content in cotton fibers was as high as 81.95%. Therefore, cotton straw biochar modified with KOH (3 mol L-1), K3PO4, and urea has great potential in the remediation of soil Cd contamination.

Improving the accuracy of cotton seedling emergence rate estimation by fusing UAV-based multispectral vegetation indices.

Timely and accurate estimation of cotton seedling emergence rate is of great significance to cotton production. This study explored the feasibility of drone-based remote sensing in monitoring cotton seedling emergence. The visible and multispectral images of cotton seedlings with 2 - 4 leaves in 30 plots were synchronously obtained by drones. The acquired images included cotton seedlings, bare soil, mulching films, and PE drip tapes. After constructing 17 visible VIs and 14 multispectral VIs, three strategies were used to separate cotton seedlings from the images: (1) Otsu's thresholding was performed on each vegetation index (VI); (2) Key VIs were extracted based on results of (1), and the Otsu-intersection method and three machine learning methods were used to classify cotton seedlings, bare soil, mulching films, and PE drip tapes in the images; (3) Machine learning models were constructed using all VIs and validated. Finally, the models constructed based on two modeling strategies [Otsu-intersection (OI) and machine learning (Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbor (KNN)] showed a higher accuracy. Therefore, these models were selected to estimate cotton seedling emergence rate, and the estimates were compared with the manually measured emergence rate. The results showed that multispectral VIs, especially NDVI, RVI, SAVI, EVI2, OSAVI, and MCARI, had higher crop seedling extraction accuracy than visible VIs. After fusing all VIs or key VIs extracted based on Otsu's thresholding, the binary image purity was greatly improved. Among the fusion methods, the Key VIs-OI and All VIs-KNN methods yielded less noises and small errors, with a RMSE (root mean squared error) as low as 2.69% and a MAE (mean absolute error) as low as 2.15%. Therefore, fusing multiple VIs can increase crop image segmentation accuracy. This study provides a new method for rapidly monitoring crop seedling emergence rate in the field, which is of great significance for the development of modern agriculture.

Improving the monitoring of root zone soil salinity under vegetation cover conditions by combining canopy spectral information and crop growth parameters.

Soil salinization is one of the main causes of land degradation in arid and semi-arid areas. Timely and accurate monitoring of soil salinity in different areas is a prerequisite for amelioration. Hyperspectral technology has been widely used in soil salinity monitoring due to its high efficiency and rapidity. However, vegetation cover is an inevitable interference in the direct acquisition of soil spectra during crop growth period, which greatly limits the monitoring of soil salinity by remote sensing. Due to high soil salinity could lead to difficulty in plants' water absorption, and inhibit plant dry matter accumulation, a method for monitoring root zone soil salinity by combining vegetation canopy spectral information and crop aboveground growth parameters was proposed in this study. The canopy spectral information was acquired by a spectroradiometer, and then variable importance in projection (VIP), competitive adaptive reweighted sampling (CARS), and random frog algorithm (RFA) were used to extract the salinity spectral features in cotton canopy spectrum. The extracted features were then used to estimate root zone soil salinity in cotton field by combining with cotton plant height, aboveground biomass, and shoot water content. The results showed that there was a negative correlation between plant height/aboveground biomass/shoot water content and soil salinity in 0-20, 0-40, and 0-60 cm soil layers at different growth stages of cotton. Spectral feature selection by the three methods all improved the prediction accuracy of soil salinity, especially CARS. The prediction accuracy based on the combination of spectral features and cotton growth parameters was significantly higher than that based on only spectral features, with R2 increasing by 10.01%, 18.35%, and 29.90% for the 0-20, 0-40, and 0-60 cm soil layer, respectively. The model constructed based on the first derivative spectral preprocessing, spectral feature selection by CARS, cotton plant height, and shoot water content had the highest accuracy for each soil layer, with R2 of 0.715,0.769, and 0.742 for the 0-20, 0-40, 0-60 cm soil layer, respectively. Therefore, the method by combining cotton canopy hyperspectral data and plant growth parameters could significantly improve the prediction accuracy of root zone soil salinity under vegetation cover conditions. This is of great significance for the amelioration of saline soil in salinized farmlands arid areas.