Ecological risk assessment of heavy metals in tea plantation soil around Tai Lake region in Suzhou, China.

Tea plant [Camellia sinensis (L.) O. Kuntze] is one of the important foliar cash crops in China, and its root system absorbs heavy metal (HM) elements enriched in the soil and transports them to the over ground part. In order to ensure the safety of the soil ecological environment and tea raw materials in the tea production area, the HM contents of soil and tea plant leaves in Suzhou tea plantations were detected, the relationship between HMs and soil physicochemical properties was analyzed, and the ecological risk of HMs in tea plantation soils was evaluated by using relevant detection techniques and evaluation models. The results showed that the average pH of tea plantation soils around Tai Lake in Suzhou was within the range suitable for the growth of tea plants. The pH, soil organic matter, total nitrogen, available phosphorus and available potassium of tea plantation soil satisfying the requirements of high quality, high efficiency and high yield ('3H') tea plantation accounted for 47.06%, 26.47%, 8.82%, 79.41% and 67.65%, respectively. Site 2 fully met the requirements of '3H' tea plantation. In addition, the contents of cadmium (Cd) and mercury (Hg) were extremely variable, and the average contents exceeded the background value of soil in Jiangsu Province, but the HM contents of tea leaves all met the pollution-free standard, and the HM contents of tea leaves around Tai Lake in Suzhou were generally at a safe level. The composite ecological risk index ranged from 0.05 to 0.60, and 32 of the 34 sample sites (except site 21 and site 23) are the most suitable agricultural land for tea plantations.

An Assessment of Relations between Vegetation Green FPAR and Vegetation Indices through a Radiative Transfer Model.

The fraction of absorbed photosynthetically active radiation (FPAR) is widely used in remote sensing-based production models to estimate gross or net primary production. The forest canopy is composed primarily of photosynthetically active vegetation (PAV, green leaves) and non-photosynthetic vegetation (NPV e.g., branches), which absorb PAR but only the PAR absorbed by PAV is used for photosynthesis. Green FPAR (the fraction of PAR absorbed by PAV) is essential for the accurate estimation of GPP. In this study, the scattering by arbitrary inclined leaves (SAIL) model was reconfigured to partition the PAR absorbed by forest canopies. The characteristics of green FPAR and its relationships with spectral vegetation indices (NDVI, EVI, EVI2, and SAVI) were analyzed. The results showed that green FPAR varied with the canopy structure. In the forests with high coverage, the green FPAR was close to the total FPAR, while in the open forests, the green FPAR was far smaller than the total FPAR. Plant area index had more important impacts on the green FPAR than the proportion of PAV and optical properties of PAV. The significant relationships were found between spectral vegetation indices and the green FPAR, but EVI was more suitable to describe the variation of canopy green FPAR.

Assessment of GF3 Full-Polarimetric SAR Data for Dryland Crop Classification with Different Polarimetric Decomposition Methods.

Crop classification is one of the most important agricultural applications of remote sensing. Many studies have investigated crop classification using SAR data, while few studies have focused on the classification of dryland crops by the new Gaofen-3 (GF3) SAR data. In this paper, taking Hengshui city as the study area, the performance of the Freeman-Durden, Sato4, Singh4 and multi-component decomposition methods for dryland crop type classification applications are evaluated, and the potential of full-polarimetric GF3 data in dryland crop type classification are also investigated. The results show that the multi-component decomposition method produces the most accurate overall classifications (88.37%). Compared with the typical polarization decomposition techniques, the accuracy of the classification results using the new decomposition method is improved. In addition, the Freeman method generally yields the third-most accurate results, and the Sato4 (87.40%) and Singh4 (87.34%) methods yield secondary results. The overall classification accuracy of the GF3 data is very positive. These results demonstrate the great promising potential of GF3 SAR data for dryland crop monitoring applications.

Influence of Industrialization and Environmental Protection on Environmental Pollution: A Case Study of Taihu Lake, China.

In order to quantitatively study the effect of environmental protection in China since the twenty-first century and the environmental pollution projected for the next ten years (under the model of extensive economic development), this paper establishes a Bayesian regulation back propagation neural network (BRBPNN) to analyze the typical pollutants (i.e., cadmium (Cd) and benzopyrene (BaP)) for Taihu Lake, a typical Chinese freshwater lake. For the periods 1950⁻2003 and 1950⁻2015, the neural network model estimated the BaP concentration for the database with Nash-Sutcliffe model efficiency (NS) = 0.99 and 0.99 and root-mean-square error (RMSE) = 3.1 and 9.3 for the total database and the Cd concentration for the database with NS = 0.93 and 0.98 and RMSE = 45.4 and 65.7 for the total database, respectively. In the model of extensive economic development, the concentration of pollutants in the sediments of Taihu reached the maximum value at the end of the twentieth century and early twenty-first century, and there was an inflection point. After the early twenty-first century, the concentration of pollutants was controlled under various environmental policies and measures. In 2015, the environmental protection ratio of Cd and BaP reached 52% and 89%, respectively. Without environmental protection measures, the concentrations of Cd and BaP obtained from the neural network model is projected to reach 2015.5 µg kg-1 and 407.8 ng g-1, respectively, in 2030. Based on the results of this study, the Chinese government will need to invest more money and energy to clean up the environment.