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Accurate and timely large-scale paddy rice maps with remote sensing are essential for crop monitoring and management and are used for assessing its impacts on food security, water resource management, and transmission of zoonotic infectious diseases. Optical image-based paddy rice mapping studies employed the unique spectral feature during the flooding/transplanting period of paddy rice. However, the lack of high-quality observations during the flooding/transplanting stage caused by rain and clouds and spectral similarity between paddy rice and natural wetlands often introduce errors in paddy rice identification, especially in paddy rice and wetland coexistent areas. In this study, we used a knowledge-based algorithm and time series observation from optical images (Sentinel-2 and Landsat 7/8) and microwave images (Sentinel-1) to address these issues. The final 10-m paddy rice map had user’s accuracy, producer’s accuracy, F1-score, and overall accuracy of 0.91 ± 0.004, 0.74 ± 0.010, 0.82, and 0.98 ± 0.001 (± value is the standard error), respectively. Over half (62.0%) of the paddy rice pixels had a confidence level of 1 (detected by both optical images and microwave images), while 38.0% had a confidence level of 0.5 (detected by either optical images or microwave images). The estimated paddy rice area in northeast China for 2020 was 60.83 ± 0.86 × 103 km2. Provincial and municipal rice areas in our data set agreed well with other existing paddy rice data sets and the Agricultural Statistical Yearbooks. These findings indicate that knowledge-based paddy rice mapping algorithms and a combination of optical and microwave images hold great potential for timely and frequently accurate paddy rice mapping in large-scale complex landscapes. 

Ancient, buried wood points to a possible low-cost method to store carbon 

This manuscript reports on the combustion of powdered iron, for the purpose of utilizing it as an environmentally friendly circular energy carrier. The conducted research investigated the spectral emissivity and temperature of iron particles, burned either individually or in groups. Combustion experiments were conducted under high heating rates in an externally-heated drop tube furnace. The pressure was atmospheric and the axial temperature was nearly-constant at ~1350 K. The oxidizer gas contained 15-100% oxygen in nitrogen diluent. Iron particles were sieve-classified in the 44-53 µm range. Results showed that, depending on the oxygen concentration, and consequently the particle temperature, the average spectral emissivities of single burning particles varied between 0.18 and 0.46, in the 600-1000 nm wavelength range. Corresponding temperatures of single particles varied between 2300 K and 2800 K, increasing with increasing oxygen concentration in the gas. In the case of groups of iron particles burning in air at different particle number densities, average spectral emissivities were found to be in the range of 0.42-0.45, with the upper value associated with denser particle clouds. Corresponding peak temperatures of particle burning in groups were found to be in the range of 2160 K to 2100 K, with the lower value attributed to denser particle clouds. 

Abstract Biodegradable plastics, perceived as ‘environmentally friendly’ materials, may end up in natural environments. This impact is often overlooked in the literature due to a lack of assessment methods. This study develops an integrated life cycle impact assessment methodology to assess the climate-change and aquatic-ecotoxicity impacts of biodegradable microplastics in freshwater ecosystems. Our results reveal that highly biodegradable microplastics have lower aquatic ecotoxicity but higher greenhouse gas (GHG) emissions. The extent of burden shifting depends on microplastic size and density. Plastic biodegradation in natural environments can result in higher GHG emissions than biodegradation in engineered end of life (for example, anaerobic digestion), contributing substantially to the life cycle GHG emissions of biodegradable plastics (excluding the use phase). A sensitivity analysis identified critical biodegradation rates for different plastic sizes that result in maximum GHG emissions. This work advances understanding of the environmental impacts of biodegradable plastics, providing an approach for the assessment and design of future plastics.