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We developed an adaptive algorithm to reduce rice loss in harvesting, promote threshing and improve the quality and efficiency of small- and medium-sized rice harvesters operating in southern China's hilly and mountainous areas. Using a fuzzy PID control algorithm, the harvester adapts to the rice harvesting conditions in southern China, and monitors rice feed volume changes and instantly adjust the traveling speed to optimize feed volume levels and threshing quality. We compared and analyzed the algorithm and the traditional PID control regulation effect in the simulation experiment. The algorithm had a quicker response speed and stable accuracy. In the field trial, the average error rate was 3.4%, and the maximum error rate was 5.1%, with most data points centered around the ideal feeding rate of 3.2 kg/s. Our results showed that the algorithm's stability, accuracy, and real-time performance met the threshing loss reduction requirements of southern China's rice harvesting operations. [ABSTRACT FROM AUTHOR]

Water clarity provides a sensitive tool to examine spatial pattern and historical trend in lakes trophic status. Yet, this metric has insufficiently been explored despite the availability of remotely-sensed data. We used three Secchi disk depth (SDD) datasets for model calibration and validation from different field campaigns mainly conducted during 2004-2018. The red/blue band ratio algorithm was applied to map SDD for lakes (> 1 ha) based on the first SDD dataset, where R2 = 0.79, RMSE = 100.3 cm, rRMSE = 61.9 %, MAE = 57.7 cm. The other two datasets were used to validate the SDD estimation model, which were indicated the model had a stable performance of temporal transferability. The annual mean SDD of lakes were retrieved across China using Landsat top of air reflectance products in GEE from 1984 to 2018. The spatiotemporal dynamics of SDD were analysed at the five lake regions and individual lake scales, and the average, changing trend, lake number and area, and spatial distribution of lake SDDs across China were presented. In 2018, we found that the lakes with SDDs < 2 m accounted for the largest proportion (80.93 %) of the total lakes, but the total area of lakes with SDD between 0-0.5 m and > 4 m were the largest, accounting for 48.28 % of the total lakes. During 1984-2018, lakes in the Tibetan-Qinghai Plateau lake region (TQR) had the clearest water with an average value of 3.32 ± 0.38 m, while that in the Northeastern lake region (NLR) exhibited the lowest SDD (mean: 0.60 ± 0.09 m). Among the 10,814 lakes with SDD results more than 10 years, 55.42 % and 3.49 % of lakes experienced significant increasing and decreasing trends, respectively. At the five lake regions, except for the Inner Mongolia-Xinjiang lake region (MXR), more than half of the total lakes in every other lake region exhibited significant increasing trends. In the Eastern lake region (ELR), NLR and Yungui Plateau lake region (YGR), almost more than 50 % of the lakes that displayed an increase or decrease in SDD were mainly distributed in an area of 0.01-1 km2, whereas that in the TQR and MXR were primarily concentrated in large lakes (> 10 km2). Spatially, lakes located in the plateau regions generally exhibited higher SDD than those situated in the flat plain regions. The dataset can now be accessed through the website of the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn): DOI: 10.11888/Hydro.tpdc.271571. [ABSTRACT FROM AUTHOR]

Aerosol particles affect the Earth’s radiative balance and represent one of the largest uncertainties in climate research. The removal of clouds is the first step and critical in the aerosol retrievals. However, the cloud detection is still challenging. Here, a novel simplified cloud detection algorithm (SCDA) is proposed to identify the cloud and clear-sky over land and based on a simplified radiative transfer model (RTM). The fewer input bands, dynamic thresholds, and only one parameter to be modified are the main advantages of the algorithm, which can be applied to different satellite sensors. In this article, we apply SCDA to the Himawari-8 data in 2016 for preliminary analysis. The detection results are validated using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) vertical feature mask (VFM) data and the National Centers for Environmental Information (NCEI) ground-based observation data. We also compare the results with the Himawari-8 cloud products from the Japan Aerospace Exploration Agency (JAXA). Compared with CALIPSO VFM data and NCEI ground-based observation data, the correct rate of SCDA cloud detection result is 86.08% and 79.86%, which are higher than that of Himawari-8 cloud products (85.71% and 78.89%). The correct rate of SCDA clear-sky detection result is 88.33% and 87.85%, which are close to the correct rate of Himawari-8 clear-sky products (90.54% and 88.63%). The overall performance of the SCDA is comparable to that of the threshold method for JAXA Himawari-8 cloud products. Therefore, the SCDA can provide accurate cloud mask with only one threshold to be modified and few input parameters. [ABSTRACT FROM AUTHOR]