Bo Jiang

Bio: Bo Jiang is an academic researcher. The author has contributed to research in topics: Image resolution. The author has an hindex of 2, co-authored 2 publications receiving 6 citations.

Evaluating traditional empirical models and BPNN models in monitoring the concentrations of chlorophyll-a and total suspended particulate of eutrophic and turbid waters

Abstract: In order to use in situ sensed reflectance to monitor the concentrations of chlorophyll-a (Chl-a) and total suspended particulate (TSP) of waters in the Pearl River Delta, which is featured by the highly developed network of rivers, channels and ponds, 135 sets of simultaneously collected water samples and reflectance were used to test the performance of the traditional empirical models (band ratio, three bands) and the machine learning models of a back-propagation neural network (BPNN). The results of the laboratory analysis with the water samples show that the Chl-a ranges from 3 to 256 µg·L−1 with an average of 39 µg·L−1 while the TSP ranges from 8 to 162 mg·L−1 and averages 42.5 mg·L−1. Ninety sets of 135 samples are used as training data to develop the retrieval models, and the remaining ones are used to validate the models. The results show that the proposed band ratio models, the three-band combination models, and the corresponding BPNN models are generally successful in estimating the Chl-a and the TSP, and the mean relative error (MRE) can be lower than 30% and 25%, respectively. However, the BPNN models have no better performance than the traditional empirical models, e.g., in the estimation of TSP on the basis of the reflectance at 555 and 750 nm (R555 and R750, respectively), the model of BPNN (R555, R750) has an MRE of 23.91%, larger than that of the R750/R555 model. These results suggest that these traditional empirical models are usable in monitoring the optically active water quality parameters of Chl-a and TSP for eutrophic and turbid waters, while the machine learning models have no significant advantages, especially when the cost of training samples is considered. To improve the performance of machine learning models in future applications on the basis of ground sensor networks, large datasets covering various water situations and optimization of input variables of band configuration should be strengthened.

Effects of Spatial Resolution on the Satellite Observation of Floating Macroalgae Blooms

Abstract: Satellite images with different spatial resolutions are widely used in the observations of floating macroalgae booms in sea surface. In this study, semi-synchronous satellite images with different resolutions (10 m, 16 m, 30 m, 50 m, 100 m, 250 m and 500 m) acquired over the Yellow Sea, are used to quantitatively assess the effects of spatial resolution on the observation of floating macroalgae blooms of Ulva prolifera. Results indicate that the covering area of macroalgae-mixing pixels (MM-CA) detected from high resolution images is smaller than that from low resolution images; however, the area affected by macroalgae blooms (AA) is larger in high resolution images than in low resolution ones. The omission rates in the MM-CA and the AA increase with the decrease of spatial resolution. These results indicate that satellite remote sensing on the basis of low resolution images (especially, 100 m, 250 m, 500 m), would overestimate the covering area of macroalgae while omit the small patches in the affected zones. To reduce the impacts of overestimation and omission, high resolution satellite images are used to show the seasonal changes of macroalgae blooms in 2018 and 2019 in the Yellow Sea.

Suspended sediment load prediction using long short-term memory neural network

Abstract: Rivers carry suspended sediments along with their flow These sediments deposit at different places depending on the discharge and course of the river However, the deposition of these sediments impacts environmental health, agricultural activities, and portable water sources Deposition of suspended sediments reduces the flow area, thus affecting the movement of aquatic lives and ultimately leading to the change of river course Thus, the data of suspended sediments and their variation is crucial information for various authorities Various authorities require the forecasted data of suspended sediments in the river to operate various hydraulic structures properly Usually, the prediction of suspended sediment concentration (SSC) is challenging due to various factors, including site-related data, site-related modelling, lack of multiple observed factors used for prediction, and pattern complexityTherefore, to address previous problems, this study proposes a Long Short Term Memory model to predict suspended sediments in Malaysia's Johor River utilizing only one observed factor, including discharge data The data was collected for the period of 1988-1998 Four different models were tested, in this study, for the prediction of suspended sediments, which are: ElasticNet Linear Regression (LR), Multi-Layer Perceptron (MLP) neural network, Extreme Gradient Boosting, and Long Short-Term Memory Predictions were analysed based on four different scenarios such as daily, weekly, 10-daily, and monthly Performance evaluation stated that Long Short-Term Memory outperformed other models with the regression values of 9201%, 9656%, 9671%, and 9945% daily, weekly, 10-days, and monthly scenarios, respectively

Investigating Spatial Distribution of Green-Tide in the Yellow Sea in 2021 Using Combined Optical and SAR Images

Abstract: Optical remote sensing is limited to clouds and rain. It is difficult to obtain ground object images in severe weather. Microwave remote sensing can penetrate clouds and rain to obtain ground object images. Therefore, this paper combines optical and microwave data to analyze the time and space of the green-tide in the Yellow Sea in 2021. Compared with a single data source, the distribution characteristics increase the frequency of time observation and show the green-tide changes in more detail. The continuous remote sensing observation time is 80 days. Ulva prolifera has experienced discovery (mid-late May), development (mid-late May to early June), outbreak (early June to mid-late June), decline (late June to mid-July), and extinction (late July to mid-August) in five stages; the development period drifts along the northeast direction, the outbreak period drifts along the northwest direction, the decline and extinction periods are mainly in the Rizhao and Qingdao waters. Ulva prolifera has a tendency to drift northward as a whole, drifting through Yancheng, Lianyungang, Linyi, Rizhao and Qingdao waters eventually landing on the coast of Qingdao and gradually disappearing.

Investigating Spatial Distribution of Green-Tide in the Yellow Sea in 2021 Using Combined Optical and SAR Images

Abstract: : Optical remote sensing is limited to clouds and rain. It is difficult to obtain ground object images in severe weather. Microwave remote sensing can penetrate clouds and rain to obtain ground object images. Therefore, this paper combines optical and microwave data to analyze the time and space of the green-tide in the Yellow Sea in 2021. Compared with a single data source, the distribution characteristics increase the frequency of time observation and show the green-tide changes in more detail. The continuous remote sensing observation time is 80 days. Ulva prolifera has experienced discovery (mid-late May), development (mid-late May to early June), outbreak (early June to mid-late June), decline (late June to mid-July), and extinction (late July to mid-August) in five stages; the development period drifts along the northeast direction, the outbreak period drifts along the northwest direction, the decline and extinction periods are mainly in the Rizhao and Qingdao waters. Ulva prolifera has a tendency to drift northward as a whole, drifting through Yancheng, Lianyungang, Linyi, Rizhao and Qingdao waters eventually landing on the coast of Qingdao and gradually disappearing.

Remote Sensing of Chlorophyll-a in Xinkai Lake Using Machine Learning and GF-6 WFV Images

Abstract: Lake ecosystem eutrophication is a crucial water quality issue that can be efficiently monitored with remote sensing. GF-6 WFV with a high spatial and temporal resolution provides a comprehensive record of the dynamic changes in water quality parameters in a lake. In this study, based on GF-6 WFV images and the field sampling data of Xingkai Lake from 2020 to 2021, the accuracy of three machine learning models (RF: random forest; SVR: support vector regression; and BPNN: back propagation neural network) was compared by considering 11 combinations of surface reflectance in different wavebands as input variables for machine learning. We mapped the spatiotemporal variations of Chl-a concentrations in Xingkai Lake from 20192021 and integrated machine learning algorithms to demonstrate that RF obtained a better degree of derived-fitting (Calibration: N = 82, RMSE = 0.82 μg/L, MAE = 0.57 μg/L, slope = 0.94, and R2 = 0.98; Validation: N = 40, RMSE = 2.12 μg/L, MAE = 1.58 μg/L, slope = 0.91, R2 = 0.89, and RPD = 2.98). The interannual variation from 2019 to 2021 showed that the Chl-a concentration in Xingkai Lake was low from June to July, while maximum values were observed from October to November, thus showing significant seasonal differences. Spatial distribution showed that Chl-a concentrations were higher in Xiao Xingkai Lake than in Da Xingkai Lake. Nutrient inputs (N, P) and other environmental factors such as high temperature could have an impact on the spatial and temporal distribution characteristics of Chl-a, therefore, combining GF-6 WFV satellite images with RF could realize large-scale monitoring and be more effective. Our results showed that remote-sensing-based machine learning algorithms provided an effective method to monitor lake eutrophication as well as technical support and methodological reference for inland lake water quality parameter inversion.