Home Water deep mapping from HJ-1B satellite data by a deep network model in the sea area of Pearl River Estuary, China
Article Open Access

Water deep mapping from HJ-1B satellite data by a deep network model in the sea area of Pearl River Estuary, China

  • Xiaoning Zhao , Daqing Wang EMAIL logo , Haoli Xu , Yue Shi , Zhengdong Deng , Zhibin Ding , Zhixin Liu , Xingang Xu , Zhao Lu , Guangyuan Wang and Zijian Cheng
Published/Copyright: July 20, 2021
Download Article (PDF)
Become an author with De Gruyter Brill
Submit Manuscript Author Information
Open Geosciences
From the journal Open Geosciences Volume 13 Issue 1

Abstract

Remote sensing (RS) water depth inversion is an important technology and the method of water depth measurement. Taking the waters around the islands outside the Pearl River Estuary as an example, five optical RS depth inversion algorithms were introduced. Then, five water depth inversion models were trained through the HJ-1B satellite RS image and the measured water depth data. The results show that the mean absolute error (MAE) of the deep learning model was the smallest (2.350 m), and that the distribution of predicted water depth points was closest to the actual value. Deep learning has been widely used in RS image classification and recognition and shows its advantages. Therefore, the deep learning model was applied to extract the depth of the shallow water. Meanwhile, the obtained inversion effect map is closest to the actual contour map. The water depth inversion performance of back propagation neural network model is better than that of the radial basis function (RBF) neural network model. Besides, the inversion accuracy of the RBF neural network may be affected due to the small amount of data and the improper number of hidden neurons. The results show broad application prospects of machine learning algorithms in RS water depth inversion. Also, this study provided data support for model optimization, training, and parameter setting.

Keywords: RS; HJ-1B; water depth inversion; deep learning

1 Introduction

The ocean plays a vital role in the strategic layout of national security and economical construction, and the water depth is the essential marine element in shallow seas. Whether in the construction of ports, docks, waterways, and anchorages, or in the safe navigation of ships, marine scientific research, marine engineering construction, and marine environmental assessment need the water depth data as the primary guarantee [1]. Traditional water depth measurement methods have large limitations and take a long time. The water depth remote sensing (RS) technology is favored because of its wide range and short-time consumptions.

Solar radiation is weakened by the atmosphere (absorption, reflection, and scattering) before reaching the surface of the water body, and only few part of the energy is reflected to the atmosphere at the water–air interface. The main energy enters the water body through water surface refraction [1]. By collecting the optical radiation information received by the sensor and performing preprocessing operations such as atmospheric correction, this study obtained some information. The information included the spectral information and the water body bottom and topography information contained in the water emission and reflection spectra of the study area. Then, the study used the water depth inversion model to invert the study area [2]. Since the 1970s, with the formation and development of RS satellite technology, the water depth RS inversion method has been known by researchers because of its unique advantages. Then, various water depth inversion models have been continuously proposed [3]. According to the principle of water depth optical inversion by RS, the methods of deriving the water depth inversion model based on the relationship between the water depth and the reflection spectrum are called the density method. The density methods were divided into three categories [4] according to the different calculation methods.

The theoretical interpretation model was derived from the radiation transmission model. The advantage of the model is that the inversion results are more accurate. The disadvantage is that the inversion process must have a certain amount of optical parameter data as a support. But the amount of the optical parameter data that the inversion process requires is so large that the model is not easy to implement, the equations are challenging to solve, and the application in the inversion process is less. Only by making the theoretical interpretation model more perfect [5], it can be more convenient for water depth inversion [6]. Polcyn et al. simplified the theoretical interpretation model by using the research of Polcyn as a basis, making it possible to apply this model to actual water depth back evolution [7].

The semi-theoretical and semi-empirical model combined theoretical interpretation models and empirical algorithms, and performed regression analysis through measured parameter data to obtain the correlation between reflectivity and water depth, and then inverted the water depth of the entire area. The theoretical interpretation model required a small number of parameters and only required that the input optical radiation data of the local water body can be used to derive the global water depth inversion results, and the inversion accuracy was better. According to the attenuation curve of the reflection spectrum of sunlight passing through water bodies at different depths, Benny and Dawson developed a single-band model (only for single-band water depth inversion) and verified its feasibility [8]. John et al. proposed a multiband water depth inversion model based on a constant ratio of the bottom water reflectivity between bands, ignoring the influence of the water substrate on the reflection spectrum. Besides, the accuracy of the inversion was verified [9]. Jingjing and Qingjiu proposed to use the B3 and B4 bands of Landsat 8 data to construct a band ratio water depth inversion model and used the inversion results to draw a water depth map [10]. After processing the water depth of Hongze Lake, the average water depth error of the inversion value was 0.35 m, and the effect was better. The assumptions of the aforementioned two models were also to ignore the influence of the bottom of the water body, so as far as possible the interference of low environmental noise and other factors on water depth inversion, with high inversion accuracy and good effect [11]. Benqing et al. applied domestic GF-1 satellite data to carry out image geometric correction, atmospheric correction, and flare correction preprocessing. Based on the dual-band water depth inversion model and the logarithmic ratio model, the water depth inversion of Jinsha Island in Xisha Islands was performed by RS, and the measured data was used to prove the Jinqing Island GF-1 satellite data based on the dual-band linear model. Water depth inversion results were significantly better than the dual-band logarithmic ratio model [12].

The statistical model is based on statistical ideas and methods. The data objects are as follows: sunlight radiating on the water surface, water body-atmospheric scattering, and water body bottom reflection, to establish the relationship between the measured area water depth value and the RS optical image spectrum. Knowing the linear relationship, the correlation of these can be used to infer the water depth value. The method was mature and widely used in water depth inversion. Based on the different types of functions used in the model, the statistical model can be divided into linear function model [13], logarithmic function model [14], exponential function model [15], power exponential function model [16], quadratic polynomial function model, and so on.

The introduction of multiple bands further weakened the interference of different substrates on light reflectance. Water depth was independent of the substrate type. During the inversion process, regression analysis was performed on the measured water depth data to calculate relevant parameters. Most of the models described earlier were based on the correlation between the optical signal’s bottom reflection spectrum information and the water depth. Therefore, the limitations of the aforementioned models were also undeniable [17]. After nearly 50 years of research, optical water depth inversion models have been continuously developed and gradually overcome. The following research introduced new methods into water depth inversion to overcome the problems existing in the previous model. Sandidge and Holyer introduced machine learning to water depth inversion, combining the neural network method with water depth RS inversion for the first time. Then Sandidge and Holyer used the water hyperspectral data and measured the water depth data of the study area to establish a neural network multilevel decision-making water depth inversion [18].

In recent years, on the basis of actual situation of the research area, scholars have constantly adjusted the neural network model to adapt to the inversion of water depth in more complex waters. In the water depth inversion of the Yangtze estuary, Wang introduced the “suspended sediment influence factor” based on the neural network model, which improved the accuracy of the neural network model in the inversion of the turbid water depth [19]. Zhicheng introduced chlorophyll and sediment into the neural network model to retrieve the depth of Dalian Bay [20]. Hongqi et al. proposed a modeling method that introduced support vector machine (SVM) into water depth inversion, constructed a SVM inversion model, and proved the potential of this model in water depth inversion [21]. Xu applied neural network technology to the domestic HJ-1A satellite for shallow water depth inversion and realized the application of HJ-1A satellite in shallow water depth inversion [22]. Yanguo and Jinxia compared the water depth inversion capability of the traditional linear model and the neural network model and proved the advantages of the neural network model in dealing with the water depth inversion problem in terms of the model’s adaptive ability and mapping ability [23]. Subsequently, Bin et al. added particle swarm algorithm to train the water depth data in the process of inverting the water depth of the neural network model, improving the iterative convergence speed of the neural network model. Bin et al. also proved that the improved algorithm was better than the traditional neural network algorithm in the depth of water depth of 30 m or less [24]. Guizhou et al. compared the back propagation (BP) and radial basis function (RBF) neural network models. They changed the parameters of the BP network through the control variable method to maximize the correlation coefficient of training. The trained BP and RBF networks were applied to the water depth inversion of Mischief Reef, and the advantages and disadvantages of the two neural networks were analyzed from different angles [25].

So far, domestic and foreign scholars had established a variety of models of RS depth inversion for different data sources and research areas and achieved good results. However, it had to be admitted that the technology of RS depth inversion was not perfect, and its accuracy and stability cannot yet replace traditional measurement methods. As a machine learning algorithm that was closer to artificial intelligence, deep learning had achieved image recognition results. These results were far exceeding the results that were achieved by previous related technologies [26]. This study attempted to combine deep learning algorithms with RS water depth inversion to explore a more accurate model.

2 Data

This section introduced the study area and the RS satellite data. The characteristics of the study area and the characteristics of the HJ-B satellite data were explained.

2.1 The overview of the study area

The sea area of the archipelago outside the Pearl River Estuary has geographical coordinates between 113°37E and 114°21E and between 21°44 N and 22°12 N (Figure 1). The maximum length of the studied area is 30 km, the maximum width is 45 km, and the average water depth is about 30 m. It has a subtropical maritime climate, with sufficient sunshine and abundant rain.

Figure 1 The map of the study area.
Figure 1

The map of the study area.

The Islands off the Pearl River Estuary, located outside the Pearl River Estuary in Guangdong Province, is the second largest archipelago after Zhoushan Islands in China. It consists of more than 150 islands. The larger ones are Hong Kong Island, Datong Island, Hengqin Island, Gaolan Island, Hebao Island, Dajin Island, Shangchuan Island, and Xiachuan Island, and the smaller islands include Wanshan Islands, Po Toi Islands, and Dangan Islands. They are administratively divided into Hong Kong, Macau, Zhuhai city, and Taishan county. Because it is affected by the North-East-South-West fault, it is arranged in a series of northeast directions, surrounded by the Pearl River Funnel Bay mouth, and stretches over 160 km. The archipelago was originally a continental mountain range (Lotus mountain) and later separated from the mainland into islands due to mountain subsidence and seawater intrusion.

The archipelago waters are good fishing grounds, and the Pearl River estuary, which is the place for fish to feed and spawn, is rich in bait. Besides, the fishing industry is pretty developed. In terms of transportation, there are highways on the island, beacon for Guangzhou and Hong Kong route navigation target. Due to its excellent geographical location, the archipelago has been a gateway for foreign trade and traffic, especially Hong Kong Island. Hong Kong Island is the world’s leading waterway and the largest trading port in the East. Wanshan archipelago is the only way for Guangzhou to voyage to sea, which is located at the mouth of the Pearl River and under the jurisdiction of Zhuhai City.

2.2 Overview of HJ-1B satellite data

China’s “environment and disaster monitoring small satellites” A and B (HJ-1A and HJ-1B) were launched in Taiyuan Satellite Launch Center on September 6, 2008. It is the first small satellite constellation dedicated to the environment and disaster monitoring and prediction in China and is another new civil satellite system launched after the meteorological, marine, and resource satellite series in China. HJ-1B is a sun synchronous orbit near noon, with an orbit height of about 650 km. It carries two widecover multispectrum cameras and an infrared sensor, with a revisit period of about 96 h. The CCD camera has a width of 360 km, the combined width of the two sets is greater than 700 km, and the spectrum range is 0.43–0.9 µm. The resulting image is divided into four bands with a resolution of 30 m under the satellite. The infrared scanner has a width of 720 km, the spectral range is 0.75–12.5 µm, and the obtained image is also divided into four bands. Subsatellite point resolution of the first three bands (near infrared, short wave infrared, and mid infrared) is 150 m, and Subsatellite point resolution of the fourth band (thermal infrared (band)) is 300 m [27]. HJ-1B satellite’s wide observation width and other characteristics can provide strong support for large-scale, all-weather dynamic monitoring of the environment.

3 The introduction of methods

The methods used in this study included deep learning, curve fitting, and simple machine learning algorithms. The calculation principle of each algorithm was different, and the characteristics of different algorithms were explained in detail.

3.1 Deep learning method

The concept of deep learning comes from the study of the artificial neural network. A multilayer sensor with multiple hidden layers is a deep learning structure. Deep learning can combine low-level features and then build more comprehensive high-level attribute features layer by layer. Deep learning model has five to ten layers or more neural networks, while traditional neural networks usually have only two or three layers. Moreover, the parameters and computing nodes of traditional neural network are limited, and its ability to learn and express complex functions is limited. The complex structure of deep learning makes it to produce more effective training mechanism. The hierarchical structure of deep learning model is only connected between adjacent layers of neurons, and the same layer and cross layer neurons are not connected with each other, which is similar to the structure of human brain, and can imitate the brain to express information efficiently and accurately [28].

3.2 Curve fitting methods

Curve fitting uses a certain model to fit a series of data into a smooth curve, to observe the internal relationship between the data and the model, and to understand the changing trend of the data. Commonly used models include linear model, polynomial model, exponential model, and Gaussian model. Generally, the model used can be determined according to the characteristics of the specialty. When the model cannot be determined, the scatter diagram can be drawn, and the appropriate curve type can be selected according to the distribution of the scatter [29].

3.3 BP neural network

The BP neural network is a kind of multilayer feed forward neural network. Its main feature is that it can forward to transmit the signal and back transmit the error [30]. It uses the error after output to estimate the error of the direct leading layer of the output layer and then uses this error to estimate the error of the previous layer, so that the error estimation of all other layers can be obtained if the layer passes backward, and the connection weight of the network can be constantly changed by the obtained error, so as to make the output of the network keep close to the expected output (Figure 2).

Figure 2 The simple structure of the BP neural network [31].
Figure 2

The simple structure of the BP neural network [31].

3.4 RBF neural network

RBF neural network is a three-layer neural network, which includes input layer, hidden layer, and output layer. Both the RBF neural network and the BP neural network belong to the feedforward neural network. Different from the BP neural network, the transformation from input space to hidden layer space of the RBF neural network is nonlinear, while the transformation from hidden space to output space is linear. In other words, RBF is used as the “base” of the hidden unit to form the hidden layer space. In this way, the input vector can be directly mapped to the hidden space without weight connection. When the center point of RBF is determined, the mapping relationship is determined. The mapping from the hidden layer space to the output space is linear, that is, the output of the network is the linear weighted sum of the output of the hidden unit, and the weight here is the adjustable parameter of the network. Among them, the function of the hidden layer is to map vector from low dimension to high dimension, so that low-dimension linear indivisibility can become linear separable from high dimension. In this way, the mapping from input to output of the network is nonlinear, while the output of the network is linear to the adjustable parameters. The weight of the network can be directly solved by the linear equations, thus speeding up the learning speed and avoiding the local minimum problem [32].

3.5 SVM neural network

SVM was first proposed by Cortes and Vapnik. It is a supervised learning model and related learning algorithm to analyze data in classification and regression analysis. The main idea of SVM is to build a classification hyperplane as the decision surface, so that the isolation edge between positive and negative examples is maximized [33].

When the data are linearly separable, the linear classifier can be learned by maximizing the hard interval, that is, the hard interval SVM; when the training data is not linearly separable but nearly linearly separable, the linear classifier can also be learned by maximizing the soft interval, that is, the soft interval SVM; when the training data are linearly separable, it can be learn by maximizing the inner product kernel and the soft interval. A nonlinear SVM is obtained. Therefore, SVM can provide good generalization performance in pattern classification, which is unique to SVM.

4 Applications and results of different methods

The water depth data are divided into the training set and the test set. The training set was used to train each model, and the test set was imported into the trained model to obtain the depth inversion results of different methods.

4.1 The results of deep learning

This study used a deep feed forward neural network (DFNN). In the regression task, it can extract advanced features from a large number of variables to obtain high prediction accuracy. This study used keras’s deep learning framework to build the DFNN model. The specific training process of deep learning includes feature learning using bottom-up and unsupervised methods. First, the first layer was trained without calibration data to learn the parameters of itself. Due to the large capacity and loose constraints of the model, the generated model can learn from the data structure itself and obtain more features than the input. After learning to obtain the N-1 layer, the output of the N-1 layer was taken as the input of the N-layer, and the N-layer was trained, thereby obtaining the parameters for each layer. In addition, the model adopted top-down supervised learning. By using labeled data for training, the error propagated backward in order, and the weight of each layer was adjusted.

The training set and the test set were used to find the optimal parameters of the model. The correlation coefficient (R 2) and MAE of the model were obtained by adjusting the number of hidden layers, neurons, and epochs. The selection of the results is presented in Table 1. When the maximum R 2 and minimum MAE of the training set appeared, the R 2 and MAE of the test set were not optimal. This result showed that the blindly pursuing of minimizing the error of training set may lead to the decline of the ability to predict unknown data (test set). Therefore, to avoid overfitting and improve the generalization ability of the model, it was important to consider the evaluation parameters of training set and test set at the same time. In this study, the following were finally selected as the initial input parameters of the model: hidden layers = 4, neurons = 36, and epochs = 50. As presented in Table 1, R 2 between the predicted depth and the measured value of the training set was 0.890, and the R 2 between the predicted depth and the measured value of the test set was 0.825. In addition, the MAE of training set and test set were 0.123 and 2.350, respectively. The result showed that the simulation accuracy of the model is high, and the prediction ability of new data was also improved.

Table 1

A selection of results of tuning the key parameters of the model

Hidden layers Neurons Epochs Train R 2 Train MAE Test R 2 Test MAE
3 36 50 0.627 1.471 0.601 5.936
3 36 100 0.711 0.684 0.697 3.215
3 36 150 0.764 0.602 0.722 3.154
4 36 50 0.890 0.123 0.825 2.350
4 36 100 0.832 0.101 0.825 2.946
4 36 150 0.816 0.166 0.790 3.054
5 36 50 0.894 0.094 0.791 3.468
5 36 100 0.887 0.167 0.727 3.271
5 36 150 0.845 0.085 0.704 3.240
… … … … … … …

The prediction result of this deep network structure is shown in Figure 3.

Figure 3 Prediction results of water depth based on deep learning model.
Figure 3

Prediction results of water depth based on deep learning model.

4.2 The results of curve fitting models

Considering the different indexes (some bands) had different correlation coefficient with the values of water deep in this area, the study referred to some researches [23,32] and did a correlation analysis, which is presented in Table 2.

Table 2

The correlation coefficient between different indexes (some bands) and water depth in this area

Correlation coefficient Inversion indexes
B1 B2 B3 B4 B1/B2 B2/B3 B3/B2 B1/B3 B1/B4 B4/B1 B2/B4
ρ 0.764 0.696 0.393 −0.661 −0.387 0.600 −0.635 0.606 0.776 −0.808 0.704

According to Table 2, the correlation coefficient between B4/B1 band ratio and water depth in this area was the highest. Therefore, the band ratio of B4/B1 was selected as the input value for the inversion of water depth in this area by the curve fitting model (Figure 4).

Figure 4 The results of curve fitting models of water deep in this study area.
Figure 4

The results of curve fitting models of water deep in this study area.

According to Table 3, the cubic model had the better results than other curve fitting models with the lower R, R 2, and so on (R = 0.875, R 2 = 0.766, R 2 (modified) = 0.761, and the standard error of the estimate = 0.353). Thus, the study wound use the cubic model to inverse the water deep value of the area. The inversion result is shown in Figure 5.

Table 3

The results of curve fitting

Names of models R R 2 R 2 (modified) The standard error of the estimate
Linear 0.808 0.654 0.651 0.427
Logarithmic 0.748 0.559 0.556 0.482
Inverse 0.675 0.456 0.452 0.535
Quadratic 0.873 0.762 0.759 0.355
Cubic 0.875 0.766 0.761 0.353
Compound 0.794 0.631 0.628 0.055
Power 0.727 0.529 0.526 0.062
S 0.651 0.423 0.419 0.068
Growth 0.794 0.631 0.628 0.055
Exponential 0.794 0.631 0.628 0.055
Logistic 0.794 0.631 0.628 0.055
Figure 5 Prediction results of the water depth based on curve fitting models.
Figure 5

Prediction results of the water depth based on curve fitting models.

4.3 The results of BP neural network

In this study, 60% of the total data and 40% of the total data were used as the training set and the test set. The structure of the BP neural network is shown in Figure 6. The B1–B4 and B4/B1 band data were used as the input of the model.

Figure 6 BP neural network structure.
Figure 6

BP neural network structure.

The MAE of training results was 1.674, and the standard deviation was 2.194; the MAE of test results was 2.455, and the standard deviation was 3.271. The importance of predictive variables of the model was 0.35, 0.25, 0.21, 0.11, and 0.09 (B4/B1, B4, B2, B3, B1, respectively). The inversion result is shown in Figure 7.

Figure 7 Prediction results of water depth based on the BP neural network.
Figure 7

Prediction results of water depth based on the BP neural network.

4.4 The results of RBF neural network

In this study, 60% of the total data and 40% of the total data were used as the training set and test set. The structure of the RBF neural network is shown in Figure 8. The B1–B4 and B4/B1 band data were used as the input of the model.

Figure 8 Structure of RBF neural network.
Figure 8

Structure of RBF neural network.

The MAE of training results was 1.838, and the standard deviation was 2.47; the MAE of test results was 2.719, and the standard deviation was 3.702. The importance of predictive variables of the model was 0.25, 0.25, 0.25, 0.17, and 0.08 (B4/B1, B2, B1, B4, and B3, respectively). The prediction result is shown in Figure 9.

Figure 9 Prediction results of water depth based on the RBF neural network.
Figure 9

Prediction results of water depth based on the RBF neural network.

4.5 The results of SVM

This study used SVM with a linear function kernel. The training set and the test set were composed of 60% of the total data and 40% of the total data. The B1–B4 and B4/B1 band data were used as the input of the model.

The MAE of training results was 2.046, and the standard deviation was 2.621; the MAE of test results was 2.451, and the standard deviation was 3.228. The importance of predictive variables of the model was 0.28, 0.27, 0.23, 0.17, and 0.04 (B4/B1, B1, B2, B3, and B4, respectively). The inversion result is shown in Figure 10.

Figure 10 Water depth prediction results based on linear SVM.
Figure 10

Water depth prediction results based on linear SVM.

5 Comparative analysis and discussion

To better reflect the accuracy of each model, for each water depth inversion algorithm, the MAE between the inversion value of all prediction points and the measured value was calculated, and the results are presented in Table 4. Besides, for each model, the measured water depth value was taken as the horizontal axis of the plane rectangular coordinate system, and the inversion value was taken as the vertical axis of the coordinate system. All points were plotted in the coordinate system to get the error between the inversion value and the measured value, as shown in Figure 11.

Table 4

The MAE of five water depth inversion models

Model MAE
Curve fitting 2.575
BP neural network 2.455
RBF neural network 2.719
Linear SVM 2.451
Deep learning 2.350
Figure 11 Deviation diagram of predicted and measured values of model water depth: (a) curve fitting model, (b) BP neural network model, (c) RBF neural network model, (d) linear SVM model, and (e) deep learning model.
Figure 11

Deviation diagram of predicted and measured values of model water depth: (a) curve fitting model, (b) BP neural network model, (c) RBF neural network model, (d) linear SVM model, and (e) deep learning model.

The performance of several depth inversion algorithms is compared in Table 4. Table 4 indicates that the MAE between the depth learning model and the measured water depth was the smallest (2.350 m), followed by the linear SVM model (2.451 m), and the BP neural network model was in the middle (2.455 m), and the errors of the curve fitting model and RBF neural network model were relatively large (2.575 m and 2.719 m, respectively). Therefore, the deep learning model was the best, followed by the linear SVM model and the BP neural network model, and the curve fitting model and RBF neural network model were poor.

Figure 11 directly reflected the deviation between the predicted water depth and the measured water depth at each inspection point. For the convenience of observation, the straight line passing through the coordinate origin, and slope of 1 was called “coincidence line” [17]. The inversion value of all inspection points on the coincidence line was equal to the measured value. The performance of several depth inversion algorithms was analyzed and compared from Figure 11.

From the curve fitting model in Figure 11(a), the distribution of prediction points was relatively scattered as a whole. When the actual water depth was less than 25 m, the prediction value of the model was generally greater than the measured value, and the fitting between the prediction point and the coincidence line was not good as a whole.

It can be seen from Figure 11(b) that for the BP neural network model, when the actual water depth value of the predicted point was greater than 35 m, the prediction error of the model was larger. When the actual water depth was about 30 m, the prediction accuracy of the model was higher. The upper prediction point fitted well with the coincidence line.

According to Figure 11(c), in the RBF neural network model, the overall distribution of prediction points was more centralized than that of the linear fitting model. Still, it was more decentralized than that of the BP neural network model. Although all prediction points were evenly distributed on both sides of the coincidence line, the error between them and the measured value was large, and on the whole, the prediction points and the coincidence line were not well fitted.

By using the linear SVM model in Figure 11(d), it turned that the distribution of prediction points was very similar to that of the BP neural network model, but the dispersion degree of prediction points when the measured water depth about 30 m was greater than that of the BP neural network model. In addition, when the water depth was less than 23 m, the predicted value of water depth at the prediction point was generally greater than the measured value, and the fitting between the prediction point and the coincidence line was general.

Through the deep learning model in Figure 11(e), it can be learned that the distribution of prediction points was dense, but when the actual water depth value was less than 23 m or greater than 33 m, the error of prediction points was larger. The distribution was very similar to that of the BP neural network model, and the prediction points and the coincidence line were generally better.

From Figure 12(a)–(f), it can be seen that the results obtained by the five water depth inversion models had certain errors compared with the actual results, but the overall error of the deep learning model and the BP neural network model was smaller. The similarity of the actual result graph was higher, and the errors of the other three models were larger.

Figure 12 Inversion results of water depth in the study area: (a) actual water depth, (b) curve fitting inversion, (c) BP neural network, (d) RBF neural network, (e) linear SVM, and (f) deep learning inversion.
Figure 12

Inversion results of water depth in the study area: (a) actual water depth, (b) curve fitting inversion, (c) BP neural network, (d) RBF neural network, (e) linear SVM, and (f) deep learning inversion.

From the comparison of Figures 11(a)–(e), 12(a)–(f), and 13, it can be seen that the deep learning model had the best fitting degree, the BP neural network model took the second place, the linear SVM model was the third, and the RBF neural network and the curve fitting model were poor.

Figure 13 Comparison between inversion results of five models and measured values.
Figure 13

Comparison between inversion results of five models and measured values.

It can be seen that deep learning was better than the traditional machine learning algorithm in RS depth inversion. The reason was that the advanced features extracted from large amounts of data were unmatched by other simple algorithm. But the deep learning model often needs a large sample size to achieve the desired effect. Simple machine learning algorithm can also backpropagate the error through continuous transmission of water depth information, so as to adjust the error. Therefore, the accuracy of traditional machine learning algorithm was higher than that of the curve fitting model.

6 Conclusion

Based on the HJ-1B satellite data and the measured water depth data in the sea area near the islands outside the Pearl River Estuary, the water depth values of the sea area were inversed by various methods. Only from the curve fitting model, the cubic model was better than other curve fitting models. However, the error of machine learning method was less than that of the curve fitting method. Of course, the curve fitting method is simpler than the machine learning method, and there is no great requirement for the computer’s computing power and machine configuration. Among the four machine learning methods, the deep learning model had the best inversion effect, followed by the BP neural network model. Therefore, the best method of this study was the deep learning model. As an advanced machine learning method, deep learning is a hot field of artificial intelligence, and its advantages in the image recognition field are far more than the previous technology. Although the RBF neural network was better than the BP neural network in generalization ability and learning speed, it was inferior to the BP neural network model in this study. The reason may be the adjustment of parameters such as the number of network layers, the number of nodes in each layer, the function type of nodes, and the epochs of the neural network model. The adjustment can achieve the best effect in the training, but cannot meet the requirements to achieve the best results in the test data. Therefore, to avoid under fitting and over fitting, the model parameters should be adjusted many times to select the most suitable model.

Acknowledgments

This research was supported by research and demonstration of ecological construction of typical islands in the South China Sea and the monitoring technology of ecological things in the South China Sea (Grant No. 2017YFC0506304); using remote sensing geology survey, the application information extraction and drawing of national defense construction (Grant No. DD2016007637); groundwater exploration technology in the water shortage region in ‘863 program (Grant No. 2012AA062601).

  1. Conflict of interest: Authors state no conflict of interest.

References

[1] Yanjiao W, Wenjie D, Peiqun Z, Feng Y. Research progress on remote sensing methods for water depth and visible light. Mar Bull. 2007;26:92–101.Search in Google Scholar

[2] Lian F, Chuanmin H, Xiaoling C, Xiaobin C, Liqiao T, Wenxia G. Assessment of inundation changes of Poyang Lake using MODIS obscrvations between 2000 and 2010. Remote Sens Environ. 2012;121:80–92.10.1016/j.rse.2012.01.014Search in Google Scholar

[3] Hongchen Z. Research on multi-level decision-making optical remote sensing water depth inversion based on WorldView2. Nanjing: Nanjing University; 2017. p. 12–5.Search in Google Scholar

[4] Ping Z. Mathematical model of visible light remote sensing. Ocean Lakes. 1982;13:225–30.Search in Google Scholar

[5] Lyzenga DR. Passive remote sensing techniques for mapping water depth and bottom features. Appl Opt. 1978;17:379–83.10.1364/AO.17.000379Search in Google Scholar

[6] Lyzenga DR. Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data. Int J Remote Sens. 1981;2:71–82.10.1080/01431168108948342Search in Google Scholar

[7] Polcyn FC, Brown WL, Sattinger IJ. The measurement of water depth by remote sensing techniques. Mich Univ Ann Arbor Inst Sci Technol. 1970;1970:7–9.Search in Google Scholar

[8] Benny AH, Dawson GJ. Satellite imagery as an aid to bathymetric charting in the Red Sea. Cartogr J. 1983;20:5–16.10.1179/caj.1983.20.1.5Search in Google Scholar

[9] John M, Paredes, Robert ES. Water depth mapping from passive remote sensing data under a generalized ratio assumption. Appl Opt. 1983;22:1134–5.10.1364/AO.22.001134Search in Google Scholar

[10] Jingjing W, Qingjiu T. Research on inversion method of shallow sea water depth and hyperspectral remote sensing in coastal zone. Geogr Sci. 2007;27:843–8.Search in Google Scholar

[11] Gang L, Fan Y, Cheng C, Dongmei W, Wenguang L. Research on depth extraction technology of Hongze Lake based on landsat-8 remote sensing image. Beijing Survey Mapp. 2017;4:13–8.Search in Google Scholar

[12] Benqing C, Yanming Y, Kai L. Retrieval of island shallow water depth from the GaoFen-1 multi-spectral imagery. J Trop Oceanogr. 2017;2:70–8.Search in Google Scholar

[13] Ying Z, Dong Z, Yanjiao W, Yong X. Research on remote sensing method of water depth in sandy waters. J Oceanogr. 2008;1:51–8.Search in Google Scholar

[14] Cracknell AP, Ibrahim M. Bathymetry studies on the coastal waters (Red Sea) of Jeddah, Saudi Arabia, using Shuttle MOMS-O1 data. Int J Remote Sens. 1988;9:1161–5.10.1080/01431168808954923Search in Google Scholar

[15] Ying Z, Yun Z, Dong Z, Yan Q. Remote sensing of water depth in the radiation sand ridges of the Southern Yellow Sea. Chin J Oceanogr. 2009;31:39–45.Search in Google Scholar

[16] Wenqian H, Di W, Yang Y, Zhicheng L, Yangyang Z. Multi-spectral remote sensing water depth inversion in shallow waters. Mar Technol. 2013;32:43–6.Search in Google Scholar

[17] Zhonghan H, Baishan X, Chenglin Y, Yongjin S. Research on reef depth retrieval of south China Sea Island based on planet multispectral image. Geomat Spat Inf Technol. 2020;43:139–142+146.Search in Google Scholar

[18] Sandidge JC, Holyer RJ. Coastal bathymetry from hyperspectral observations of water radiance. Remote Sens Environ. 1998;65:341–52.10.1016/S0034-4257(98)00043-1Search in Google Scholar

[19] Wang Y. Research on remote sensing method of water depth based on the impact of suspended sediment. Doctoral dissertation. Nanjing: Nanjing Normal University; 2010. p. 75–90.Search in Google Scholar

[20] Zhicheng L, Wenqian H, Yang Y, Yangyang Z. Multi-factor remote sensing water depth inversion model based on neural network technology. Survey Mapp Eng. 2012;21:17–21.Search in Google Scholar

[21] Hongqi S, Chunxia C, Long Y. Research on water depth inversion method of hyperspectral image based on SVR. N Industrial. 2014;1:75–8.Search in Google Scholar

[22] Xu L. Multi-factor remote sensing water depth inversion based on neural network technology. Master degree thesis. Beijing: China University of Petroleum; 2014. p. 7–13.Search in Google Scholar

[23] Yanguo F, Jinxia L. Research on remote sensing water depth inversion model based on neural network technology. Mar Survey Mapp. 2015;35:20–3.Search in Google Scholar

[24] Bin C, Zhenge Q, Shulong Z, Bincai C. Improvement of BP neural network remote sensing water depth inversion algorithm. Bull Survey Mapp. 2017;2:40–4.Search in Google Scholar

[25] Guizhou Z, Xiaodong L, Hongping W, Weihua H. Inversion of water depth from WorldView02 satellite imagery based on BP and RBF neural network. Earth Sci. 2017;12:2345–53.Search in Google Scholar

[26] Chen X. Research on deep learning algorithm and application based on convolutional neural network. Zhejiang: Zhejiang Gongshang University; 2014.Search in Google Scholar

[27] Tingxu D, Hongbo J, Chao C, Qiming Q. Inversion of shallow snow depth based on measured spectral analysis of HJ-1B data. Spectrosc Spectr Anal. 2011;31:2784–8.Search in Google Scholar

[28] Runping S, Anqi H, Bolun L, Jia G. Construction of a drought monitoring model using deep learning based on multi-source remote sensing data. Int J Appl Earth Obs Geoinf. 2019;79:48–57.10.1016/j.jag.2019.03.006Search in Google Scholar

[29] Haixiang L, Caiming Z, Xiuxia L. Conic fitting of scattered data points on a plane. J Comput Des Graph (Chin). 2004;16:1594–8.Search in Google Scholar

[30] Xiaochuan W, Feng S, Lei Y, Yang L. MATLAB neural network 30 case analysis. Beijing: Beihang University Press; 2010.Search in Google Scholar

[31] Bin C, Zhenge Q, Bincai C. Comparison of four remote sensing algorithms for shallow water depth inversion. J Geomat Sci Technol. 2016;33:388–93.Search in Google Scholar

[32] Zhengdong D, Xin Y, Hongjun G, Dehao Y. Remote sensing of water depth based on RBF neural network. J PLA Univ Sci Technol (Nat Sci Ed). 2013;14:101–6.Search in Google Scholar

[33] Jinshang Z, Xuanyu J, Zhenzhen S. Water depth inversion based on support vector machine and BP neural network. Geomat Spat Inf Technol. 2019;6:11–4.Search in Google Scholar
Received: 2020-10-21
Revised: 2021-06-02
Accepted: 2021-06-04
Published Online: 2021-07-20

© 2021 Xiaoning Zhao et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

  1. Regular Articles
  2. Lithopetrographic and geochemical features of the Saalian tills in the Szczerców outcrop (Poland) in various deformation settings
  3. Spatiotemporal change of land use for deceased in Beijing since the mid-twentieth century
  4. Geomorphological immaturity as a factor conditioning the dynamics of channel processes in Rządza River
  5. Modeling of dense well block point bar architecture based on geological vector information: A case study of the third member of Quantou Formation in Songliao Basin
  6. Predicting the gas resource potential in reservoir C-sand interval of Lower Goru Formation, Middle Indus Basin, Pakistan
  7. Study on the viscoelastic–viscoplastic model of layered siltstone using creep test and RBF neural network
  8. Assessment of Chlorophyll-a concentration from Sentinel-3 satellite images at the Mediterranean Sea using CMEMS open source in situ data
  9. Spatiotemporal evolution of single sandbodies controlled by allocyclicity and autocyclicity in the shallow-water braided river delta front of an open lacustrine basin
  10. Research and application of seismic porosity inversion method for carbonate reservoir based on Gassmann’s equation
  11. Impulse noise treatment in magnetotelluric inversion
  12. Application of multivariate regression on magnetic data to determine further drilling site for iron exploration
  13. Comparative application of photogrammetry, handmapping and android smartphone for geotechnical mapping and slope stability analysis
  14. Geochemistry of the black rock series of lower Cambrian Qiongzhusi Formation, SW Yangtze Block, China: Reconstruction of sedimentary and tectonic environments
  15. The timing of Barleik Formation and its implication for the Devonian tectonic evolution of Western Junggar, NW China
  16. Risk assessment of geological disasters in Nyingchi, Tibet
  17. Effect of microbial combination with organic fertilizer on Elymus dahuricus
  18. An OGC web service geospatial data semantic similarity model for improving geospatial service discovery
  19. Subsurface structure investigation of the United Arab Emirates using gravity data
  20. Shallow geophysical and hydrological investigations to identify groundwater contamination in Wadi Bani Malik dam area Jeddah, Saudi Arabia
  21. Consideration of hyperspectral data in intraspecific variation (spectrotaxonomy) in Prosopis juliflora (Sw.) DC, Saudi Arabia
  22. Characteristics and evaluation of the Upper Paleozoic source rocks in the Southern North China Basin
  23. Geospatial assessment of wetland soils for rice production in Ajibode using geospatial techniques
  24. Input/output inconsistencies of daily evapotranspiration conducted empirically using remote sensing data in arid environments
  25. Geotechnical profiling of a surface mine waste dump using 2D Wenner–Schlumberger configuration
  26. Forest cover assessment using remote-sensing techniques in Crete Island, Greece
  27. Stability of an abandoned siderite mine: A case study in northern Spain
  28. Assessment of the SWAT model in simulating watersheds in arid regions: Case study of the Yarmouk River Basin (Jordan)
  29. The spatial distribution characteristics of Nb–Ta of mafic rocks in subduction zones
  30. Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods
  31. Extraction of fractional vegetation cover in arid desert area based on Chinese GF-6 satellite
  32. Detection and modeling of soil salinity variations in arid lands using remote sensing data
  33. Monitoring and simulating the distribution of phytoplankton in constructed wetlands based on SPOT 6 images
  34. Is there an equality in the spatial distribution of urban vitality: A case study of Wuhan in China
  35. Considering the geological significance in data preprocessing and improving the prediction accuracy of hot springs by deep learning
  36. Comparing LiDAR and SfM digital surface models for three land cover types
  37. East Asian monsoon during the past 10,000 years recorded by grain size of Yangtze River delta
  38. Influence of diagenetic features on petrophysical properties of fine-grained rocks of Oligocene strata in the Lower Indus Basin, Pakistan
  39. Impact of wall movements on the location of passive Earth thrust
  40. Ecological risk assessment of toxic metal pollution in the industrial zone on the northern slope of the East Tianshan Mountains in Xinjiang, NW China
  41. Seasonal color matching method of ornamental plants in urban landscape construction
  42. Influence of interbedded rock association and fracture characteristics on gas accumulation in the lower Silurian Shiniulan formation, Northern Guizhou Province
  43. Spatiotemporal variation in groundwater level within the Manas River Basin, Northwest China: Relative impacts of natural and human factors
  44. GIS and geographical analysis of the main harbors in the world
  45. Laboratory test and numerical simulation of composite geomembrane leakage in plain reservoir
  46. Structural deformation characteristics of the Lower Yangtze area in South China and its structural physical simulation experiments
  47. Analysis on vegetation cover changes and the driving factors in the mid-lower reaches of Hanjiang River Basin between 2001 and 2015
  48. Extraction of road boundary from MLS data using laser scanner ground trajectory
  49. Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud
  50. Research on the conservation and sustainable development strategies of modern historical heritage in the Dabie Mountains based on GIS
  51. Cenozoic paleostress field of tectonic evolution in Qaidam Basin, northern Tibet
  52. Sedimentary facies, stratigraphy, and depositional environments of the Ecca Group, Karoo Supergroup in the Eastern Cape Province of South Africa
  53. Water deep mapping from HJ-1B satellite data by a deep network model in the sea area of Pearl River Estuary, China
  54. Identifying the density of grassland fire points with kernel density estimation based on spatial distribution characteristics
  55. A machine learning-driven stochastic simulation of underground sulfide distribution with multiple constraints
  56. Origin of the low-medium temperature hot springs around Nanjing, China
  57. LCBRG: A lane-level road cluster mining algorithm with bidirectional region growing
  58. Constructing 3D geological models based on large-scale geological maps
  59. Crops planting structure and karst rocky desertification analysis by Sentinel-1 data
  60. Physical, geochemical, and clay mineralogical properties of unstable soil slopes in the Cameron Highlands
  61. Estimation of total groundwater reserves and delineation of weathered/fault zones for aquifer potential: A case study from the Federal District of Brazil
  62. Characteristic and paleoenvironment significance of microbially induced sedimentary structures (MISS) in terrestrial facies across P-T boundary in Western Henan Province, North China
  63. Experimental study on the behavior of MSE wall having full-height rigid facing and segmental panel-type wall facing
  64. Prediction of total landslide volume in watershed scale under rainfall events using a probability model
  65. Toward rainfall prediction by machine learning in Perfume River Basin, Thua Thien Hue Province, Vietnam
  66. A PLSR model to predict soil salinity using Sentinel-2 MSI data
  67. Compressive strength and thermal properties of sand–bentonite mixture
  68. Age of the lower Cambrian Vanadium deposit, East Guizhou, South China: Evidences from age of tuff and carbon isotope analysis along the Bagong section
  69. Identification and logging evaluation of poor reservoirs in X Oilfield
  70. Geothermal resource potential assessment of Erdaobaihe, Changbaishan volcanic field: Constraints from geophysics
  71. Geochemical and petrographic characteristics of sediments along the transboundary (Kenya–Tanzania) Umba River as indicators of provenance and weathering
  72. Production of a homogeneous seismic catalog based on machine learning for northeast Egypt
  73. Analysis of transport path and source distribution of winter air pollution in Shenyang
  74. Triaxial creep tests of glacitectonically disturbed stiff clay – structural, strength, and slope stability aspects
  75. Effect of groundwater fluctuation, construction, and retaining system on slope stability of Avas Hill in Hungary
  76. Spatial modeling of ground subsidence susceptibility along Al-Shamal train pathway in Saudi Arabia
  77. Pore throat characteristics of tight reservoirs by a combined mercury method: A case study of the member 2 of Xujiahe Formation in Yingshan gasfield, North Sichuan Basin
  78. Geochemistry of the mudrocks and sandstones from the Bredasdorp Basin, offshore South Africa: Implications for tectonic provenance and paleoweathering
  79. Apriori association rule and K-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping
  80. Lithology classification of volcanic rocks based on conventional logging data of machine learning: A case study of the eastern depression of Liaohe oil field
  81. Sequence stratigraphy and coal accumulation model of the Taiyuan Formation in the Tashan Mine, Datong Basin, China
  82. Influence of thick soft superficial layers of seabed on ground motion and its treatment suggestions for site response analysis
  83. Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine
  84. Research on the traditional zoning, evolution, and integrated conservation of village cultural landscapes based on “production-living-ecology spaces” – A case study of villages in Meicheng, Guangdong, China
  85. A prediction method for water enrichment in aquifer based on GIS and coupled AHP–entropy model
  86. Earthflow reactivation assessment by multichannel analysis of surface waves and electrical resistivity tomography: A case study
  87. Geologic structures associated with gold mineralization in the Kirk Range area in Southern Malawi
  88. Research on the impact of expressway on its peripheral land use in Hunan Province, China
  89. Concentrations of heavy metals in PM2.5 and health risk assessment around Chinese New Year in Dalian, China
  90. Origin of carbonate cements in deep sandstone reservoirs and its significance for hydrocarbon indication: A case of Shahejie Formation in Dongying Sag
  91. Coupling the K-nearest neighbors and locally weighted linear regression with ensemble Kalman filter for data-driven data assimilation
  92. Multihazard susceptibility assessment: A case study – Municipality of Štrpce (Southern Serbia)
  93. A full-view scenario model for urban waterlogging response in a big data environment
  94. Elemental geochemistry of the Middle Jurassic shales in the northern Qaidam Basin, northwestern China: Constraints for tectonics and paleoclimate
  95. Geometric similarity of the twin collapsed glaciers in the west Tibet
  96. Improved gas sand facies classification and enhanced reservoir description based on calibrated rock physics modelling: A case study
  97. Utilization of dolerite waste powder for improving geotechnical parameters of compacted clay soil
  98. Geochemical characterization of the source rock intervals, Beni-Suef Basin, West Nile Valley, Egypt
  99. Satellite-based evaluation of temporal change in cultivated land in Southern Punjab (Multan region) through dynamics of vegetation and land surface temperature
  100. Ground motion of the Ms7.0 Jiuzhaigou earthquake
  101. Shale types and sedimentary environments of the Upper Ordovician Wufeng Formation-Member 1 of the Lower Silurian Longmaxi Formation in western Hubei Province, China
  102. An era of Sentinels in flood management: Potential of Sentinel-1, -2, and -3 satellites for effective flood management
  103. Water quality assessment and spatial–temporal variation analysis in Erhai lake, southwest China
  104. Dynamic analysis of particulate pollution in haze in Harbin city, Northeast China
  105. Comparison of statistical and analytical hierarchy process methods on flood susceptibility mapping: In a case study of the Lake Tana sub-basin in northwestern Ethiopia
  106. Performance comparison of the wavenumber and spatial domain techniques for mapping basement reliefs from gravity data
  107. Spatiotemporal evolution of ecological environment quality in arid areas based on the remote sensing ecological distance index: A case study of Yuyang district in Yulin city, China
  108. Petrogenesis and tectonic significance of the Mengjiaping beschtauite in the southern Taihang mountains
  109. Review Articles
  110. The significance of scanning electron microscopy (SEM) analysis on the microstructure of improved clay: An overview
  111. A review of some nonexplosive alternative methods to conventional rock blasting
  112. Retrieval of digital elevation models from Sentinel-1 radar data – open applications, techniques, and limitations
  113. A review of genetic classification and characteristics of soil cracks
  114. Potential CO2 forcing and Asian summer monsoon precipitation trends during the last 2,000 years
  115. Erratum
  116. Erratum to “Calibration of the depth invariant algorithm to monitor the tidal action of Rabigh City at the Red Sea Coast, Saudi Arabia”
  117. Rapid Communication
  118. Individual tree detection using UAV-lidar and UAV-SfM data: A tutorial for beginners
  119. Technical Note
  120. Construction and application of the 3D geo-hazard monitoring and early warning platform
  121. Enhancing the success of new dams implantation under semi-arid climate, based on a multicriteria analysis approach: Case of Marrakech region (Central Morocco)
  122. TRANSFORMATION OF TRADITIONAL CULTURAL LANDSCAPES - Koper 2019
  123. The “changing actor” and the transformation of landscapes
https://doi.org/10.1515/geo-2020-0267
Keywords for this article
RS; HJ-1B; water depth inversion; deep learning
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Lithopetrographic and geochemical features of the Saalian tills in the Szczerców outcrop (Poland) in various deformation settings
  3. Spatiotemporal change of land use for deceased in Beijing since the mid-twentieth century
  4. Geomorphological immaturity as a factor conditioning the dynamics of channel processes in Rządza River
  5. Modeling of dense well block point bar architecture based on geological vector information: A case study of the third member of Quantou Formation in Songliao Basin
  6. Predicting the gas resource potential in reservoir C-sand interval of Lower Goru Formation, Middle Indus Basin, Pakistan
  7. Study on the viscoelastic–viscoplastic model of layered siltstone using creep test and RBF neural network
  8. Assessment of Chlorophyll-a concentration from Sentinel-3 satellite images at the Mediterranean Sea using CMEMS open source in situ data
  9. Spatiotemporal evolution of single sandbodies controlled by allocyclicity and autocyclicity in the shallow-water braided river delta front of an open lacustrine basin
  10. Research and application of seismic porosity inversion method for carbonate reservoir based on Gassmann’s equation
  11. Impulse noise treatment in magnetotelluric inversion
  12. Application of multivariate regression on magnetic data to determine further drilling site for iron exploration
  13. Comparative application of photogrammetry, handmapping and android smartphone for geotechnical mapping and slope stability analysis
  14. Geochemistry of the black rock series of lower Cambrian Qiongzhusi Formation, SW Yangtze Block, China: Reconstruction of sedimentary and tectonic environments
  15. The timing of Barleik Formation and its implication for the Devonian tectonic evolution of Western Junggar, NW China
  16. Risk assessment of geological disasters in Nyingchi, Tibet
  17. Effect of microbial combination with organic fertilizer on Elymus dahuricus
  18. An OGC web service geospatial data semantic similarity model for improving geospatial service discovery
  19. Subsurface structure investigation of the United Arab Emirates using gravity data
  20. Shallow geophysical and hydrological investigations to identify groundwater contamination in Wadi Bani Malik dam area Jeddah, Saudi Arabia
  21. Consideration of hyperspectral data in intraspecific variation (spectrotaxonomy) in Prosopis juliflora (Sw.) DC, Saudi Arabia
  22. Characteristics and evaluation of the Upper Paleozoic source rocks in the Southern North China Basin
  23. Geospatial assessment of wetland soils for rice production in Ajibode using geospatial techniques
  24. Input/output inconsistencies of daily evapotranspiration conducted empirically using remote sensing data in arid environments
  25. Geotechnical profiling of a surface mine waste dump using 2D Wenner–Schlumberger configuration
  26. Forest cover assessment using remote-sensing techniques in Crete Island, Greece
  27. Stability of an abandoned siderite mine: A case study in northern Spain
  28. Assessment of the SWAT model in simulating watersheds in arid regions: Case study of the Yarmouk River Basin (Jordan)
  29. The spatial distribution characteristics of Nb–Ta of mafic rocks in subduction zones
  30. Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods
  31. Extraction of fractional vegetation cover in arid desert area based on Chinese GF-6 satellite
  32. Detection and modeling of soil salinity variations in arid lands using remote sensing data
  33. Monitoring and simulating the distribution of phytoplankton in constructed wetlands based on SPOT 6 images
  34. Is there an equality in the spatial distribution of urban vitality: A case study of Wuhan in China
  35. Considering the geological significance in data preprocessing and improving the prediction accuracy of hot springs by deep learning
  36. Comparing LiDAR and SfM digital surface models for three land cover types
  37. East Asian monsoon during the past 10,000 years recorded by grain size of Yangtze River delta
  38. Influence of diagenetic features on petrophysical properties of fine-grained rocks of Oligocene strata in the Lower Indus Basin, Pakistan
  39. Impact of wall movements on the location of passive Earth thrust
  40. Ecological risk assessment of toxic metal pollution in the industrial zone on the northern slope of the East Tianshan Mountains in Xinjiang, NW China
  41. Seasonal color matching method of ornamental plants in urban landscape construction
  42. Influence of interbedded rock association and fracture characteristics on gas accumulation in the lower Silurian Shiniulan formation, Northern Guizhou Province
  43. Spatiotemporal variation in groundwater level within the Manas River Basin, Northwest China: Relative impacts of natural and human factors
  44. GIS and geographical analysis of the main harbors in the world
  45. Laboratory test and numerical simulation of composite geomembrane leakage in plain reservoir
  46. Structural deformation characteristics of the Lower Yangtze area in South China and its structural physical simulation experiments
  47. Analysis on vegetation cover changes and the driving factors in the mid-lower reaches of Hanjiang River Basin between 2001 and 2015
  48. Extraction of road boundary from MLS data using laser scanner ground trajectory
  49. Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud
  50. Research on the conservation and sustainable development strategies of modern historical heritage in the Dabie Mountains based on GIS
  51. Cenozoic paleostress field of tectonic evolution in Qaidam Basin, northern Tibet
  52. Sedimentary facies, stratigraphy, and depositional environments of the Ecca Group, Karoo Supergroup in the Eastern Cape Province of South Africa
  53. Water deep mapping from HJ-1B satellite data by a deep network model in the sea area of Pearl River Estuary, China
  54. Identifying the density of grassland fire points with kernel density estimation based on spatial distribution characteristics
  55. A machine learning-driven stochastic simulation of underground sulfide distribution with multiple constraints
  56. Origin of the low-medium temperature hot springs around Nanjing, China
  57. LCBRG: A lane-level road cluster mining algorithm with bidirectional region growing
  58. Constructing 3D geological models based on large-scale geological maps
  59. Crops planting structure and karst rocky desertification analysis by Sentinel-1 data
  60. Physical, geochemical, and clay mineralogical properties of unstable soil slopes in the Cameron Highlands
  61. Estimation of total groundwater reserves and delineation of weathered/fault zones for aquifer potential: A case study from the Federal District of Brazil
  62. Characteristic and paleoenvironment significance of microbially induced sedimentary structures (MISS) in terrestrial facies across P-T boundary in Western Henan Province, North China
  63. Experimental study on the behavior of MSE wall having full-height rigid facing and segmental panel-type wall facing
  64. Prediction of total landslide volume in watershed scale under rainfall events using a probability model
  65. Toward rainfall prediction by machine learning in Perfume River Basin, Thua Thien Hue Province, Vietnam
  66. A PLSR model to predict soil salinity using Sentinel-2 MSI data
  67. Compressive strength and thermal properties of sand–bentonite mixture
  68. Age of the lower Cambrian Vanadium deposit, East Guizhou, South China: Evidences from age of tuff and carbon isotope analysis along the Bagong section
  69. Identification and logging evaluation of poor reservoirs in X Oilfield
  70. Geothermal resource potential assessment of Erdaobaihe, Changbaishan volcanic field: Constraints from geophysics
  71. Geochemical and petrographic characteristics of sediments along the transboundary (Kenya–Tanzania) Umba River as indicators of provenance and weathering
  72. Production of a homogeneous seismic catalog based on machine learning for northeast Egypt
  73. Analysis of transport path and source distribution of winter air pollution in Shenyang
  74. Triaxial creep tests of glacitectonically disturbed stiff clay – structural, strength, and slope stability aspects
  75. Effect of groundwater fluctuation, construction, and retaining system on slope stability of Avas Hill in Hungary
  76. Spatial modeling of ground subsidence susceptibility along Al-Shamal train pathway in Saudi Arabia
  77. Pore throat characteristics of tight reservoirs by a combined mercury method: A case study of the member 2 of Xujiahe Formation in Yingshan gasfield, North Sichuan Basin
  78. Geochemistry of the mudrocks and sandstones from the Bredasdorp Basin, offshore South Africa: Implications for tectonic provenance and paleoweathering
  79. Apriori association rule and K-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping
  80. Lithology classification of volcanic rocks based on conventional logging data of machine learning: A case study of the eastern depression of Liaohe oil field
  81. Sequence stratigraphy and coal accumulation model of the Taiyuan Formation in the Tashan Mine, Datong Basin, China
  82. Influence of thick soft superficial layers of seabed on ground motion and its treatment suggestions for site response analysis
  83. Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine
  84. Research on the traditional zoning, evolution, and integrated conservation of village cultural landscapes based on “production-living-ecology spaces” – A case study of villages in Meicheng, Guangdong, China
  85. A prediction method for water enrichment in aquifer based on GIS and coupled AHP–entropy model
  86. Earthflow reactivation assessment by multichannel analysis of surface waves and electrical resistivity tomography: A case study
  87. Geologic structures associated with gold mineralization in the Kirk Range area in Southern Malawi
  88. Research on the impact of expressway on its peripheral land use in Hunan Province, China
  89. Concentrations of heavy metals in PM2.5 and health risk assessment around Chinese New Year in Dalian, China
  90. Origin of carbonate cements in deep sandstone reservoirs and its significance for hydrocarbon indication: A case of Shahejie Formation in Dongying Sag
  91. Coupling the K-nearest neighbors and locally weighted linear regression with ensemble Kalman filter for data-driven data assimilation
  92. Multihazard susceptibility assessment: A case study – Municipality of Štrpce (Southern Serbia)
  93. A full-view scenario model for urban waterlogging response in a big data environment
  94. Elemental geochemistry of the Middle Jurassic shales in the northern Qaidam Basin, northwestern China: Constraints for tectonics and paleoclimate
  95. Geometric similarity of the twin collapsed glaciers in the west Tibet
  96. Improved gas sand facies classification and enhanced reservoir description based on calibrated rock physics modelling: A case study
  97. Utilization of dolerite waste powder for improving geotechnical parameters of compacted clay soil
  98. Geochemical characterization of the source rock intervals, Beni-Suef Basin, West Nile Valley, Egypt
  99. Satellite-based evaluation of temporal change in cultivated land in Southern Punjab (Multan region) through dynamics of vegetation and land surface temperature
  100. Ground motion of the Ms7.0 Jiuzhaigou earthquake
  101. Shale types and sedimentary environments of the Upper Ordovician Wufeng Formation-Member 1 of the Lower Silurian Longmaxi Formation in western Hubei Province, China
  102. An era of Sentinels in flood management: Potential of Sentinel-1, -2, and -3 satellites for effective flood management
  103. Water quality assessment and spatial–temporal variation analysis in Erhai lake, southwest China
  104. Dynamic analysis of particulate pollution in haze in Harbin city, Northeast China
  105. Comparison of statistical and analytical hierarchy process methods on flood susceptibility mapping: In a case study of the Lake Tana sub-basin in northwestern Ethiopia
  106. Performance comparison of the wavenumber and spatial domain techniques for mapping basement reliefs from gravity data
  107. Spatiotemporal evolution of ecological environment quality in arid areas based on the remote sensing ecological distance index: A case study of Yuyang district in Yulin city, China
  108. Petrogenesis and tectonic significance of the Mengjiaping beschtauite in the southern Taihang mountains
  109. Review Articles
  110. The significance of scanning electron microscopy (SEM) analysis on the microstructure of improved clay: An overview
  111. A review of some nonexplosive alternative methods to conventional rock blasting
  112. Retrieval of digital elevation models from Sentinel-1 radar data – open applications, techniques, and limitations
  113. A review of genetic classification and characteristics of soil cracks
  114. Potential CO2 forcing and Asian summer monsoon precipitation trends during the last 2,000 years
  115. Erratum
  116. Erratum to “Calibration of the depth invariant algorithm to monitor the tidal action of Rabigh City at the Red Sea Coast, Saudi Arabia”
  117. Rapid Communication
  118. Individual tree detection using UAV-lidar and UAV-SfM data: A tutorial for beginners
  119. Technical Note
  120. Construction and application of the 3D geo-hazard monitoring and early warning platform
  121. Enhancing the success of new dams implantation under semi-arid climate, based on a multicriteria analysis approach: Case of Marrakech region (Central Morocco)
  122. TRANSFORMATION OF TRADITIONAL CULTURAL LANDSCAPES - Koper 2019
  123. The “changing actor” and the transformation of landscapes
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 8.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/geo-2020-0267/html
Scroll to top button