CN112161998A - Soil moisture content measuring method and device, electronic equipment and storage medium - Google Patents

Soil moisture content measuring method and device, electronic equipment and storage medium Download PDF

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CN112161998A
CN112161998A CN202010911955.8A CN202010911955A CN112161998A CN 112161998 A CN112161998 A CN 112161998A CN 202010911955 A CN202010911955 A CN 202010911955A CN 112161998 A CN112161998 A CN 112161998A
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王舒
喻小勇
叶勤玉
冯伟
何文春
刘媛媛
徐拥军
韩同欣
王�琦
刘鑫
郑波
倪学磊
李江涛
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Abstract

One or more embodiments of the present specification provide a soil moisture content measuring method, including: aiming at a region, acquiring a passive microwave remote sensing image and soil roughness data corresponding to the region; extracting brightness temperature data from the passive microwave remote sensing image; taking the brightness temperature data and the soil roughness data as input characteristics of a soil water content prediction model; determining the soil water content of the area according to the input characteristics based on the soil water content prediction model; wherein the soil water content prediction model is a residual network model for determining soil water content based on input features. Corresponding to the soil moisture content measuring method, the specification also provides a soil moisture content measuring device, electronic equipment and a computer readable medium.

Description

Soil moisture content measuring method and device, electronic equipment and storage medium
Technical Field
One or more embodiments of the present disclosure relate to the field of remote sensing image inversion technologies, and in particular, to a method for measuring moisture content in soil, a device for measuring moisture content in soil, an electronic device, and a computer-readable storage medium.
Background
Soil moisture is the most important component in a land ecosystem, is an important parameter in researches such as hydrology, meteorology and agriculture, and particularly has important significance in crop yield assessment models and agricultural drought monitoring researches. Therefore, how to effectively measure the water content of the soil with high precision becomes the most concerned problem in the current research.
The traditional soil moisture content measuring method is obtained by taking soil samples from the ground and measuring and weighing in a laboratory. Although the method has high precision, the method is time-consuming and labor-consuming, and the task of measuring the soil water content in a large range is difficult to satisfy.
With the development of remote sensing technology, a large-scale soil moisture content measurement is made possible. At present, optical remote sensing and microwave remote sensing are two main flow directions for soil water content inversion based on remote sensing means. The soil water content inversion method based on optical remote sensing is mainly used for inverting the soil water content by establishing the relationship among vegetation indexes, surface temperature and soil water content. However, since optical remote sensing is easily affected by cloud, rain, aerosol, etc., the soil moisture content obtained by inversion has a certain influence on accuracy. The soil water content inversion method based on microwave remote sensing is mainly used for inverting the soil water content through multi-frequency bright temperature combination. Because microwave remote sensing is less influenced by cloud, rain, aerosol and the like, the soil water content inversion by utilizing the microwave remote sensing is one of the relatively effective soil water content inversion methods at present.
Disclosure of Invention
In view of this, one or more embodiments of the present disclosure provide a method for measuring a soil moisture content, which can determine a soil moisture content of a certain area according to a passive microwave remote sensing image and soil roughness data.
The method for measuring the water content of the soil, which is described in one or more embodiments of the specification, may include: aiming at a region, acquiring a passive microwave remote sensing image and soil roughness data corresponding to the region; extracting brightness temperature data from the passive microwave remote sensing image; taking the brightness temperature data and the soil roughness data as input characteristics of a soil water content prediction model; determining the soil water content of the area according to the input characteristics based on the soil water content prediction model; wherein the soil water content prediction model is a residual network model for determining soil water content based on input features.
In some embodiments of the present description, acquiring a passive microwave remote sensing image corresponding to the region includes: and acquiring a passive microwave remote sensing image which corresponds to the area and is shot by an advanced microwave scanning radiometer 2 satellite (AMSR2) and/or a passive microwave remote sensing image which is shot by a soil moisture and ocean salinity satellite (SMOS).
In some embodiments of the present description, extracting brightness and temperature data from the passive microwave remote sensing image includes: extracting C-band brightness temperature data from the passive microwave remote sensing image which is shot by the AMSR2 and corresponds to the region; and/or extracting brightness temperature data of an L wave band from the passive microwave remote sensing image which is shot by the SMOS and corresponds to the area.
In some embodiments of the present description, the method may further comprise: removing radio interference of the C-band bright temperature data by using the bright temperature data of other bands except the C-band in the passive microwave remote sensing image shot by the AMSR 2; and/or eliminating the pixel data when the electromagnetic interference index of a certain pixel is greater than a preset threshold value by utilizing an L3-level electromagnetic interference quality control mechanism in the SMOS.
In some embodiments of the present description, the method may further comprise: extracting backscattering coefficients of H polarization and V polarization of a C wave band from a passive microwave remote sensing image shot by the AMSR2 to serve as one of input features of the soil water content prediction model; and/or extracting backscattering coefficients of L-wave band H polarization and V polarization from the passive microwave remote sensing image shot by the SMOS to serve as one of input features of the soil water content prediction model.
In some embodiments of the present description, the method may further comprise: and acquiring soil texture data corresponding to the area as one of input characteristics of the soil water content prediction model.
In some embodiments of the present description, the residual network model comprises: the device comprises an input layer, N residual error units and an output layer, wherein the N residual error units and the output layer are connected in a cascading mode; wherein N is a natural number greater than 1;
the output of the input layer is connected to the input of the 1 st residual unit; the output of the nth residual error unit is connected to the input of the (N + 1) th residual error unit, wherein N is more than or equal to 1 and less than N; the output of the Nth residual error unit is connected to the input of the output layer; wherein the content of the first and second substances,
the residual unit includes: the device comprises at least two hidden layers, a feature superposition layer and an activation function layer which are connected in a cascade mode; the hidden layer is used for expanding the features input by the input layer according to different dimensions and extracting high-dimensional features; the characteristic superposition layer is used for superposing the output characteristics of the input layer or the previous residual error unit with the output characteristics of the last hidden layer which is connected with the characteristic superposition layer in a cascade mode; the activation function of the activation function layer is relu (x) ═ max (0, x).
Corresponding to the above-mentioned soil moisture content measuring method, one or more embodiments of the present specification further disclose a soil moisture content measuring apparatus including:
the characteristic acquisition module is used for acquiring a passive microwave remote sensing image and soil roughness data corresponding to a region aiming at the region;
the characteristic extraction module is used for extracting brightness temperature data from the passive microwave remote sensing image and taking the brightness temperature data and the soil roughness data as input characteristics of a soil water content prediction model;
the prediction module is used for determining the soil water content of the area according to the input characteristics based on the soil water content prediction model; wherein the soil water content prediction model is a residual network model for determining soil water content based on input features.
One or more embodiments of the present specification also provide an electronic device, which may include: a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the soil moisture content measuring method described above.
One or more embodiments of the present specification also provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the soil moisture content measurement method described above.
It can be seen that the soil water content measuring method adopts the residual network model obtained by the supervised training mode as the soil water content prediction model, can fully utilize the characteristic that the input value is added again in the residual network before the output value is activated, not only solves the problems of weak generalization ability, low precision and low reliability when other neural networks are adopted for soil water content prediction, but also can effectively solve the problems of gradient disappearance, network degradation and the like generated when the number of layers of the network is increased in the neural network. Experiments prove that the residual error network model is very suitable for predicting the water content of the soil and has the characteristics of strong generalization capability, high precision and strong reliability.
In addition, in the soil water content measuring method, the passive microwave remote sensing image is used as one of the input characteristics of the soil water content prediction model. Because the microwave remote sensing image is less influenced by cloud, fog, overcast and rainy, aerosol and the like, the soil water content obtained by predicting the soil water content according to the passive microwave remote sensing image is higher in precision.
Furthermore, besides the passive microwave remote sensing image, the soil moisture content measuring method also utilizes the soil roughness as the input of the soil moisture content prediction model, fully considers that the soil roughness has great influence on the radiation brightness temperature detected by the passive microwave remote sensing satellite, and can further improve the accuracy of soil moisture content prediction.
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In order to more clearly illustrate one or more embodiments or prior art solutions of the present specification, the drawings that are needed in the description of the embodiments or prior art will be briefly described below, and it is obvious that the drawings in the following description are only one or more embodiments of the present specification, and that other drawings may be obtained by those skilled in the art without inventive effort from these drawings.
FIG. 1 is a schematic flow diagram of a soil moisture content measurement method according to some embodiments of the present disclosure;
fig. 2 shows an internal structure of the residual error unit according to one or more embodiments of the present disclosure.
FIG. 3 is a schematic flow chart of a soil moisture content measuring method according to further embodiments of the present disclosure;
FIG. 4 is a graph of the relationship between predicted soil moisture content and measured soil moisture content using the soil moisture content prediction method described in the examples herein;
FIG. 5 is a schematic diagram illustrating a training process of a soil moisture content prediction model according to one or more embodiments of the present disclosure;
FIG. 6 is a schematic view of the internal structure of a soil moisture content measuring device according to one or more embodiments of the present disclosure;
fig. 7 is a schematic diagram of a hardware structure of an electronic device according to one or more embodiments of the present disclosure.
Detailed Description
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
It is to be noted that unless otherwise defined, technical or scientific terms used in one or more embodiments of the present specification should have the ordinary meaning as understood by those of ordinary skill in the art to which this disclosure belongs. The use of "first," "second," and similar terms in one or more embodiments of the specification is not intended to indicate any order, quantity, or importance, but rather is used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
As mentioned above, soil moisture is the most important component in the land ecosystem, is an important parameter in hydrology, meteorology, agriculture and other researches, and has a particularly important meaning in crop yield assessment models and agricultural drought monitoring researches. Therefore, how to effectively measure the water content of the soil with high precision becomes the most concerned problem in the current research.
One or more embodiments of the present disclosure provide a method for measuring a soil moisture content, which may determine a soil moisture content of a certain area according to a passive microwave remote sensing image and soil roughness data.
Fig. 1 shows a flow of implementing the soil moisture content measuring method according to one or more embodiments of the present disclosure. As shown in fig. 1, the method may include:
in step 102, a passive microwave remote sensing image and soil roughness data corresponding to a region are obtained.
In the embodiments of the present specification, the above-mentioned region may be any specified region of the earth's surface, for example, an upstream region of a black river valley, or the like.
In an embodiment of the present specification, the passive microwave remote sensing image may be obtained by at least one of the following two ways.
Route 1: obtained by Advanced Microwave Scanning Radiometer 2 satellite (AMSR 2).
The AMSR2 sensor is mounted on the GCOM-W1 satellite of Japan and is launched and tracked in 2012. The transit time per day is about 1:30 in the morning (falling rail) and 13:30 in the afternoon (rising rail) locally. Wherein the track ascending and descending data can cover most of the global area except the polar region within two days. The observation incident angle is 55 degrees, the working frequency comprises 14 dual-polarized channels (6.925GHz, 7.3GHz, 10.65GHz, 18.7GHz, 23.8GHz, 36.5GHz and 89GHz), and the multi-band earth surface observation light temperature can be provided. In the embodiment of the present specification, a passive microwave remote sensing image taken by AMSR2 corresponding to the above-mentioned region may be obtained as the passive microwave remote sensing image in step 102.
Route 2: successfully launched by the european space in 11 months of 2009 by Soil Moisture and Ocean Salinity satellites (SMOS). The SMOS sensor is firstly carried with an L-band synthetic aperture radiometer, the working frequency of the SMOS sensor is 1.4GHz, and the transit time of a satellite is 6:00am (ascending orbit) and 6:00pm (descending orbit) of the satellite at the local place. Therefore, in the embodiment of the present specification, a passive microwave remote sensing image corresponding to the area captured by the SMOS may be acquired as the passive microwave remote sensing image in step 102.
In embodiments of the present description, soil roughness data may be indexed by root mean square height of the surface relief. Therefore, in the embodiment of the present specification, in order to improve the accuracy of the soil moisture content measurement, in addition to the passive microwave remote sensing image, soil roughness data corresponding to the area is further selected as one of the bases for the soil moisture content measurement.
In some embodiments of the present disclosure, the soil roughness data corresponding to the above-mentioned region may be calculated using C-band H-polarization and V-polarization brightness temperatures of the advanced microwave scanning radiometer AMSR-E. In order to simplify the calculation, in the embodiment of the present specification, the influence of the frequency on the soil roughness may be ignored, and therefore, soil roughness data calculated by C-band H-polarization and V-polarization brightness temperatures may be used as the soil roughness data.
In other embodiments of the present disclosure, soil roughness data corresponding to the above-mentioned area may also be obtained from a soil information collection site. The soil information acquisition station can measure the roughness of the soil in the area under the jurisdiction by various methods, so that the data of the roughness of the soil in the area under the jurisdiction can be determined and stored.
In step 104, brightness temperature data is extracted from the passive microwave remote sensing image.
In an embodiment of the present specification, the extracting of the brightness temperature data from the passive microwave remote sensing image may include: extracting brightness temperature data of a C wave band from a passive microwave remote sensing image shot by AMSR 2; and/or extracting brightness temperature data of the L wave band from the passive microwave remote sensing image shot by the SMOS.
Compared with brightness temperature data of other frequencies in AMSR2, the passive microwave remote sensing image shot by AMSR2 has the advantages that the wavelength corresponding to the C-band (the working frequency is 6.9GHz) is relatively long, the penetrating power is strong, the influence of vegetation and the atmosphere is minimal, and the passive microwave remote sensing image is more suitable for soil moisture inversion. Therefore, in the embodiment of the present specification, the C-band light temperature data may be extracted from the passive microwave remote sensing image captured by the AMSR2 as one of the input features of the soil water content prediction model.
For the passive microwave remote sensing image shot by the SMOS, the response degree to the soil moisture is higher due to the fact that the wavelength of the L wave band is smaller. Therefore, in the embodiments of the present specification, the light temperature data of the L band may be extracted from the passive microwave remote sensing image captured by SMOS as one of the input features of the soil water content prediction model.
In addition, in the embodiments of the present specification, in order to further improve the prediction accuracy of the soil water content and remove the influence of radio interference (RFI), for the brightness and temperature data of the C-band, after extracting the brightness and temperature data of the C-band, according to the AMSR2 radio removal method, the pearson correlation value of the brightness and temperature data with the operating frequencies of 7.3GHz and 10.65GHz in the passive microwave remote sensing image captured by the AMSR2 may be used to determine that the brightness and temperature data of the C-band is subjected to a strong or medium degree of radio interference, and then the removal is performed.
Regarding the brightness temperature data of the L-band, after the brightness temperature data of the L-band is extracted, a L3-level RFI quality control mechanism (e.g., RFI _ Prob) in the SMOS may be further utilized to perform a judgment, and when the electromagnetic interference index RFI _ Prob of a certain pixel is greater than a preset threshold, for example, 30%, the pixel data may be considered to be seriously affected by electromagnetic interference, and the pixel data may be rejected.
In step 106, the brightness temperature data and the soil roughness data are used as input characteristics of a soil water content prediction model.
In the embodiment of the present specification, the input features of the soil water content prediction model, which is obtained by extracting the C-band brightness temperature data from the passive microwave remote sensing image captured by AMSR2, and/or the input features of the soil water content prediction model, which is obtained by extracting the L-band brightness temperature data from the passive microwave remote sensing image captured by SMOS, are the cases where the surface temperature is most accurately reflected by the C-band brightness temperature data in the passive microwave remote sensing image captured by AMSR2 and the L-band brightness temperature data in the passive microwave remote sensing image captured by SMOS, taking into account that the surface water content is strongly correlated with the surface temperature, and therefore, a higher prediction accuracy can be obtained by using the brightness temperature data as the input features of the soil water content prediction model.
On the other hand, in the embodiments of the present disclosure, only the partial band of the brightness temperature data is extracted from the passive microwave remote sensing image as the input features of the soil moisture content prediction model, instead of the whole band of the brightness temperature data, so that the number of the input features of the soil moisture content prediction model can be greatly reduced, the complexity of the soil moisture content prediction model can be reduced, and the training and prediction efficiency of the soil moisture content prediction model can be improved.
In addition, in order to ensure the scientificity and feasibility of the input features of the soil water content prediction model, in some embodiments of the present specification, feature fusion may be further performed on the brightness and temperature data from the multi-source satellite and the soil roughness data.
In an embodiment of the present specification, the feature fusion may specifically be normalization of the input features. Specifically, the input features of the soil moisture content prediction model may be normalized in one of the following two normalization manners.
The first normalization mode is as follows: and (6) normalizing the linear function. That is, the original data is linearly transformed, and the original data is mapped into the range of [0,1 ]. Specifically, the normalization can be realized by the following formula (1):
Figure BDA0002663621150000081
wherein X represents raw data; xmaxAnd XminRepresenting the maximum and minimum values of the raw data.
The second normalization mode is as follows: normalized by standard deviation, also called Z-Score. Specifically, the normalization can be realized by the following formula (2):
Figure BDA0002663621150000082
wherein μ represents the mean of the raw data; σ represents the variance of the original data; x denotes the raw data. In general, Z-Score normalization performs better when distance is used to measure similarity in classification, clustering, and algorithms or when dimension reduction is performed using a covariance analysis (PCA) technique.
In step 108, determining the soil moisture content of the area according to the input features based on the soil moisture content prediction model; the soil water content prediction model is a residual error network model used for determining the soil water content based on the input characteristics.
It is an object of embodiments of the present description to perform quantitative inversion of soil water content through multi-feature inputs. The inversion is a complex model, and the current inversion models combined with the neural network are simple, so that the problems of weak generalization capability, low precision and low reliability of the model are solved. And increasing the number of layers of the network can cause the problems of gradient disappearance and network degradation of the network model. Therefore, a residual network model is introduced in embodiments of the present description to improve the inversion capability of the network.
In an embodiment of the present specification, the residual network model includes: the device comprises an input layer, at least two residual error units and an output layer, wherein the at least two residual error units are connected in a cascading mode. Assume that there are N residual units, where N is a natural number greater than 1. Wherein, the output of the input layer is connected to the input of the 1 st residual error unit connected in a cascade mode; the output of the nth residual error unit connected in a cascade mode is connected to the input of the (N + 1) th residual error unit, wherein N is more than or equal to 1 and is less than N; the output of the nth residual unit connected in a cascade manner is connected to the input of the output layer.
Fig. 2 shows an internal structure of the residual error unit according to one or more embodiments of the present disclosure. As shown in fig. 2, the residual unit may include: at least two hidden layers 202, a feature superposition layer 204 and an activation function layer 206 connected in a cascaded manner. The hidden layer 202 is configured to expand the features input by the input layer according to different dimensions, and extract high-dimensional features; the feature superposition layer 204 is configured to superpose the features output by the input layer or the previous residual unit of the current residual unit with the features output by the last hidden layer connected in a cascade manner; the activation function of the activation function layer 206 is relu (x) ═ max (0, x).
It can be seen that in the embodiments of the present disclosure, the residual error network model is a complete residual error network formed by connecting such individual residual error units as shown in fig. 2. It should be noted that only two hidden layers are shown in fig. 2, and in practice, the number of hidden layers may be more than two, for example, there may be 3 or even more. As can be seen from fig. 2, X is the input of this one residual unit, and f (X) is the output before being linearly changed and activated by the hidden layer 202. In the residual unit shown in fig. 2, before the activation function layer 206 is linearly changed and activated, the feature superposition layer 204 adds the input X of the residual unit on the basis of f (X), and then the residual unit is activated and output by the activation function layer 206. The input X is added before the output value is active, this path is called a Shortcut (Shortcut) connection. The residual error unit adds an identity mapping layer to the residual error network model through shortcut connection, and mainly solves the problems of gradient disappearance and network degradation caused by the increase of the number of layers of the traditional neural network.
It can be seen that the soil water content measuring method adopts the residual network model obtained by the supervised training mode as the soil water content prediction model, and can fully utilize the characteristic that the residual network is added with the input value again before the output value is activated, thereby not only solving the problems of weak generalization capability, low precision and low reliability when other neural networks are adopted for soil water content prediction, but also effectively solving the problems of gradient disappearance, network degradation and the like generated when the number of layers of the network is increased by the neural network. Experiments prove that the residual error network model is very suitable for predicting the water content of the soil and has the characteristics of strong generalization capability, high precision and strong reliability.
In addition, in the soil water content measuring method, the passive microwave remote sensing image is used as one of the input characteristics of the soil water content prediction model. Because the microwave remote sensing image is less influenced by cloud, fog, rain, aerosol and the like, the accuracy of the soil water content obtained by predicting the soil water content according to the passive microwave remote sensing image is higher.
Furthermore, besides the passive microwave remote sensing image, the soil moisture content measuring method also utilizes the soil roughness as the input of the soil moisture content prediction model. The method makes full use of the fact that the rough degree of the soil has great influence on the radiation brightness temperature detected by the passive microwave remote sensing satellite, and therefore the accuracy of soil water content prediction can be further improved.
In other embodiments of the present disclosure, in order to further improve the accuracy of predicting the soil water content, the method may further include, in addition to the brightness temperature data of the C-band and the L-band: extracting backscattering coefficients of H polarization and V polarization of a C wave band from the passive microwave remote sensing image shot by the AMSR2 to serve as one of input features of the soil water content prediction model; and/or extracting backscattering coefficients of L-wave band H polarization and V polarization from the passive microwave remote sensing image shot by the SMOS to serve as one of input features of the soil water content prediction model.
In these embodiments, further selecting the C-band H-polarization and V-polarization backscattering coefficients in the passive microwave remote sensing image captured by the AMSR2 and/or the L-band H-polarization and V-polarization backscattering coefficients in the passive microwave remote sensing image captured by the SMOS as one of the input features of the soil water content prediction model takes into account the capability of the convolutional neural network based on the residual error structure to extract the parameter features. By inputting the backscattering coefficients of the H polarization and the V polarization of the wave bands and combining the characteristic extraction capability of the network, the high-dimensional characteristics in the original data can be automatically extracted to carry out soil water content inversion, so that the prediction accuracy of the soil water content is further improved.
In addition, in some embodiments of the present disclosure, in order to further improve the accuracy of the prediction of the soil moisture content, in addition to the backscattering coefficients of the H polarization and the V polarization or on the basis of the brightness temperature data and the soil roughness data, the method may further include: and acquiring soil texture data corresponding to the area as one of input characteristics of the soil water content prediction model. This is because the change of soil quality also has a crucial influence on the water content of the soil, and different soil qualities cause different water retention properties of the soil. Therefore, in the embodiments of the present specification, the soil texture classification data corresponding to the above-mentioned region, that is, the volume weight (g/cm) of the soil can be further obtained3) The soil quality data are used as one of the input characteristics of the soil water content prediction model. In the embodiment of the present specification, the soil texture data corresponding to the above-described region may be acquired from the soil texture classification data. As will be appreciated by those skilled in the art, soil texture classification data is generally provided by the Food and Agricultural Organization (FAO).
As can be seen from the above description, the input features of the soil moisture content prediction model at least include: the brightness temperature data and the soil roughness data. In some embodiments of the present disclosure, the input features of the soil moisture content prediction model may further include: and the backscattering coefficients of C-band H polarization and V polarization in the passive microwave remote sensing image shot by the AMSR2 and/or the backscattering coefficients of L-band H polarization and V polarization in the passive microwave remote sensing image shot by the SMOS. In other embodiments of the present disclosure, the input features of the soil moisture content prediction model may further include: corresponding to the soil property data of the area.
Based on the above input features, some embodiments of the present description provide a soil moisture content measuring method. Fig. 3 shows a flow chart of the implementation of the soil moisture content measuring method according to other embodiments of the present disclosure. As shown in fig. 3, the method may include:
in step 302, for a region, the brightness temperature data of the C-band and the H-polarization and V-polarization backscatter coefficients thereof in the passive microwave remote sensing image corresponding to the region captured by AMSR2, the brightness temperature data of the L-band and the H-polarization and V-polarization backscatter coefficients thereof in the passive microwave remote sensing image corresponding to the region captured by SMOS, the soil roughness data corresponding to the region, and the soil texture data corresponding to the region are obtained.
In step 304, the brightness temperature data of the C-band and the H-polarization and V-polarization backscatter coefficients thereof in the passive microwave remote sensing image corresponding to the region captured by the AMSR2, the brightness temperature data of the L-band and the H-polarization and V-polarization backscatter coefficients thereof in the passive microwave remote sensing image corresponding to the region captured by the SMOS, the soil roughness data corresponding to the region, and the soil texture data corresponding to the region are normalized.
In an embodiment of the present specification, the normalization process may include: linear function normalization or standard deviation normalization.
In step 306, the data obtained after the normalization process is used as the input features of the soil water content prediction model.
In step 308, determining the soil moisture content of the area according to the input features based on the soil moisture content prediction model; the soil water content prediction model is a residual error network model used for determining the soil water content based on the input characteristics.
It should be noted that, the specific implementation method of the steps 302-308 can refer to the steps 102-108, and the description is not repeated here.
It can be seen that the soil water content measuring method adopts the residual network model obtained by the supervised training mode as the soil water content prediction model, and can fully utilize the characteristic that the residual network is added with the input value again before the output value is activated, thereby not only solving the problems of weak generalization capability, low precision and low reliability when other neural networks are adopted for soil water content prediction, but also effectively solving the problems of gradient disappearance, network degradation and the like generated when the number of layers of the network is increased by the neural network. Experiments prove that the residual error network model is very suitable for predicting the water content of the soil and has the characteristics of strong generalization capability, high precision and strong reliability.
In addition, in the soil water content measuring method, the passive microwave remote sensing image is used as one of the input characteristics of the soil water content prediction model, and the microwave remote sensing image is less influenced by cloud, rain, aerosol and the like, so that the soil water content obtained by predicting the soil water content according to the passive microwave remote sensing image is high in precision.
In addition, the soil water content measuring method selects the brightness temperature data acquired by the radiometers carried by the two satellites and selects the brightness temperature data of the wave band which can most accurately reflect the surface temperature condition as the input characteristics of the soil water content prediction model, so that the complexity of the soil water content prediction model can be reduced, and higher prediction precision can be obtained.
In addition, by further inputting the backscattering coefficient of the wave band into the soil water content prediction model, the high-dimensional characteristics in the original data can be automatically extracted by combining the characteristic extraction capability of the network to carry out soil water content inversion, so that the prediction precision of the soil water content can be further improved.
Furthermore, besides the passive microwave remote sensing image, the soil water content measuring method also utilizes the soil roughness and the soil quality data as the input of the soil water content prediction model, fully considers that the soil roughness has great influence on the radiation brightness temperature detected by the passive microwave remote sensing satellite, and utilizes the characteristics of the soil roughness and the correlation between the soil quality data and the soil water content, thereby further improving the accuracy of soil water content prediction.
As can be understood by those skilled in the art, the construction of the network model structure is an experimental process from simple to complex, and the number of layers and parameters of the network can affect the inversion accuracy of the network. For example, as the number of network layers and parameters increase, the feature extraction capability of the network and the learning capability of the training samples are greatly improved. However, if the number of network layers or parameters is too large, the problems of network non-convergence, overfitting and the like are caused. Therefore, the residual network model used in the embodiments of the present specification is obtained by repeated experiments according to the response degree of the image characteristics, the soil roughness, the soil texture characteristics, and the like used in the soil water content testing method to the soil water content.
Specifically, in the embodiment of the present specification, through multiple simulation experiments, the structure of the residual network model is optimized according to the predicted soil water content and the actual soil water content, so as to obtain the residual network model structure shown in table 1 below. Experiments prove that the residual error network model with the network structure can be rapidly converged, and meanwhile, the accurate prediction of the soil water content is guaranteed.
Figure BDA0002663621150000121
Figure BDA0002663621150000131
Figure BDA0002663621150000141
TABLE 1
As can be seen from table 1, the total number of the residual network models used in the embodiments of the present disclosure is 101489, wherein the residual network models include 10 residual units. The 10 residual single units respectively comprise 2 to 3 hidden layers, and totally comprise 28 hidden layers. Specifically, the remaining 8 residual units each contain 3 hidden layers, except that the second and sixth residual units contain only 2 hidden layers.
Further, in the embodiment of the present specification, the activation function of the activation function layer described above may be set to relu (x) max (0, x). The network training can be faster by adopting the activation function. This is because the derivative of the activation function is better solved than the activation functions such as Sigmoid, Tanh, etc., which makes the back propagation simpler and the training faster. In addition, the activation function is a nonlinear function, and the activation function is added into a neural network to enable the network to fit nonlinear mapping, so that the nonlinearity of the network can be increased by adopting the activation function. Furthermore, the activation function is an unsaturated activation function, which can prevent the inverse of the activation function from approaching 0 when the value is too large or too small, thereby causing the problem of gradient disappearance. Finally, the part of the activation function smaller than 0 is 0, and the part larger than 0 has a value, so that overfitting of a residual network model can be reduced, and the grid has sparsity.
Fig. 4 is a graph showing the relationship between the predicted soil moisture content and the measured soil moisture content by using the soil moisture content prediction method described in the embodiment of the present specification. Wherein the Mean Square Error (MSE) between the predicted soil water content and the actually measured soil water content is about 0.002, and the coefficient R is determined2About 0.645 and a prediction accuracy of about 86.7%. As can be seen from fig. 4, the soil moisture content prediction using the soil moisture content prediction model described above can obtain a high prediction accuracy.
The training method of the residual error network model is described in detail below with reference to the accompanying drawings. Fig. 5 illustrates a method for training a residual network model according to one or more embodiments of the present disclosure.
In step 502, input features of the residual error network model corresponding to the plurality of regions are obtained as a plurality of training samples of the residual error network model.
In an embodiment of the present specification, the residual network model input features may include: and brightness temperature data and soil roughness data extracted from the passive microwave remote sensing image corresponding to the region. The brightness temperature data may be brightness temperature data of a C-band in the passive microwave remote sensing image corresponding to the region captured by AMSR2 and/or brightness temperature data of an L-band in the passive microwave remote sensing image corresponding to the region captured by SMOS.
In an embodiment of the present specification, the input feature may further include: and the soil property data and/or the backscattering coefficients of H polarization and V polarization corresponding to brightness temperature data extracted from the passive microwave remote sensing image corresponding to the region.
At step 504, actual soil moisture content corresponding to each of the plurality of training samples is obtained.
In the practice of this specification, the actual soil moisture content may be obtained from a soil information collection site. For example, the actual soil moisture content upstream of the black river basin can be obtained by 40 sites of the eight-treasure river basin upstream of the black river.
In step 506, the input features are input into a residual error network model to be trained, and soil water content predicted values corresponding to the training samples and output by the residual error network model are obtained.
In step 508, a predefined loss function is used to determine the difference between the output of the residual network model and the actual soil moisture content of the area, and parameters of the residual network model are adjusted according to the back propagation of the difference, thereby completing the training of the soil moisture content prediction model.
In some examples of the present specification, the predefined Loss function may be a Regression Loss (Regression Loss) function, and specifically, a Mean Absolute Error (MAE) may be used as the Loss function, which may be expressed by the following expression (3).
Figure BDA0002663621150000161
Wherein the content of the first and second substances,
Figure BDA0002663621150000162
an output representing the residual network model corresponding to the ith training sample; y isiRepresenting the actual soil moisture content corresponding to the ith training sample; n represents the number of training samples.
It should be noted that the mean absolute error is a loss function used in the regression model and represents the sum of the absolute values of the differences between the target variable and the predicted variable. Thus, it measures the average magnitude of the error in a set of predictions, regardless of the direction of the error, and the loss range is also 0 to ∞. Therefore, using the absolute error as a loss function may improve the robustness of the data.
In practical application, 3000 iterations can be set for the training of the residual error network model, each iteration is monitored, and finally, the hyper-parameter with the highest precision is stored. Experiments prove that the prediction precision can reach 86.7 percent by adopting the residual error network model of the network structure to predict the water content of the soil.
Based on the soil moisture content measuring method, one or more embodiments of the present disclosure further provide a soil moisture content measuring device, an internal structure of which is shown in fig. 6, and mainly includes:
the characteristic acquisition module 602 is configured to acquire, for an area, a passive microwave remote sensing image and soil roughness data corresponding to the area;
a feature extraction module 604, configured to extract bright temperature data from the passive microwave remote sensing image, and use the bright temperature data and the soil roughness data as input features of a soil water content prediction model; and
a prediction module 606, configured to determine, based on the soil water content prediction model, a soil water content of the area according to the input feature; the soil water content prediction model is a residual error network model used for determining the soil water content based on the input characteristics.
It should be noted that, the concrete implementation method of each module of the soil moisture content measuring device can refer to the foregoing embodiments, and the description is not repeated here.
It should be noted that the method of one or more embodiments of the present disclosure may be performed by a single device, such as a computer or server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method according to one or more embodiments of the present disclosure, and the multiple devices may interact with each other to complete the method.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, the functionality of the modules may be implemented in the same one or more software and/or hardware implementations in implementing one or more embodiments of the present description.
The apparatus of the foregoing embodiment is used to implement the corresponding method in the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Fig. 7 is a schematic diagram of a more specific hardware structure of an electronic device according to an embodiment of the present disclosure, where the electronic device may include: processor 710, memory 720, input/output interface 730, communication interface 740, and bus 750. Wherein processor 710, memory 720, input/output interface 730, and communication interface 740 are communicatively coupled to each other within the device via bus 750.
The processor 710 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the soil moisture content measuring method provided in the embodiments of the present disclosure.
The Memory 720 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, or the like. Memory 720 may store an operating system and other application programs, and when the soil moisture content measurement method provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in memory 720 and called by processor 710 for execution.
The input/output interface 730 is used for connecting an input/output module to realize information input and output. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 740 is used for connecting a communication module (not shown in the figure) to implement communication interaction between the present device and other devices. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
Bus 750 includes a path that transfers information between various components of the device, such as processor 710, memory 720, input/output interface 730, and communication interface 740.
It should be noted that although the above-described device only shows the processor 710, the memory 720, the input/output interface 730, the communication interface 740, and the bus 750, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
Computer-readable media of the present embodiments, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the spirit of the present disclosure, features from the above embodiments or from different embodiments may also be combined, steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present description as described above, which are not provided in detail for the sake of brevity.
In addition, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown in the provided figures, for simplicity of illustration and discussion, and so as not to obscure one or more embodiments of the disclosure. Furthermore, devices may be shown in block diagram form in order to avoid obscuring the understanding of one or more embodiments of the present description, and this also takes into account the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the one or more embodiments of the present description are to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the disclosure, it should be apparent to one skilled in the art that one or more embodiments of the disclosure can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the discussed embodiments.
It is intended that the one or more embodiments of the present specification embrace all such alternatives, modifications and variations as fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements, and the like that may be made without departing from the spirit and principles of one or more embodiments of the present disclosure are intended to be included within the scope of the present disclosure.

Claims (10)

1. A method for measuring water content in soil, comprising:
aiming at a region, acquiring a passive microwave remote sensing image and soil roughness data corresponding to the region;
extracting brightness temperature data from the passive microwave remote sensing image;
taking the brightness temperature data and the soil roughness data as input characteristics of a soil water content prediction model;
determining the soil water content of the area according to the input characteristics based on the soil water content prediction model; wherein the soil water content prediction model is a residual network model for determining soil water content based on input features.
2. The method of claim 1, wherein obtaining a passive microwave remote sensing image corresponding to the region comprises: and acquiring a passive microwave remote sensing image shot by an advanced microwave scanning radiometer 2 satellite, AMSR2 and/or a passive microwave remote sensing image shot by a soil moisture and ocean salinity satellite, SMOS and corresponding to the region.
3. The method of claim 2, wherein extracting brightness temperature data from the passive microwave remote sensing image comprises:
extracting C-band brightness temperature data from the passive microwave remote sensing image which is shot by the AMSR2 and corresponds to the region; and/or
And extracting brightness temperature data of an L wave band from the passive microwave remote sensing image which is shot by the SMOS and corresponds to the area.
4. The method of claim 3, wherein the method further comprises:
removing radio interference of the C-band bright temperature data by using the bright temperature data of other bands except the C-band in the passive microwave remote sensing image shot by the AMSR 2; and/or
And by utilizing the L3-level electromagnetic interference quality control mechanism in the SMOS, when the electromagnetic interference index of a certain pixel is greater than a preset threshold value, the pixel data are rejected.
5. The method of claim 3, wherein the method further comprises:
extracting backscattering coefficients of H polarization and V polarization of a C wave band from a passive microwave remote sensing image shot by the AMSR2 to serve as one of input features of the soil water content prediction model; and/or
And extracting backscattering coefficients of L-wave band H polarization and V polarization from the passive microwave remote sensing image shot by the SMOS to be used as one of input features of the soil water content prediction model.
6. The method of claim 1, wherein the method further comprises: and acquiring soil texture data corresponding to the area as one of input characteristics of the soil water content prediction model.
7. The method of claim 1, wherein the residual network model comprises: the device comprises an input layer, N residual error units and an output layer, wherein the N residual error units and the output layer are connected in a cascading mode; wherein N is a natural number greater than 1;
the output of the input layer is connected to the input of the 1 st residual unit; the output of the nth residual error unit is connected to the input of the (N + 1) th residual error unit, wherein N is more than or equal to 1 and less than N; the output of the Nth residual error unit is connected to the input of the output layer; wherein the content of the first and second substances,
the residual unit includes: the device comprises at least two hidden layers, a characteristic superposition layer and an activation function layer which are connected in a cascading mode; the hidden layer is used for expanding the features input by the input layer according to different dimensions and extracting high-dimensional features; the characteristic superposition layer is used for superposing the output characteristics of the input layer or the previous residual error unit and the output characteristics of the last hidden layer which is connected with the characteristic superposition layer in a cascade mode; the activation function of the activation function layer is relu (x) ═ max (0, x).
8. A soil moisture content measuring device comprising:
the characteristic acquisition module is used for acquiring a passive microwave remote sensing image and soil roughness data corresponding to a region aiming at the region;
the characteristic extraction module is used for extracting brightness temperature data from the passive microwave remote sensing image and taking the brightness temperature data and the soil roughness data as input characteristics of a soil water content prediction model;
the prediction module is used for determining the soil water content of the area according to the input characteristics based on the soil water content prediction model; wherein the soil water content prediction model is a residual network model for determining soil water content based on input features.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the soil moisture content measuring method according to any one of claims 1 to 7.
10. A non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium stores computer instructions for causing the computer to perform the soil moisture content measuring method of any one of claims 1 to 7.
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