A method for regional soil moisture monitoring in airborne spectral reconstruction optical satellite remote sensing

Abstract

The present invention discloses a method for monitoring regional soil moisture by airborne spectral reconstruction optical satellite remote sensing, which relates to the technical field of soil moisture monitoring. This method obtains satellite remote sensing data of the area to be monitored and collects unmanned aerial vehicle (UAV) remote sensing data of a local area in the area to be monitored; according to the UAV remote sensing data of the local area, spectral reconstruction is carried out on the satellite remote sensing data of the area to be monitored to obtain reconstructed satellite spectral data of the satellite remote sensing data; the reconstructed satellite spectral data is input into a pre-trained soil moisture inversion model to obtain the soil moisture content of the area to be monitored. This method can accurately and quickly monitor soil moisture.

Description

Technical Field

[0001] The present invention relates to the technical field of soil moisture monitoring, and particularly to a method for monitoring regional soil moisture by airborne spectral reconstruction of optical satellite remote sensing. Background Art

[0002] Soil moisture is usually quantified by soil water content, which has important research value in the fields of crop growth monitoring, regional drought monitoring and early warning, efficient utilization of water resources, precision irrigation, and smart agriculture.

[0003] Currently, optical satellite remote sensing can be used to monitor soil moisture in large-scale irrigation areas. However, due to the limitations of spatial resolution and the dynamic changes in vegetation coverage (surface spatial heterogeneity), the accuracy of soil moisture monitoring is relatively low. Low-altitude unmanned aerial vehicle (UAV) remote sensing has the characteristics of ultra-high spatial resolution and fast response speed, and can capture rich surface information, with higher accuracy in monitoring soil moisture compared to satellite remote sensing. However, for large-scale irrigation areas, it is time-consuming and laborious to collect UAV remote sensing data covering the entire irrigation area, and it is impossible to ensure the timeliness of soil moisture monitoring products.

[0004] Therefore, there is an urgent need for a method that can accurately and quickly monitor soil moisture. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide a method for monitoring regional soil moisture by airborne spectral reconstruction of optical satellite remote sensing, which can accurately and quickly monitor soil moisture.

[0006] The present invention adopts the following technical solutions:

[0007] The present invention provides a method for monitoring regional soil moisture by airborne spectral reconstruction of optical satellite remote sensing, including:

[0008] Obtaining satellite remote sensing data of the area to be monitored and collecting UAV remote sensing data of a local area in the area to be monitored;

[0009] Performing spectral reconstruction on the satellite remote sensing data of the area to be monitored according to the UAV remote sensing data of the local area to obtain reconstructed satellite spectral data of the satellite remote sensing data;

[0010] Inputting the reconstructed satellite spectral data into a pre-trained soil moisture inversion model to obtain the soil water content of the area to be monitored.

[0011] Preferably, performing spectral reconstruction on the satellite remote sensing data of the area to be monitored according to the UAV remote sensing data of the local area to obtain reconstructed satellite spectral data of the satellite remote sensing data, including:

[0012] Upscale the UAV remote sensing data of the local area to determine the UAV spectral data of the area to be monitored;

[0013] Determine the satellite spectral data of the area to be monitored according to the satellite remote sensing data of the area to be monitored;

[0014] Reconstruct the satellite spectral data through the UAV spectral data to obtain the reconstructed satellite spectral data.

[0015] Preferably, the spectral data includes band reflectance and spectral index; reconstructing the satellite spectral data through the UAV spectral data to obtain the reconstructed satellite spectral data includes:

[0016] Reconstruct the satellite band reflectance through the UAV spectral data to obtain the reconstructed satellite band reflectance;

[0017] Determine the reconstructed satellite spectral index according to the reconstructed satellite band reflectance and a preset conversion function.

[0018] Preferably, the calculation method of the reconstructed satellite band reflectance is:

[0019] B r = B o {1 + k·[FVC / (B u ·B o )]}

[0020] where B r is the reconstructed satellite band reflectance; B0 is the satellite band reflectance; FVC is the fractional vegetation cover corresponding to the spatial resolution scale of the optical satellite; k is an adjustment factor, B u is the upscaled UAV spectral data, including the upscaled UAV band reflectance and spectral index.

[0021] Preferably, the construction process of the soil moisture inversion model includes:

[0022] Obtain the original satellite training sample data and UAV training sample data, and the corresponding soil moisture content;

[0023] Determine the original satellite sample spectral data according to the original satellite training sample data;

[0024] Spectrally reconstruct the original satellite sample spectral data according to the UAV training sample data to obtain the reconstructed satellite sample spectral data;

[0025] Train the extreme learning machine model through the reconstructed satellite sample spectral data to obtain the soil moisture inversion model.

[0026] Preferably, the method further includes:

[0027] Before training the soil moisture inversion model, the correlation analysis is separately performed on the original satellite sample spectral data and the reconstructed satellite sample spectral data with the soil moisture content.

[0028] Preferably, the method further includes:

[0029] Training the extreme learning machine model with the original satellite sample spectral data to obtain the soil moisture inversion model under the original satellite sample spectral data;

[0030] Training the extreme learning machine model with the reconstructed satellite sample spectral data to obtain the soil moisture inversion model under the reconstructed satellite sample spectral data;

[0031] Performing performance tests on the soil moisture inversion model under the original satellite sample spectral data and the soil moisture inversion model under the reconstructed satellite sample spectral data to obtain the original performance test results of the soil moisture inversion model under the original satellite sample spectral data and the performance test results of the soil moisture inversion model under the reconstructed satellite sample spectral data.

[0032] The present invention provides a regional soil moisture monitoring device for airborne spectral reconstruction of optical satellite remote sensing, including:

[0033] An acquisition module, configured to acquire satellite remote sensing data of the area to be monitored and collect unmanned aerial vehicle remote sensing data of a local area in the area to be monitored;

[0034] A reconstruction module, configured to perform spectral reconstruction on the satellite remote sensing data of the area to be monitored according to the unmanned aerial vehicle remote sensing data of the local area to obtain the reconstructed satellite spectral data of the satellite remote sensing data;

[0035] A monitoring module, configured to input the reconstructed satellite spectral data into a pre-trained soil moisture inversion model to obtain the soil moisture content of the area to be monitored.

[0036] The present invention provides a computer-readable storage medium, where the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned regional soil moisture monitoring method for airborne spectral reconstruction of optical satellite remote sensing is implemented.

[0037] The present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the above-mentioned regional soil moisture monitoring method for airborne spectral reconstruction of optical satellite remote sensing is implemented.

[0038] At least one of the above technical solutions adopted by the present invention can achieve the following beneficial effects:

[0039] Spectral reconstruction of satellite remote sensing data for the area to be monitored is carried out using the unmanned aerial vehicle (UAV) remote sensing data of a local area in the area to be monitored, so that the reconstructed satellite spectral data is integrated into the UAV remote sensing data of the local area. In this way, it will be more accurate to determine the soil moisture content of the area to be monitored based on the reconstructed satellite spectral data, and the timeliness is ensured, thus realizing accurate and rapid soil moisture monitoring of the area to be monitored. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings described herein are used to provide a further understanding of the present invention, form a part of the present invention, and the schematic embodiments of the present invention and their descriptions are used to explain the present invention, and do not constitute an improper limitation of the present invention. In the drawings:

[0041] Figure 1 It is a schematic flow chart of a method for monitoring regional soil moisture by airborne spectral reconstruction of optical satellite remote sensing provided by the present invention;

[0042] Figure 2 It is a schematic flow chart of another method for monitoring regional soil moisture by airborne spectral reconstruction of optical satellite remote sensing provided by the present invention;

[0043] Figure 3 It is a schematic flow chart of another method for monitoring regional soil moisture by airborne spectral reconstruction of optical satellite remote sensing provided by the present invention;

[0044] Figure 4 It is a schematic matrix diagram of the correlation analysis between the original and reconstructed band reflectance and spectral index and soil moisture content provided by the present invention;

[0045] Figure 5 It is a scatter precision comparison chart of soil moisture inversion models under the original and reconstructed satellite spectral data provided by the present invention;

[0046] Figure 6 It is a schematic diagram of a device for monitoring regional soil moisture by airborne spectral reconstruction of optical satellite remote sensing provided by the present invention;

[0047] Figure 7 It is a schematic diagram of a computer device for implementing a method for monitoring regional soil moisture by airborne spectral reconstruction of optical satellite remote sensing provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0049] Multi-scale fusion of satellite and UAV optical remote sensing data can greatly improve the accuracy of satellite remote sensing for monitoring soil moisture. However, for large-scale irrigation areas, collecting UAV remote sensing data for full coverage of the irrigation area is time-consuming and laborious, and it is impossible to ensure the timeliness of soil moisture monitoring products.

[0050] Therefore, the present invention provides a method for monitoring regional soil moisture by airborne spectral reconstruction of optical satellite remote sensing. This method integrates UAV optical remote sensing data of local areas to reconstruct the satellite remote sensing band reflectance and spectral indices, so as to improve the accuracy of satellite remote sensing for monitoring soil moisture and achieve high spatio-temporal resolution and high-precision monitoring of soil moisture in large-scale irrigation areas.

[0051] The following will, in conjunction with the accompanying drawings, elaborate on the technical solutions provided by each embodiment of the present invention.

[0052] Figure 1 It is a schematic flowchart of a method for monitoring regional soil moisture by airborne spectral reconstruction of optical satellite remote sensing in the present invention, which specifically includes the following steps:

[0053] S101, Obtain the satellite remote sensing data of the area to be monitored and collect the UAV remote sensing data of the local area in the area to be monitored.

[0054] Among them, the area to be monitored is the area where soil moisture is to be monitored, and the local area can be a representative area with soil moisture preset in the area to be monitored.

[0055] The remote sensing data of the area to be monitored can be directly collected by the satellite, and the remote sensing data of the local area in the area to be monitored can be collected by the UAV. After the satellite collects the remote sensing data of the area to be monitored, it is sent to the server to obtain the satellite remote sensing data of the area to be monitored; after the UAV collects the remote sensing data of the local area in the area to be monitored, it can be sent to the server to obtain the UAV remote sensing data of the local area in the area to be monitored.

[0056] The server mentioned in the present invention can be a server set up on the business platform, or a device such as a desktop computer or a laptop computer that can execute the solution of the present invention. For the convenience of description, only the server will be used as the execution entity for description below.

[0057] S102, According to the UAV remote sensing data of the local area, perform spectral reconstruction on the satellite remote sensing data of the area to be monitored to obtain the reconstructed satellite spectral data of the satellite remote sensing data.

[0058] Among them, as Figure 2 shown, performing spectral reconstruction on the satellite remote sensing data of the area to be monitored according to the UAV remote sensing data of the local area to obtain the reconstructed satellite spectral data of the satellite remote sensing data includes the following steps:

[0059] S201. Upscale the UAV remote sensing data of the local area to determine the UAV spectral data of the area to be monitored.

[0060] Among them, the UAV remote sensing data of the local area can be converted into the UAV spectral data of the area to be monitored through upscaling. The upscaling factor can be determined according to requirements. For example, the resolution is increased from 5 cm to 10 m.

[0061] Optionally, the UAV remote sensing data of the local area can also be input into the first learning model for upscaling to obtain the UAV spectral data of the area to be monitored. Among them, the UAV spectral data includes the spectral data of all areas within the area to be monitored. Specifically, in the first learning model, first, according to the UAV remote sensing data of the local area, determine the UAV spectral data of the local area, and then let the other areas of the area to be monitored learn from the UAV spectral data of the local area to obtain the UAV spectral data of the other areas of the area to be monitored, so as to obtain the UAV spectral data of all areas within the area to be monitored.

[0062] Or, the UAV remote sensing data of the local area can also be input into the second learning model for upscaling to obtain the UAV spectral data of the area to be monitored. Among them, the UAV spectral data includes the spectral data of all areas within the area to be monitored. Specifically, in the second learning model, the other areas of the area to be monitored first learn from the UAV remote sensing data of the local area to obtain the UAV remote sensing data of the other areas of the area to be monitored, so as to obtain the UAV remote sensing data of all areas within the area to be monitored, and then analyze the UAV remote sensing data of all areas within the area to be monitored to obtain the UAV spectral data of the area to be monitored.

[0063] S202. Determine the satellite spectral data of the area to be monitored according to the satellite remote sensing data of the area to be monitored.

[0064] Among them, the satellite remote sensing data of the area to be monitored may include satellite spectral data.

[0065] S203. Reconstruct the satellite spectral data through the UAV spectral data to obtain the reconstructed satellite spectral data.

[0066] Among them, the spectral data includes band reflectance and spectral index. Reconstructing the satellite spectral data through the UAV spectral data to obtain the reconstructed satellite spectral data includes: reconstructing the satellite band reflectance through the UAV spectral data to obtain the reconstructed satellite band reflectance; determining the reconstructed satellite spectral index according to the reconstructed satellite band reflectance and a preset conversion function.

[0067] Among them, the calculation method of the reconstructed satellite band reflectance is as follows:

[0068] B r = B o {1 + k·[FVC / (B u ·B o )]} (1)

[0069] Among them, B r is the reconstructed satellite band reflectance; B0 is the satellite band reflectance; FVC is the fractional vegetation cover corresponding to the spatial resolution scale of the optical satellite; k is the adjustment factor, B u is the UAV band reflectance or the UAV spectral index.

[0070] The reconstructed spectral index is a function of the reconstructed band reflectance, that is, the conversion function is shown in formula (2).

[0071] S rec = f(B r ) (2)

[0072] Among them, S rec is the reconstructed satellite spectral index, f(·) is the function from B r to S rec , and the reconstructed satellite spectral index can include vegetation index, salinity index and brightness index.

[0073] For example, as shown in Table 1, Table 1 is the conversion function from band reflectance to spectral index.

[0074] Table 1

[0075]

[0076] Among them, B, R, G, and NIR are the band reflectances corresponding to Blue, Red, Green, and Near Infrared (NIR) respectively.

[0077] S103. Input the reconstructed satellite spectral data into the pre-trained soil moisture inversion model to obtain the soil moisture content of the area to be monitored.

[0078] Input the reconstructed satellite band reflectance and the reconstructed satellite spectral index into the pre-trained soil moisture inversion model to obtain the soil moisture content of the area to be monitored output by the soil moisture inversion model.

[0079] Preferably, the construction process of the soil moisture inversion model includes: obtaining the original satellite training sample data and the UAV training sample data, as well as the corresponding soil moisture content; determining the original satellite sample spectral data according to the original satellite training sample data, and performing spectral reconstruction on the original satellite sample spectral data according to the UAV training sample data to obtain the reconstructed satellite sample spectral data; training the extreme learning machine model with the reconstructed satellite sample spectral data to obtain the soil moisture inversion model.

[0080] Specifically, first, obtain the UAV multispectral remote sensing data, optical satellite remote sensing images, and soil moisture content data with local representativeness (under different vegetation coverage conditions), and perform preprocessing. Among them, the measured soil moisture content and the UAV multispectral remote sensing data are obtained through field experiments, and the satellite remote sensing data is obtained by downloading from the official website of satellite remote sensing data. The number of UAV upscaled to the satellite remote sensing scale pixels is not less than 1000, and the number of ground soil moisture samplings is not less than 120. Use this UAV multispectral remote sensing data as the UAV training sample data, and use this optical satellite remote sensing image as the satellite training sample data.

[0081] First, perform spectral reconstruction on the satellite training sample data according to the UAV training sample data to obtain the reconstructed satellite sample spectral data; it should be noted that the method for obtaining the reconstructed satellite sample spectral data is the same as the method for obtaining the reconstructed satellite spectral data of the satellite remote sensing data in the above embodiment, and this embodiment will not be elaborated here. The reconstructed satellite sample spectral data includes the reconstructed satellite sample band reflectance and the reconstructed satellite sample spectral index.

[0082] The k value in the reconstructed satellite sample band reflectance, that is, the k value in formula (1), is determined by constructing a linear correlation function r(k). The linear correlation function is constructed by formulas (3)-(6). Formula (3) is the calculation of the linear correlation coefficient between the reconstructed satellite sample band reflectance and the corresponding soil moisture content.

[0083]

[0084] where, S m is the soil mass moisture content.

[0085]

[0086] According to formula (4), formula (3) can be converted into formula (5).

[0087]

[0088] When the sampling number n is given, M0, M1, M2, M3, M4, and M5 are also determined accordingly, and at this time they all become constants, then equation (5) is expressed as equation (6):

[0089]

[0090] Wherein, r(k) is the linear correlation function between the reconstructed band reflectance and the soil moisture content, and r(k) ∈ [-1, 1].

[0091] Differentiating both sides of equation (6) with respect to k, formula (7) can be obtained:

[0092]

[0093] Equation (7) has an extreme value (maximum value) of the correlation r(k). Let dr / dk = 0 in equation (7), and equation (8) is obtained:

[0094]

[0095] For the linear correlation function r(k), there are the following four special cases: (i), (ii), (iii), and (iv).

[0096] (i) When k = (M1M4 - M2M5) / (M2M4 - M1M3) in equation (6), formula (9) is obtained:

[0097]

[0098] Wherein, (k e , r e ) is the only extreme point of the function r(k) within the real number range, and r e is the only maximum or minimum value of the function r(k).

[0099] (ii) When k = 0 in equation (6), formula (10) is obtained:

[0100]

[0101] Wherein, r O represents the linear correlation coefficient between B o and S m , that is, the correlation coefficient between the original satellite band reflectance and the soil moisture.

[0102] (iii) When k = -M1 / M2 in equation (6), r(k) = 0. At this time, there is no linear correlation between the reconstructed satellite band reflectance and the soil moisture.

[0103] (iv) When k → ∞ in equation (6), formula (11) is obtained:

[0104]

[0105] Wherein, r ∞Denote B r and S m The linear correlation coefficient at plus and minus infinity.

[0106] The reconstructed satellite sample spectral data includes the reconstructed satellite sample band reflectance and the reconstructed satellite sample spectral index; the k calculated according to formula (8) m , the original satellite band reflectance B0 corresponding to the satellite training sample data, the vegetation coverage FVC, and the UAV multi-spectral band reflectance or spectral index B corresponding to the upscaled UAV training sample data u Calculate the reconstructed satellite sample band reflectance according to formula (1), and calculate the reconstructed satellite sample spectral index based on the reconstructed satellite sample band reflectance.

[0107] After obtaining the reconstructed satellite sample spectral data, train the extreme learning machine model with the reconstructed satellite sample spectral data to obtain a soil moisture inversion model under the reconstructed satellite sample spectral data. Specifically, use machine learning algorithms such as the extreme learning machine to construct a soil moisture inversion regression model for the reconstructed satellite sample spectral data to obtain a soil moisture inversion model.

[0108] In an exemplary embodiment, the embodiment includes: before training the soil moisture inversion model, perform correlation analysis on the original satellite sample spectral data and the reconstructed satellite sample spectral data with the soil moisture content respectively. Among them, the correlation analysis uses Pearson correlation.

[0109] And, train the extreme learning machine model with the original satellite sample spectral data to obtain a soil moisture inversion model under the original satellite sample spectral data; train the extreme learning machine model with the reconstructed satellite sample spectral data to obtain a soil moisture inversion model under the reconstructed satellite sample spectral data; perform performance tests on the soil moisture inversion model under the original satellite sample spectral data and the soil moisture inversion model under the reconstructed satellite sample spectral data to obtain the original performance test results of the soil moisture inversion model under the original satellite sample spectral data and the performance test results of the soil moisture inversion model under the reconstructed satellite sample spectral data.

[0110] Among them, the soil moisture inversion model under the original satellite sample spectral data and the soil moisture inversion model under the reconstructed satellite sample spectral data are an inversion regression model.

[0111] Optionally, evaluate the accuracy of the soil moisture inversion model constructed using the original satellite sample spectral data and the soil moisture inversion model constructed using the reconstructed satellite sample spectral data respectively through the coefficient of determination (R 2 ) and the root mean square error (RMSE), and use R 2The increase or decrease of RMSE is used to evaluate the improvement effect of the soil moisture inversion model under the reconstructed satellite sample spectral data compared with the soil moisture inversion model under the original satellite sample spectral data.

[0112] In an exemplary embodiment, Figure 3 As shown, the present invention also provides a regional soil moisture monitoring method of airborne spectral reconstruction optical satellite remote sensing, specifically, obtaining satellite images of the sample area collected by the satellite, and then preprocessing the satellite images to obtain original spectral data, and obtaining drone images of the sample area collected by a drone, and preprocessing the drone images to obtain preprocessed drone image data, and upscaling the drone image processing to obtain upscaled drone multispectral data.

[0113] Then, the original satellite spectral data was reconstructed through the upscaled UAV multispectral data to obtain the reconstructed satellite spectral data. The original satellite spectral data and the reconstructed spectral data were respectively correlated with the measured soil moisture content. The original satellite spectral data and the reconstructed satellite spectral data were used as independent variables, and the measured soil moisture content was used as the dependent variable. The soil moisture inversion model under the original satellite spectral data and the soil moisture inversion model under the reconstructed satellite spectral data were trained. Then, the evaluation index (R 2 The soil moisture inversion model under the original satellite spectral data and the soil moisture inversion model under the reconstructed satellite spectral data were evaluated by using the mean square error (RMSE) and RMSE to determine that the soil moisture inversion model under the reconstructed satellite spectral data has better performance.

[0114] The regional soil moisture monitoring method using airborne spectral reconstruction optical satellite remote sensing provided by the present invention is further described below through a specific embodiment.

[0115] Firstly, the local (four plots with different vegetation coverage were selected) unmanned aerial vehicle multispectral remote sensing data obtained from the Jiefangzha Shahaoqu irrigation area in the Hetao irrigation area of site A, the measured soil moisture data on four typical plots collected simultaneously, and the downloaded cloud-free Landsat 8 satellite multispectral remote sensing data corresponding to the study area were used to construct an irrigation area soil moisture inversion model based on the original satellite spectral data and an irrigation area soil moisture inversion model based on the satellite spectral data reconstructed by airborne spectrum, respectively. The inversion accuracy and improvement effect of each model were compared through evaluation indicators.

[0116] Step 1, soil samples were collected and UAV flight tests were carried out in four selected typical fields from August 12th to 15th, 2019. The visible light bands of the UAV multispectral data, namely Blue, Green, and Red, were used for upscaling. The five-point sampling method was used to collect soil samples at a depth of 0 - 10 cm. The mass water content S of the soil was calculated by the drying method (constant temperature treatment at 105 °C for 24 h). m , and the calculation formula is:

[0117]

[0118] where m1 is the weight of the wet soil plus the empty aluminum box, m2 is the weight of the dry soil plus the empty aluminum box, and m3 is the weight of the empty aluminum box.

[0119] Landsat 8 multispectral satellite remote sensing data was downloaded from USGS Earth Explorer. The obtained Landsat 8 satellite image is a Collection 2 Level-1 product that has been geometrically corrected. The imaging time of the image is August 15th, 2019, which is close to the field sample collection time. The Landsat 8 satellite image data was further processed through ENVI software, including radiometric calibration, atmospheric correction, cropping, etc., and then the reflectance data of each band at the sampling points was extracted. In this invention, the Blue, Green, Red, Near Infrared (NIR), Shortwave Infrared-1 (SWIR1), and Shortwave Infrared-2 (SWIR2) of the Landsat 8 optical satellite data were selected for reconstruction.

[0120] Step 2, FVC was obtained by extracting the proportion of vegetation elements in the high-resolution UAV multispectral images at a given scale (30 m). The calculation formula is as follows.

[0121]

[0122] In the formula: N v and N no-v are the numbers of vegetation and non-vegetation pixels corresponding to the Landsat 8 satellite spatial resolution scale, respectively.

[0123] B u In this invention, the S3 salinity index calculated from the upscaled UAV remote sensing data is selected, that is, S3 u , and the calculation formula is as follows:

[0124] S3 u = Green u × Red u / Blue u (14)

[0125] Among them, Blue u , Green u and Red u are the reflectance of the blue, green, and red bands in the UAV remote sensing data upscaled to a spatial resolution of 30 m, respectively. The upscaling method used is the pixel aggregation method.

[0126] The k value of the reflectance of each reconstructed band is calculated using the k value formula at the extreme point of the linear r(k) function, that is, k m , k m-Blue , k m-Green , k m-Red , k m-NIR , k m-SWIR1 and k m-SWIR2 . Finally, the reflectance of each reconstructed band is obtained, as shown in formulas (15)-(20).

[0127] Blue re = Blue{1 + k m-Blue ·[FVC / (S3 u ·Blue)]} (15)

[0128] Green re = Green{1 + k m-Green ·[FVC / (S3 u ·Green)]} (16)

[0129] Red re = Red{1 + k m-Red ·[FVC / (S3 u ·Red)]} (17)

[0130] NIR re = NIR{1 + k m-NIR ·[FVC / (S3 u ·NIR)]} (18)

[0131] SWIR1 re = SWIR1{1 + k m-SWIR1 ·[FVC / (S3 u ·SWIR1)]} (19)

[0132] SWIR2 re = SWIR2{1 + k m-SWIR2 ·[FVC / (S3 u ·SWIR2)]} (20)

[0133] Step 3: Construct 8 original spectral indices (including 4 salinity indices, 3 vegetation indices, and 1 brightness index) based on the band reflectance of the original Landsat 8 optical satellite data; construct 8 reconstructed spectral indices (including 4 reconstructed salinity indices, 3 reconstructed vegetation indices, and 1 reconstructed brightness index) based on the band reflectance of the reconstructed Landsat 8 optical satellite data. The specific calculation formulas are shown in Table 2:

[0134] Table 2

[0135]

[0136]

[0137] Among them, B, G, R, and NIR respectively represent the original blue, green, red, and near-infrared band reflectances, that is, B = Blue, G = Green, R = Red; B re , G re , R re , and NIR re respectively represent the reconstructed blue, green, red, and near-infrared band reflectances, that is, B re = Blue re , G re = Green re , R re = Red re .

[0138] Step 4: Conduct a correlation analysis between the measured soil moisture content and the original and reconstructed satellite band reflectances and spectral indices respectively. The results are shown through a Pearson correlation matrix diagram, as shown in Figure 4 , where the larger the circle, the stronger the correlation. As can be seen from Figure 4 , compared with the original satellite band reflectances and spectral indices, the correlations between the reconstructed satellite band reflectances and spectral indices and the soil moisture content are significantly enhanced.

[0139] Step 5, The extreme learning machine is an artificial neural network model training algorithm, and its composition usually includes an input layer, a hidden layer, and an output layer. During the model training process, the connection weights between the input layer and the hidden layer are random and do not need to be adjusted. Only by setting the number of hidden layer nodes in the network can a global optimal solution be generated. In the present invention, the number of hidden layer nodes is uniformly set to 20, and the extreme learning machine model is constructed through MATLAB software. The ratio of the number of samples in the modeling set to the verification set is set to 2:1, that is, the number of samples in the modeling set is 79, and the number of samples in the verification set is 40. Using all the original band reflectances and spectral indices as independent variables and soil moisture content as the dependent variable, an extreme learning machine regression algorithm is used to construct a soil moisture inversion model under the original satellite data; using all the reconstructed band reflectances and spectral indices as independent variables and soil moisture content as the dependent variable, an extreme learning machine regression algorithm is used to construct a soil moisture inversion model under the reconstructed satellite data, and the results are as Figure 5 shown.

[0140] Step 6, The coefficient of determination can reflect the fitting effect of the model. The closer R 2 is to 1 and the smaller the RMSE, the better the model prediction effect and the smaller the error between the predicted value and the measured value. Calculate the R 2 and RMSE between the measured value and the predicted value of soil moisture content, as shown in Table 3.

[0141]

[0142] Among them, y i represents the measured value, represents the predicted value, represents the average value of the measured values, and n represents the number of samples.

[0143] Table 3

[0144]

[0145] It should be noted that the results in Table 3 are the increase and decrease conditions of R 2 and RMSE of the extreme learning machine model constructed using the reconstructed satellite spectral data compared with the extreme learning machine model constructed using the original satellite spectral data; + indicates an increase, and - indicates a decrease.

[0146] Combined with Figure 5 and Table 3, it can be seen that the R 2 of the verification set of the soil moisture inversion model constructed only using the original satellite data is 0.441, and the RMSE is 0.034 cm 3 / cm 3 . In contrast, the R 2 of the verification set of the soil moisture inversion model constructed using the extreme learning machine regression algorithm under the reconstructed satellite data is 0.663, and the RMSE is 0.025 cm 3 / cm 3 The validation set R 2 increased by 0.222 (an increase of approximately 50.34%), and the RMSE decreased by 0.009 cm 3 / cm 3 (a decrease of approximately 26.47%). It can be seen that integrating the multi-spectral data of local area drones can effectively improve the accuracy of the soil moisture inversion model of single satellite optical remote sensing data, enhance the accuracy of satellite remote sensing monitoring of soil moisture, and achieve high spatio-temporal resolution and high-precision monitoring of soil moisture in large-scale irrigation areas. The present invention can provide a theoretical basis and technical support for improving the monitoring accuracy of regional soil moisture content based on satellite remote sensing spectral data.

[0147] When applying the method for monitoring regional soil moisture by airborne spectral reconstruction optical satellite remote sensing provided by the present invention, it is not necessary to execute according to Figure 1 the order of the steps shown. The specific execution order of each step can be determined according to needs, and the present invention does not limit this.

[0148] The above is the method for monitoring regional soil moisture by airborne spectral reconstruction optical satellite remote sensing provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding device for monitoring regional soil moisture by airborne spectral reconstruction optical satellite remote sensing, as Figure 6 shown.

[0149] Figure 6 It is a schematic diagram of a device for monitoring regional soil moisture by airborne spectral reconstruction optical satellite remote sensing provided by the present invention. The device 600 includes:

[0150] An acquisition module 601, configured to acquire satellite remote sensing data of the area to be monitored and collect unmanned aerial vehicle remote sensing data of a local area in the area to be monitored;

[0151] A reconstruction module 602, configured to perform spectral reconstruction on the satellite remote sensing data of the area to be monitored according to the unmanned aerial vehicle remote sensing data of the local area to obtain reconstructed satellite spectral data of the satellite remote sensing data;

[0152] A monitoring module 603, configured to input the reconstructed satellite spectral data into a pre-trained soil moisture inversion model to obtain the soil moisture content of the area to be monitored.

[0153] For the specific limitations of the regional soil moisture monitoring device for airborne spectral reconstruction optical satellite remote sensing, reference can be made to the limitations of the regional soil moisture monitoring method for airborne spectral reconstruction optical satellite remote sensing in the above text, which will not be elaborated here. Each module in the above-mentioned regional soil moisture monitoring device for airborne spectral reconstruction optical satellite remote sensing can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0154] The present invention also provides a computer-readable storage medium, which stores a computer program that can be used to execute the above-mentioned Figure 1 regional soil moisture monitoring method for airborne spectral reconstruction optical satellite remote sensing provided.

[0155] The present invention also provides Figure 7 the structural schematic diagram of the computer device shown in, as Figure 7 shown, at the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include other hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the above-mentioned Figure 1 regional soil moisture monitoring method for airborne spectral reconstruction optical satellite remote sensing provided.

[0156] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned method embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned method embodiments. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided by the present invention can include at least one of non-volatile and volatile memories. The non-volatile memory can include a read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. The volatile memory can include a random access memory (RAM) or an external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as a static random access memory (SRAM) or a dynamic random access memory (DRAM), etc.

[0157] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in the present invention.

Claims

1. A method for monitoring regional soil moisture by airborne spectral reconstruction optical satellite remote sensing, characterized in that, The method includes: Obtaining satellite remote sensing data of the area to be monitored and collecting unmanned aerial vehicle (UAV) remote sensing data of a local area in the area to be monitored; According to the UAV remote sensing data of the local area, performing spectral reconstruction on the satellite remote sensing data of the area to be monitored to obtain reconstructed satellite spectral data of the satellite remote sensing data; the spectral data includes band reflectance; the calculation method of the reconstructed satellite band reflectance is: B r = B o {1 + k·[FVC / (B u ·B o )]} Among them, B r is the reconstructed satellite band reflectance; B0 is the satellite band reflectance; FVC is the fractional vegetation cover corresponding to the spatial resolution scale of the optical satellite; k is the adjustment factor, B u is the UAV band reflectance or the UAV spectral index; Inputting the reconstructed satellite spectral data into a pre-trained soil moisture inversion model to obtain the soil moisture content of the area to be monitored.

2. The method according to claim 1, wherein, The step of performing spectral reconstruction on the satellite remote sensing data of the area to be monitored according to the UAV remote sensing data of the local area to obtain the reconstructed satellite spectral data of the satellite remote sensing data includes: Performing upscaling conversion on the UAV remote sensing data of the local area to determine the UAV spectral data of the area to be monitored; According to the satellite remote sensing data of the area to be monitored, determining the satellite spectral data of the area to be monitored; Reconstructing the satellite spectral data through the UAV spectral data to obtain the reconstructed satellite spectral data.

3. The method according to claim 2, characterized in that, The spectral data further includes spectral indices; the step of reconstructing the satellite spectral data through the UAV spectral data to obtain the reconstructed satellite spectral data includes: Reconstructing the satellite band reflectance through the UAV spectral data to obtain the reconstructed satellite band reflectance; Determining the reconstructed satellite spectral indices according to the reconstructed satellite band reflectance and a preset conversion function.

4. The method according to claim 1, characterized in that, The construction process of the soil moisture inversion model includes: Obtaining original satellite training sample data, UAV training sample data, and the corresponding soil moisture content; Determining original satellite sample spectral data according to the original satellite training sample data; Performing spectral reconstruction on the original satellite sample spectral data according to the UAV training sample data to obtain reconstructed satellite sample spectral data; Training an extreme learning machine model through the reconstructed satellite sample spectral data to obtain the soil moisture inversion model.

5. The method according to claim 4, characterized in that, The method further includes: Before training the soil moisture inversion model, performing correlation analysis on the original satellite sample spectral data and the reconstructed satellite sample spectral data respectively with the soil moisture content.

6. The method according to claim 4, wherein The method further includes: Training an extreme learning machine model through the original satellite sample spectral data to obtain a soil moisture inversion model under the original satellite sample spectral data; Training an extreme learning machine model through the reconstructed satellite sample spectral data to obtain a soil moisture inversion model under the reconstructed satellite sample spectral data; Performing performance testing on the soil moisture inversion model under the original satellite sample spectral data and the soil moisture inversion model under the reconstructed satellite sample spectral data to obtain the original performance test results of the soil moisture inversion model under the original satellite sample spectral data and the performance test results of the soil moisture inversion model under the reconstructed satellite sample spectral data.

7. An airborne spectral reconstruction optical satellite remote sensing-based regional soil moisture monitoring device, characterized in that, It includes: An acquisition module, configured to obtain satellite remote sensing data of the area to be monitored and collect UAV remote sensing data of a local area in the area to be monitored; A reconstruction module, configured to perform spectral reconstruction on the satellite remote sensing data of the area to be monitored according to the UAV remote sensing data of the local area, so as to obtain the reconstructed satellite spectral data of the satellite remote sensing data; the spectral data includes band reflectance; the calculation method of the reconstructed satellite band reflectance is as follows: B r = B o {1 + k·[FVC / (B u ·B o )]} Among them, B r is the reconstructed satellite band reflectance; B0 is the satellite band reflectance; FVC is the fractional vegetation cover corresponding to the spatial resolution scale of the optical satellite; k is the adjustment factor, B u is the UAV band reflectance or the UAV spectral index; A monitoring module, configured to input the reconstructed satellite spectral data into a pre-trained soil moisture inversion model to obtain the soil moisture content of the area to be monitored.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

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