Soil water holding capacity remote sensing monitoring method based on exponential decay model

Abstract

The application discloses a soil water holding capacity remote sensing monitoring method based on an exponential decay model, and comprises the following steps: step one, selecting a Sentinel-2A satellite remote sensing image in a target area; step two, cutting the processed remote sensing image according to the target area dry field block vector range; step three, determining an exponential decay model that is consistent with an NDWI time sequence curve; step four, according to the characteristics that the NDWI water decay curve rapidly decreases and then becomes flat and stable; step five, processing soil temperature and humidity sensor measured data; step six, based on the soil water holding capacity remote sensing monitoring result; the application proposes a soil water holding capacity remote sensing monitoring index, combines time sequence data of a remote sensing index NDWI and an exponential decay model, realizes nondestructive, rapid and large-area monitoring of the soil water holding capacity, and overcomes the limitations of traditional methods. By introducing the NDWI water decay curve and combining the exponential decay model, the change trend of the soil water holding capacity can be more accurately reflected.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing monitoring of soil water holding capacity, and in particular to a remote sensing monitoring method of soil water holding capacity based on an exponential decay model. Background Art

[0002] Soil water-holding capacity is a key physical property of soil, critically impacting agricultural production, the hydrological cycle, and the ecological environment. Traditional soil water-holding capacity monitoring relies primarily on laboratory analysis to draw soil moisture characteristic curves using methods such as drying and tensiometers. While these methods offer high accuracy, they are time-consuming, costly, and limited to obtaining localized data. With the development of remote sensing technology, which can rapidly acquire soil information over large areas, its application in soil moisture monitoring has gradually gained attention. However, there are currently no methods for remote sensing monitoring of soil water-holding capacity, and a direct and effective monitoring model for soil water-holding capacity is lacking. Currently, remote sensing technology primarily focuses on monitoring soil moisture, and methods are primarily based on vegetation indices or single remote sensing images. These methods are significantly affected by factors such as vegetation cover and soil texture, resulting in limited monitoring accuracy. Existing remote sensing models, in particular, struggle to accurately reflect the spatial variation of soil water-holding capacity in areas with varying types and degrees of soil degradation. In view of this, this patent proposes a remote sensing monitoring method for soil water holding capacity based on an exponential decay model, aiming to overcome the shortcomings of existing technologies, improve the accuracy and efficiency of remote sensing monitoring of soil water holding capacity, and provide a new technical means for soil water holding capacity monitoring. Summary of the Invention

[0003] The purpose of the present invention is to provide a remote sensing monitoring method for soil water holding capacity based on an exponential decay model to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a remote sensing monitoring method for soil water holding capacity based on an exponential decay model, comprising the following steps:

[0005] Step 1: Select Sentinel-2A satellite remote sensing images of the target area. The images are acquired during the three periods of each year, before, during, and after snow cover melts. The acquired images are ensured to be free of cloud contamination to prevent cloud cover from obstructing and interfering with surface reflectance information. The images are preprocessed using radiometric calibration, atmospheric correction, and geometric correction. The Normalized Difference Water Index (NDWI) of each preprocessed image is calculated based on the green and near-infrared bands.

[0006] Step 2: Cropping the processed remote sensing image according to the vector range of the dryland plots in the target area. Based on the NDWI value calculated in step 1, extract the NDWI of the cropped image, and then obtain the NDWI time series curve of each pixel in the cropped image. Sorting the NDWI time series curves of all pixels with "row" as time and "column" as pixel to obtain the period sorting of the cropped image. After observing the NDWI time series curves, it was found that the best time series window that can reflect the dynamic changes of soil moisture is from early March to late May each year.

[0007] Step 3: Determine an exponential decay model that fits the NDWI time series curve. Use this model to fit the NDWI time series curve of each pixel in step 2 into an NDWI moisture decay curve. Analysis shows that the NDWI moisture decay curve and the soil moisture characteristic curve are highly similar. The former represents the change in soil moisture status over time, while the latter represents the change in soil moisture content as water potential increases. The shapes and characteristics of the two are consistent. According to the characteristics of the exponential decay model parameters b representing the curve decline rate and c representing the curve intercept, it is found that the decline rate and intercept of the NDWI moisture decay curve can reflect the strength of the soil water holding capacity.

[0008] Step 4: Based on the characteristics of the NDWI moisture attenuation curve from a rapid decline to a gentle and stable state, determine the corresponding time series date and NDWI value. Specifically, determine the time point when the curve begins to decline. , the time point when the decline ends , and the time point of slow decline , and obtain the NDWI value corresponding to the time point, recorded as and , combining model parameters b and c to construct a soil water holding capacity remote sensing index model, using this model to monitor soil water holding capacity remote sensing, extract soil water holding capacity remote sensing images, the calculation equation is:

[0009]

[0010] Where, is the point in time when the curve begins to decline. is the time point when the curve ends its decline. is the time point when the curve changes to a flat state. for The corresponding NDWI fitting value, for The corresponding NDWI fitting value, for The corresponding NDWI fitting value, b is the time constant of the exponential decay model, and c is the intercept of the exponential decay model;

[0011] Step 5: Process the measured soil temperature and humidity sensor data, plot the changes in soil moisture content after rainfall into a curve, and use an exponential decay model to fit it to the soil moisture attenuation curve. It is found that the variation pattern of the soil moisture attenuation curve is consistent with the NDWI moisture attenuation curve. The humidity data corresponding to the characteristics of the soil moisture attenuation curve are input into the SWRCI model. Using the independent sample verification method, R and RMSE are used as evaluation indicators to verify and evaluate the model accuracy between the calculated results of the measured soil moisture content and the remote sensing monitoring results of soil water holding capacity.

[0012] Step 6: Based on the remote sensing monitoring results of soil water holding capacity, correlation analysis is conducted with the field measured soil organic matter data, soil texture data, and crop yield data. 2 , RMSE were used as evaluation indicators and displayed through scatter plots. Based on the good results of correlation analysis, the accuracy of soil water holding capacity remote sensing images was proved.

[0013] Preferably, in step 1, the wavelength of the green light band is 0.559–0.570 μm; the wavelength of the near-infrared band is 0.833–0.864 μm.

[0014] Preferably, in step 1, the normalized water index NDWI is calculated using the following formula:

[0015]

[0016] Where GREEN is the green band and NIR is the near-infrared band, which correspond to bands 3 and 8 in Sentinel-2 images, respectively.

[0017] Preferably, the exponential decay model in step 3 is calculated using the following equation:

[0018]

[0019] Where, It is the period before, during and after snow cover and melting; is the NDWI reconstructed fitting value, as changes with the changes of is the initial value of the NDWI moisture attenuation curve; It is the time constant, reflecting the decay rate of the moisture decay curve. The smaller the value, the faster the decay rate. is the curve intercept, which is also the final stable value. Approaching infinity Approaching ;

[0020] The exponential decay model of step 5 is calculated as follows:

[0021]

[0022] Where, It is the time after rainfall when soil moisture continues to decrease and level off; is the reconstructed fitted value of soil moisture, as changes with the changes of is the initial value of the soil moisture decay curve; It is the time constant, reflecting the decay rate of the soil moisture decay curve. The smaller the value, the faster the decay rate. is the curve intercept, which is also the final stable value. Approaching infinity Approaching .

[0023] Preferably, the calculation equations for the independent sample verification in step 5 and the correlation evaluation in step 6 are:

[0024]

[0025]

[0026]

[0027]

[0028]

[0029] Where, is the standardized soil water holding capacity remote sensing index model result; is the original output value of the model, is the mean of the model output values, is the standard deviation of the model output; is the calculated result of the normalized measured soil moisture content; is the measured soil moisture content, is the average value of the measured soil moisture content, is the standard deviation of the measured soil moisture content; It is used to measure the linear correlation between the model prediction value and the measured value. is the coefficient of determination, which is used to quantify the correlation between the model results and the measured data on soil properties and crop yields. 、 They are The results of the standardized soil water holding capacity remote sensing index model and the calculated results of the measured soil organic matter data, soil texture data, and crop yield data of the samples were used. and are the means of the standardized soil water holding capacity remote sensing index model results and the measured soil organic matter data, soil texture data, and crop yield data results, respectively. is the sample size; is the root mean square error, which is used to evaluate the average error between the predicted values ​​of the soil water holding capacity remote sensing index model and the measured values ​​of soil moisture content; It is also used to evaluate the average error between the predicted value of the soil water holding capacity remote sensing index model and the measured soil organic matter data, soil texture data, and crop yield data. The model predicts the The value of the sample, Indicates the measured The value of a sample.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] This paper proposes a remote sensing monitoring index for soil water holding capacity. By combining remote sensing NDWI time series data with an exponential decay model, it enables non-destructive, rapid, and large-scale monitoring of soil water holding capacity, overcoming the limitations of traditional methods. By incorporating the NDWI moisture decay curve and combining it with the exponential decay model, it can more accurately reflect the changing trends in soil water holding capacity. Furthermore, independent sample validation demonstrates the accuracy and reliability of this method, providing strong technical support for soil moisture monitoring and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a time series curve graph of the NDWI of each pixel obtained in step 2 of the present invention;

[0033] Figure 2 is a time series curve graph of the measured soil moisture content obtained in step five of the present invention;

[0034] Figure 3 This is a reconstructed fitting NDWI moisture attenuation curve of the present invention; the circles in the figure represent five time phases, and the solid line represents the reconstructed curve of the five time phases;

[0035] Figure 4 This is a time series curve of soil moisture content reconstructed and fitted in hours by the present invention; the circles in the figure represent 12 hours; the solid line represents the reconstructed curve for 12 hours;

[0036] Figure 5 This is the spatial distribution map of soil water holding capacity in 2022 of the present invention example;

[0037] Figure 6 This is a scatter plot for verifying the accuracy of the soil water holding capacity remote sensing monitoring model of the present invention;

[0038] Figure 7It is a scatter plot of the correlation between the soil water holding capacity remote sensing monitoring model of the present invention and soil organic matter;

[0039] Figure 8 It is a scatter plot of the correlation between the soil water holding capacity remote sensing monitoring model of the present invention and soil texture;

[0040] Figure 9 It is a scatter plot of the correlation between the soil water holding capacity remote sensing monitoring model of the present invention and crop yield. DETAILED DESCRIPTION

[0041] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0042] The present invention provides a remote sensing monitoring method for soil water holding capacity based on an exponential decay model, comprising the following steps:

[0043] Step 1: Select Sentinel-2A satellite remote sensing images of the target area. The image acquisition time is three periods each year: before, during, and after snow cover melts. The acquired images are ensured to be free of cloud contamination to avoid cloud obstruction and interference with surface reflectance information. The images are then preprocessed for radiometric calibration, atmospheric correction, and geometric correction. The normalized difference water index (NDWI) of each preprocessed image is calculated based on the green band and near-infrared band. The wavelength of the green band is 0.559–0.570 μm, and the wavelength of the near-infrared band is 0.833–0.864 μm. The normalized difference water index (NDWI) is calculated using the following equation:

[0044]

[0045] Where GREEN is the green band and NIR is the near-infrared band, which correspond to bands 3 and 8 in Sentinel-2 images respectively.

[0046] Step 2: Cropping the processed remote sensing image according to the vector range of the dryland plots in the target area. Based on the NDWI value calculated in step 1, extract the NDWI of the cropped image, and then obtain the NDWI time series curve of each pixel in the cropped image. Sorting the NDWI time series curves of all pixels with "row" as time and "column" as pixel to obtain the period sorting of the cropped image. After observing the NDWI time series curves, it was found that the best time series window that can reflect the dynamic changes of soil moisture is from early March to late May each year.

[0047] Step 3: Determine an exponential decay model that fits the NDWI time series curve. Use this model to fit the NDWI time series curve of each pixel in step 2 into the NDWI moisture decay curve. Analysis shows that the NDWI moisture decay curve is highly similar to the soil moisture characteristic curve. The former represents the change in soil moisture status over time, while the latter represents the change in soil moisture content as water potential increases. The shapes and characteristics of the two are consistent. Through the analysis of the exponential decay model, it is found that the model parameter b represents the rate of decline of the curve, that is, the time constant b determines the speed of the curve decay, and the model parameter c represents the intercept of the curve, that is, it determines the position and height of the curve. The calculation equation of the exponential decay model is:

[0048]

[0049] Where, It is the period before, during and after snow cover and melting; is the NDWI reconstructed fitting value, as changes with the changes of is the initial value of the NDWI moisture attenuation curve; It is the time constant, reflecting the decay rate of the moisture decay curve. The smaller the value, the faster the decay rate. is the curve intercept, which is also the final stable value. Approaching infinity Approaching ;

[0050] Step 4: Based on the characteristics of the NDWI moisture attenuation curve from a rapid decline to a gentle and stable state, determine the corresponding time series date and NDWI value. Specifically, determine the time point when the curve begins to decline. , the time point when the decline ends , and the time point of slow decline , and obtain the NDWI value corresponding to the time point, recorded as and , combining model parameters b and c to construct a soil water holding capacity remote sensing index model, using this model to monitor soil water holding capacity remote sensing, extract soil water holding capacity remote sensing images, the calculation equation is:

[0051]

[0052] Where, is the point in time when the curve begins to decline. is the time point when the curve ends its decline. is the time point when the curve changes to a flat state. for The corresponding NDWI fitting value, for The corresponding NDWI fitting value, for The corresponding NDWI fitting value, b is the time constant of the exponential decay model, and c is the intercept of the exponential decay model;

[0053] Step 5: Process the measured data from the soil temperature and humidity sensors, plot the change in soil moisture content after rainfall into a curve, and use the exponential decay model to fit it to the soil moisture attenuation curve. It is found that the change pattern of the soil moisture attenuation curve is consistent with the change of the NDWI moisture attenuation curve. The humidity data corresponding to the characteristics of the soil moisture attenuation curve are input into the soil water holding capacity remote sensing index model. The independent sample verification method is used to verify and evaluate the model accuracy of the calculated results of the measured soil moisture content and the soil water holding capacity remote sensing monitoring results using R and RMSE as evaluation indicators; the exponential decay model, its calculation equation is:

[0054]

[0055] Where, It is the time after rainfall when soil moisture continues to decrease and level off; is the reconstructed fitted value of soil moisture, as changes with the changes of is the initial value of the soil moisture decay curve; It is the time constant, reflecting the decay rate of the soil moisture decay curve. The smaller the value, the faster the decay rate. is the curve intercept, which is also the final stable value. Approaching infinity Approaching ; Perform independent sample verification on model accuracy, using R and RMSE as evaluation indicators, and the calculation equations are:

[0056]

[0057]

[0058]

[0059]

[0060] Where, is the standardized soil water holding capacity remote sensing index model result; is the original output value of the model, is the mean of the model output values, is the standard deviation of the model output; is the calculated result of the normalized measured soil moisture content; is the measured soil moisture content, is the average value of the measured soil moisture content, is the standard deviation of the measured soil moisture content; It is used to measure the linear correlation between the model prediction value and the measured value; is the root mean square error, which is used to evaluate the average error between the predicted value of the soil water holding capacity remote sensing index model and the measured value of soil moisture content. The model predicts the The value of the sample, Indicates the measured The value of a sample.

[0061] Step 6: Based on the remote sensing monitoring results of soil water holding capacity, correlation analysis is conducted with the field measured soil organic matter data, soil texture data, and crop yield data. 2 , RMSE were used as evaluation indicators and displayed through scatter plots. Based on the good results of correlation analysis, the accuracy of remote sensing images of soil water holding capacity was proved; R 2 , RMSE are evaluation indicators, and the calculation equations are:

[0062]

[0063]

[0064] Where, is the coefficient of determination, which is used to quantify the correlation between the model results and the measured data. 、 They are The results of the standardized soil water holding capacity remote sensing index model and the calculation results of soil organic matter content, soil texture content, and crop yield of samples were and are the mean values ​​of the standardized soil water holding capacity remote sensing index model results and the calculated results of soil organic matter content, soil texture content, and crop yield, respectively. is the number of samples; RMSE is the root mean square error, which is used to evaluate the average error between the predicted value of the soil water holding capacity remote sensing index model and the soil organic matter content data, soil texture content data, and crop yield data. The model predicts the The value of the sample, Indicates the measured The value of a sample.

[0065] Example 2:

[0066] See also Figure 1-9In a specific embodiment of the present invention, remote sensing images of the soil of Friendship Farm with different degrees of soil degradation in 2022 before, during, and after snow cover and melting are selected, NDWI time series data is calculated, an exponential decay model is used to fit the NDWI moisture decay curve, key points of the curve are analyzed, variables are input into the constructed soil water holding capacity remote sensing index model, the 2022 soil water holding capacity remote sensing monitoring results are extracted, and the model is verified using the measured data of soil temperature and humidity sensors from 2022 to 2024. The correlation between the mean water holding capacity of dryland soil of Friendship Farm from 2020 to 2025 and the measured soil organic matter, soil texture, crop yield and other data is analyzed. The specific steps are as follows:

[0067] Step 1: Obtain Sentinel-2A satellite remote sensing images of Youyi Farm in 2022, covering the period before, during, and after snow cover melts. Ensure that the images are not contaminated by clouds and perform data preprocessing for radiometric calibration, atmospheric correction, and geometric correction. Calculate the Normalized Difference Water Index (NDWI) for each preprocessed image. NDWI is calculated based on the green and near-infrared bands using the following equation:

[0068]

[0069] Where GREEN is the green light band (wavelength 0.559–0.570 μm) and NIR is the near-infrared band (wavelength 0.833–0.864 μm), which correspond to bands 3 and 8 in Sentinel-2 images, respectively.

[0070] Step 2: Crop the processed remote sensing image according to the vector range of the dryland plot of Friendship Farm in 2022. Extract the NDWI of the cropped image based on the NDWI value calculated in step 1, and then obtain the NDWI time series of each pixel in the cropped image. Sort the NDWI time series curves of all pixels by "row" for time and "column" for pixel to obtain the period sorting of the cropped image. Figure 1 As shown in Figure 2, observing the characteristics of the NDWI time series curve, it is found that it shows a downward trend over time and stabilizes;

[0071] Step 3: Determine an exponential decay model that fits the NDWI time series curve. Use this model to fit the NDWI time series curve of each pixel in step 2 into the NDWI moisture decay curve. Analysis shows that the NDWI moisture decay curve is highly similar to the soil moisture characteristic curve. The former represents the change in soil moisture status over time, while the latter represents the change in soil moisture content as water potential increases. The shapes and characteristics of the two are consistent. Through the analysis of the exponential decay model, it is found that the model parameter b represents the rate of decline of the curve, that is, the time constant b determines the speed of the curve decay, and the model parameter c represents the intercept of the curve, that is, it determines the position and height of the curve. The calculation equation of the exponential decay model is:

[0072]

[0073] Where, It is the period before, during and after snow cover and melting; is the NDWI reconstructed fitting value, as changes with the changes of is the initial value of the NDWI moisture attenuation curve; It is the time constant, reflecting the decay rate of the moisture decay curve. The smaller the value, the faster the decay rate. is the curve intercept, which is also the final stable value. Approaching infinity Approaching . Figure 3 The NDWI water attenuation curves for different degradation levels show different decline rates. The curves for severe degradation have a faster decline rate and smaller parameters b and c, indicating that the water holding capacity of severely degraded areas is weaker, while the curves for non-degraded areas have a slower decline rate and larger parameters b and c, indicating that the water holding capacity of non-degraded areas is stronger.

[0074] Step 4: Based on the changing characteristics of the NDWI moisture attenuation curves of different soil degradation levels in 2022, determine the corresponding time series date and NDWI value. Specifically, the time point when the curve begins to decline It is the 10th day after the snow melts, when the curve ends its decline. It is the 28th day after the snow melts, when the curve stabilizes It is the 82nd day after the snow melts. The NDWI values ​​corresponding to these three time points are obtained and recorded as , combined with model parameters b and c, a soil water holding capacity remote sensing index model was constructed. This model was used to monitor soil water holding capacity remotely, and the remote sensing image of soil water holding capacity of the dry land of Friendship Farm in 2022 was extracted. The calculation equation is:

[0075]

[0076] Where, is the point in time when the curve begins to decline. is the time point when the curve ends its decline. is the time point when the curve changes to a flat state. for The corresponding NDWI fitting value, for The corresponding NDWI fitting value, for The corresponding NDWI fitting value, b is the time constant of the exponential decay model, and c is the intercept of the exponential decay model. After running the model, the spatial distribution results of soil water holding capacity in 2022 are as follows Figure 5 As shown, we can see the spatial distribution pattern and trend of soil water holding capacity;

[0077] Step 5: Process the measured data of the soil temperature and humidity sensor and plot the change of soil moisture content on July 2, 2022 (after rainfall) into a curve, such as Figure 2 As shown in the figure, it is found that the curve shows a downward trend until it is flat, and the exponential decay model is used to fit it to obtain Figure 4 , it was found that the change pattern of the soil moisture attenuation curve was consistent with the change of the NDWI moisture attenuation curve. The humidity data corresponding to the soil moisture attenuation curve characteristics were input into the soil water holding capacity remote sensing index model. The independent sample verification method was used, and R and RMSE were used as evaluation indicators to verify and evaluate the model accuracy of the calculated results of the measured soil moisture content and the soil water holding capacity remote sensing monitoring results. After calculation, it was found that the model R was 0.82 and the RMSE was 0.023. The exponential decay model calculation equation is:

[0078]

[0079] Where, It is the time after rainfall when soil moisture continues to decrease and level off; is the reconstructed fitted value of soil moisture, as changes with the changes of is the initial value of the soil moisture decay curve; It is the time constant, reflecting the decay rate of the soil moisture decay curve. The smaller the value, the faster the decay rate. is the curve intercept, which is also the final stable value. Approaching infinity Approaching ;

[0080] Independent sample verification was performed, with R and RMSE as evaluation indicators, and the calculation equations were:

[0081]

[0082]

[0083]

[0084]

[0085] Where, is the standardized soil water holding capacity remote sensing index model result; is the original output value of the model, is the mean of the model output values, is the standard deviation of the model output; is the calculated result of the normalized measured soil moisture content; is the measured soil moisture content, is the average value of the measured soil moisture content, is the standard deviation of the measured soil moisture content; It is used to measure the linear correlation between the model prediction value and the measured value; is the root mean square error, which is used to evaluate the average error between the predicted value of the soil water holding capacity remote sensing index model and the measured value of soil moisture content. The model predicts the The value of the sample, Indicates the measured The value of a sample.

[0086] Step 6: Based on the remote sensing monitoring results of the mean soil water holding capacity of Friendship Farm from 2020 to 2025, a correlation analysis was conducted with the field measured soil organic matter data, soil texture data, and crop yield data. 2 , RMSE is used as the evaluation index and displayed through a scatter plot. The soil water holding capacity and soil organic matter content R are calculated. 2 is 0.71, RMSE is 0.023; and soil sand content R 2 is 0.64, RMSE is 0.026; and soil silt content R 2 is 0.62, RMSE is 0.027; and soil clay content R 2 is 0.59, RMSE is 0.035; and crop yield R 2 The correlation analysis results are good, which proves the accuracy of soil water holding capacity remote sensing images. 2 , RMSE calculation equations are:

[0087]

[0088]

[0089] Where R 2 is the coefficient of determination, which is used to quantify the correlation between the model results and the measured data. 、 They are The results of the standardized soil water holding capacity remote sensing index model and the calculation results of soil organic matter content, soil texture content, and crop yield for each sample and are the mean values ​​of the standardized soil water holding capacity remote sensing index model results and the calculated results of soil organic matter content, soil texture content, and crop yield, respectively. is the number of samples; RMSE is the root mean square error, which is used to evaluate the average error between the predicted value of the soil water holding capacity remote sensing index model and the soil organic matter content, soil texture content, and crop yield. The model predicts the The value of the sample, Indicates the measured The value of a sample.

[0090] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A remote sensing monitoring method for soil water holding capacity based on an exponential decay model, characterized by: The following steps are involved: Step 1: Select Sentinel-2A satellite remote sensing images of the target area. The images are acquired during the three periods of each year, before, during, and after snow cover melts. The acquired images are ensured to be free of cloud contamination to avoid cloud obstruction and interference with surface reflectance information. The images are preprocessed using radiometric calibration, atmospheric correction, and geometric correction. The normalized water index (NDWI) of each preprocessed image is calculated based on the green and near-infrared bands. Step 2: Cropping the processed remote sensing image according to the vector range of the dryland plots in the target area. Based on the NDWI value calculated in Step 1, the NDWI of the cropped image is extracted to obtain the NDWI time series curve for each pixel in the cropped image. The NDWI time series curves of all pixels are sorted by "row" representing time and "column" representing pixels to obtain the period order of the cropped image. After observing the NDWI time series curves, it was found that the best time series window for reflecting the dynamic changes of soil moisture is from early March to late May each year. Step 3: Determine an exponential decay model that fits the NDWI time series curve. Use this model to fit the NDWI time series curve of each pixel in step 2 into an NDWI moisture decay curve. Analysis shows that the NDWI moisture decay curve and the soil moisture characteristic curve have highly similar characteristics. The former represents the change in soil moisture status over time, while the latter represents the change in soil volumetric water content as water potential increases. The two characteristics are consistent. According to the characteristics of the exponential decay model parameters b representing the curve decline rate and c representing the curve intercept, it is found that the difference in the decline rate and intercept of the NDWI moisture decay curve can reflect the strength of the soil water holding capacity. Step 4: Based on the characteristics of the NDWI moisture attenuation curve from a rapid decline to a gentle and stable state, determine the corresponding time series date and NDWI value. Specifically, determine the time point when the curve begins to decline. , the time point when the decline ends , and the time point of slow decline , and obtain the NDWI value corresponding to the time point, recorded as and , combining model parameters b and c to construct a soil water holding capacity remote sensing index model, using this model to monitor soil water holding capacity remote sensing, extract soil water holding capacity remote sensing images, the calculation equation is: Where, is the point in time when the curve begins to decline. is the time point when the curve ends its decline. is the time point when the curve changes to a flat state. for The corresponding NDWI fitting value, for The corresponding NDWI fitting value, for The corresponding NDWI fitting value, b is the time constant of the exponential decay model, and c is the intercept of the exponential decay model; Step 5: Process the measured soil temperature and humidity sensor data, plot the changes in soil moisture content after rainfall into a curve, and use an exponential decay model to fit it to the soil moisture attenuation curve. It is found that the variation pattern of the soil moisture attenuation curve is consistent with the NDWI moisture attenuation curve. The humidity data corresponding to the characteristics of the soil moisture attenuation curve are input into the SWRCI model. Using the independent sample verification method, R and RMSE are used as evaluation indicators to verify and evaluate the model accuracy between the calculated results of the measured soil moisture content and the remote sensing monitoring results of soil water holding capacity. Step 6: Based on the remote sensing monitoring results of soil water holding capacity, correlation analysis is conducted with the measured soil organic matter data, soil texture data, and crop yield data. 2 , RMSE were used as evaluation indicators and displayed through scatter plots. Based on the good results of correlation analysis, the accuracy of soil water holding capacity remote sensing images was proved.

2. The method for remote sensing monitoring of soil water holding capacity based on an exponential decay model according to claim 1, characterized in that: In the step 1, the wavelength of the green light band is 0.559–0.570 μm; the wavelength of the near-infrared band is 0.833–0.864 μm.

3. The method for remote sensing monitoring of soil water holding capacity based on an exponential decay model according to claim 1, characterized in that: In the step 1, the normalized water index NDWI is calculated as follows: Where GREEN is the green band and NIR is the near-infrared band, which correspond to bands 3 and 8 in Sentinel-2 images, respectively.

4. The method for remote sensing monitoring of soil water holding capacity based on an exponential decay model according to claim 1, characterized in that: The exponential decay model in step 3 is calculated as follows: ， Where, It is the period before, during and after snow cover and melting; is the NDWI reconstructed fitting value, as changes with the changes of is the initial value of the NDWI moisture attenuation curve; It is the time constant, reflecting the decay rate of the moisture decay curve. The smaller the value, the faster the decay rate. is the curve intercept, which is also the final stable value. Approaching infinity Approaching ; The exponential decay model of step 5 is calculated as follows: ， Where, It is the time after rainfall when soil moisture continues to decrease and level off; is the reconstructed fitted value of soil moisture, as changes with the changes of is the initial value of the soil moisture decay curve; It is the time constant, reflecting the decay rate of the soil moisture decay curve. The smaller the value, the faster the decay rate. is the curve intercept, which is also the final stable value. Approaching infinity Approaching .

5. The method for remote sensing monitoring of soil water holding capacity based on an exponential decay model according to claim 1, characterized in that: The calculation equations for the independent sample verification in step 5 and the correlation evaluation in step 6 are: ， ， ， ， ， Where, is the standardized soil water holding capacity remote sensing index model result; is the original output value of the model, is the mean of the model output values, is the standard deviation of the model output; is the calculated result of the normalized measured soil moisture content; is the measured soil moisture content, is the average value of the measured soil moisture content, is the standard deviation of the measured soil moisture content; It is used to measure the linear correlation between the model prediction value and the measured value. is the coefficient of determination, which is used to quantify the correlation between the model results and the measured data on soil properties and crop yields. 、 They are The results of the standardized soil water holding capacity remote sensing index model and the calculated results of the measured soil organic matter data, soil texture data, and crop yield data of the samples were used. and are the means of the standardized soil water holding capacity remote sensing index model results and the measured soil organic matter data, soil texture data, and crop yield data results, respectively. is the sample size; is the root mean square error, which is used to evaluate the average error between the predicted values ​​of the soil water holding capacity remote sensing index model and the measured values ​​of soil moisture content; It is also used to evaluate the average error between the predicted value of the soil water holding capacity remote sensing index model and the measured soil organic matter data, soil texture data, and crop yield data. The model predicts the The value of the sample, Indicates the measured The value of a sample.

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