Crop flood disaster large-area quantitative evaluation method and system

Abstract

The invention discloses a crop flood disaster large-area quantitative evaluation method and system, and the method comprises the steps: obtaining flood disaster data and SAR time sequence remote sensing data, obtaining a flood disaster submerging space range based on a deep learning image semantic segmentation model according to an SAR remote sensing image, and obtaining a crop flood disaster flood area based on landform and river water level observation data. Estimating the flood submerging depth of the DEM pixel scale by using a submerging depth model; recognizing a flood disaster area by combining SAR (Synthetic Aperture Radar) time sequence remote sensing data, the precipitation observed on the ground and the water level change of a river channel; core evaluation indexes of crop flood submerging range, submerging duration and submerging depth are calculated, flood disaster events are associated with crop periods, and a flood hazard degree equation is constructed by taking flood stress sensitivity as a basis. According to the method, local crop characteristics of a research area are fused, a flood disaster event is associated with a crop period, and the flood hazard degree equation is constructed by taking the flood stress sensitivity as one of parameters, so that quantitative calculation of the flood hazard degree is realized.

Description

Technical Field

[0001] The present invention relates to the field of assessment, and in particular to a method and system for quantitatively assessing a large area of ​​crops affected by floods. Background Art

[0002] Under today's complex and changeable climate conditions, crop yields are vulnerable to extreme weather. In many areas, floods caused by heavy rains are the main natural disasters that cause the greatest crop losses. In order to quantitatively assess the losses caused by large-scale agricultural floods in the region, GIS and remote sensing technology provide rich data and analysis tools for determining the spatial range of crop flooding and the spatial distribution of crop damage. GIS can be used to carry out flood risk zoning and flood evolution simulation. In real-time flood disaster monitoring, it can assist in obtaining flood characteristic parameters such as flooding range and water depth by combining with remote sensing images. First, the spatial range of crop flooding is obtained based on remote sensing images, and then the flooding depth and flooding time are determined at the pixel scale. The crop damage degree function is applied pixel by pixel to achieve quantitative spatial mapping of crop damage caused by floods.

[0003] In the existing technology, the step of extracting the flood range from remote sensing images is mostly based on the vegetation index or water body index of optical remote sensing data. However, during rainstorms and floods, cloud cover is usually severe, which has a great impact on the quality of optical remote sensing images. Polarimetric radar remote sensing data is not restricted by weather conditions and has nearly all-weather observation capabilities, which has great advantages in flood disaster monitoring. In estimating the flooding time, traditional methods rely on the difference in water signals from time-series remote sensing images. Due to the time interval restrictions of time-series remote sensing data, there are great limitations in its application. Therefore, a quantitative assessment method for large areas of crop flooding is needed. Summary of the invention

[0004] The purpose of the present invention is to provide a method for quantitatively evaluating large-scale crop flood damage.

[0005] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0006] The present invention comprises the following steps:

[0007] A obtains flood disaster data and SAR time series remote sensing data, obtains the spatial scope of flood disaster inundation from SAR remote sensing images based on a deep learning image semantic segmentation model, and estimates the flood inundation depth at the DEM pixel scale using an inundation depth model based on topography and river water level observation data; the deep learning image semantic segmentation model is trained and constructed using the flood disaster data;

[0008] B. Combine the flood disaster areas identified by SAR time series remote sensing data with the precipitation and river water level changes observed on the ground to estimate the flooding duration;

[0009] C calculates the core assessment indicators of crops, associates flood disaster events with crop periods, uses flood stress sensitivity to construct a flood hazard degree equation, determines the crop flood disaster degree equation, and completes spatial mapping of the disaster degree; the core assessment indicators include flood inundation range, inundation duration, and inundation depth.

[0010] Furthermore, the method for estimating the flooding duration includes:

[0011] The prerequisite is that rainfall occurs continuously every day within a period of time and the river water level is higher than the warning water level;

[0012] The SAR time series remote sensing data is input into the deep belief network based on the Boltzmann machine, which consists of a hidden layer and a display layer;

[0013] Compute the joint probability density between the hidden and visible layers:

[0014]

[0015]

[0016] The ath hidden layer is z a , the ath display layer is x a , the bias of the qth hidden layer is v q , the bias of the ath display layer is w a , No. a The weight between the hidden layer and the qth display layer is l aq , the number of hidden layers is The number of displayed layers is The parameters are The network function is The normalization factor is E, and the probability joint density between the hidden layer z and the display layer x is p(x, z);

[0017] By learning the probability joint density between the hidden layer and the display layer, the feature extraction of SAR time series remote sensing data is carried out to obtain the characteristics of the flooded area;

[0018] Clustering is used to reduce the dimension of flood area characteristics to obtain reduced dimension features, the expression is:

[0019]

[0020] The number of flood area characteristics is N, and the characteristic of the u-th flood area is h u , the center value of the c dimension of the y-th cluster is b y (c) The value of the cth dimension of the uth flood area feature is h u (c), the yth cluster is K y , flood area characteristics uBelongs to cluster d y The distance is d y (h u ), the u-th dimension reduction feature of the y-th cluster is D iy ;

[0021] The flood area recognition is completed for two periods of continuous time series remote sensing images. The flood area recognition results of the two periods of images are compared. The expression is:

[0022]

[0023] The time variable of the u-th dimension reduction feature is The u-th dimension reduction feature at the s-th time is h u.s , the u-th dimension reduction feature at the s+j-th time is h u，s+j , the number of dimension reduction features that change is N 1 , the control coefficient is ζ;

[0024] Obtain overlapping flood areas and non-overlapping flood areas based on time-varying variables;

[0025] For overlapping flood areas, where the flood inundation duration is greater than or equal to the remote sensing time series interval, the time increment Δs based on the SAR time series remote sensing interval time t is obtained, and the final inundation duration is t+Δs;

[0026] For non-overlapping areas, the flooding duration estimation method is used.

[0027] Furthermore, the method for obtaining the core evaluation indicators includes:

[0028] Calculate the similarity of flood disaster data:

[0029]

[0030] The rth flood disaster data is h r , the fth flood disaster data is h f , flood disaster data r and flood disaster data f The similarity is Flood disaster data r and flood disaster data f The distance is D(h r ,h f ), flood disaster data r The modulus is h r |, Flood disaster data f The modulus is h f |, the tuning parameter is σ, and the kernel width is φ;

[0031] The flood disaster data with the largest number of similarities is taken as the classification cluster center, and the remaining flood disaster data is classified into the classification cluster with the largest similarity to the classification cluster center to obtain the classification cluster;

[0032] Calculate the importance of classification clusters:

[0033]

[0034] The importance of the u-th classification cluster is Z u , the rth flood disaster data is h r , transposed to T, the unit matrix is ​​E, and the transposed of the rth flood disaster data is The cluster center is h o , the number of flood disaster data in the u-th classification cluster is n r , the importance parameter is μ, and the error coefficient is ε;

[0035] The flood disaster data of the classification clusters with importance greater than 0.318 are taken as key data, and the flood relevance of the key data is calculated:

[0036]

[0037] The number of key data is n 1 , the flood relevance of the rth key data is The flood severity is Flood disaster data r and flood severity The joint probability distribution of Flood disaster data r The marginal probability distribution of is p(h r ), flood severity The marginal probability distribution of

[0038] The key data with flood correlation greater than 0.647 are output as core evaluation indicators.

[0039] Furthermore, the flood inundation depth model is used to determine the potential inundation depth within the river basin. It is assumed that floods cause the river water level to rise and flow from high-lying areas to low-lying areas within the basin, which can be expressed as:

[0040] FID ij =ΔH ij -ΔDEM ij

[0041] Where ΔH=H max -H min ΔDEM is the maximum water level minus the minimum water level. ij It is the difference between the DEM at the (i, j) location and the DEM at the monitoring point.

[0042] Furthermore, basin analysis using the Hydrological Analyst tool of ArcGIS was used, combined with DEM data to calculate the range of each river basin within the study area and calculate the potential flooding depth of each pixel.

[0043] Furthermore, the flood hazard equation is:

[0044] L = f(g, l, d)

[0045] The degree of crop flood damage is L, the growth and development period is g, the flooding duration is l, and the flooding depth is d;

[0046] The flood damage equation outputs a quantitative description of the degree of damage expressed as a percentage.

[0047] Furthermore, the flood-inundated areas were treated as new land cover categories for independent model training.

[0048] Furthermore, the ground observed precipitation data and river water level observation data are used to construct a flooding duration estimation model.

[0049] The second aspect is a large-scale quantitative assessment system for crop flood damage, including:

[0050] Acquisition module: used to train and build a deep learning image semantic segmentation model based on the public flood disaster dataset, and apply it to the SAR remote sensing images of the target flood disaster event to obtain the spatial scope of flood disaster inundation. Based on the topography and river water level observation data, the flood inundation depth model is used to estimate the flood inundation depth at the DEM pixel scale;

[0051] Calculation module: used to combine the flood disaster areas identified by time-series remote sensing data with the precipitation and river water level changes observed on the ground to comprehensively estimate the flooding duration;

[0052] Output module: used to calculate the three core evaluation indicators of crop flood inundation range, inundation duration and inundation depth, associate flood disaster events with crop periods, construct flood hazard degree equation based on flood stress sensitivity, determine crop flood disaster degree equation, and complete spatial mapping of disaster degree.

[0053] The beneficial effects of the present invention are:

[0054] The present invention is a method and system for quantitatively assessing large areas of crop flood damage. Compared with the prior art, the present invention has the following technical effects:

[0055] The near-real-time loss assessment method for crop flood disasters proposed in the present invention has strong universality and can be applied to the quantitative assessment of losses caused by large-scale agricultural flood disasters. For different regions or different crops, it is only necessary to obtain the data required by the local meteorological, hydrological, topographic and geomorphological models and crop characteristics, and adjust the parameters of the crop disaster degree function. The analysis results can be used by agricultural management departments and agricultural insurance companies to conduct real-time assessments of agricultural disaster losses and assist in formulating response measures.

[0056] In addition, in the absence of historical data on agricultural losses caused by flood disasters, the method of the present invention can be applied to evaluate and analyze flood disaster cases in different regions, so as to obtain the spatiotemporal characteristics of agricultural flood disaster losses in different regions, and then apply agricultural insurance product development tools to realize regional personalized agricultural insurance product development. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A flowchart of the steps of a method for quantitatively assessing a large area of ​​crop flood damage according to the present invention;

[0058] Figure 2 A structural diagram of a U-net model for water body identification in a method for quantitatively assessing large areas of crop flooding disasters in the present invention;

[0059] Figure 3 This is a schematic diagram of the FID calculation principle in a large-scale quantitative assessment method for crop flood damage in the present invention. DETAILED DESCRIPTION

[0060] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0061] A method and system for quantitatively assessing large areas of crop flood damage in the present invention comprises the following steps:

[0062] like Figure 1 As shown, in this embodiment, the following steps are included:

[0063] A obtains flood disaster data and SAR time series remote sensing data, obtains the spatial scope of flood disaster inundation from SAR remote sensing images based on a deep learning image semantic segmentation model, and estimates the flood inundation depth at the DEM pixel scale using an inundation depth model based on topography and river water level observation data; the deep learning image semantic segmentation model is trained and constructed using the flood disaster data;

[0064] B. Combine the flood disaster areas identified by SAR time series remote sensing data with the precipitation and river water level changes observed on the ground to estimate the flooding duration;

[0065] C calculates the core assessment indicators of crops, associates flood disaster events with crop periods, uses flood stress sensitivity to construct a flood hazard degree equation, determines the crop flood disaster degree equation, and completes spatial mapping of the disaster degree; the core assessment indicators include flood inundation range, inundation duration, and inundation depth.

[0066] In this embodiment, the method for estimating the submergence duration includes:

[0067] The prerequisite is that rainfall occurs continuously every day within a period of time and the river water level is higher than the warning water level;

[0068] The SAR time series remote sensing data is input into the deep belief network based on the Boltzmann machine, which consists of a hidden layer and a display layer;

[0069] Compute the joint probability density between the hidden and visible layers:

[0070]

[0071]

[0072] The ath hidden layer is z a , the ath display layer is x a , the bias of the qth hidden layer is v q , the bias of the ath display layer is w a , the weight value between the ath hidden layer and the qth display layer is l aq , the number of hidden layers is The number of displayed layers is The parameters are The network function is The normalization factor is E, and the probability joint density between the hidden layer z and the display layer x is p(x, z);

[0073] By learning the probability joint density between the hidden layer and the display layer, the feature extraction of SAR time series remote sensing data is carried out to obtain the characteristics of the flooded area;

[0074] Clustering is used to reduce the dimension of flood area characteristics to obtain reduced dimension features, the expression is:

[0075]

[0076] The number of flood area characteristics is N, and the characteristic of the u-th flood area is h u , the center value of the c dimension of the y-th cluster is b y (c) The value of the cth dimension of the uth flood area feature is h u (c), the yth cluster is K y , flood area characteristics u Belongs to cluster dy The distance is d y (h u ), the u-th dimension reduction feature of the y-th cluster is D iy ;

[0077] The flood area recognition is completed for two periods of continuous time series remote sensing images. The flood area recognition results of the two periods of images are compared. The expression is:

[0078]

[0079] The time variable of the u-th dimension reduction feature is The u-th dimension reduction feature at the s-th time is h u.s , the u-th dimension reduction feature at the s+j-th time is h u，s+j , the number of dimension reduction features that change is N 1 , the control coefficient is ζ;

[0080] Obtain overlapping flood areas and non-overlapping flood areas based on time-varying variables;

[0081] For overlapping flood areas, where the flood inundation duration is greater than or equal to the remote sensing time series interval, the time increment Δs based on the SAR time series remote sensing interval time t is obtained, and the final inundation duration is t+Δs;

[0082] For non-overlapping areas, the flooding duration estimation method is used.

[0083] In this embodiment, the method for obtaining the core evaluation index includes:

[0084] Calculate the similarity of flood disaster data:

[0085]

[0086] The rth flood disaster data is h r , the fth flood disaster data is h f , flood disaster data r and flood disaster data f The similarity is Flood disaster data r and flood disaster data f The distance is D(h r ,h f ), flood disaster data r The modulus is h r |, Flood disaster data f The modulus is h f |, the tuning parameter is σ, and the kernel width is φ;

[0087] The flood disaster data with the largest number of similarities is taken as the classification cluster center, and the remaining flood disaster data is classified into the classification cluster with the largest similarity to the classification cluster center to obtain the classification cluster;

[0088] Calculate the importance of classification clusters:

[0089]

[0090] The importance of the u-th classification cluster is Z u , the rth flood disaster data is h r , transposed to T, the unit matrix is ​​E, and the transposed of the rth flood disaster data is The cluster center is h o , the number of flood disaster data in the u-th classification cluster is n r , the importance parameter is μ, and the error coefficient is ε;

[0091] The flood disaster data of the classification clusters with importance greater than 0.318 are taken as key data, and the flood relevance of the key data is calculated:

[0092]

[0093] The number of key data is n 1 , the flood relevance of the rth key data is The flood severity is Flood disaster data r and flood severity The joint probability distribution of Flood disaster data r The marginal probability distribution of is p(h r ), flood severity The marginal probability distribution of

[0094] The key data with flood correlation greater than 0.647 are output as core evaluation indicators.

[0095] In this embodiment, the flood inundation depth model is used to determine the potential inundation depth within the river basin. Assuming that floods cause the river water level to rise and flow from high-lying areas to low-lying areas within the basin, it can be expressed as:

[0096] FID ij =ΔH ij -ΔDEM ij

[0097] Where ΔH=H max -H min ΔDEM is the maximum water level minus the minimum water level. ijIt is the difference between the DEM at the (i, j) location and the DEM at the monitoring point.

[0098] In this embodiment, the basin analysis of the hydrological tool Hydrological Analyst of ArcGIS is used, and the DEM data is combined to calculate the range of each river basin within the study area and calculate the potential flooding depth of each pixel.

[0099] In this embodiment, the flood damage equation is:

[0100] L = f(g, l, d)

[0101] The degree of crop flood damage is L, the growth and development period is g, the flooding duration is l, and the flooding depth is d;

[0102] The flood damage equation outputs a quantitative description of the degree of damage expressed as a percentage.

[0103] In this embodiment, the flood-inundated area is treated as a new land cover category and model training is performed independently.

[0104] In this embodiment, the ground observed precipitation data and the river water level observation data are used to construct a flooding duration estimation model.

[0105] The second aspect is a large-scale quantitative assessment system for crop flood damage, including:

[0106] Acquisition module: used to train and build a deep learning image semantic segmentation model based on the public flood disaster dataset, and apply it to the SAR remote sensing images of the target flood disaster event to obtain the spatial scope of flood disaster inundation. Based on the topography and river water level observation data, the flood inundation depth model is used to estimate the flood inundation depth at the DEM pixel scale;

[0107] Calculation module: used to combine the flood disaster areas identified by time-series remote sensing data with the precipitation and river water level changes observed on the ground to comprehensively estimate the flooding duration;

[0108] Output module: used to calculate the three core evaluation indicators of crop flood inundation range, inundation duration and inundation depth, associate flood disaster events with crop periods, construct flood hazard degree equation based on flood stress sensitivity, determine crop flood disaster degree equation, and complete spatial mapping of disaster degree.

[0109] Taking rice as an example, multi-source data such as crop distribution, ground hydrological observations, precipitation observations, river network distribution, and SAR time-series remote sensing images were obtained during the flood period, and the three core evaluation indicators of crop flood inundation range, inundation duration, and inundation depth were calculated respectively. Finally, a crop disaster degree equation was constructed for spatial mapping of the crop disaster degree.

[0110] 1. Extraction of flooded area

[0111] The present invention trains a deep learning model based on the Cloud to Street flood remote sensing data set to identify the surface water coverage area. The model uses a CNN neural network based on the U-Net structure to perform semantic segmentation on the image and divide the pixels into two categories: water bodies and non-water bodies. After the model is trained, the water body coverage before and after the disaster is identified respectively, and the flood disaster inundation area can be obtained by clipping the water body coverage before the disaster from the water body coverage after the disaster.

[0112] The U-net model construction process is as follows: create a 3-channel image by superimposing SAR remote sensing VH, VV polarization images and DEM data images. Preprocess the data, including Min-Max scaling, flipping and rotation enhancement of each channel pixel value, train three randomly initialized U-Net convolutional neural networks using the same dice loss function, use different training-test splits, and use the average of the three model outputs as the final prediction result.

[0113] like Figure 2 As shown in the figure, the left side is the backbone network block, which is connected to the decoder part on the right side through a jump connection. The input is a 512×512 pixel image of three channels, VH, VV, and DEM, and the output is the prediction results of two categories, water body and non-water body. During the model training process, three backbone structures were tested separately, including ResNet34, SeresNet34, and EfficientNetB4, and the best model was selected for water body recognition in the case.

[0114] 2. Calculation of submergence duration

[0115] The present invention overcomes the limitation of determining flood inundation duration based solely on time-series remote sensing images. Since the time interval of remote sensing observation is fixed, it is difficult to apply to flood disaster cases where the flood inundation duration is less than the time-series interval. The present invention is designed to add an inundation duration estimation step based on ground meteorological and hydrological data on the basis of time-series remote sensing image recognition to comprehensively determine the inundation duration.

[0116] First, obtain the spatial interpolation time series data of daily precipitation in the study area within the target date range, as well as the watershed division map. The basic flooding duration estimation method is: continuous rainfall occurs every day within a period of time, and the river water level is higher than the warning water level;

[0117] Specifically:

[0118] Step 1: Use two periods of continuous time-series remote sensing images to complete flood area identification. Compare the flood area identification results of the two periods of images to obtain overlapping flood areas and non-overlapping flood areas;

[0119] Step 2: For overlapping flood areas, the flood inundation duration is greater than or equal to the remote sensing time series interval. In this case, it is necessary to obtain the time increment Δs based on the time series remote sensing interval t; this time increment uses the above-mentioned basic inundation duration estimation method; the final inundation duration is t+Δs.

[0120] Step 3: For non-overlapping areas, use the basic flooding duration estimation method described above.

[0121] 3. Submergence Depth Model

[0122] The present invention uses the flood inundation depth (FID) model to determine the potential inundation depth within a river basin. The model assumes that floods cause the river water level to rise and flow from high-lying areas to low-lying areas within the basin, as follows Figure 3 As shown;

[0123] The model can be expressed as:

[0124] FID ij =ΔH ij -ΔDEM ij

[0125] Where ΔH=H max -H min ΔDEM is the maximum water level minus the minimum water level. ij is the difference between the DEM at the (i, j) location and the DEM at the monitoring point;

[0126] like Figure 3 As shown in the figure, before using the FID model to calculate the potential flooding depth, it is necessary to determine which basin the target area belongs to; the present invention uses the basin analysis of the Hydrological Analyst hydrological tool of ArcGIS, combined with DEM data to calculate the basin range of each river within the study area. On this basis, the potential flooding depth of each pixel is calculated.

[0127] 4. Construction of flood hazard equation

[0128] Taking the assessment parameters of crop flood damage in a basin as an example, this case uses the relationship between the damage degree and the flooding time and depth published by the local government, as well as the crop height data at different growing times.

[0129] The degree of crop flood damage L is a function of the growth period g, flooding duration l and flooding depth d, that is:

[0130] L = f(g, l, d)

[0131] The present invention determines the parameters of the equation based on the research on the characteristics of crop varieties in the study area, and outputs a quantitative description of the degree of damage in percentage. For example, for rice crops, the relationship between the degree of damage and the duration and depth of flooding can be referred to the following table:

[0132] Table 1 Relationship between rice damage and flooding duration and depth (Philippine Department of Agriculture)

[0133]

[0134]

[0135] Finally, combined with the estimated results of the disaster-stricken area, that is, the flooded range, the crop loss assessment of the entire study area was completed.

[0136] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for quantitatively assessing large areas of crop flood damage, characterized in that: The following steps are involved: A obtains flood disaster data and SAR time series remote sensing data, obtains the spatial scope of flood disaster inundation from SAR remote sensing images based on a deep learning image semantic segmentation model, and estimates the flood inundation depth at the DEM pixel scale using an inundation depth model based on topography and river water level observation data; the deep learning image semantic segmentation model is trained and constructed using the flood disaster data; B. Combine the flood disaster areas identified by SAR time series remote sensing data with the precipitation and river water level changes observed on the ground to estimate the flooding duration; C calculates the core assessment indicators of crops, associates flood disaster events with crop periods, constructs a flood damage degree equation using flood stress sensitivity, determines the crop flood damage degree equation, and completes spatial mapping of the damage degree.

2. A method for quantitatively assessing large areas of crop flood damage according to claim 1, characterized in that: The method for estimating the flooding duration includes: The prerequisite is that rainfall occurs continuously every day within a period of time and the river water level is higher than the warning water level; The SAR time series remote sensing data is input into the deep belief network based on the Boltzmann machine, which consists of a hidden layer and a display layer; Compute the joint probability density between the hidden and visible layers: The ath hidden layer is z a , the ath display layer is x a , the bias of the qth hidden layer is v q , the bias of the ath display layer is w a , the weight value between the ath hidden layer and the qth display layer is l aq , the number of hidden layers is The number of displayed layers is The parameters are The network function is The normalization factor is E, and the probability joint density between the hidden layer z and the display layer x is p(x,z); By learning the probability joint density between the hidden layer and the display layer, the feature extraction of SAR time series remote sensing data is carried out to obtain the characteristics of the flooded area; Clustering is used to reduce the dimension of flood area characteristics to obtain reduced dimension features, the expression is: The number of flood area characteristics is N, and the characteristic of the u-th flood area is h u , the center value of the c dimension of the y-th cluster is b y (c) The value of the cth dimension of the uth flood area feature is h u (c), the yth cluster is K y , flood area characteristics u Belongs to cluster d y The distance is d y (h u ), the u-th dimension reduction feature of the y-th cluster is D iy ; The flood area recognition is completed for two periods of continuous time series remote sensing images. The flood area recognition results of the two periods of images are compared. The expression is: The time variable of the u-th dimension reduction feature is The u-th dimension reduction feature at the s-th time is h u.s , the u-th dimension reduction feature at the s+j-th time is h u，s+j , the number of reduced dimensionality features that change is N1, and the control coefficient is ζ; Obtain overlapping flood areas and non-overlapping flood areas based on time-varying variables; For overlapping flood areas, where the flood inundation duration is greater than or equal to the remote sensing time series interval, the time increment Δs based on the SAR time series remote sensing interval time t is obtained, and the final inundation duration is t+Δs; For non-overlapping areas, the flooding duration estimation method is used.

3. A method for quantitatively assessing large areas of crop flood damage according to claim 1, characterized in that: The method for obtaining the core evaluation indicators includes: Calculate the similarity of flood disaster data: The rth flood disaster data is h r , the fth flood disaster data is h f , flood disaster data r and flood disaster data f The similarity is Flood disaster data r and flood disaster data f The distance is D(h r ,h f ), flood disaster data r The modulus is h r |, Flood disaster data f The modulus is h f |, the tuning parameter is σ, and the kernel width is φ; The flood disaster data with the largest number of similarities is taken as the classification cluster center, and the remaining flood disaster data is classified into the classification cluster with the largest similarity to the classification cluster center to obtain the classification cluster; Calculate the importance of classification clusters: The importance of the u-th classification cluster is Z u , the rth flood disaster data is h r , transposed to T, the unit matrix is ​​E, and the transposed of the rth flood disaster data is The cluster center is h o , the number of flood disaster data in the u-th classification cluster is n r , the importance parameter is μ, and the error coefficient is ε; The flood disaster data of the classification clusters with importance greater than 0.318 are taken as key data, and the flood relevance of the key data is calculated: The number of key data is n1, and the flood relevance of the rth key data is The flood level is Flood disaster data r and flood severity The joint probability distribution of Flood disaster data r The marginal probability distribution of is p(h r ), flood severity The marginal probability distribution of The key data with flood correlation greater than 0.647 are output as core evaluation indicators.

4. A method for quantitatively assessing large areas of crop flood damage according to claim 1, characterized in that: The flood inundation depth model is used to determine the potential inundation depth within the river basin. It is assumed that floods cause the river water level to rise and flow from high-lying areas to low-lying areas within the basin, which can be expressed as: FID ij =ΔH ij -ΔDEM ij Where ΔH=H max -H min ΔDEM is the maximum water level minus the minimum water level. ij is the difference between the DEM at the (i, j) location and the DEM at the monitoring point.

5. A method for quantitatively assessing large areas of crop flood damage according to claim 3, characterized in that: The basin analysis of the Hydrological Analyst tool of ArcGIS was used, and the DEM data was combined to calculate the basin range of each river channel within the study area and the potential flooding depth of each pixel.

6. A method for quantitatively assessing large areas of crop flood damage according to claim 1, characterized in that: The flood hazard equation is: L=f(g,l,d) The degree of crop flood damage is L, the growth and development period is g, the flooding duration is l, and the flooding depth is d; The flood damage equation outputs a quantitative description of the degree of damage expressed as a percentage.

7. A method for quantitatively assessing large areas of crop flood damage according to claim 1, characterized in that: The flooded areas were treated as new land cover categories and the model was trained independently.

8. A method for quantitatively assessing large areas of crop flood damage according to claim 1, characterized in that: The ground-observed precipitation data and river water level observation data are used to construct a flooding duration estimation model.

9. A system for quantitatively assessing large areas of crop flood damage, used to implement the method according to any one of claims 1 to 8, characterized in that: include: Acquisition module: used to train and build a deep learning image semantic segmentation model based on the public flood disaster dataset, and apply it to the SAR remote sensing images of the target flood disaster event to obtain the spatial scope of flood disaster inundation. Based on the topography and river water level observation data, the flood inundation depth model is used to estimate the flood inundation depth at the DEM pixel scale; Calculation module: used to combine the flood disaster areas identified by time-series remote sensing data with the precipitation and river water level changes observed on the ground to comprehensively estimate the flooding duration; Output module: used to calculate the three core evaluation indicators of crop flood inundation range, inundation duration and inundation depth, associate flood disaster events with crop periods, construct flood hazard degree equation based on flood stress sensitivity, determine crop flood disaster degree equation, and complete spatial mapping of disaster degree.

Images