CN105809148B - Crop drought recognition and risk assessment method based on remote sensing space-time spectrum fusion - Google Patents

Crop drought recognition and risk assessment method based on remote sensing space-time spectrum fusion Download PDF

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CN105809148B
CN105809148B CN201610187807.XA CN201610187807A CN105809148B CN 105809148 B CN105809148 B CN 105809148B CN 201610187807 A CN201610187807 A CN 201610187807A CN 105809148 B CN105809148 B CN 105809148B
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王树东
张立福
杨邦会
张潇元
田静国
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Abstract

The invention provides a crop drought identification and risk assessment method based on remote sensing space-time spectrum fusion, which comprises the following steps: respectively acquiring remote sensing data of high space, high time and high spectral resolution of a target area to be detected; performing space-time fusion on the remote sensing data with high space and high time resolution; performing space-spectrum fusion on the data subjected to space-time fusion and the remote sensing data with high spectral resolution; acquiring crop planting area information according to the remote sensing data with high time, high space and high spectral resolution after space-spectrum fusion; according to the crop planting area information, recognizing the crop drought area by using a preset drought recognition model and recognizing the crop drought areas in different historical periods; and performing risk assessment on crop drought according to the identification result. The method applies the fused remote sensing data with high time, space and spectral resolution to carry out drought identification and risk assessment on the crops in the target area to be detected, and the result is more accurate and the timeliness is stronger.

Description

Crop drought recognition and risk assessment method based on remote sensing space-time spectrum fusion
Technical Field
The invention relates to the technical field of remote sensing and ecological agriculture, in particular to a crop drought recognition and risk assessment method based on remote sensing space-time spectrum fusion.
Background
The time, space and spectrum scale problems of the remote sensing data are one of the important reasons for limiting the agricultural drought monitoring and evaluation precision and maintaining the timeliness. Due to spatial heterogeneity of crop distribution, sensitivity of spectra and dynamics of growth in different growth cycles, remote sensing data with high temporal, spatial and spectral resolution is required, however, due to the limitation of sensor design, temporal, spatial and spectral resolution have mutual exclusion, and three indexes cannot be obtained simultaneously.
At present, remote sensing data obtained by an aviation or aerospace sensor for drought identification, such as land satellite Landsat data, medium-resolution imaging spectrometer (MODIS) data, National Oceanic and atomic administration of sea (NOAA) satellite data and the like, obtain important results in regional or global drought crop monitoring and evaluation, however, due to the limitation of data acquisition by a single sensor, high-time, high-space and high-spectrum remote sensing data cannot be effectively obtained, and thus, monitoring accuracy and timeliness are obviously insufficient.
In view of this, how to obtain remote sensing data of high time, high space and high spectrum, and identify crop drought in the remote sensing data and evaluate risks becomes a technical problem to be solved at present.
Disclosure of Invention
In order to solve the technical problems, the invention provides a crop drought identification and risk assessment method based on remote sensing space-time spectrum fusion, which can be used for identifying and assessing the risk of crop drought in remote sensing data with high time, space and high spectral resolution after fusion of a target area to be detected, and has more accurate result and higher timeliness.
In a first aspect, the invention provides a crop drought identification and risk assessment method based on remote sensing spatio-temporal spectrum fusion, which comprises the following steps:
respectively acquiring remote sensing data with high spatial resolution of a target area to be detected, remote sensing data with high temporal resolution of the target area to be detected and remote sensing data with high spectral resolution of the target area to be detected;
performing space-time fusion on the remote sensing data with the high spatial resolution and the remote sensing data with the high temporal resolution;
performing space-spectrum fusion on the data subjected to space-time fusion and the remote sensing data with the high spectral resolution to obtain the remote sensing data with high time, high space and high spectral resolution of the target area to be measured;
acquiring crop planting area information according to the remote sensing data with high time, high space and high spectral resolution;
according to the crop planting area information, recognizing crop drought by using a preset drought recognition model, and recognizing drought in different historical periods;
and performing risk assessment on the crop drought according to the identification result of the crop drought.
Optionally, after the obtaining of the remote sensing data with high spatial resolution of the target area to be measured, the remote sensing data with high temporal resolution of the target area to be measured, and the remote sensing data with high spectral resolution of the target area to be measured, before performing space-time fusion on the remote sensing data with high spatial resolution and the remote sensing data with high temporal resolution, the method further includes:
filtering the remote sensing data with high time resolution;
correspondingly, performing space-time fusion on the remote sensing data with the high spatial resolution and the remote sensing data with the high temporal resolution, specifically:
performing space-time fusion on the remote sensing data with the high spatial resolution and the filtered remote sensing data with the high temporal resolution;
correspondingly, the space-spectrum fusion is performed on the data after the space-time fusion and the remote sensing data with the high spectral resolution to obtain the remote sensing data with high time, high space and high spectral resolution of the target area to be measured, and the method specifically comprises the following steps:
and performing space-spectrum fusion on the data subjected to space-time fusion and the filtered remote sensing data with the high spectral resolution to obtain the remote sensing data with high time, high space and high spectral resolution of the target area to be detected.
Optionally, the filtering the remote sensing data with high time resolution includes:
and filtering the remote sensing data with high time resolution by using an S-G filtering algorithm.
Optionally, the performing space-time fusion on the remote sensing data with the high spatial resolution and the remote sensing data with the high temporal resolution includes:
performing space-time fusion on the remote sensing data with the high spatial resolution and the remote sensing data with the high temporal resolution by utilizing an improved self-adaptive remote sensing image space-time fusion model ESTRAFM algorithm;
and/or the presence of a gas in the gas,
the space-spectrum fusion of the data after the space-time fusion and the remote sensing data with the high spectral resolution comprises the following steps:
and performing space-spectrum fusion on the data subjected to space-time fusion and the remote sensing data with the high spectral resolution by using a multi-spectral image Spectral Resolution Enhancement Method (SREM) fusion algorithm.
Optionally, the obtaining of the crop planting area information according to the remote sensing data with high time, high space and high spectral resolution includes:
acquiring EVI time sequence data of a target area to be detected according to the remote sensing data with high time, high space and high spectral resolution;
generating an EVI time sequence curve of a normal crop which is not stressed by drought and an EVI time sequence curve of a crop to be identified by drought according to the EVI time sequence data, and extracting vegetation in the remote sensing data with high time, high space and high spectral resolution by the slope change of the generated EVI time sequence curve and the comparison of the phenological period of the crop which is not stressed by drought, wherein the vegetation comprises: part of natural vegetation and all field crops;
extracting field crops from the extracted vegetation by using a width-at-height algorithm of 1/3;
according to the digital elevation model and the gradient map, distinguishing slope farmland and flat farmland from the extracted farmland crops;
the slope calculation formula of the constructed EVI time sequence curve is as follows:
wherein E ist2-t1、Et3-t2Respectively, points (t1, EVI)t1) And point (t2, EVI)t2) The value of the slope between and the point (t2, EVI)t2) And point (t3, EVI)t3) The slope values between t1, t2 and t3 are all time points, wherein t2 is the time point of sowing; EVIt1、EVIt2、EVIt3EVI values corresponding to time points t1, t2, and t3, respectively; a is more than or equal to t2-t1, a is less than a first preset value, and Et2-t1Δ E, ≦ Δ E being an allowable error; t3-t2 ═ b, Et3-t2>0, when b is larger than a threshold value c, determining that the plant is a part of and all farmlands of natural vegetation, wherein c is an empirical value;
wherein, using the width algorithm at height 1/3 to extract the field crop from the extracted vegetation comprises:
assuming that the left-hand simulation function of the crop to be drought-identified is FhF (t, EVI) formula one
The simulation function on the right side of the crop to be drought-identified is GhG (t, EVI) formula two
The division point of the two is t ═ tv，tvFor the highest point EVI of the growth period curve of the non-drought cropsmaxThe corresponding time point value;
the intersection points of the left side and the right side of the first formula, the second formula and the third formula are respectively as follows: (t)m,EVIm)，G(tn,EVIn)；
If there is no drought-enduring cropThe side-known intersections are: (t)m-n,EVIm-n)，G(tn-n,EVIn-n)，tn-n-tm-n＝d；
When t isn-tm>d + delta is natural vegetation, and conversely is crops;
where Δ is an allowable error value.
Optionally, the identifying the crop drought by using a preset drought identification model according to the crop planting area information includes:
identifying crop drought of the extracted sloping fields by using a preset drought identification model according to the crop planting area information;
and identifying the crop drought of the extracted flat cultivated land by using a preset drought identification model according to the crop planting area information.
Optionally, the identifying, according to the crop planting area information, the crop drought of the sloping field by using a preset drought identification model includes:
acquiring the precipitation distance flat percentage of a target region to be detected in a period to be detected;
when the precipitation distance flat percentage is smaller than a preset first threshold value, determining that the extracted slope farmland is drought;
when the extracted sloping farmland is drought, judging the drought level according to the precipitation rate flat percentage and the agricultural drought level standard issued by the state;
the precipitation distance flat percentage Pa of the target region to be measured in the period to be measured is calculated by a first formula, where the first formula is:
wherein M is the accumulated precipitation amount in a certain time period estimated by the precipitation satellite data,d0days of Observation, SAdEstimating the amount of precipitation on day d for the application of precipitation satellite data, d being denoted as day d;
for the average precipitation of the contemporaneous remote sensing inversion in a certain time period,y0is the number of years observed; SAyAnd (4) counting the y year, wherein the rainfall is obtained by remote sensing inversion in a certain time period, and y is a time variable and is expressed as the y year.
Optionally, the identifying, according to the crop planting area information, the crop drought of the extracted flat cultivated land by using a preset drought identification model includes:
acquiring EVI time sequence curves of drought-stricken years and non-drought-stricken years in a historical preset time period of a target area to be detected;
acquiring the intersection point of the EVI time sequence curves of the drought-stricken year and the non-drought-stricken year;
obtaining the drought stress degree A through a second formula according to the intersection pointgh；
In AghWhen the value is larger than a preset second threshold value, determining that the extracted flat plowed land is dry;
wherein the preset second threshold is the threshold of the drought-stricken crops and the non-drought-stricken crops;
the second formula is:
wherein d1 and d2 are respectively the time points of the intersection of the growth curves of the non-drought crops and the drought crops; t is a time variable; EVIntAnd EVIatEVI values of the non-drought crops and the drought crops on the t day are respectively; snThe accumulated value of the non-drought crops from the time d1 to the time d2 is calculated by a third formula;
the third formula is:
optionally, the performing risk assessment on the crop drought according to the identification result of the crop drought includes:
acquiring the drought area of the crop according to the identification result of the drought of the crop;
and acquiring drought-affected risk values of the crops according to the areas of the crops in different historical periods during drought.
Optionally, the obtaining of drought-stricken risk values of the crops according to areas of drought of the crops in different historical periods includes:
dividing the target area of risk assessment into grid units again, and obtaining the drought-stricken risk value R of the crops through a fourth formula according to the proportion of the drought-stricken areas in the grid in different periodsi,j；
Wherein the fourth formula is:
wherein S isi,jIs the area of the evaluated grid cell; delta Si,j,kThe area of drought in the grid in the evaluation unit is shown, wherein i, j and k are respectively the ith row, the jth column and the kth grid unit of the grid to be evaluated and are obtained by calculation through a fifth formula; n isaIs the counted number of years;
the fifth formula is:
Pixelthe area of each pixel of the remote sensing image is shown; j. the design is a squareo.pFor the drought discrimination function, o and p represent the pixels of row o and column p, respectively, Jo.pTake 0 as non-drought and 1 as drought.
According to the technical scheme, the crop drought identification and risk assessment method based on remote sensing space-time spectrum fusion can identify and assess the crop drought in the fused remote sensing data with high time, high space and high spectral resolution by performing space-time and space-spectrum fusion on the remote sensing data with high spatial resolution, the remote sensing data with high time resolution and the remote sensing data with high spectral resolution in the target area to be detected, and the result is more accurate and the timeliness is higher.
Drawings
FIG. 1 is a schematic flow chart of a crop drought identification and risk assessment method based on remote sensing spatiotemporal spectrum fusion according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an enhanced vegetation index EVI timing curve according to an embodiment of the present invention;
fig. 3 is a schematic diagram of EVI time series curves of a drought-stricken year and a non-drought-stricken year according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 shows a schematic flow chart of a crop drought recognition and risk assessment method based on remote sensing spatio-temporal spectrum fusion according to a first embodiment of the present invention, and as shown in fig. 1, the crop drought recognition and risk assessment method based on remote sensing spatio-temporal spectrum fusion according to the present embodiment is as follows.
101. Respectively obtaining remote sensing data with high spatial resolution of a target area to be measured, remote sensing data with high time resolution of the target area to be measured and remote sensing data with high spectral resolution of the target area to be measured.
In a specific application, for example, the remote sensing data with high spatial resolution, the remote sensing data with high temporal resolution and the remote sensing data with high spectral resolution of the target area to be measured can be respectively obtained by using a terrestrial satellite Landsat, a medium resolution imaging spectrometer MODIS, a NOAA satellite, and the like.
102. And performing space-time fusion on the remote sensing data with the high spatial resolution and the remote sensing data with the high temporal resolution.
103. And performing space-spectrum fusion on the data subjected to space-time fusion and the remote sensing data with the high spectral resolution to obtain the remote sensing data with high time, high space and high spectral resolution of the target area to be measured.
104. And acquiring crop planting area information according to the remote sensing data with high time, high space and high spectral resolution.
105. And identifying the crop drought by using a preset drought identification model according to the crop planting area information, and identifying the drought in different historical periods.
106. And performing risk assessment on the crop drought according to the identification result of the crop drought.
According to the crop drought identification and risk assessment method based on remote sensing space-time spectrum fusion, the remote sensing data with high spatial resolution, the remote sensing data with high temporal resolution and the remote sensing data with high spectral resolution of the target area to be measured are subjected to space-time spectrum fusion, crop drought in the fused remote sensing data with high temporal resolution, high temporal resolution and high spectral resolution can be identified and risk assessment can be carried out, the result is more accurate, and the timeliness is higher.
In a specific application, between the step 101 and the step 102, a step S1 not shown in the figure may be further included:
s1, filtering the remote sensing data with high time resolution;
correspondingly, the step 102 specifically includes:
performing space-time fusion on the remote sensing data with the high spatial resolution and the filtered remote sensing data with the high temporal resolution;
correspondingly, the step 103 specifically includes:
and performing space-spectrum fusion on the data subjected to space-time fusion and the filtered remote sensing data with the high spectral resolution to obtain the remote sensing data with high time, high space and high spectral resolution of the target area to be detected.
For example, in the step S1, the high-time-resolution remote sensing data may be filtered by using an S-G filtering algorithm; specifically, the EVI time sequence data can be reconstructed through TIMEAT software by using an S-G filtering algorithm;
the S-G filtering algorithm may specifically be to reconstruct the time series data using the following formula:
wherein the content of the first and second substances,reconstructing time series data, Tj+1As raw time series data, CiN is the amount of time series data within the sliding window.
It can be understood that due to the influence of factors such as a sensor and cloud layer atmosphere, the acquired remote sensing data inevitably generates some noises, the S-G filter has a good removing effect on the noises, the reconstructed time series data can clearly describe the long-term change trend of the sequence and local mutation information, and the reconstruction of the vegetation index time series data has good applicability.
In a specific application, the step 102 may include:
and performing space-time Fusion on the remote sensing data with the high Spatial resolution and the remote sensing data with the high Temporal resolution by using an improved Adaptive remote sensing image space-time Fusion Model (ESTRAFM) algorithm.
It can be understood that, the estramfm fusion model is developed based on the STARFM model, the time difference between the analog data and the reference data is considered, and the mixed pixel decomposition theory is introduced, so that the defect that the STARFM is poor in applicability under the condition that the ground object type is complex is overcome to a certain extent, in the above step 102 of the embodiment, the estramfm can simulate the Landsat reflectivity data of the corresponding time phase of the MODIS data by utilizing the difference of the Landsat and MODIS reflectivity data in the information of pixel distance, spectrum, acquisition time and the like;
when the pixel is pure, t0The Landsat and MODIS reflectance data for time have the following relationship:
then there is an analog tkThe Landsat data for time were:
wherein L and M are Landsat and MODIS surface reflectivities, respectively, (x)i,yj) The position of the pixel is shown, and a and b are coefficients (caused by wave band width, geometric errors and the like) of linear relation between reflectivity data of the two sensors;
considering that in practice, most of the pixels are mixed pixels, the ground cover condition is likely to change along with time, and the position of the sensor changes along with time, therefore, an ESTRAFM fusion model establishes a local sliding window, finds out the spectrum similar adjacent pixels of the central pixel, and gives different weights to the adjacent pixels according to the spectrum difference of Landsat and MODIS, the time difference of MODIS of reference time and simulation time, and the spatial Euclidean geometric distance between the central pixel and the adjacent pixels to obtain the reflectivity of the simulated central pixel;
when the adjacent pixel is selected, the following conditions are satisfied:
wherein L is Landsat reflectivity, (x)i,yj) Is the pixel position, w is the local sliding window size, t0Is time, σ (B)n) The standard deviation of the reflectivity data of the nth wave band is obtained;
then there is a simulated center reflectivity of:
wherein the content of the first and second substances,as a central pixel (x)w/2,yw/2) At simulation time tpThe surface reflectivity of Landsat of (a),as a central pixel (x)w/2,yw/2) At a reference time t0The surface reflectivity of Landsat of (a),are respectively a picture element (x)i,yj) At t0，tpMODIS surface reflectance of time, ViIs a linear coefficient, W, obtained by mixed pixel decompositionijkIs a weight;
Cijk＝Sijk*Tijk*Dijk
Sijk＝|L(xi,yj,tk)-M(xi,yj,tk)|
Tijk＝|M(xi,yj,tk)-M(xi,yj,t0)|
Sijkfor a given position in (x)i,yj) The difference between the MODIS and Landsat earth surface reflectivity can be measured by the parameter, the smaller the value is, the similarity of the adjacent pixels at a given position is high, and the higher weight is given; t isijkRepresenting the reflectivity difference between two periods of MODIS data, wherein the smaller the value is, the smaller the spectral change in the period is, and the higher weight is given in the calculation; dijkThe geometric distance between the central pixel point and the pixel point participating in the calculation is given, and the smaller the value is, the higher the weight is given;
and then according to the phase weighting, the following can be obtained:
wherein the content of the first and second substances,
m and n are two time phases, p simulates the time phase, and B is a wave band.
ESTRAFM can simulate Landsat data, i.e. input Tm,TnTemporal Landsat and MODIS reference images and TkMODIS of time to obtain TkSimulated Landsat data for time. Landsat data missing due to cloud pollution or revisit cycle limitation can be obtained through the ESTRAFM, for example, 92 Landsat data of 2013 and 2014 can be obtained, the spatial resolution of the data is 30m, and the time resolution is 8 days. The data of high time and high space can overcome the condition of the mixed pixel to a certain extent, and meanwhile, the method has the potential of monitoring the change of drought disasters which occur in a short time.
In a specific application, the step 103 may include:
and performing space-spectrum fusion on the data subjected to space-time fusion and the remote sensing data with the high spectral resolution by using a multi-spectral image spectral resolution enhancement method (SREM for short) fusion algorithm.
It can be understood that the basic idea of the SREM fusion algorithm is to extract end members according to a hyperspectral image by using a Vertex Component Analysis algorithm (VCA) or the like, extract a certain number of pixel spectra of different ground feature end members from an original hyperspectral image, and resample the pixel spectra according to a spectral response function of the multispectral image to obtain corresponding multispectral image end member spectral data. Or directly extracting the end member spectrum from the registered multispectral image according to the extracted end member hyperspectral pixel position. The spectrum vectors are divided into N groups according to the types of ground object end members, each spectrum is represented by a column vector, and the relation of multi/high spectrum data of each ground object type can be represented as
G′(q)M(q)＝H(q)+r(q)
Wherein the content of the first and second substances,representing sets of multi-spectral vectorsAn L × W dimensional matrix of (1);representing a set of hyperspectral vectorsA K × W dimensional matrix of; w is the number of spectra extracted for the ground object end-member type q (q takes on values of 1 to N). As long as the value of W is greater than or equal to L, a particular transformation matrix G' (q) for the terrain type q can be calculated:
G′(q)＝H(q)M(q)T(M(q)M(q)T)-1；
n conversion matrixes can be obtained by selecting N types of ground object end members in the image. Then, multiplying G' (q) by a pixel spectral vector with L wave bands in the multispectral imageA spectral vector with K bands can be reconstructed
However, N transformation matrices G' (q) may be calculated based on different end-member types, and for each multispectral image pixel the correct single transformation matrix must be selected to complete the reconstruction process. The key to this choice is that the type of the feature of a certain multispectral image element must be obtained, which can be achieved by performing mixed spectral unmixing on multispectral data. However, since the number of bands of the multispectral data is usually small, when the types of the ground object end members are many, the accuracy of directly unmixing the multispectral data is difficult to guarantee. Therefore, the non-negative matrix factorization NMF algorithm can be used for performing spectrum unmixing on the hyperspectral data after spatial resampling to obtain the end member abundance of each pixel; and then selecting P end member types with higher abundance values in each pixel as spectrum mixing end members of the corresponding multispectral data pixel, performing pixel-by-pixel unmixing on the multispectral data by using an NMF algorithm again, and finally selecting the end member type with the highest abundance value of the multispectral data pixel as the ground object type of the pixel so as to select the corresponding conversion matrix G' (q).
In a specific application, the step 104 may include steps P1-P4 not shown in the figure:
and P1, acquiring Enhanced Vegetation Index (EVI) time sequence data of the target area to be measured according to the remote sensing data with high time, high space and high spectral resolution.
Understandably, the EVI has the advantages of being not easy to saturate, sensitive to crop coverage, canopy water, chlorophyll and the like, and is used for drought monitoring;
where ρ is the surface reflectance, ρNIRSurface reflectance, p, for the near infrared bandredSurface reflectance in the red band, pblueThe surface reflectivity of blue light wave band, L the soil background regulation coefficient, C1,C2For fitting coefficients, L ═ 1, C may be taken1＝6,C2＝7.5,G＝2.5。
The EVI compensates the absorption of the residual aerosol to the red light according to the difference of the blue light and the red light passing through the aerosol, and corrects the atmospheric aerosol influence on the red waveband. The EVI overcomes the influence of the soil background to a certain extent, can reduce the influence of the Vegetation indexes such as Normalized Difference Vegetation Index (NDVI) which are easily saturated in a high coverage area, and is often used for optimizing Vegetation signals and enhancing Vegetation monitoring. And calculating to obtain EVI time sequence data with high space, high time and high spectral resolution based on the space-time fusion data, and using the EVI time sequence data to extract crop areas and drought areas.
And P2, generating an EVI time sequence curve of a normal crop without drought stress and a crop EVI time sequence curve to be subjected to drought identification (for example, as shown in figure 2) according to the EVI time sequence data, and extracting vegetation in the remote sensing data with high time, high space and high spectral resolution through the slope change of the generated EVI time sequence curve and the comparison of the phenological period of the crop without drought stress, wherein the vegetation comprises: part of natural vegetation and all field crops.
It can be understood that, for crops with different growth cycles, due to different starting points of the growth cycles and different rising and falling positions of the EVI, the types of the crops can be judged by the slope change of the EVI time sequence curve and the actual phenological period of the crops, and the vegetation in the remote sensing data with high time, high space and high spectral resolution can be extracted, wherein the vegetation comprises: part of natural vegetation and all field crops.
In a specific application, the slope calculation formula of the constructed EVI timing curve is as follows:
wherein E ist2-t1、Et3-t2Respectively, points (t1, EVI)t1) And point (t2, EVI)t2) The value of the slope between and the point (t2, EVI)t2) And point (t3, EVI)t3) The slope values between t1, t2 and t3 are all time points, wherein t2 is the time point of sowing; EVIt1、EVIt2、EVIt3EVI values corresponding to time points t1, t2, and t3, respectively; a is more than or equal to t2-t1, and a is less than a first preset value(the first preset value may preferably be 16), and Et2-t1Δ E, ≦ Δ E being an allowable error; t3-t2 ═ b, Et3-t2>0, b may be 10, 20, 30, 40, 50, and gradually increase with b, Et3-t2And also directly increases, when the value of b is greater than the threshold value c, which is an empirical value and is related to the type of crop, it is determined to be part and all of the field of natural vegetation.
P3 field crops are extracted from the extracted vegetation using the 1/3 height width algorithm.
In a specific application, the extraction of field crops from the extracted vegetation using the 1/3 height width algorithm of step P3 may include:
assuming that the left-hand simulation function of the crop to be drought-identified is FhF (t, EVI) formula one
The simulation function on the right side of the crop to be drought-identified is GhG (t, EVI) formula two
The division point of the two is t ═ tv，tvThe time point value corresponding to the highest point of the growth period curve of the normal crop (i.e. the non-drought-stricken crop);
the intersection points of the left side and the right side of the first formula, the second formula and the third formula are respectively as follows: (t)m,EVIm)，G(tn,EVIn)；
The known intersection points on the two sides of the normal crop are respectively: (t)m-n,EVIm-n)，G(tn-n,EVIn-n)，
tn-n-tm-n＝d；
When t isn-tm>d + delta is natural vegetation, and conversely is crops;
where Δ is an allowable error value.
And P4, distinguishing slope farmland and flat farmland from the extracted farmland crops according to a Digital Elevation Model (DEM for short) and a slope map.
In a specific application, the step P4 of distinguishing the slope land and the flat land from the extracted field crops according to the DEM and the slope map may include:
in the extracted farmland crops, if the gradient is less than or equal to e, the farmland crops are flat ploughed; and if the gradient is greater than e, the slope is cultivated land, and the value range of e is 6-25 degrees.
It is understood that after the slope and flat lands in the field crop are extracted in step P4, the area of the slope land and the area of the flat land can be obtained.
In a specific application, the step 105 may include steps Q1 and Q2 not shown in the figure:
and Q1, identifying the crop drought of the extracted sloping field by using a preset drought identification model according to the crop planting area information.
In a specific application, the step Q1 may include steps B1-B3:
and B1, acquiring the precipitation rate from the flat percentage of the target region to be measured in the measuring period.
It should be noted that, although the drought-stricken crops are mostly spatially distributed on the slope farmland, since the crop plants are sparsely broken in the slope farmland in the mountain area and it is difficult to obtain pure pixels, the slope farmland is mostly only nourished by rainwater, and therefore, when weather drought occurs, the slope farmland is considered to be also arid.
Weather drought is used as the starting point of agricultural drought, and weather satellite data can be adopted to judge whether a certain area has weather drought according to the precipitation rate flat percentage Pa. The precipitation distance flat percentage Pa is the deviation degree of precipitation in a certain period of time and the average condition of the local climate, can visually reflect weather drought caused by abnormal precipitation, and is represented by the percentage (%) of the difference between the precipitation and the average precipitation of the same-period climate in the same period of the year to the average precipitation of the same-period climate in the same period of the year:
wherein Pa is a percentage (%) of precipitation in a certain period; p is the cumulative precipitation (mm) of a certain period of time;the average precipitation (mm) of the same weather month is a certain period.
With the weather satellite data product, the daily precipitation D can be expressed as:
wherein h is00The daily observation frequency, i.e. the number of scenes of precipitation data,h0estimating a time span of precipitation products, namely representing the precipitation duration by each scene data; SAhThe rainfall represented by certain scene data is represented, and h is an observed scene number variable;
the cumulative precipitation M for a certain period of time (in days) can be expressed as:
wherein d is0Is a time period (which may be expressed as days); SAh,dD is the precipitation amount of day d, d is a time variable and is expressed as day d;
the mean precipitation for the contemporaneous climate for a certain period of time (in years) can be expressed as:
wherein, y0Is the number of years observed; SAh,d,yThe amount of precipitation in a certain period of time (in days) counted for the y year, y is a time variable and is expressed in years as the y year. Thus, the moment average can be expressed as:
and B2, when the precipitation distance Pa is smaller than a preset first threshold value, determining that the extracted slope land is drought.
And B3, when the extracted sloping farmland is drought, judging the drought level according to the precipitation rate flat percentage and the agricultural drought level standard issued by the country (see the table 1).
TABLE 1
And Q2, identifying the crop drought of the extracted flat cultivated land by using a preset drought identification model according to the crop planting area information.
In a specific application, the step Q2 may include steps C1-C3:
acquiring EVI time sequence curves of drought-stricken years and non-drought-stricken years in a historical preset time period of a target area to be detected;
and C1, acquiring the intersection point of the EVI time sequence curves of the drought year and the non-drought year.
The EVI time sequence curves of the non-drought crops and the drought crops can be extracted by repeatedly comparing the EVI time sequence curves of the crops in the drought-stricken year and the non-drought year in the same region, and referring to fig. 3, fig. 3 shows a schematic diagram of the EVI time sequence curves of the drought-stricken year and the non-drought-stricken year, wherein the EVI of the non-drought-stricken crops in the diagram presents a unimodal curve, and the EVI of the crops in the jointing-up to maturity stage reaches the maximum value.
C2, obtaining drought stress degree A through a second formula according to the intersection pointgh。
Wherein the preset second threshold is a threshold of the drought-stricken crops and the non-drought-stricken crops, and is specifically related to the types of the crops;
the second formula is:
wherein d1 and d2 are respectively the time points of the intersection of the growth curves of the non-drought crops and the drought crops; t is a time variable; EVIntAnd EVIatEVI values of the non-drought crops and the drought crops on the t day are respectively; snThe accumulated value of the non-drought crops from the time d1 to the time d2 is calculated by a third formula;
the third formula is:
c3 at AghAnd when the value is larger than a preset second threshold value, determining that the extracted flat plowed land is arid.
In a specific application, the step 106 may include steps a1 and a2 that are not shown in the figure:
and A1, acquiring the drought area of the crop according to the identification result of the drought of the crop.
In a specific application, in step a1, the areas S of the sloping fields and the drought can be obtained according to the identification result of the crop droughtdArea S of peace and arable land droughtp(ii) a And obtaining the total drought area S of the crops in the target area to be measured by the following formulaa。
A2, acquiring drought-affected risk values of the crops according to the drought areas of the crops in different historical periods.
In a specific application, the step a2 may include:
for convenience of evaluation, a target area of risk evaluation is divided into grid units again, and the drought-stricken risk value R of the crop is obtained through a fourth formula according to the proportion of the drought-stricken areas in the grid in different periodsi,j；
Wherein the fourth formula is:
wherein S isi,jIs the area of the evaluated grid cell; delta Si,j,kThe area of drought in the grid in the evaluation unit is shown, wherein i, j and k are respectively the ith row, the jth column and the kth grid unit of the grid to be evaluated and are obtained by calculation through a fifth formula; n isaIs the counted number of years;
the fifth formula is:
Pixelthe area of each pixel of the remote sensing image is shown; j. the design is a squareo.pFor the drought discrimination function, o and p represent the pixels of row o and column p, respectively, Jo.pTake 0 as non-drought and 1 as drought.
According to the crop drought identification and risk assessment method based on remote sensing space-time spectrum fusion, the remote sensing data with high spatial resolution, the remote sensing data with high temporal resolution and the remote sensing data with high spectral resolution of the target area to be measured are subjected to space-time spectrum fusion, crop drought in the fused remote sensing data with high temporal resolution, high temporal resolution and high spectral resolution can be identified and risk assessment can be carried out, the result is more accurate, and the timeliness is higher.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (8)

1. A crop drought identification and risk assessment method based on remote sensing space-time spectrum fusion is characterized by comprising the following steps:
respectively acquiring remote sensing data with high spatial resolution of a target area to be detected, remote sensing data with high temporal resolution of the target area to be detected and remote sensing data with high spectral resolution of the target area to be detected;
performing space-time fusion on the remote sensing data with the high spatial resolution and the remote sensing data with the high temporal resolution;
performing space-spectrum fusion on the data subjected to space-time fusion and the remote sensing data with the high spectral resolution to obtain the remote sensing data with high time, high space and high spectral resolution of the target area to be measured;
acquiring crop planting area information according to the remote sensing data with high time, high space and high spectral resolution;
according to the crop planting area information, recognizing crop drought by using a preset drought recognition model, and recognizing drought in different historical periods;
according to the identification result of the crop drought, performing risk assessment on the crop drought, comprising the following steps: acquiring the drought area of the crop according to the identification result of the drought of the crop; acquiring drought-affected risk values of crops according to drought areas of the crops in different historical periods, wherein the drought-affected risk values comprise the following steps: dividing the target area of risk assessment into grid units again, and obtaining the drought-stricken risk value R of the crops through a fourth formula according to the proportion of the drought-stricken areas in the grid in different periodsi,j；
Wherein the fourth formula is:
wherein S isi,jIs the area of the evaluated grid cell; delta Si,j,kThe area of drought in the grid in the evaluation unit is shown, wherein i, j and k are respectively the ith row, the jth column and the kth grid unit of the grid to be evaluated and are obtained by calculation through a fifth formula; n isaIs the counted number of years;
the fifth formula is:
Pixelthe area of each pixel of the remote sensing image is shown; j. the design is a squareo.pFor the drought discrimination function, o and p represent the pixels of row o and column p, respectively, Jo.pTake 0 as non-drought and 1 as drought.
2. The method according to claim 1, wherein after the obtaining of the remote sensing data with high spatial resolution of the target area to be measured, the remote sensing data with high temporal resolution of the target area to be measured, and the remote sensing data with high spectral resolution of the target area to be measured, respectively, and before the performing of the space-time fusion of the remote sensing data with high spatial resolution and the remote sensing data with high temporal resolution, the method further comprises:
filtering the remote sensing data with high time resolution;
correspondingly, performing space-time fusion on the remote sensing data with the high spatial resolution and the remote sensing data with the high temporal resolution, specifically:
performing space-time fusion on the remote sensing data with the high spatial resolution and the filtered remote sensing data with the high temporal resolution;
correspondingly, the space-spectrum fusion is performed on the data after the space-time fusion and the remote sensing data with the high spectral resolution to obtain the remote sensing data with high time, high space and high spectral resolution of the target area to be measured, and the method specifically comprises the following steps:
and performing space-spectrum fusion on the data subjected to space-time fusion and the filtered remote sensing data with the high spectral resolution to obtain the remote sensing data with high time, high space and high spectral resolution of the target area to be detected.
3. The method of claim 2, wherein the filtering the high temporal resolution telemetry data comprises:
and filtering the remote sensing data with high time resolution by using an S-G filtering algorithm.
4. The method of claim 1, wherein the spatiotemporal fusion of the high spatial resolution telemetry data with the high temporal resolution telemetry data comprises:
performing space-time fusion on the remote sensing data with the high spatial resolution and the remote sensing data with the high temporal resolution by utilizing an improved self-adaptive remote sensing image space-time fusion model ESTRAFM algorithm;
and/or the presence of a gas in the gas,
the space-spectrum fusion of the data after the space-time fusion and the remote sensing data with the high spectral resolution comprises the following steps:
and performing space-spectrum fusion on the data subjected to space-time fusion and the remote sensing data with the high spectral resolution by using a multi-spectral image Spectral Resolution Enhancement Method (SREM) fusion algorithm.
5. The method according to claim 1, wherein the obtaining of the crop planting area information from the high time, high space, high spectral resolution remote sensing data comprises:
acquiring EVI time sequence data of a target area to be detected according to the remote sensing data with high time, high space and high spectral resolution;
generating an EVI time sequence curve of a normal crop which is not stressed by drought and an EVI time sequence curve of a crop to be identified by drought according to the EVI time sequence data, and extracting vegetation in the remote sensing data with high time, high space and high spectral resolution by the slope change of the generated EVI time sequence curve and the comparison of the phenological period of the crop which is not stressed by drought, wherein the vegetation comprises: part of natural vegetation and all field crops;
extracting field crops from the extracted vegetation by using a width-at-height algorithm of 1/3;
according to the digital elevation model and the gradient map, distinguishing slope farmland and flat farmland from the extracted farmland crops;
the slope calculation formula of the constructed EVI time sequence curve is as follows:
wherein E ist2-t1、Et3-t2Respectively, points (t1, EVI)t1) And point (t2, EVI)t2) The value of the slope between and the point (t2, EVI)t2) And point (t3, EVI)t3) The slope values between t1, t2 and t3 are all time points, wherein t2 is the time point of sowing; EVIt1、EVIt2、EVIt3EVI values corresponding to time points t1, t2, and t3, respectively; a is more than or equal to t2-t1, a is less than a first preset value, and Et2-t1Δ E, ≦ Δ E being an allowable error; t3-t2 ═ b, Et3-t2If b is greater than a threshold value c, determining that the vegetation is a part of or all farmlands of natural vegetation, wherein c is an empirical value;
wherein, using the width algorithm at height 1/3 to extract the field crop from the extracted vegetation comprises:
assuming that the left-hand simulation function of the crop to be drought-identified is FhF (t, EVI) formula one
The simulation function on the right side of the crop to be drought-identified is GhG (t, EVI) formula two
The division point of the two is t ═ tv，tvFor the highest point EVI of the growth period curve of the non-drought cropsmaxThe corresponding time point value;
the intersection points of the left side and the right side of the first formula, the second formula and the third formula are respectively as follows: f (t)m,EVIm)，G(tn,EVIn)；
The known intersection points on the two sides of the non-drought crops are respectively: f (t)m-n,EVIm-n)，G(tn-n,EVIn-n)，tn-n-tm-n＝d；
When t isn-tmWhen d + delta is larger than d + delta, the vegetation is natural vegetation, and the opposite is crops;
where Δ is an allowable error value.
6. The method according to claim 5, wherein the identifying the crop drought by using a preset drought identification model according to the crop planting area information comprises:
identifying crop drought of the extracted sloping fields by using a preset drought identification model according to the crop planting area information;
and identifying the crop drought of the extracted flat cultivated land by using a preset drought identification model according to the crop planting area information.
7. The method according to claim 6, wherein the identifying the crop drought of the sloping field by using a preset drought identification model according to the crop planting area information comprises:
acquiring a precipitation distance flat percentage Pa of a target region to be detected in a period to be detected;
when the precipitation distance flat percentage is smaller than a preset first threshold value, determining that the extracted slope farmland is drought;
when the extracted sloping farmland is drought, judging the drought level according to the precipitation rate flat percentage and the agricultural drought level standard issued by the state;
the precipitation distance flat percentage Pa of the target region to be measured in the period to be measured is calculated by a first formula, where the first formula is:
wherein M is the accumulated precipitation amount in a certain time period estimated by the precipitation satellite data,d0days of Observation, SAdFor applicationD is the day d precipitation estimated by the water satellite data;
for the average precipitation of the contemporaneous remote sensing inversion in a certain time period,y0is the number of years observed; SAyAnd (4) counting the y year, wherein the rainfall is obtained by remote sensing inversion in a certain time period, and y is a time variable and is expressed as the y year.
8. The method according to claim 6, wherein the identifying the crop drought of the extracted cultivated land according to the crop planting area information by using a preset drought identification model comprises:
acquiring EVI time sequence curves of drought-stricken years and non-drought-stricken years in a historical preset time period of a target area to be detected;
acquiring the intersection point of the EVI time sequence curves of the drought-stricken year and the non-drought-stricken year;
obtaining the drought stress degree A through a second formula according to the intersection pointgh；
In AghWhen the value is larger than a preset second threshold value, determining that the extracted flat plowed land is dry;
wherein the preset second threshold is the threshold of the drought-stricken crops and the non-drought-stricken crops;
the second formula is:
wherein d1 and d2 are respectively the time points of the intersection of the growth curves of the non-drought crops and the drought crops; t is a time variable; EVIntAnd EVIatEVI values of the non-drought crops and the drought crops on the t day are respectively; snThe accumulated value of the non-drought crops from the time d1 to the time d2 is calculated by a third formula;
the third formula is:
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CN115393732A (en) * 2022-08-19 2022-11-25 南京林业大学 Rice information extraction method based on MODIS time sequence image
CN116935214B (en) * 2023-06-27 2024-04-12 福建鼎旸信息科技股份有限公司 Space-time spectrum fusion method for satellite multi-source remote sensing data

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102915529A (en) * 2012-10-15 2013-02-06 黄波 Integrated fusion technique and system based on remote sensing of time, space, spectrum and angle
CN103325096A (en) * 2013-06-25 2013-09-25 中国科学院遥感与数字地球研究所 Method for reconstructing wide hyperspectral image based on fusion of multispectral/hyperspectral images
CN103345707A (en) * 2013-06-04 2013-10-09 中国科学院遥感与数字地球研究所 Crop maturation stage remote sensing prediction method based on multi-source remote sensing data
CN104715467A (en) * 2015-03-06 2015-06-17 中国科学院遥感与数字地球研究所 Improved multi-source remote sensing data space-time fusion method
CN105005978A (en) * 2015-07-15 2015-10-28 天津大学 Spectrum real-time filtering method based on Savitzky-Golay filter parameter optimization

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102915529A (en) * 2012-10-15 2013-02-06 黄波 Integrated fusion technique and system based on remote sensing of time, space, spectrum and angle
CN103345707A (en) * 2013-06-04 2013-10-09 中国科学院遥感与数字地球研究所 Crop maturation stage remote sensing prediction method based on multi-source remote sensing data
CN103325096A (en) * 2013-06-25 2013-09-25 中国科学院遥感与数字地球研究所 Method for reconstructing wide hyperspectral image based on fusion of multispectral/hyperspectral images
CN104715467A (en) * 2015-03-06 2015-06-17 中国科学院遥感与数字地球研究所 Improved multi-source remote sensing data space-time fusion method
CN105005978A (en) * 2015-07-15 2015-10-28 天津大学 Spectrum real-time filtering method based on Savitzky-Golay filter parameter optimization

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
"A UNIFIED FRAMEWORK FOR SPATIO-TEMPORAL-SPECTRAL FUSION OF REMOTE SENSING IMAGES";Xiangchao Meng 等;《2015 IEEE International Geosience and Remote Sensing Symposium(IGARSS)》;20151112;第4.3节 *
"An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions";Xiaolin Zhu 等;《Remote Sensing of Environment》;20101115;第114卷(第11期);第2611页左栏第1段、最后一段 *
"基于ＳＲＥＭ融合数据的矿物蚀变信息提取";王桂珍 等;《地球科学———中国地质大学学报》;20150831;第40卷(第8期);参见第1331页左栏第2-3段 *
"多源卫星数据在甘蔗干旱遥感监测中的应用";匡昭敏 等;《中国农业气象》;20071231;第28卷(第01期);第2段，第2.2-2.3、3.1节 *

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