CN114936765A - Agricultural drought index construction method considering spatial heterogeneity - Google Patents
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Abstract
The invention provides an agricultural drought index construction method considering spatial heterogeneity, which comprises the following steps of constructing a generalized agricultural drought index GSMSDI: determining agricultural drought index SMSDI corresponding to different thresholds; obtaining an MODIS13A3 product, and extracting monthly-scale normalized vegetation index data; obtaining MOD12Q1 products, and extracting a land cover data set of each year; extracting the optimal threshold value defining the drought index pixel by using an optimal threshold value extraction method; and obtaining the optimal threshold value of the agricultural drought index defined under different underlying surfaces. The invention provides an agricultural drought index construction method considering spatial heterogeneity, which is used for constructing a drought index based on time series soil moisture by means of vegetation index data, so that the index has a remarkable agricultural drought indication effect.
Description
Technical Field
The invention relates to the technical field of drought monitoring, in particular to an agricultural drought index construction method considering spatial heterogeneity.
Background
The drought index is an index indicating a drought state, and there are many drought indexes at present. In agricultural drought monitoring applications, the indices may be generally classified as precipitation-based indices, soil moisture or soil water balance-based indices, and vegetation growth status-based indices. These indices are focused on a certain or part of the agricultural drought process (precipitation-soil moisture-vegetation growth-crop yield).
Among the three agricultural drought indices above, an index based on precipitation, which generally defines the degree of drought in terms of precipitation, does not allow an efficient spread of the indicated drought conditions and ultimately affects the final crop yield, since, on the one hand, precipitation is lost by means of runoff or the like; on the other hand, different types of vegetation have different sensitivities to rainfall response under the influence of soil texture, vegetation root distribution and the like. The drought index defined based on the vegetation growth condition effectively reflects the influence of drought, but from the perspective of water management, when the vegetation has received the influence of drought, the significance for guiding water management and relieving agricultural drought is limited. Therefore, soil moisture-based indices are generally more useful, one being because soil moisture can effectively affect the growth of vegetation, which can be used to predict crop yield; and secondly, the occurrence of drought can be effectively warned by monitoring the soil moisture, and the method is very important for water management. In view of the fact that long-time-series soil moisture data can be widely obtained at present, development of drought indexes based on soil moisture time-series statistical data can be more widely applied.
In the current indexes based on soil moisture development, the definition of drought occurrence is not uniform, namely the threshold value is uncertain when the indexes are defined. Currently, a commonly used index determination method generally constructs an index by taking a certain quantile value as a threshold (for example, a 10 quantile value of historical time series data) based on historical statistical data. The index constructed by this method is spatially comparable, but considering that drought indicated by the drought index is ultimately manifested as crop yield, it is a better agricultural drought index only if it is well predictive of crop yield or vegetation productivity. Therefore, the drought index is defined as the difference in response of vegetation to drought, and heterogeneity exists in space. Therefore, the differences are fully considered, more reasonable drought indexes are extracted, and the method has important significance for drought early warning, crop yield prediction and the like.
The invention patent application with publication number CN113919146A discloses an agricultural drought index construction method based on soil quick-acting water, which adopts WDt index to construct agricultural drought index. A Modified Soil Water Deficit Index (MSWDI) suitable for agricultural drought monitoring was constructed based on root bed soil water content simulated by a crop growth model (wofors). Although the method also considers the vegetation type (such as root system depth and the like), the method needs simulation parameters and soil texture parameters, the parameters are difficult to obtain by using a remote sensing technology, and large uncertainty exists on a macroscopic scale. .
The invention patent application with publication number CN113095621A discloses an agricultural drought detection method based on soil moisture to meteorological time lag, developing a CADIi drought index, wherein the index simultaneously considers three parameters of precipitation, evapotranspiration and soil moisture, and the finally obtained index has a better comparison effect with SPEI, but does not consider the difference of vegetation response drought, namely does not consider spatial heterogeneity like the method.
Disclosure of Invention
In order to solve the technical problems, the invention provides an agricultural drought index construction method considering spatial heterogeneity, and the drought index is constructed by means of vegetation index data based on time series soil moisture, so that the index has a remarkable agricultural drought indication effect.
The invention provides an agricultural drought index construction method considering spatial heterogeneity, which comprises the following steps of constructing a generalized agricultural drought index GSMSDI:
step 1: determining agricultural drought index SMSDI corresponding to different thresholds;
step 2: obtaining an MODIS13A3 product, and extracting monthly-scale normalized vegetation index data;
and step 3: obtaining MOD12Q1 products, and extracting a land cover data set of each year;
and 4, step 4: extracting the optimal threshold value defining the drought index pixel by using an optimal threshold value extraction method;
and 5: and obtaining the optimal threshold value of the agricultural drought index defined under different underlying surfaces.
Preferably, the generalized agricultural drought index GMSDI is constructed based on time series soil moisture, and is defined as
Wherein, P sm Is the corresponding percentile value, T, of soil moisture in time sequence data 0 Is a percentile threshold value, and the generalized expression is the percentile T at the moment 0 Has not been determined.
In any of the above embodiments, it is preferable that when the soil moisture percentage value is lower than T 0 Time, GSMSDI<0, when the percentage value of the soil moisture is higher than T 0 Time, GSMSDI>And otherwise, GSMSDI is 0.
In any of the above schemes, the value range of the GSMSDI index is preferably-1, and the closer to 1, the wetter, and the closer to-1, the drought.
In any of the above schemes, preferably, the step 1 includes selecting T for the generalized drought index GSMSDI 0 And (5), 6,7, … and 50th, determining the corresponding agricultural drought index SMSDI under different thresholds every step 1, thereby generating a total of 46 time sequences.
In any of the above schemes, preferably, the step 2 includes calculating NDVI abnormality index NDVIA using NDVI data, where the NDVI abnormality index NDVIA is expressed by the formula
Wherein NDVIA i,j Is NDVI abnormality index of j month of i year, i is chronological order, j is j period of growth season, and growth season is 4-9Month, NDVI ave,j Is the mean of the years NDVI for the jth period.
In any of the above schemes, preferably, the step 2 further includes obtaining the year-scale NDVIA by calculating a month average value after the month-scale NDVIA is obtained by calculation.
In any of the above schemes, preferably, the optimal threshold extraction method is to calculate the correlation between the multi-year time series SMSDI data and the NDVIA data under different threshold conditions, and a corresponding threshold when the correlation between the two reaches a maximum value is taken as an optimal threshold for defining the drought index.
In any of the above schemes, preferably, the calculation formula of the correlation r is:
wherein, SMSDI i Is year i SMSDI, NDVIA i NDVIA in year i;
is the average value of the SMSDI,
respectively the average values of NDVIA; r is max For the maximum correlation coefficient, n is the number of years.
In any of the above schemes, preferably, the maximum correlation coefficient r is max Is calculated by the formula
r max ＝max{r T0＝5th ,r T0＝6th ,r T0＝7th ,…,r T0＝50th }
Wherein r is T0＝5th ,r T0＝6th ,r T0＝7th ,…,r T0＝50th Is when T 0 And (3) taking correlation coefficients of the SMSDI and the NDVIA obtained by calculation when the 5,6,7, … and 50 percentile values are taken respectively, wherein max is a function of the maximum value in the set.
In any of the above solutions, preferably, the step 5 includes the following sub-steps:
step 51: respectively determining an optimal threshold value pixel by pixel;
step 52: calculating the mean value of the optimal threshold values of all pixels under a specific underlying surface by combining land coverage data;
and step 53, determining an optimal threshold value defined for the drought index of the specific underlying surface by using the average value.
In any of the above schemes, preferably, the step 51 is to determine the optimal threshold value by pixel using the optimal threshold value extraction method.
The invention provides an agricultural drought index construction method considering spatial heterogeneity, and overfitting problems caused by insufficient micro-expression data sets can be made up by performing transfer learning from macro expression. Existing macroexpression and microexpression data sets can meet experimental requirements after using migration learning.
The MODIS13A3 product refers to a Vegetation Index product provided by a Terra/Aqua medium resolution imaging spectrometer, and comprises a Normalized Difference Vegetation Index (NDVI) with a spatial resolution of 1 km and a temporal resolution of months. Other satellites or reanalysis vegetation index products can also be utilized, and the corresponding vegetation index can reflect the vegetation growth condition.
The MOD12Q1 product refers to a land cover product provided by a resolution imaging spectrometer in Terra/Aqua, and comprises a land cover data set with a spatial resolution of 1 kilometer, and one land cover classification image per year. Other satellites or re-analysis of the land cover data set may also be used in order to obtain the land cover type.
NDVI, Normalized Difference Vegetation Index, refers to the monthly-scale Normalized Vegetation Index.
Drawings
Fig. 1 is a flowchart of a preferred embodiment of the agricultural drought index construction method considering spatial heterogeneity according to the present invention.
Fig. 2 is a schematic diagram showing changes of SMSDI corresponding to different thresholds extracted from soil moisture in a time series according to a free embodiment of the agricultural drought index construction method considering spatial heterogeneity of the present invention.
Fig. 3 is a schematic diagram of the change situation of the correlation coefficient between NDVIA and SMSDI under a certain pixel along with the difference of the SMSDI threshold value according to a preferred embodiment of the agricultural drought index construction method considering spatial heterogeneity of the present invention.
FIG. 4 is a statistical schematic diagram of the number of optimal threshold pixels under the area scale according to a preferred embodiment of the agricultural drought index construction method considering spatial heterogeneity of the present invention.
Fig. 5 is a schematic diagram comparing change curves of SPEI and SMSDI at three sites according to a preferred embodiment of the agricultural drought index construction method considering spatial heterogeneity of the present invention.
Detailed Description
The invention is further illustrated with reference to the figures and the specific examples.
Example one
As shown in FIG. 1, step 100 is performed to construct a generalized agricultural drought index GSMSDI based on time series soil moisture, the generalized agricultural drought index GMSDI being defined as
Wherein, P sm Is the corresponding percentile value, T, of soil moisture in time sequence data 0 Is a percentile threshold value, and the generalized expression is the percentile T at the moment 0 Has not been determined. When the percentage value of soil moisture is lower than T 0 Time, GSMSDI<0, when the percentage value of the soil moisture is higher than T 0 Time, GSMSDI>And otherwise, GSMSDI is 0. The value range of the GSMSDI index is-1, and the closer to 1, the wetter the GSMSDI index is, and the closer to-1, the drought the GSMSDI index is.
Executing step 110, determining agricultural drought index SMSDI corresponding to different thresholds, and selecting T aiming at generalized drought index GSMSDI 0 And (5), 6,7, … and 50th, determining the corresponding agricultural drought index SMSDI under different thresholds every step 1, thereby generating a total of 46 time sequences.
And executing the step 120, obtaining a MODIS13A3 product, and extracting month scale normalized vegetation index data. Calculating the NDVI abnormality index NDVIA using the NDVI data by the formula
Wherein NDVIA i,j Is NDVI abnormality index of j month of i year, i is chronological order, j is j period of growth season, growth season is 4-9 months, NDVI ave,j Is the mean of the years NDVI for the jth period.
After the month scale NDVIA is calculated, the year scale NDVIA is then obtained by calculating the average value of the month (only growth season phases are considered, i.e. months 4-9).
Executing step 130, obtaining MOD12Q1 products, and extracting a land cover data set of each year;
executing step 140, extracting the optimal threshold defining the drought index pixel by using an optimal threshold extraction method, wherein the optimal threshold extraction method is to calculate the correlation between the many-year time series SMSDI data and the NDVIA data under different threshold conditions (46 thresholds corresponding to the 46 time series generated in step 110), the threshold corresponding to the maximum correlation is used as the optimal threshold defining the drought index, and the calculation formula of the correlation r is as follows:
wherein, SMSDI i Is year i SMSDI, NDVIA i NDVIA in year i;
is the average value of the SMSDI,
respectively, the average values of NDVIA; r is max For the maximum correlation coefficient, n is the number of years.
The maximum correlation coefficient r max Is calculated by the formula
r max ＝max{r T0＝5th ,r T0＝6th ,r T0＝7th ,…,r T0＝50th }
Wherein r is T0＝5th ,r T0＝6th ,r T0＝7th ,…,r T0＝50th Is when T 0 And (3) taking correlation coefficients of the SMSDI and the NDVIA obtained by calculation when 5,6,7, … and 50 percentile values are respectively taken, wherein max is a function of the maximum value in the set.
Executing step 150, obtaining the optimal threshold value of the agricultural drought index defined under different underlying surfaces, comprising the following substeps: step 151 is executed to determine optimal threshold values pixel by using the optimal threshold value extraction method; step 152 is executed, and the average value of the optimal threshold values of all the pixels under a specific underlying surface is counted by combining the land coverage data; step 153 is performed to determine the optimal threshold value defined for the drought index of the particular underlying surface from the mean value.
Example two
The invention aims to determine an index construction method considering spatial heterogeneity. By the method, the drought index is constructed by means of vegetation index data based on time series soil moisture, so that the index has a remarkable agricultural drought indication effect.
In order to achieve the above object, the present invention provides the following solutions:
S1A Generalized agricultural drought Index (GSMSDI) is first constructed based on the time series Soil moisture. The drought index is defined as follows:
in the formula, P sm The corresponding percentile value of the soil moisture in the time sequence data is obtained; t is 0 Is a percentile threshold value, when the soil moisture percentile value is lower than the threshold value, the GSMSDI<0, GSMSDI when the soil moisture percentage value is higher than the threshold value>0, otherwise, GSMSDI ═ 0. The value range of the GSMSDI index is-1, and the closer to 1, the wetter and the closer to-1, the drought. The generalized agricultural drought index has an uncertain drought threshold.
S2A generalized drought index definition formula mentioned in S1 is adopted, and T is selected 0 The value of (A) is from 5 to 50, the value is taken every 1 step, and finally, the corresponding agricultural drought Index (SMSDI) under different thresholds is determined. This gives a total of 46 time sequences (T) 0 5,6,7, …,50th), as shown in fig. 2, 5 of which time series, i.e., T, are selected 0 10,20,30,40 and 50th, and therefore, the drought index time series obtained by different threshold values have differences, and the judgment of the 'when to enter the drought state' (SMSDI) by different threshold values is determined<0) With a greater effect).
S3 obtains MODIS13A3 product, and extracts monthly Normalized Difference Vegetation Index (NDVI) data. The NDVI anomaly index (NDVI) is then calculated by the following formula using the NDVI data.
Wherein i is the chronological order; j is the jth period (month) of the growing season (months 4-9); NDVIA i,j NDVI anomaly index for the jth period of year i. NDVI ave,j Mean values of the years NDVI for the jth period. The NDVI abnormality index obtained by the formula (2) can eliminate the influence of the growth of the plant in the growing season, so that the indexes can be compared in time. After the monthly NDVIA is calculated, the annual NDVIA is then obtained by calculating the monthly mean (only growth season phases are considered, i.e. months 4-9).
S4 obtains MOD12Q1 product, extracts the year land cover data sets.
S5 extracting the optimal threshold value of the drought index by pixel: and calculating the correlation between the SMSDI data and the NDVIA data of the time series of years, and taking a corresponding threshold value when the correlation between the SMSDI data and the NDVIA data reaches the maximum value as an optimal threshold value for defining the index. As shown in fig. 3, a certain pixel of a grassland with an underlying surface is selected, the correlation between the SMSDI and the NDVIA shows unimodal variation characteristics along with the variation of the threshold, when the threshold T0 is 25th, the correlation between the drought index and the vegetation growth condition is strongest, the threshold at this time is defined as an optimal threshold, and the method ensures that the drought index determined by the selected threshold has the greatest influence on the vegetation.
S6 obtaining the optimal threshold value of the agricultural drought index defined under different underlying surfaces. The heterogeneity of each pixel is also large due to the optimal threshold determined under the area scale, and the method determines the optimal threshold by the method mentioned in S5 respectively pixel by pixel, and then calculates the average value of the optimal thresholds of all pixels under a specific underlying surface type. As shown in fig. 4, the statistical result of the inner Mongolia region shows that the optimal threshold of most pixels under the grassland type is 10-19th, and the average value is 18th, then the optimal threshold defined by the drought index corresponding to the underlying surface of the grassland is 18 th).
Verification and comparison of the results of S7. As shown in fig. 5, three sites are selected, and compared with the widely used SPEI index, the SMSDI and the SPEI are found to have more consistent trend and better correlation. Meanwhile, the index has strong correlation with the vegetation growth condition and is simple to calculate. For a better understanding of the present invention, the foregoing detailed description has been given in conjunction with specific embodiments thereof, but not with the intention of limiting the invention thereto. Any simple modifications of the above embodiments according to the technical essence of the present invention still fall within the scope of the technical solution of the present invention. In the present specification, each embodiment is described with emphasis on differences from other embodiments, and the same or similar parts between the respective embodiments may be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
Claims (10)
1. An agricultural drought index construction method considering spatial heterogeneity comprises the step of constructing a generalized agricultural drought index GSMSDI, and is characterized by further comprising the following steps:
step 1: determining agricultural drought index SMSDI corresponding to different thresholds;
step 2: obtaining an MODIS13A3 product, and extracting monthly-scale normalized vegetation index data;
and 3, step 3: obtaining MOD12Q1 products, and extracting a land cover data set of each year;
and 4, step 4: extracting the optimal threshold value of the defined drought index pixel by using an optimal threshold value extraction method;
and 5: and obtaining the optimal threshold value of the agricultural drought index defined under different underlying surfaces.
2. The method of constructing an agricultural drought index taking into account spatial heterogeneity according to claim 1, wherein the generalized agricultural drought index GMSDI defined as GMSDI is constructed based on time-series soil moisture
Wherein, P sm Is the corresponding percentile value, T, of soil moisture in time sequence data 0 Is a percentile threshold.
3. The method for constructing an agricultural drought index considering spatial heterogeneity according to claim 2, wherein when the soil moisture percentage value is lower than T 0 Time, GSMSDI<0, when the percentage value of the soil moisture is higher than T 0 Time, GSMSDI>And otherwise, GSMSDI is 0.
4. The method for constructing the agricultural drought index considering spatial heterogeneity according to claim 3, wherein the GSMSDI index value range is-1, and the closer to 1, the wetter, and the closer to-1, the more drought.
5. The method for constructing an agricultural drought index taking into account spatial heterogeneity according to claim 4, wherein the step 1 comprises selecting T for the generalized drought index GSMSDI 0 5,6,7, … and 50th, taking values every 1 step, determining corresponding agricultural drought indexes SMSDI under different thresholds, and generating 46 time sequences in total。
6. The method for constructing an agricultural drought index taking spatial heterogeneity of claim 5, wherein step 2 comprises calculating NDVI abnormality index NDVIA by using NDVI data, and the formula of NDVI abnormality index NDVIA is
Wherein NDVIA i,j Is NDVI abnormality index of j month of i year, i is chronological order, j is j period of growth season, growth season is 4-9 months, NDVI ave,j Is the mean of the years NDVI for the jth period.
7. The method for constructing an agricultural drought index taking spatial heterogeneity into account of claim 6, wherein the step 2 further comprises obtaining the annual NDVIA by calculating a monthly average after the monthly NDVIA is obtained by calculation.
8. The agricultural drought index construction method considering spatial heterogeneity as claimed in claim 7, wherein the optimal threshold extraction method is to calculate correlations between the multi-year time series SMSDI data and NDVIA data under different threshold conditions, and the corresponding threshold when the correlations between the SMSDI data and the NDVIA data reach the maximum value is used as the optimal threshold for defining the drought index.
9. The agricultural drought index construction method considering spatial heterogeneity as claimed in claim 8, wherein the step 5 comprises the sub-steps of:
step 51: respectively determining an optimal threshold value pixel by pixel;
step 52: calculating the mean value of the optimal threshold values of all pixels under a specific underlying surface by combining land coverage data;
and step 53, determining an optimal threshold value defined for the drought index of the specific underlying surface by using the average value.
10. The method for constructing an agricultural drought index taking into account spatial heterogeneity according to claim 9, wherein said step 51 is to determine an optimal threshold value pixel by pixel using said optimal threshold value extraction method.
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