CN112730465A - Agricultural drought monitoring method for SMAP L waveband brightness temperature - Google Patents
Agricultural drought monitoring method for SMAP L waveband brightness temperature Download PDFInfo
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Abstract
The invention discloses an agricultural drought monitoring method based on SMAP L-band light temperature, which is characterized by utilizing soil moisture, soil temperature and vegetation information carried in the L-band light temperature to represent a drought state and construct a Standardized Microwave bright temperature drought index (SMTBDI). Which comprises the following steps: acquiring SMAP brightness temperature time sequence data of each grid in the area at a certain time scale in a certain month for many years; performing Gaussian distribution inspection on the time series data, and judging whether the independent sample corresponding to each grid conforms to Gaussian distribution or not; calculating SMTBDI for the grid passing the Gaussian distribution test; and judging the drought degree according to the SMTBDI value, wherein the smaller the index value is, the more drought is.
Description
Technical Field
The invention belongs to the technical field of drought monitoring, and particularly discloses a drought monitoring method based on SMAP L waveband brightness temperature. According to the method, soil moisture, soil temperature and vegetation information carried in L-band light temperature are used for representing the drought state, and a Standardized Microwave bright temperature drought index (SMTBDI) is constructed.
Background
Soil moisture is an important data source for agricultural drought monitoring. The lack of rainfall causes the soil water to be deficient, the plants can not absorb sufficient water, and the healthy growth of the plants can be threatened. Therefore, soil moisture is a key and timely monitoring index as a first characterization of drought. Among soil moisture products, the microwave remote sensing soil moisture product is a very superior product. The passive microwave data processing is simple, the algorithm is mature, the precision is relatively high, the coverage area is large, and the time resolution is small. The SMAP soil moisture product covers the whole world, and the drought condition of China can be monitored in time by detecting the soil moisture product through the L wave band. In recent years, many scholars have begun to investigate drought monitoring based on SMAP soil moisture dynamics. However, drought monitoring only by using the soil moisture of the SMAP has certain limitation, which is mainly reflected in two aspects of the penetration depth of the bright temperature of the SMAP and the inversion accuracy of the soil moisture of the SMAP. Soil moisture based on microwave brightness temperature has poor inversion accuracy in open water areas, steep terrains, dense vegetation areas, snow coverage areas and the like, and auxiliary information such as vegetation, roughness and the like also influences the soil moisture inversion accuracy. For example, in the southern area of China, the vegetation coverage is high, the clay content is high, the microwave brightness temperature penetration depth is low, meanwhile, the water area is rich, the mountainous area is large, and the soil moisture inversion accuracy is reduced due to the complex microwave brightness temperature of the earth surface. In addition, the surface soil moisture content is an important factor in the germination stage of crops, but the root zone soil moisture becomes more important for irrigation management and yield evaluation and other scenes. Therefore, it is necessary to explore another drought monitoring idea based on the SMAP product and make up for the shortcomings of the drought monitoring method based on the SMAP soil moisture.
Disclosure of Invention
The L-band bright temperature in the SMAP product carries the water content information of a soil system, and also carries the water content of the soil temperature and the water content of a vegetation system, and the three are important factors of agricultural drought, so that the L-band bright temperature can be directly used as an agricultural drought monitoring data source. The change rule of the three and the microwave brightness temperature is as follows: the water content of the soil is reduced, the emissivity of the soil is increased, and the microwave brightness temperature is increased; the water content of the vegetation is increased, the soil emissivity is increased, and the microwave brightness temperature is increased; soil temperature increases, soil emissivity decreases, but soil emission also increases, and the total result is drought that increases the microwave light temperature. In addition, compared with the prior art, the high-frequency microwave band is severely changed by plant growth, and the low-frequency microwave band is more sensitive to the information of soil water content and vegetation water content. The SMAP L wave band frequency is lower, so that the lower SMAP L wave band brightness temperature can reflect the soil moisture dynamic of deeper layers and can also participate in the construction of agricultural drought indexes. The time sequence standardization method is an important method for constructing the drought index, and tests show that compared with SMAP soil moisture, SMAP brightness and temperature are more in accordance with Gaussian distribution in a plurality of experimental areas, the standardization drought index can be constructed, and drought monitoring is realized.
The invention aims to provide an agricultural drought monitoring method, which is used for directly monitoring drought based on SMAP L-band bright temperature, standardizing the L-band bright temperature, constructing a standard microwave bright temperature agricultural drought index (SMTBDI) to represent a drought state, and rapidly monitoring agricultural drought.
The technical scheme adopted by the invention is as follows:
an agricultural drought monitoring method directly based on SMAP L waveband brightness temperature is characterized by comprising the following steps:
and (1) acquiring SMAP brightness temperature time sequence data of each grid in the region at a certain time scale in a certain month for many years to form an independent sample for each grid. The step (1) may include the steps of:
step (11), acquiring a certain month of SMAP product for many years;
step (12), geometric correction and cutting are carried out on the SMAP product, and SMAP L wave band brightness temperature data in the region are extracted;
and (13) selecting time scales (such as a 3-day scale, a 7-day scale and a month scale), calculating an average value of the SMAP brightness and temperature in the time scales, and acquiring SMAP brightness and temperature time series data of each grid in the region in a certain time scale.
And (2) carrying out Gaussian distribution detection on the time sequence data in the step (1) and judging whether the independent sample corresponding to each grid conforms to Gaussian distribution or not. The SMAP brightness and temperature time sequence data of a certain time scale in the same month in different years are assumed to follow Gaussian distribution, and the land vegetation type and the agricultural planting structure in each grid are assumed to be negligible; here, the simultaneous time scale time series data for each grid constitutes an independent sample, rather than data for all months of all grids over a large range, to avoid introducing errors due to factors such as climate, land cover, etc. The step (2) may include the steps of:
and (21) carrying out Gaussian distribution test on the SMAP brightness temperature independent sample. The following methods may be employed:
judging whether the sample accords with Gaussian distribution by using Shapiro-Wilk test, and simultaneously, making a zero hypothesis as that the sample accords with the Gaussian distribution; the Shapiro-Wilk test is a correlation-based method, similar to the linear regression method, and the closer the calculated correlation coefficient is to 1, the better the sample data fits to the Gaussian distribution.
And (22) screening the grids passing the Gaussian distribution test in the region. The following methods may be employed:
selecting a significance level of alpha being 0.05 as a judgment basis for judging whether the significance level accords with Gaussian distribution; if the p value is less than 0.05, rejecting a null hypothesis, and proving that the sample data does not accord with Gaussian distribution; if the p value in the Shapiro-Wilk test is greater than 0.05, a null hypothesis is received, demonstrating that the sample data conforms to a Gaussian distribution.
And (3) calculating a standardized microwave bright temperature drought index (SMTBDI) aiming at the grids passing the Gaussian distribution test. The step (3) may include the steps of:
step (31), calculating the standard deviation and the average value of the SMAP brightness temperature time series data in the step (1);
step (32), calculating the SMTBDI value by using the mean value and the standard deviation in the step (31), wherein the specific formula is as follows:
wherein Tb is a value in the SMAP brightness temperature independent samples in the step (1),
is the mean value of the independent samples in the step (1), and sigma is the standard deviation of the independent samples in the step (1).
Step (4), calculating SMTBDI value of the grid screened in the step (3), and judging drought degree, wherein the smaller the index value is, the more drought is, the specific way of judging the drought grade is as follows:
TABLE 1 SMTBDI drought grade grading Standard
The method is used for drought monitoring directly based on SMAP L-band brightness temperature, and a standard microwave brightness temperature agricultural drought index (SMTBDI) is constructed to represent the drought state by standardizing the L-band brightness temperature. The method is a simple, rapid and effective agricultural drought monitoring method, and can also be used for monitoring drought in combination with other drought indexes to make up for the defects of a single drought monitoring method. Compared with the prior art, the invention has the following characteristics and beneficial effects:
1. the single drought monitoring method has limitations, an agricultural drought monitoring method directly based on the SMAP L-band light temperature is constructed, a standard microwave light temperature agricultural drought index (SMTBDI) is constructed by utilizing the Gaussian distribution characteristic of the L-band light temperature and the negative correlation characteristic of the microwave light temperature and the drought state, the standard microwave light temperature agricultural drought index (SMTBDI) can be mutually compensated with the monitoring method based on the SMAP soil moisture, different drought information is reflected from different angles, and drought monitoring, forecasting and early warning are realized.
2. In addition, the drought index construction method avoids the inversion process of the soil moisture, directly calculates the SMAP bright temperature data, avoids the uncertainty problem of the precision in soil moisture inversion, and is a simpler, quicker and near-real-time agricultural drought monitoring method.
Drawings
FIG. 1 is a flow chart of drought index calculation based on SMAP brightness temperature proposed by the present invention;
FIG. 2 is an overview of the study area in accordance with an embodiment of the present invention; wherein part (a) is a spatial distribution map of a research area, and part (b) is a land utilization coverage map of the research area;
FIG. 3 is a Gaussian distribution-compliant grid spatial distribution diagram for an embodiment of the present invention, with a month of 8 months;
FIG. 4 is a graph of the SMTBDI drought monitoring profile at 8 months in 2015-2019 of an embodiment of the invention. Wherein (a) the segment is the SMTBDI drought monitoring profile at 8 months of 2015, (b) the segment is the SMTBDI drought monitoring profile at 8 months of 2016, (c) the segment is the SMTBDI drought monitoring profile at 8 months of 2017, (d) the segment is the SMTBDI drought monitoring profile at 8 months of 2018, and (e) the segment is the SMTBDI drought monitoring profile at 8 months of 2019.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments. It should be noted that the embodiments described herein are only for illustrating the present invention and are not to be construed as limiting the present invention.
According to the drought monitoring method based on the SMAP L-band light temperature, soil moisture, soil temperature and vegetation information carried in the SMAP L-band light temperature are used for representing the drought state, the drought monitoring method based on the SMAP L-band light temperature is constructed, and the drought is monitored by using a standardized microwave light temperature drought index (SMTBDI), so that the drought monitoring method is very convenient and fast, and is very important for drought forecast and early warning.
The drought detection method of the present invention is further described below with reference to the study area shown in fig. 2. It comprises the following steps:
step (1), acquiring SMAP brightness temperature time sequence data of each grid in a certain time scale in a certain month for many years in an area to form an independent sample for each grid;
step (2), carrying out Gaussian distribution inspection on the time sequence data in the step (1), and judging whether the independent sample corresponding to each grid conforms to Gaussian distribution or not;
step (3), calculating a standardized microwave bright temperature drought index (SMTBDI) aiming at the grids passing the Gaussian distribution test;
and (4) calculating the SMTBDI value of the grid screened in the step (3), and judging the drought degree, wherein the smaller the index value is, the more drought is.
The embodiment of the invention mainly selects the southern area of China as a test area, and the L-waveband brightness temperature product selects an SMAP L3_ SM _ P _ E product (9km) in SMAP (soil Moisture Active passive) soil Moisture, the influence of RFI on China is large, the influence of RFI on the soil Moisture product is relieved by the SMAP, the precision is relatively high, and the method is suitable for the drought research of China.
The process of the invention is shown in figure 1 and comprises the following steps:
step (1), taking southern China as a main research area, and obtaining the SMAP bright-warm month scale time sequence data of 8 months in 2019 on each grid 2015-and-year basis, wherein the overview of the research area is shown in FIG. 2.
And (11) obtaining the SMAP product of 8 months in 2015-2019.
And (12) performing geometric correction and cutting on the SMAP product, and extracting SMAP L wave band brightness temperature data in the research area.
And (13) selecting a month scale as a time scale, selecting 8 months, and obtaining the average value of the SMAP brightness temperature of 2015-year 2019-month 8 of each grid in the research area to form an independent sample with 5 numerical values.
And (2) carrying out Gaussian distribution test on the independent sample composed of each grid in the step (1).
And (21) judging whether the Gaussian distribution is met by using a Shapiro-Wilk test in the Gaussian distribution test process.
And (22) screening the grids passing the Gaussian distribution test in the region, wherein the spatial distribution of the grids conforming to the Gaussian distribution is shown in figure 3.
And (3) calculating a standardized microwave bright temperature drought index (SMTBDI) aiming at the grids passing the Gaussian distribution test.
And (31) calculating the standard deviation and the average value of the SMAP brightness temperature time series data in the step (1).
And (32) calculating the SMTBDI value by using the mean value and the standard deviation in the step (31), wherein the SMTBDI spatial distribution is as shown in the figure 4.
And (4) calculating the SMTBDI value of the grid screened in the step (3), and judging the drought degree, wherein the smaller the index value is, the more drought is.
Claims (5)
1. An agricultural drought monitoring method based on SMAP L waveband brightness temperature is characterized by comprising the following steps:
step (1), acquiring SMAP brightness temperature time sequence data of each grid in a certain time scale in a certain month for many years in an area to form an independent sample for each grid;
step (2), carrying out Gaussian distribution inspection on the time sequence data in the step (1), and judging whether the independent sample corresponding to each grid conforms to Gaussian distribution or not;
step (3), calculating a standardized microwave bright temperature drought index (SMTBDI) aiming at the grids passing the Gaussian distribution test;
and (4) calculating the SMTBDI value of the grid screened in the step (3), and judging the drought degree, wherein the smaller the index value is, the more drought is.
2. The agricultural drought monitoring method based on the SMAP L waveband brightness temperature as claimed in claim 1, wherein: the step (1) of obtaining the SMAP brightness and temperature time sequence data of a certain time scale in a certain month for many years of each grid in the region to form an independent sample for each grid specifically comprises the following substeps:
step (11), acquiring a certain month of SMAP product for many years;
step (12), geometric correction and cutting are carried out on the SMAP product, and SMAP L wave band brightness temperature data in the region are extracted;
and (13) selecting a time scale, calculating an SMAP brightness-temperature average value in the time scale, and acquiring SMAP brightness-temperature time sequence data of each grid in the region in a certain time scale.
3. The agricultural drought monitoring method based on the SMAP L waveband brightness temperature as claimed in claim 1, wherein: performing Gaussian distribution inspection on the time sequence data in the step (1), and judging whether the independent sample corresponding to each grid conforms to Gaussian distribution, wherein the method specifically comprises the following substeps:
step (21), performing Gaussian distribution inspection on the SMAP brightness temperature independent sample, wherein the method specifically comprises the following steps:
judging whether the sample accords with Gaussian distribution or not by using Shapiro-Wilk test, and meanwhile, making a zero hypothesis that the sample accords with the Gaussian distribution, wherein the more the calculated correlation coefficient is close to 1, the better the sample data is fitted with the Gaussian distribution;
step (22), the grids passing the Gaussian distribution test in the region are screened, and the specific method is as follows:
if the p value in the Shapiro-Wilk test is smaller than the threshold value, rejecting a zero hypothesis, and proving that the sample data does not accord with Gaussian distribution; if the p value in the Shapiro-Wilk test is greater than the threshold, a null hypothesis is received, confirming that the sample data conforms to a Gaussian distribution.
4. The agricultural drought monitoring method based on the SMAP L-band brightness temperature as claimed in claim 1, wherein the significance level of α -0.05 is selected as the criterion for judging whether the Gaussian distribution is satisfied, and the threshold value of the P value is 0.05.
5. The agricultural drought monitoring method based on the SMAP L-band brightness temperature as claimed in claim 1, wherein SMTBDI score is calculated for the grid screened in step (3), and the drought degree is judged, and the specific way of judging the drought grade is that the smaller the index value is, the more drought is:
TABLE 1 SMTBDI drought grade grading Standard
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