CN115855841B - Summer corn drought unmanned aerial vehicle rapid monitoring and distinguishing method based on leaf area index - Google Patents
Summer corn drought unmanned aerial vehicle rapid monitoring and distinguishing method based on leaf area index Download PDFInfo
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Abstract
The invention provides a summer corn drought unmanned aerial vehicle rapid monitoring and distinguishing method based on leaf area indexes, which comprises the following steps: 1) Multispectral images and ground actual measurement foliar indexes (LAI) obtained through the unmanned aerial vehicle multi-load low-altitude remote sensing technology are calculated, and NDVI, EVI, SAVI and TVI vegetation indexes are calculated; 2) The vegetation index and the actually measured leaf area index are selected to construct regression equations in different growth periods, and the regression equation with the highest correlation in each growth period is selected as the LAI calculation optimal model equation in the growth period; 3) Inverting the LAI of each period according to an optimal model equation, and calibrating leaf area index thresholds among different drought grades; 4) And calculating a required vegetation index by acquiring multispectral images of the land block to be detected through real-time monitoring, inverting to obtain an LAI value, comparing the LAI value with a threshold value, and judging the real-time drought grade. The method is used for monitoring and judging the drought of summer corns, and has the advantages of high accuracy, high speed and strong practicability.
Description
Technical Field
The invention belongs to the technical field of drought monitoring, and particularly relates to summer corn drought monitoring, in particular to a rapid monitoring and judging method of a summer corn drought unmanned aerial vehicle based on leaf area indexes.
Background
Drought is a main natural factor affecting the growth and the yield of crops, and the long-term and large-range drought can cause the crops to greatly reduce the yield. Precipitation and irrigation are main sources of moisture in the growth and development process of crops, corn is a water-loving crop and is sensitive to water stress, and water deficiency can inhibit normal physiological metabolism of the corn, influence normal growth and development of the corn, reduce the yield by 20% -30% in general years, reduce the yield by a large area in serious years, and even prevent the granule from being harvested. The agricultural infrastructure of China is weak, the irrigation technology and the system are not perfect, and the traditional mode of flood irrigation is still adopted in most areas at present, so that the irrigation is uneven, and the water resource utilization efficiency is low. The corn has different water demand at different stages, the corn has small plants and slow growth, the water demand on the corn is less, the soil moisture is controlled to be about 60% of the water holding capacity in the field, the corn heading period to the milk ripe period are the rapid plant growth period, the corn is very sensitive to the moisture, the water demand in the heading and flowering period reaches the highest value, the water deficiency can cause serious influence, the yield of the corn is reduced by 20% -50%, the water demand in the ripe period is only 4% -7%, and therefore, the normal growth of the corn plant can be ensured by timely irrigation according to the different water demand of the corn. The soil moisture condition can reflect the vegetation moisture condition in the corn growth and development process, if the soil moisture is insufficient, the physiological water required by crops is not supplied, the normal growth and development and the production capacity of the crops are seriously affected, the inhibition effect is represented by physiological parameters and appearance forms, however, the change of the parameters is quite complex, the time and the labor are wasted, the space representativeness is poor and plants are easy to damage when the parameters are simply observed by manpower, and the vegetation is difficult to observe effectively and accurately for a long time.
At present, corn drought monitoring is mainly implemented through soil moisture content monitoring and evaluation, vegetation indexes and other remote sensing monitoring, a physical mechanism is not clear enough, time and labor are wasted, and large-scale drought monitoring precision is low, and monitoring efficiency is low.
The remote sensing technology can better reflect the change of soil moisture, can rapidly, efficiently and nondestructively acquire drought information, but the remote sensing image is influenced by factors such as resolution, weather, revisit period and the like, and the problems of data loss, time discontinuity, low spatial resolution and the like are easily caused. The vegetation drought remote sensing monitoring method has two major types, namely vegetation drought monitoring based on moisture content, and the other type is the remote sensing drought index method which is most widely applied in the actual drought monitoring system at present. The index can better reflect the change of soil moisture, but due to insufficient mechanical property of the agricultural drought remote sensing monitoring of the remote sensing index and the lack of the agricultural drought remote sensing monitoring index, the deep application of the agricultural drought remote sensing monitoring is limited.
Crop monitoring based on unmanned aerial vehicle remote sensing is now becoming the key point of research, and unmanned aerial vehicle can acquire high-precision remote sensing data at any time, can exert the advantage of fine scale in farmland and dynamic continuous monitoring based on unmanned aerial vehicle's low altitude remote sensing technology, can provide quick and convenient service for information monitoring in the field area.
The low-altitude remote sensing monitoring based on the unmanned aerial vehicle platform has the unique technical advantages of regional monitoring, high space-time resolution, cloud operation, flexibility, low cost, rapidness, high efficiency and the like, can realize fine-scale and dynamic continuous monitoring of farmlands, and is widely applied to drought monitoring. However, the existing unmanned aerial vehicle drought monitoring is mainly concentrated on onions, fruit trees and the like, is relatively few in field crop monitoring, and cannot meet the requirement of agricultural precise irrigation. At present, the unmanned aerial vehicle is used for monitoring summer corns in a low-altitude remote sensing mode, the leaf area index research for defining different drought grade thresholds is relatively less, and the unmanned aerial vehicle has the defect of water for crop irrigation.
Disclosure of Invention
The invention aims at: based on the technical defects, the method for rapidly monitoring and judging the drought of the summer corns through the leaf surface index analysis is provided, and the detection precision and the monitoring efficiency are improved under different drought conditions.
The purpose of the invention is realized in the following way:
a summer corn drought unmanned aerial vehicle rapid monitoring and distinguishing method based on leaf area indexes comprises the following steps:
1) And (3) data acquisition: the method comprises the steps of obtaining multispectral image data and ground actual measurement foliar index data through an unmanned aerial vehicle multi-load low-altitude remote sensing technology: calculating NDVI, EVI, SAVI and TVI vegetation indexes through multispectral image data;
2) Constructing Leaf Area Index (LAI) inversion models of different drought degrees at each growth stage of summer corns:
removing soil background by using a collaborative registration NDVI-OTSU method, extracting pixel values of pure vegetation indexes of a summer corn canopy, selecting NDVI, EVI, SAVI and TVI vegetation indexes and actually measured leaf area indexes respectively to construct 3 types of regression equations in different growth periods, and selecting a regression equation with highest correlation with the Leaf Area Index (LAI) in each growth period as an optimal model equation of the growth period; the different growth periods refer to corn: a jointing period, a heading period, a spinning period and a maturation period; the 3 types of regression equations are linear equations, exponential equations and logarithmic equations;
3) Rating of leaf area index thresholds for different drought classes: inverting Leaf Area Indexes (LAI) of each period according to the optimal model equation of each growth period obtained in the step 2), and calibrating leaf area index thresholds among different drought grades;
4) Real-time discrimination of drought level: the multispectral image of the land block to be detected is obtained through real-time monitoring, a required vegetation index is calculated, the vegetation index is carried into an optimal model equation of the corresponding growth period which is set in the step 2) to be inverted to obtain a Leaf Area Index (LAI) value, and then the leaf area index value is compared with a threshold value of the period in the step 3) to judge the real-time drought grade.
Further optimizing, step 1) the multispectralObtaining image data: integrated carrying MicaSense RedEdge-M of four-rotor unmanned aerial vehicle applying FL-81 TM The multispectral camera acquires multispectral aerial photo, the flying height is set to be 55m, the corresponding ground resolution is 4cm, and the wavelength which can be acquired by the lens is respectively blue light wave band, green light wave band, red side wave band and near infrared wave band.
Further, the measured leaf area index in step 1): the leaf area index value was calculated using the portable plant canopy analyzer LAI-2200C of LI-COR, 20 measurements per plot, with 20 measurements as input data.
Further, in the step 2), the optimal model equation in the jointing period is an exponential regression equation of EVI and LAI, the optimal model equation in the heading period is a linear regression equation of NDVI and LAI, the optimal model equation in the spinning period is an exponential regression equation of SAVI and LAI, the optimal model equation in the maturation period is a linear regression equation of TVI and LAI, and the LAI represents a leaf area index.
Further, the leaf area index threshold value between different drought levels rated in step 3) is: leaf area index for the jointing period: normal condition is more than 3.5, and the drought is 3.0-3.5; 2.3 to 3.0 percent of Zhongyeng; the heavy drought is less than 2.3; leaf area index for heading stage: normal condition is more than 3.1, and the drought is 2.8-3.1; 2.5 to 2.8 percent of Zhongyeng; the heavy drought is less than 2.5; leaf area index for the laying period: normal condition is more than 3.7, and the drought is 2.9-3.7; 2.2 to 2.9 percent of Zhongyeng; the heavy drought is less than 2.2; leaf area index for maturity: normal condition is more than 2.8, and the drought is 2.5-2.8; 2.2 to 2.5 percent of Zhongyeng; the heavy drought is less than 2.2;
further, the calibrating method of the leaf area index threshold value between different drought grades calibrated in the step 3) comprises the following steps: firstly, calculating the average value of inversion results of land blocks with different drought grades, and then respectively calculating the median value of the average value of two adjacent land blocks with the drought grades, wherein the median value is used as a threshold value between two adjacent drought grades.
Taking threshold values of the jointing period as an example, calculating NDVI, EVI, SAVI and TVI vegetation indexes, carrying out regression analysis on the vegetation indexes and actually measured LAI, constructing a model based on linearity, indexes and logarithms, determining that a regression model with highest relevance of the jointing period is an EVI and LAI index model, carrying out normal, light drought, medium drought and heavy drought threshold value grade division on summer corn results with different drought degrees by inversion LAI of the EVI, firstly calculating average values of all drought grades, then calculating median values of normal average values and light drought average values, and taking the median values as threshold values for distinguishing two drought grades, wherein the light drought, medium drought and heavy drought results are the same.
The beneficial effects of the invention are as follows:
the method provided by the invention has higher precision when being used for monitoring and distinguishing summer corn drought. The extraction threshold is flexible, stable and quick, the optimal leaf area index inversion model is obtained through comparative analysis, and leaf area index thresholds of different drought grades are calibrated, so that high-precision drought condition quick monitoring and distinguishing are realized.
Drawings
The invention is further described below with reference to the drawings and examples.
FIG. 1 is a view showing the outline of the investigation region of example 1 of the present invention;
FIG. 2 is a plot design of a study area according to example 1 of the present invention;
FIG. 3 is a graph showing the correlation of 3 models of vegetation indexes and leaf area indexes in the late stage of jointing according to example 1 of the present invention;
FIG. 4 is a graph showing the correlation of 3 models of each vegetation index and leaf area index in the heading stage of example 1;
FIG. 5 is a correlation of 3 models of vegetation indexes and leaf area indexes during laying period in example 1 of the present invention;
FIG. 6 is a graph showing the correlation of 3 models of vegetation indexes and leaf area indexes in the mature period of example 1;
FIG. 7 inversion of leaf area index at late stage of the joint;
FIG. 8 inversion of leaf area index during heading stage;
FIG. 9 inversion of leaf area index during laying;
FIG. 10 inversion of leaf area index at maturity.
Detailed Description
Example 1:
a summer corn drought unmanned aerial vehicle rapid monitoring and distinguishing method based on leaf area index,
selection of a study area and preparation in the early stage:
the research area of this example is located in the yellow pumping irrigation area (109 deg. 10'-110 deg. 10' E, 34 deg. 41'-35 deg. 00' N) of the east Lei Erqi of Fu Ping county, wei He, shanxi province, the eastern part of the Guangzhou province, the yellow river, the North part of the loess wave table area, the Wei He North, and the elevation 385 m-635 m. The eastern yellow river, the western city, the Fu-Ping county, the southern neighborhood, the Shanxi province intersection, the Wei irrigation district, and the Wei nan city, the Luo Hui Qu irrigation district, the North He Qiao Shan, the topography northwest, and the southeast. Belongs to temperate continental monsoon climate, cold and dry winter, hot and rainy summer, more than 50% of rainfall is concentrated in summer, the annual average rainfall is 519mm-552mm, which is far less than the annual average evaporation (1700 mm-2000 mm), and is a typical semiarid region. The total area of the total irrigation area is 1469.81km 2 . The crops in the irrigated area are mainly wheat and corn.
(1) Land block design
The experimental plots are selected to be positioned on open fields and have flat land features, the soil loam is the soil of a large scale, the buried depth of underground water is below 2m, water can be discharged and irrigated, and two plots with the area of 10m and 16m are arranged, and an openable rain shelter is built above the plots to eliminate the influence of precipitation. The plots were evenly divided into 11 small blocks of 4m by 4m, and to ensure the implementation of the moisture control experiment, the plots were kept 2m between the cells to prevent leakage and to isolate the plots (fig. 2). The experiment field is internally provided with a soil humidity sensor 17 set, wherein the rain root equipment is provided with 11 plots, the family hundred is provided with 6 plots, and the resolution of the observation time is 30 minutes. The corn is planted as a local dominant variety Yufeng 620, the corn row spacing is 70cm, the sowing depth is 3-5cm, the corn is sowed in the period of 6 months and 27 days, and the conventional field management is carried out by referring to the management measures of local farmers in the corn growing process.
(2) Moisture control design
The whole growth period of corn is divided into 4 growth stages, the names and detailed division of each growth stage (table 1), and 5 drought degree treatments (normal, light drought, medium drought, heavy drought and extreme drought) are respectively designed to ensure complete emergence of seedlings and to treat water until maturity in the jointing period. The drought degree division of corns is carried out according to agricultural meteorological observation standard corns-QX/T361-2016, soil humidity probes with burial depths of 10cm, 20cm and 40cm are arranged on each land block and used for measuring the soil water content of different depths, and the upper limit and the lower limit of irrigation are determined according to the percentage of the field water holding capacity (29.5%).
Table 1 summer corn moisture control degree partitioning
The rapid drought monitoring and distinguishing method specifically comprises the following steps:
1) And (3) data acquisition: the method comprises the steps of obtaining multispectral image data and ground actual measurement foliar index data through an unmanned aerial vehicle multi-load low-altitude remote sensing technology: calculating NDVI, EVI, SAVI and TVI vegetation indexes through multispectral image data; the acquisition of the multispectral image data: the FL-81 quad-rotor unmanned aerial vehicle is used for integrally carrying a multispectral camera to obtain multispectral aerial photos, the flying height is set to be 55m, the corresponding ground resolution is 4cm, and the available wavelengths of the lens are respectively blue light wave bands, green light wave bands, red edges and near infrared wave bands. Using a portable plant canopy analyzer LAI-2200C of LI-COR, measuring 20 times per plot, and calculating leaf area index value by taking 20 times of measured values as input data; the calculation formula of each vegetation index in this embodiment is calculated using the formula in table 2.
Table 2 calculation formulas for each vegetation index
Note that: ρ NIR 、ρ R 、ρ G 、ρ B and ρ RE the reflectivity of the near infrared band, the red band, the green band, the blue band and the red band respectively.
2) Constructing Leaf Area Index (LAI) inversion models of different drought degrees at each growth stage of summer corns: removing soil background by using a collaborative registration NDVI-Otsu method, extracting pixel values of pure vegetation indexes of a summer maize canopy, selecting NDVI, EVI, SAVI and TVI vegetation indexes and actually measured leaf area indexes respectively to construct 3 types of regression equations in different growth periods, and selecting the regression equation with the highest correlation with the Leaf Area Index (LAI) in each growth period as an optimal model equation of the growth period; the different growth periods refer to corn: a jointing period, a heading period, a spinning period and a maturation period; the 3 types of regression equations are linear equations, exponential equations and logarithmic equations;
and (3) regression analysis is carried out on the vegetation indexes NDVI, EVI, SAVI and TVI calculated from the multispectral data of different growth periods and the measured LAI value, so as to obtain 3 regression models of the vegetation indexes and LAI of different drought grades in different growth periods. As shown in figures 3-6, the vegetation indexes of the rest growing periods except the mature period have a correlation with the LAI index model which is greater than that of the linear and logarithmic regression models, wherein the overall correlation of the jointing period is the best, the correlation of EVI and the LAI model is higher than that of other vegetation index models, and R 2 All the indexes in the heading period are more than 0.86, the correlation between each index and the LAI model is lower than that of other growth periods, R 2 Near 0.5, SAVI inversion effect is low, overall correlation in the laying period is good, R 2 The correlation of the linear regression model in the maturity is higher than that of the exponential and logarithmic model, and the optimal model equation is shown in Table 3.
Inversion R of different indexes in late stage of jointing 2 All are larger than 0.8, and the overall inversion result is good. The vegetation indexes in the jointing period have better correlation with the index model and the linear model of the LAI, the regression model with the highest correlation is the EVI and LAI index model, R 2 = 0.9113, and secondly TVI and LAI models, SAVI and LAI models, NDVI and LAI models, so EVI is selected as a vegetation index to invert late-stage LAI, and leaf area index threshold division between different drought grades is calibrated, so that drought degrees of different plots in late stage of the jointing are monitored. The overall inversion effect of each index in the heading period is good, the correlation between SAVI index and LAI is low, the correlation between NDVI and LAI linear model is optimal, R 2 =0.6325, evi and LAI models and TVI and LAI models are not very different, R 2 At 0.5 up and down, SAVI has the lowest correlation with the LAI model. Selecting and usingAnd taking the NDVI as a vegetation index to invert the LAI in the heading period, and monitoring the drought degree of different plots in the heading period. The overall inversion effect of each vegetation index in the laying period is good, and the correlation of the index models of different vegetation indexes and LAI is higher than that of linear and logarithmic models. The index model of SAVI and LAI has the best correlation, R 2 = 0.8619, followed by NDVI and LAI models, TVI and LAI models EVI and LAI models. SAVI is selected as a vegetation index to invert summer corn LAI in the heading stage, and drought degree of different plots in the spinning stage is monitored. The total inversion effect of different indexes in the maturity period is good, the difference between each vegetation index and 3 models of leaf area indexes is not obvious, the correlation of the linear model is slightly higher than that of an index and logarithmic model, wherein the correlation of the linear model of TVI and LAI is optimal, R 2 = 0.7049. And inverting summer corn LAI in the heading period by taking TVI as a vegetation index, and monitoring drought degree of different plots in the mature period.
TABLE 3 optimal regression equation for vegetation indices and LAI for different growth periods
Note that: x is the vegetation index and y is the inverted leaf area index
3) Rating of leaf area index thresholds for different drought classes: inverting the Leaf Area Index (LAI) of each period according to the optimal model equation of each growth period obtained in the step 2), and calibrating the leaf area index threshold value among different drought grades. Unmanned aerial vehicle multispectral images obtained based on the flying dragon-81 are inverted to obtain crop leaf area indexes, and drought degree division is carried out according to the threshold value rated in the table 4.
TABLE 4 summer corn leaf area index threshold for different drought degrees after unmanned aerial vehicle data rating
4) Real-time discrimination of drought level: the multispectral image of the land block to be detected is obtained through real-time monitoring, a required vegetation index is calculated, the vegetation index is carried into an optimal model equation of the corresponding growth period which is set in the step 2) to be inverted to obtain a Leaf Area Index (LAI) value, and then the leaf area index value is compared with a threshold value of the period in the step 3) to judge the real-time drought grade.
7-10, it can be known that, in western plots, the plot of Rixi 3 and the plot of West 4 are arid plots, the plot of West 3 is a medium and arid plot, the plot of West 4 is a heavy arid plot, the plot of Ridong 1 and Dong 2 are heavy arid plots, and the plot of Dong 6 is a light arid plot, so that the practical experimental design is consistent, and the EVI index model can well estimate the summer corn LAI in the late stage of jointing; the heading period is in a western land block, lixi 2, a light dry land block, an eastern land block, lidong 1 and Dong 2, a heavy dry land block, and Dong 3 and Dong 4 respectively, which are medium dry land blocks and heavy dry land blocks, and the NDVI index model is in accordance with the actual land block, so that summer corn LAI in the heading period can be estimated well; the silk-laying period is characterized in that a light dry land block is arranged in a western land block, the east land block, namely, the east 1 and the east 2, is a heavy dry land block, and the east 3 and the east 4 are respectively a medium dry land block and a heavy dry land block, so that the experimental design is met, and the SAVI index model can well estimate the summer corn LAI in the heading period; mature period experiment design is that in western land parcels, rixi 2 is light dry land parcels, and in western land parcels 3 is medium dry land parcels, east 1 and east 2 are heavy dry land blocks, east 3 is medium dry land block and heavy dry land block respectively, the inversion result shows that most plots are in light drought and normal because the plots are affected by rainfall at the moment, and the TVI linear model can well estimate the summer maize LAI in the mature period consistent with the result. When the unmanned aerial vehicle inverts the LAI with different drought degrees in each growth period, the index model and the linear model can estimate the LAI, and the model applicability is good.
Finally, it should be noted that the above only illustrates the technical solution of the present invention and is not limiting, and although the present invention has been described in detail with reference to the preferred arrangement, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (5)
1. A summer corn drought unmanned aerial vehicle rapid monitoring and distinguishing method based on leaf area indexes is characterized by comprising the following steps of: the method comprises the following steps:
1) And (3) data acquisition: the method comprises the steps of obtaining multispectral image data and ground actual measurement foliar index data through an unmanned aerial vehicle low-altitude remote sensing technology: calculating NDVI, EVI, SAVI and TVI vegetation indexes through multispectral image data;
2) Constructing leaf area index inversion models of different drought degrees at each growth stage of summer corns: removing soil background pixels, extracting pure vegetation index pixels of summer corn canopy, selecting NDVI, EVI, SAVI and TVI vegetation indexes and actually measured leaf area indexes respectively to construct 3 types of regression equations in different growth periods, and selecting the regression equation with highest correlation with the leaf area index in each growth period as an optimal model equation of the growth period; the different growth periods refer to corn: a jointing period, a heading period, a spinning period and a maturation period; the 3 types of regression equations are linear equations, exponential equations and logarithmic equations;
the optimal model equation in the jointing period in the step 2) is an exponential regression equation of EVI and LAI, the optimal model equation in the heading period is a linear regression equation of NDVI and LAI, the optimal model equation in the spinning period is an exponential regression equation of SAVI and LAI, the optimal model equation in the maturation period is a linear regression equation of TVI and LAI, and the LAI is expressed as a leaf area index;
3) Rating of leaf area index thresholds for different drought classes: inverting the leaf area index of each growth period according to the optimal model equation of each growth period obtained in the step 2), and calibrating the leaf area index threshold value among different drought grades;
4) Real-time discrimination of drought level: the multispectral image of the land block to be measured is obtained through real-time monitoring,
and calculating a required vegetation index, carrying the vegetation index into the optimal model equation of the corresponding growth period calibrated in the step 2), inverting to obtain a leaf area index value, and comparing the leaf area index value with the threshold value of each growth period in the step 3) to judge the real-time drought grade.
2. The summer corn drought unmanned aerial vehicle rapid monitoring and judging method based on leaf area index according to claim 1The method is characterized in that: the acquisition of the multispectral image data in step 1): integration carrying MicasenseRedEdge-M by using FL-81 quad-rotor unmanned aerial vehicle TM The multispectral camera acquires multispectral aerial photo, the flying height is set to be 55m, the corresponding ground resolution is 4cm, and the wavelength which can be acquired by the lens is respectively blue light wave band, green light wave band, red edge and near infrared wave band.
3. The rapid monitoring and distinguishing method for summer corn drought unmanned aerial vehicle based on leaf area index according to claim 1, wherein the method comprises the following steps: the measured leaf area index in step 1): the leaf area index value was calculated using the portable plant canopy analyzer LAI-2200C of LI-COR, 20 measurements per plot, with 20 measurements as input data.
4. The rapid monitoring and distinguishing method for summer corn drought unmanned aerial vehicle based on leaf area index according to claim 1, wherein the method comprises the following steps: the leaf area index threshold value between different drought grades rated in step 3) is: leaf area index for the jointing period: normal condition is more than 3.5, and the drought is 3.0-3.5; 2.3 to 3.0 percent of Zhongyeng; the heavy drought is less than 2.3; leaf area index for heading stage: normal condition is more than 3.1, and the drought is 2.8-3.1; 2.5 to 2.8 percent of Zhongyeng; the heavy drought is less than 2.5; leaf area index for the laying period: normal condition is more than 3.7, and the drought is 2.9-3.7; 2.2 to 2.9 percent of Zhongyeng; the heavy drought is less than 2.2; leaf area index for maturity: normal condition is more than 2.8, and the drought is 2.5-2.8; 2.2 to 2.5 percent of Zhongyeng; the heavy drought is less than 2.2.
5. The rapid monitoring and distinguishing method for summer corn drought unmanned aerial vehicle based on leaf area index according to claim 1, wherein the method comprises the following steps: the calibrating method of the leaf area index threshold value between different drought grades calibrated in the step 3) comprises the following steps: firstly, calculating leaf area index average values obtained by inversion of land blocks with different drought grades, and then respectively calculating the median value of the average values of two adjacent drought grades, wherein the median value is used as a threshold value between adjacent drought grades.
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