A method for agricultural drought monitoring based on multi-source remote sensing data

Abstract

The invention discloses an agricultural drought monitoring method based on multi-source remote sensing data, and relates to the technical field of agricultural drought monitoring. The invention comprises land status monitoring, crop analysis in normal areas, land monitoring in cracked areas, crop selection in cracked land, early warning prompts and a display center. By analyzing the land assessment coefficients corresponding to each sub-area of ​​a target farmland, a more accurately judged land status corresponding to each sub-area of ​​the target farmland is obtained, and then each normal sub-area of ​​the target farmland and each cracked sub-area of ​​the target farmland are obtained more quickly, so that corresponding crop selection and planting are performed, and thus the agricultural economic development and usability are better guaranteed, and the shortcomings in the current technology are solved. In addition, the multi-source remote sensing data technology effectively improves the agricultural drought monitoring efficiency and monitoring accuracy, and realizes the multi-dimensional monitoring guarantee of agricultural drought, so as to timely prevent and improve agricultural drought and improve the benefits of agricultural development.

Description

Technical Field

[0001] The invention relates to the technical field of agricultural drought monitoring, and in particular to an agricultural drought monitoring method using multi-source remote sensing data. Background Art

[0002] In today's era, agricultural development has become a very important part of economic development, so agricultural monitoring is very important. With the continuous changes in the social environment, the state of agriculture has also changed accordingly. Among them, agricultural drought is a prominent representative. Therefore, the intelligent use of multi-source remote sensing data in agricultural drought monitoring can achieve more comprehensive, accurate and multi-dimensional agricultural monitoring, improve monitoring efficiency and monitoring accuracy, and timely prevent and improve agricultural drought.

[0003] Current technology for monitoring agricultural drought is mainly through manual on-site inspections, and the agricultural drought situation is analyzed based on inspection experience and inspection phenomena. This monitoring method is relatively slow, and the agricultural data and scope that can be monitored are also relatively limited. It is impossible to achieve multi-dimensional monitoring and analysis of agricultural drought, and it is impossible to accurately judge the degree of drought corresponding to each agricultural sub-region, and it is impossible to carry out effective drought improvement, which greatly reduces the economic development capacity of agriculture. Summary of the invention

[0004] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide an agricultural drought monitoring method based on multi-source remote sensing data.

[0005] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides an agricultural drought monitoring method based on multi-source remote sensing data, including: Step 1, land status monitoring: dividing the target farmland into regions to obtain sub-regions, and obtaining the land information corresponding to each sub-region of the target farmland, and then analyzing to obtain the land assessment coefficient corresponding to each sub-region of the target farmland, and screening to obtain normal sub-regions of the target farmland and cracked sub-regions of the target farmland.

[0006] Step 2: Analysis of crops in normal areas: Obtain information corresponding to each crop in each normal sub-area of ​​the target farmland, and then analyze and obtain the variety evaluation coefficient corresponding to each crop in each normal sub-area of ​​the target farmland, and screen and obtain each crop corresponding to each normal sub-area of ​​the target farmland.

[0007] Step 3. Land monitoring in cracked areas: obtain the root information corresponding to each crop in each cracked sub-area of ​​the target farmland, and then analyze and obtain the root water demand corresponding to each crop in each cracked sub-area of ​​the target farmland, and obtain the capacity information corresponding to each cracked sub-area of ​​the target farmland, and then analyze and obtain the capacity influencing factors corresponding to each cracked sub-area of ​​the target farmland.

[0008] Step 4. Cracked land crop selection: According to the root water demand of each crop in each cracked sub-area of ​​the target farmland and the capacity influencing factors of each cracked sub-area of ​​the target farmland, the water adjustment value corresponding to each crop in each cracked sub-area of ​​the target farmland is analyzed and the corresponding crops in each cracked sub-area of ​​the target farmland are screened.

[0009] Step 5. Early warning: When the land corresponding to a certain area of ​​the target farmland is in a severe drought or drought state, an early warning will be issued.

[0010] Step 6: Display center: Display the crops corresponding to the normal sub-areas of the target farmland and the crops corresponding to the cracked sub-areas of the target farmland.

[0011] Preferably, the analysis obtains the land assessment coefficient corresponding to each sub-area of ​​the target farmland, and the specific analysis process is as follows: by calculating the formula The land evaluation coefficient χ corresponding to the vth sub-area of ​​the target farmland is obtained by analysis. v , v represents the number of each sub-area, v = 1, 2.....r, r is any integer greater than 2, ε1, ε2, ε3 are the weight factors of the set water lock time, heat capacity, and soil moisture, respectively, m v is the water lock duration corresponding to the vth sub-area of ​​the target farmland, k v is the heat capacity corresponding to the vth sub-area of ​​the target farmland, u v is the soil moisture corresponding to the vth sub-area of ​​the target farmland, c v is the illumination corresponding to the vth sub-area of ​​the target farmland, e is a natural constant, 0<ε1<1, 0<ε2<1, 0<ε3<1, m′ is the set reference water lock time, k′ is the set reference heat capacity, and u′ is the set reference soil moisture.

[0012] Preferably, the screening obtains normal sub-regions of the target farmland and cracked sub-regions of the target farmland, and the specific judgment process is as follows: the land assessment coefficient corresponding to each sub-region of the target farmland is compared with the land assessment coefficient interval stored in the database. If the land assessment coefficient corresponding to a sub-region of the target farmland is lower than the lower limit of the land assessment coefficient interval stored in the database, it is determined that the land corresponding to the sub-region is in a severe drought state, and an early warning is issued. If the land assessment coefficient corresponding to a sub-region of the target farmland is within the lower limit of the land assessment coefficient interval stored in the database, it is determined that the land corresponding to the sub-region is in a drought state, and an early warning is issued, and the sub-region of the target farmland is recorded as a cracked sub-region to obtain the cracked sub-regions of the target farmland. If the agricultural assessment coefficient corresponding to a sub-region of the target farmland is higher than the upper limit of the agricultural land assessment coefficient interval stored in the database, it is determined that the agriculture corresponding to the sub-region is in a normal state, and the sub-region of the target farmland is recorded as a normal sub-region to obtain normal sub-regions of the target farmland.

[0013] Preferably, the analysis obtains the variety evaluation coefficient corresponding to each crop in each normal sub-area of ​​the target farmland. The specific analysis process is as follows: By calculating the formula The analysis results show that the variety evaluation coefficient corresponding to the sth crop in the fth normal sub-area of ​​the target farmland is obtained. f is the number of each normal sub-area, f = 1, 2...p, p is any integer greater than 2, s is the number of each crop, s = 1, 2.......w, s is any integer greater than 2, y' is the historical reference crop yield, j' is the historical reference crop growth cycle, h' is the historical reference crop marketing price, q1, q2, q3 are the set weight factors of crop yield, crop growth cycle, and crop marketing price, respectively, y sf is the historical crop yield corresponding to the sth crop in the fth normal sub-area of ​​the target farmland, j sf is the historical crop growth cycle corresponding to the sth crop in the fth normal sub-region of the target farmland, h sf is the historical crop marketing price corresponding to the sth crop in the fth normal sub-area of ​​the target farmland, c sf is the land area corresponding to the sth crop in the fth normal sub-area of ​​the target farmland, 0<q1<1, 0<q2<1, 0<q3<1.

[0014] Preferably, the screening obtains each crop corresponding to each normal sub-area of ​​the target farmland, and the specific screening process is as follows: the variety evaluation coefficient corresponding to each crop in each normal sub-area of ​​the target farmland is compared with the variety evaluation coefficient interval corresponding to each crop stored in the database. If the variety evaluation coefficient corresponding to a crop in a normal sub-area of ​​the target farmland is greater than the upper limit of the variety evaluation coefficient interval corresponding to the crop stored in the database, then the crop stored in the database is used as the crop corresponding to a normal sub-area of ​​the target farmland, thereby obtaining each crop corresponding to each normal sub-area of ​​the target farmland.

[0015] Preferably, the analysis obtains the root water requirement corresponding to each crop in each cracked sub-region of the target farmland. The specific analysis process is as follows: a root system scanner is used to obtain the root system image corresponding to each crop in each cracked sub-region of the target farmland, and the root distribution area and rhizome depth corresponding to each crop in each cracked sub-region of the target farmland are extracted from the root system image, so as to obtain the root water requirement position corresponding to each crop in each cracked sub-region of the target farmland, and compare it with the soil depth layer of each cracked sub-region of the target farmland to obtain the distance between the soil depth layer d of the cracked sub-region and the crop root water requirement r, which is recorded as d represents the number corresponding to each cracked sub-region, d=1, 2...g, g represents the number of cracked sub-regions, r represents the number corresponding to each crop root system, r=1, 2...n, n represents the number of crop roots.

[0016] Preferably, the analysis obtains the capacity influencing factors corresponding to each cracked sub-region of the target farmland, and the specific analysis process is as follows: by calculating the formula The capacity influencing factor σ corresponding to the dth cracking sub-area of ​​the target farmland is obtained by analysis. d , d is the number corresponding to each crack sub-area, d = 1, 2...g, g is any integer greater than 2, θ′ is the historical reference penetration depth, is the historical reference penetration velocity, τ1 and τ2 are the weight factors of the historical penetration depth and penetration velocity, respectively, and θ d is the historical penetration depth corresponding to the dth cracked sub-area of ​​the target farmland, is the historical infiltration velocity corresponding to the d-th cracked sub-area of ​​the target farmland, 0<τ1<1, 0<τ2<1.

[0017] Preferably, the analysis obtains the water adjustment value corresponding to each crop in each cracked sub-region of the target farmland. The specific analysis process is as follows: According to the calculation formula Calculate the water adjustment value corresponding to the rth crop in the dth cracked sub-area of ​​the target farmland Ω1 represents the compensation factor of the distance between the soil depth layer of the cracked sub-region and the crop root system, η represents the distance between each cracked sub-region and each crop root system in history, e is a natural constant, σ d is the capacity influencing factor corresponding to the dth cracked sub-area of ​​the target farmland.

[0018] Preferably, the screening obtains the planted crops corresponding to each cracked sub-region of the target farmland, and the specific screening process is as follows: the water adjustment value corresponding to each crop in each cracked sub-region of the target farmland is compared with the water adjustment value interval corresponding to the crops stored in the database. If the water adjustment value corresponding to a crop in a cracked sub-region of the target farmland is within the water adjustment value interval corresponding to the crop stored in the database, then the crop stored in the database is used as the crop corresponding to the cracked sub-region of the target farmland, thereby obtaining the planted crops corresponding to each cracked sub-region of the target farmland.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides an agricultural drought monitoring method based on multi-source remote sensing data, which analyzes the land assessment coefficients corresponding to each sub-region of the target farmland, thereby obtaining a more accurately judged land status corresponding to each sub-region of the target farmland, and thereby more quickly obtaining each normal sub-region of the target farmland and each cracked sub-region of the target farmland, so as to select and plant corresponding crops, thereby better ensuring the development and usability of the agricultural economy, solving the deficiencies in the current technology, and the multi-source remote sensing data technology effectively improves the efficiency and accuracy of agricultural drought monitoring, reduces the input of manpower and material resources, reduces the cost of agricultural drought monitoring, and realizes multi-dimensional monitoring and guarantee of agricultural drought, so as to timely prevent and improve agricultural drought, improve the benefits of agricultural development, and provide rich data support for agricultural planting selection. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 The present invention is a schematic flow chart of the steps for implementing the method. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Step 1: Land status monitoring: Divide the target farmland into regions to obtain sub-regions, and obtain the land information corresponding to each sub-region of the target farmland, and then analyze and obtain the land assessment coefficient corresponding to each sub-region of the target farmland, and screen out the normal sub-regions and cracked sub-regions of the target farmland.

[0024] It should be noted that land information includes water lock duration, heat capacity, soil moisture, and light intensity.

[0025] It should also be noted that the microwave remote sensing sensor emits microwave radiation to monitor the land status of each sub-area of ​​the target farmland, and then obtains the soil moisture time and soil water loss time, and subtracts the soil water loss time from the soil moisture time to obtain the corresponding water lock time of each sub-area of ​​the target farmland. A heat capacity meter is installed in the target farmland to obtain the heat capacity corresponding to each sub-area of ​​the target farmland, a humidity sensor is installed in the target farmland to obtain the soil humidity corresponding to each sub-area of ​​the target farmland, and an illuminance meter is used to obtain the light intensity corresponding to each sub-area of ​​the target farmland.

[0026] It should be noted again that land is the most direct indicator of agricultural drought, so it is necessary to monitor and analyze the land information corresponding to each sub-area of ​​the target farmland, so as to better understand the land drought status corresponding to each sub-area of ​​the target farmland.

[0027] As an optional implementation, the analysis obtains the land assessment coefficient corresponding to each sub-area of ​​the target farmland. The specific analysis process is as follows: By calculating the formula The land evaluation coefficient χ corresponding to the vth sub-area of ​​the target farmland is obtained by analysis. v , v represents the number of each sub-area, v = 1, 2.....r, r is any integer greater than 2, ε1, ε2, ε3 are the weight factors of the set water lock time, heat capacity, and soil moisture, respectively, m v is the water lock duration corresponding to the vth sub-area of ​​the target farmland, k v is the heat capacity corresponding to the vth sub-area of ​​the target farmland, u v is the soil moisture corresponding to the vth sub-area of ​​the target farmland, c v is the illumination corresponding to the vth sub-area of ​​the target farmland, e is a natural constant, 0<ε1<1, 0<ε2<1, 0<ε3<1, m′ is the set reference water lock time, k′ is the set reference heat capacity, and u′ is the set reference soil moisture.

[0028] As an optional implementation method, the screening obtains normal sub-regions of the target farmland and cracked sub-regions of the target farmland, and the specific judgment process is as follows: the land assessment coefficient corresponding to each sub-region of the target farmland is compared with the land assessment coefficient interval stored in the database. If the land assessment coefficient corresponding to a sub-region of the target farmland is lower than the lower limit of the land assessment coefficient interval stored in the database, it is determined that the land corresponding to the sub-region is in a severe drought state, and an early warning is issued. If the land assessment coefficient corresponding to a sub-region of the target farmland is within the lower limit of the land assessment coefficient interval stored in the database, it is determined that the land corresponding to the sub-region is in a drought state, and an early warning is issued, and the sub-region of the target farmland is recorded as a cracked sub-region to obtain the cracked sub-regions of the target farmland. If the agricultural assessment coefficient corresponding to a sub-region of the target farmland is higher than the upper limit of the agricultural land assessment coefficient interval stored in the database, it is determined that the agriculture corresponding to the sub-region is in a normal state, and the sub-region of the target farmland is recorded as a normal sub-region to obtain normal sub-regions of the target farmland.

[0029] Step 2: Analysis of crops in normal areas: Obtain information corresponding to each crop in each normal sub-area of ​​the target farmland, and then analyze and obtain the variety evaluation coefficient corresponding to each crop in each normal sub-area of ​​the target farmland, and screen and obtain each crop corresponding to each normal sub-area of ​​the target farmland.

[0030] It should be noted that crop information includes historical crop yields, historical crop growth cycles, historical crop marketing prices, and land area.

[0031] It should also be noted that the historical crop yield and land area are obtained from the farmland management center corresponding to each normal sub-area of ​​the target farmland, the historical crop growth cycle corresponding to each normal sub-area of ​​the target farmland is obtained from the historical crop purchase information, the corresponding selling price in the historical crop sales market is obtained and the average is calculated to obtain the historical crop marketing price corresponding to each normal sub-area of ​​the target farmland.

[0032] As an optional implementation, the analysis obtains the crop assessment coefficient corresponding to each normal sub-area of ​​the target farmland. The specific analysis process is as follows: By calculating the formula The variety evaluation coefficient corresponding to the sth crop in the fth normal sub-area of ​​the target farmland is obtained by analysis. f is the number of each normal sub-area, f = 1, 2...p, p is any integer greater than 2, s is the number of each crop, s = 1, 2.......w, s is any integer greater than 2, y' is the historical reference crop yield, j' is the historical reference crop growth cycle, h' is the historical reference crop marketing price, q1, q2, q3 are the set weight factors of crop yield, crop growth cycle, and crop marketing price, respectively, y sfis the historical crop yield corresponding to the sth crop in the fth normal sub-area of ​​the target farmland, j sf is the historical crop growth cycle corresponding to the sth crop in the fth normal sub-region of the target farmland, h sf is the historical crop marketing price corresponding to the sth crop in the fth normal sub-area of ​​the target farmland, c sf is the land area corresponding to the sth crop in the fth normal sub-area of ​​the target farmland, 0<q1<1, 0<q2<1, 0<q3<1.

[0033] As an optional implementation, the screening obtains the crops corresponding to each normal sub-area of ​​the target farmland, and the specific screening process is as follows: the variety evaluation coefficient corresponding to each crop in each normal sub-area of ​​the target farmland is compared with the variety evaluation coefficient interval corresponding to each crop stored in the database. If the variety evaluation coefficient corresponding to a crop in a normal sub-area of ​​the target farmland is greater than the upper limit of the variety evaluation coefficient interval corresponding to the crop stored in the database, then the crop stored in the database is used as the crop corresponding to a normal sub-area of ​​the target farmland, so as to obtain the crops corresponding to each normal sub-area of ​​the target farmland.

[0034] Step 3. Land monitoring in cracked areas: obtain the root information corresponding to each crop in each cracked sub-area of ​​the target farmland, and then analyze and obtain the root water demand corresponding to each crop in each cracked sub-area of ​​the target farmland, and obtain the capacity information corresponding to each cracked sub-area of ​​the target farmland, and then analyze and obtain the capacity influencing factors corresponding to each cracked sub-area of ​​the target farmland.

[0035] It should be noted that the capacity information includes historical penetration depth and historical penetration speed, and the crop information includes root depth and root distribution area.

[0036] It should also be noted that the double-ring infiltration meter was used to obtain the historical infiltration depth and historical infiltration rate corresponding to each cracked sub-area of ​​the target farmland.

[0037] As an optional implementation, the analysis obtains the root water requirement corresponding to each crop in each cracked sub-region of the target farmland. The specific analysis process is as follows: a root system scanner is used to obtain the root system image corresponding to each crop in each cracked sub-region of the target farmland, and the root distribution area and rhizome depth corresponding to each crop in each cracked sub-region of the target farmland are extracted from the root system image, thereby obtaining the root water requirement corresponding to each crop in each cracked sub-region of the target farmland, and comparing it with the soil depth layer of each cracked sub-region of the target farmland to obtain the distance between the soil depth layer d of the cracked sub-region and the crop root water requirement r, which is recorded as d represents the number corresponding to each cracked sub-region, d=1, 2...g, g represents the number of cracked sub-regions, r represents the number corresponding to each crop root system, r=1, 2...n, n represents the number of crop roots.

[0038] As an optional implementation, the analysis obtains the capacity influencing factors corresponding to each cracked sub-region of the target farmland. The specific analysis process is as follows: By calculating the formula The capacity influencing factor σ corresponding to the dth cracking sub-area of ​​the target farmland is obtained by analysis. d , d is the number corresponding to each crack sub-area, d = 1, 2...g, g is any integer greater than 2, θ′ is the historical reference penetration depth, is the historical reference penetration velocity, τ1 and τ2 are the weight factors of the historical penetration depth and penetration velocity, respectively, and θ d is the historical penetration depth corresponding to the dth cracked sub-area of ​​the target farmland, is the historical infiltration velocity corresponding to the d-th cracked sub-area of ​​the target farmland, 0<τ1<1, 0<τ2<1.

[0039] Step 4. Cracked land crop selection: According to the root water demand of each crop in each cracked sub-area of ​​the target farmland and the capacity influencing factors of each cracked sub-area of ​​the target farmland, the water adjustment value corresponding to each crop in each cracked sub-area of ​​the target farmland is analyzed and the corresponding crops in each cracked sub-area of ​​the target farmland are screened.

[0040] As an optional implementation, the analysis obtains the water adjustment value corresponding to each crop in each cracked sub-area of ​​the target farmland. The specific analysis process is as follows: According to the calculation formula Calculate the water adjustment value corresponding to the rth crop in the dth cracked sub-area of ​​the target farmland Ω1 represents the compensation factor of the distance between the soil depth layer of the cracked sub-region and the crop root system, η represents the distance between each cracked sub-region and each crop root system in history, e is a natural constant, σ d is the capacity influencing factor corresponding to the dth cracked sub-area of ​​the target farmland.

[0041] As an optional implementation, the screening obtains the planted crops corresponding to each cracked sub-region of the target farmland, and the specific screening process is as follows: the water adjustment value corresponding to each crop in each cracked sub-region of the target farmland is compared with the water adjustment value interval corresponding to the crops stored in the database. If the water adjustment value corresponding to a crop in a cracked sub-region of the target farmland is within the water adjustment value interval corresponding to the crop stored in the database, then the crop stored in the database is used as the crop corresponding to a cracked sub-region of the target farmland, so as to obtain the planted crops corresponding to each cracked sub-region of the target farmland.

[0042] Step 5: Early warning: when the land corresponding to a certain area of ​​the target farmland is in a severe drought or drought state, an early warning is issued;

[0043] Step 6: Display center: Display the crops corresponding to the normal sub-areas of the target farmland and the crops corresponding to the cracked sub-areas of the target farmland.

[0044] The present invention provides a specific embodiment, which analyzes the land assessment coefficients corresponding to each sub-region of the target farmland, thereby obtaining a more accurately judged land status corresponding to each sub-region of the target farmland, and thereby more quickly obtaining each normal sub-region of the target farmland and each cracked sub-region of the target farmland, so as to select and plant corresponding crops, thereby better ensuring the development and usability of the agricultural economy, solving the deficiencies in the current technology, and multi-source remote sensing data technology effectively improves the efficiency and accuracy of agricultural drought monitoring, reduces the input of manpower and material resources, reduces the cost of agricultural drought monitoring, and realizes multi-dimensional monitoring and guarantee of agricultural drought, so as to timely prevent and improve agricultural drought, improve the efficiency of agricultural development, and provide rich data support for agricultural planting selection.

[0045] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they shall all fall within the protection scope of the present invention.

Claims

1. A method for monitoring agricultural drought using multi-source remote sensing data, characterized in that: include: Step 1: Land status monitoring: Divide the target farmland into regions to obtain sub-regions, and obtain the land information corresponding to each sub-region of the target farmland, and then analyze and obtain the land assessment coefficient corresponding to each sub-region of the target farmland, and screen out each normal sub-region of the target farmland and each dry and cracked sub-region of the target farmland; Step 2: Analysis of crops in normal areas: Obtain information corresponding to each crop in each normal sub-area of ​​the target farmland, and then analyze and obtain the variety evaluation coefficient corresponding to each crop in each normal sub-area of ​​the target farmland, and screen and obtain each crop corresponding to each normal sub-area of ​​the target farmland; Step 3: Land monitoring in cracked areas: obtain the root information corresponding to each crop in each cracked sub-area of ​​the target farmland, and then analyze and obtain the root water demand corresponding to each crop in each cracked sub-area of ​​the target farmland, and obtain the capacity information corresponding to each cracked sub-area of ​​the target farmland, and then analyze and obtain the capacity influencing factors corresponding to each cracked sub-area of ​​the target farmland; Step 4: Crop selection on cracked land: According to the root water requirement of each crop in each cracked sub-area of ​​the target farmland and the capacity influencing factor of each cracked sub-area of ​​the target farmland, the water adjustment value corresponding to each crop in each cracked sub-area of ​​the target farmland is analyzed and the corresponding crops in each cracked sub-area of ​​the target farmland are screened; Step 5: Early warning: when the land corresponding to a certain area of ​​the target farmland is in a severe drought or drought state, an early warning is issued; Step 6, display center: display each crop corresponding to each normal sub-area of ​​the target farmland, and each planted crop corresponding to each cracked sub-area of ​​the target farmland; The analysis obtains the land assessment coefficient corresponding to each sub-area of ​​the target farmland. The specific analysis process is as follows: By calculating the formula The land evaluation coefficient χ corresponding to the vth sub-area of ​​the target farmland is obtained by analysis. v , v represents the number of each sub-area, v = 1, 2.....r, r is any integer greater than 2, ε1, ε2, ε3 are the weight factors of the set water lock time, heat capacity, and soil moisture, respectively, m v is the water lock duration corresponding to the vth sub-area of ​​the target farmland, k v is the heat capacity corresponding to the vth sub-area of ​​the target farmland, u v is the soil moisture corresponding to the vth sub-area of ​​the target farmland, c v is the illumination corresponding to the vth sub-area of ​​the target farmland, e is a natural constant, 0<ε1<1, 0<ε2<1, 0<ε3<1, m′ is the set reference water lock time, k′ is the set reference heat capacity, and u′ is the set reference soil moisture.

2. The agricultural drought monitoring method based on multi-source remote sensing data according to claim 1, characterized in that: The screening obtains each normal sub-region of the target farmland and each cracked sub-region of the target farmland, and the specific judgment process is as follows: The land assessment coefficient corresponding to each sub-region of the target farmland is compared with the land assessment coefficient interval stored in the database. If the land assessment coefficient corresponding to a sub-region of the target farmland is lower than the lower limit of the land assessment coefficient interval stored in the database, it is determined that the land corresponding to the sub-region is in a severe drought state, and an early warning is issued. If the land assessment coefficient corresponding to a sub-region of the target farmland is within the lower limit of the land assessment coefficient interval stored in the database, it is determined that the land corresponding to the sub-region is in a drought state, and an early warning is issued. The sub-region of the target farmland is recorded as a cracked sub-region, and each cracked sub-region of the target farmland is obtained. If the agricultural assessment coefficient corresponding to a sub-region of the target farmland is higher than the upper limit of the agricultural land assessment coefficient interval stored in the database, it is determined that the agriculture corresponding to the sub-region is in a normal state, and the sub-region of the target farmland is recorded as a normal sub-region, and each normal sub-region of the target farmland is obtained.

3. The agricultural drought monitoring method based on multi-source remote sensing data as claimed in claim 1, characterized in that: The analysis obtains the variety evaluation coefficient corresponding to each crop in each normal sub-area of ​​the target farmland. The specific analysis process is as follows: By calculating the formula The analysis results show that the variety evaluation coefficient corresponding to the sth crop in the fth normal sub-area of ​​the target farmland is obtained. f is the number of each normal sub-area, f = 1, 2...p, p is any integer greater than 2, s is the number of each crop, s = 1, 2.......w, s is any integer greater than 2, y' is the historical reference crop yield, j' is the historical reference crop growth cycle, h' is the historical reference crop marketing price, q1, q2, q3 are the set weight factors of crop yield, crop growth cycle, and crop marketing price, respectively, y sf is the historical crop yield corresponding to the sth crop in the fth normal sub-area of ​​the target farmland, j sf is the historical crop growth cycle corresponding to the sth crop in the fth normal sub-region of the target farmland, h sf is the historical crop marketing price corresponding to the sth crop in the fth normal sub-area of ​​the target farmland, c sf is the land area corresponding to the sth crop in the fth normal sub-area of ​​the target farmland, 0<q1<1, 0<q2<1, 0<q3<1.

4. The agricultural drought monitoring method based on multi-source remote sensing data as claimed in claim 1, characterized in that: The screening obtains the crops corresponding to the normal sub-areas of the target farmland. The specific screening process is as follows: The variety evaluation coefficient corresponding to each crop in each normal sub-area of ​​the target farmland is compared with the variety evaluation coefficient interval corresponding to each crop stored in the database. If the variety evaluation coefficient corresponding to a crop in a normal sub-area of ​​the target farmland is greater than the upper limit of the variety evaluation coefficient interval corresponding to the crop stored in the database, the crop stored in the database will be used as the crop corresponding to a normal sub-area of ​​the target farmland, so as to obtain the crops corresponding to each normal sub-area of ​​the target farmland.

5. The agricultural drought monitoring method based on multi-source remote sensing data according to claim 1, characterized in that: The analysis obtains the root water requirement of each crop in each cracked sub-region of the target farmland. The specific analysis process is as follows: A root scanner is used to obtain the root images corresponding to each crop in each cracked sub-area of ​​the target farmland, and the root distribution area and rhizome depth corresponding to each crop in each cracked sub-area of ​​the target farmland are extracted from the root images, thereby obtaining the root water requirement position corresponding to each crop in each cracked sub-area of ​​the target farmland, and comparing it with the soil depth layer of each cracked sub-area of ​​the target farmland, and obtaining the distance between the soil depth layer d of the cracked sub-area and the crop root water requirement r, which is recorded as d represents the number corresponding to each cracked sub-region, d=1, 2...g, g represents the number of cracked sub-regions, r represents the number corresponding to each crop root system, r=1, 2...n, n represents the number of crop roots.

6. The agricultural drought monitoring method based on multi-source remote sensing data according to claim 1, characterized in that: The analysis obtains the capacity influencing factors corresponding to each cracking sub-area of ​​the target farmland. The specific analysis process is as follows: By calculating the formula The capacity influencing factor σ corresponding to the dth cracking sub-area of ​​the target farmland is obtained by analysis. d , d is the number corresponding to each crack sub-area, d = 1, 2...g, g is any integer greater than 2, θ′ is the historical reference penetration depth, is the historical reference penetration velocity, τ1 and τ2 are the weight factors of the historical penetration depth and penetration velocity, respectively, and θ d is the historical penetration depth corresponding to the dth cracked sub-area of ​​the target farmland, is the historical infiltration velocity corresponding to the d-th cracked sub-area of ​​the target farmland, 0<τ1<1, 0<τ2<1.

7. The agricultural drought monitoring method based on multi-source remote sensing data as claimed in claim 5, characterized in that: The analysis obtains the water adjustment value corresponding to each crop in each cracked sub-area of ​​the target farmland. The specific analysis process is as follows: According to the calculation formula Calculate the water adjustment value corresponding to the rth crop in the dth cracked sub-area of ​​the target farmland Ω1 represents the compensation factor of the distance between the soil depth layer of the cracked sub-region and the crop root system, η represents the distance between each cracked sub-region and each crop root system in history, e is a natural constant, σ d is the capacity influencing factor corresponding to the dth cracked sub-area of ​​the target farmland.

8. The agricultural drought monitoring method based on multi-source remote sensing data according to claim 1, characterized in that: The screening obtains the various crops corresponding to the cracked sub-regions of the target farmland, and the specific screening process is as follows: The water adjustment value corresponding to each crop in each cracked sub-area of ​​the target farmland is compared with the water adjustment value interval corresponding to the crops stored in the database. If the water adjustment value corresponding to a crop in a cracked sub-area of ​​the target farmland is within the water adjustment value interval corresponding to the crops stored in the database, the crop stored in the database will be used as the crop corresponding to the cracked sub-area of ​​the target farmland, so as to obtain the planted crops corresponding to each cracked sub-area of ​​the target farmland.

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