Forestry precision irrigation method based on combination of satellite remote sensing and ground sensing network

Abstract

This invention discloses a precision irrigation method for forestry, comprising the following steps: acquiring remote sensing data of vegetation in different target areas and transmitting it to an Internet of Things (IoT) cloud platform; acquiring real-time environmental data in different target areas and transmitting it to the IoT cloud platform; the IoT cloud platform processing the received data; acquiring meteorological data of different target areas through a meteorological station and optimizing the irrigation amount information of different target areas based on the meteorological data to obtain optimized irrigation amount information for different target areas; the IoT cloud platform generating corresponding execution control commands based on the optimized irrigation amount information and sending the commands to the irrigation execution module; the irrigation execution module generating corresponding equipment control commands to control valves and water pumps to perform irrigation operations. The method provided by this invention, by accurately assessing irrigation amounts, supplies the required water only to the areas requiring irrigation and during the required irrigation periods, avoiding the ineffective loss of water resources.

Description

Technical Field

[0001] This invention relates to the field of forestry irrigation technology, specifically to a precision forestry irrigation method based on the combination of satellite remote sensing and ground sensor networks. Background Technology

[0002] Forestry, as a core carrier of ecological civilization construction, not only undertakes the ecological functions of conserving water resources, maintaining soil and water, and regulating climate, but also serves as an important foundation for ensuring timber security and enhancing the value of ecological products. With the intensification of global climate change and the frequent occurrence of extreme weather events, the contradiction between water scarcity and uneven distribution is becoming increasingly prominent. Especially in arid and semi-arid regions, water has become a core bottleneck restricting the survival rate, growth quality, and ecological function of trees, making the need for precise and intelligent forestry irrigation increasingly urgent.

[0003] Currently, there are two main technical approaches in the field of forestry irrigation: traditional irrigation mode and single sensor / remote sensing irrigation mode. However, both have significant drawbacks and are difficult to meet the needs of modern forestry for high-quality development and precision irrigation.

[0004] Traditional forestry irrigation mainly employs extensive methods such as flood irrigation and furrow irrigation, accounting for more than 70% of existing irrigation methods. This model relies entirely on manual experience to determine the timing and amount of irrigation, without considering key factors such as tree growth cycles, soil moisture differences, vegetation type distinctions, and microclimate changes. This not only results in extremely low water resource utilization, with an effective irrigation water utilization coefficient of only 0.45, far below the 0.8 level in developed countries, but also wastes more than 10 billion cubic meters of water annually due to inefficient irrigation. Furthermore, it easily leads to secondary environmental problems such as soil salinization and compaction, while increasing manual management costs. Moreover, it is difficult to adapt to large-scale and complex forest areas, resulting in uneven tree growth, with afforestation survival rates in some arid areas hovering below 60% for a long time.

[0005] To address the problem of inefficient traditional irrigation, the industry has gradually seen attempts at irrigation based on single satellite remote sensing technology or single ground sensor network technology. However, both of these single technology approaches have inherent limitations and cannot achieve truly precise irrigation. Summary of the Invention

[0006] To address the aforementioned problems, this invention provides a precision irrigation method for forestry based on the combination of satellite remote sensing and ground sensor networks.

[0007] The objective of this invention is achieved through the following technical solution: A precision irrigation method for forestry based on the combination of satellite remote sensing and ground sensor networks is applied to a precision irrigation system for forestry. The precision irrigation system includes: a satellite remote sensing module, a ground sensor network module, an Internet of Things (IoT) cloud platform, and an irrigation execution module. The precision irrigation method for forestry includes the following steps: The overall target area is divided into multiple target areas; The satellite remote sensing module acquires vegetation remote sensing data in different target areas and transmits the vegetation remote sensing data to the Internet of Things cloud platform. The ground sensor module acquires real-time environmental data in different target areas and transmits the real-time environmental data to the Internet of Things cloud platform. The IoT cloud platform processes the received vegetation remote sensing data and real-time environmental data, and evaluates the irrigation amount information in different target areas based on the processing results. The IoT cloud platform also obtains meteorological data from different target areas through the meteorological station, and optimizes the irrigation amount information in different target areas by combining the meteorological data to obtain optimized irrigation amount information in different target areas. The IoT cloud platform generates corresponding execution control instructions based on the optimized irrigation amount information, and sends the execution control instructions to the irrigation execution module; The irrigation execution module generates corresponding equipment control commands based on the received execution control commands, and controls the valves and water pumps to perform irrigation operations, so as to achieve precise irrigation of vegetation in different target areas.

[0008] The precision irrigation method for forestry based on the combination of satellite remote sensing and ground sensor network provided by this invention, compared with traditional extensive irrigation methods such as flood irrigation and sprinkler irrigation, accurately assesses the irrigation volume and supplies the required amount of water only to the areas that need irrigation and at the time when irrigation is needed, thus avoiding the ineffective loss of water resources and improving the water resource utilization rate by more than 30%. It is especially suitable for forestry areas with scarce water resources such as mountains and hills.

[0009] Preferably, the step of acquiring vegetation remote sensing data within different target areas through the satellite remote sensing module specifically involves: Remote sensing data of vegetation in different target areas is acquired by using multispectral sensors, thermal infrared sensors, hyperspectral sensors and visible light cameras carried on artificial satellites. The vegetation remote sensing data includes: vegetation spectral index, canopy temperature, soil surface moisture, topography and landforms, and vegetation coverage.

[0010] Preferably, the ground sensor module includes a wireless sensor network consisting of multiple sensor nodes deployed in different target areas and a base station device. The sensor nodes are used to collect real-time environmental data of their location and transmit it to the base station device. The base station device is deployed at the center of the total target area and aggregates the real-time environmental data collected by each sensor node and transmits it to the Internet of Things cloud platform.

[0011] Preferably, the sensor node includes one or more of the following: soil moisture sensor, soil temperature sensor, air temperature and humidity sensor, light sensor, wind speed sensor, and rainfall sensor.

[0012] Preferably, the IoT cloud platform processes the received vegetation remote sensing data and real-time environmental data, specifically: Based on the received vegetation remote sensing data and the real-time environmental data, the water resource loss value of vegetation in different target areas is calculated using the following formula. , In the formula, Let i be the water resource loss value of the target area. Let be the soil thermal inertia coefficient of target region i, used to correct for the influence of soil type on heat conduction. Let be the surface albedo of target region i. Let be the amount of shortwave solar radiation received by target region i. Let be the amount of longwave solar radiation received by target region i. Let be the soil heat flux of target region i. This is the constant of the wet-bulb and dry-bulb thermometer; The real-time air temperature of target area i, in degrees Celsius; Let be the average surface wind speed in target area i; Let be the saturated water vapor pressure at the temperature corresponding to target region i; Let i be the actual water vapor pressure in the target region. Let be the slope of the saturated water vapor pressure versus temperature curve within the target region i. The actual soil volumetric water content of target region i; Let be the soil saturation water content of target area i; Let i be the soil moisture attenuation coefficient within the target region i. This is the slope correction function for the target region i; These are vegetation physiological state correction coefficients, dimensionless coefficients used to characterize the impact of the current vegetation health status in target area i on water demand, with values ​​ranging from 0 to 1. Let be the reflectance value in the near-infrared band of the remote sensing image of target region i. The reflectance value of the red band in the remote sensing image of target area i is used to ensure vegetation coverage and health.

[0013] The above formula allows for the calculation of water resource loss in vegetation within each target area, providing a precise understanding of water resource loss in each area. This facilitates adjustments to irrigation amounts for each target area based on its specific conditions, improving the scientific rigor and foresight of irrigation decisions. The formula integrates solar shortwave / longwave radiation, surface reflectivity, soil heat flux, air temperature, and wind speed, comprehensively reconstructing the energy exchange process between vegetation and soil in different target areas, and accurately quantifying water loss in each area. It also incorporates saturated vapor pressure difference and the ratio of soil volumetric water content to saturated water content, directly linking to the water supply capacity of vegetation transpiration and soil evaporation. Forestry scenarios are often located in hilly or mountainous areas, where uneven terrain leads to uneven distribution of sunlight and water, posing a challenge to precision irrigation. The formula innovatively incorporates a slope correction function. This function quantifies the impact of terrain slope on the angle of solar radiation incidence and gravitational water loss. On steep, sunny slopes, the system automatically increases... The system calculates the weights to provide early warnings of water loss risks; in low-lying, shady slopes, it appropriately reduces irrigation demand. This terrain-adaptive correction function effectively eliminates the interference of terrain on water distribution, fills the technological gap in precision irrigation under complex mountainous terrain, and ensures that every inch of land in the forest area receives water on demand.

[0014] Preferably, the base station equipment is located at the center of the total target area, and each sensor node is randomly deployed in different target areas, ensuring that at least one sensor node is deployed in each target area. There are 10 sensor nodes, among which... The following formula is used to calculate: in, Let i be the number of sensors within the target region i. Let i be the area of ​​the target region. The effective sensing radius of the sensor node. This is the redundancy coefficient, and its value ranges from 1.1 to 1.2. This is the sensor attenuation coefficient in a ground sensor network, which is determined by the type of sensor used in the ground sensor network. This is the threshold value for the ground sensor network, which is related to the accuracy that the ground sensor network needs to achieve. Its value ranges from [0,1], and the higher the accuracy, the smaller its value. Let be the slope influence factor of target region i. This is the floor function.

[0015] In a target area, deploying too few sensors will fail to accurately reflect the actual situation, while deploying too many will increase costs and waste resources. In the above embodiment, the minimum number of sensors required for each target area is calculated using the above formula, allowing staff to deploy the required number of sensors, achieving a balance between accurate measurement and cost. Simultaneously, the formula considers the area of ​​the current region and the effective sensing radius of the sensor nodes, breaking away from the drawbacks of traditional "experience-based" and "average" sensor deployment. It can accurately calculate the required number of sensors for target areas of different sizes, ensuring that the coverage of sensor nodes in each target area precisely matches the area size, fundamentally avoiding "sensing blind spots" and "duplication of coverage" problems, and ensuring the integrity and accuracy of monitoring data. The influence of terrain is also considered. On steep slopes, signal propagation paths are longer and more easily obstructed, and soil moisture loss is rapid, making monitoring crucial. Introducing a slope influence factor allows for increasing the number of sensor nodes deployed based on terrain conditions, while reducing the configuration in flat areas as needed. This achieves dynamic adaptation of sensor deployment to terrain features, ensuring the accuracy and effectiveness of monitoring data in complex terrain, supporting the scientific basis for subsequent decisions such as precision irrigation.

[0016] Preferably, after the sensor nodes and base station equipment are deployed, a clustered wireless sensor network is constructed according to a preset clustering mechanism, and the real-time environmental data collection and transmission in different target areas are completed through the wireless sensor network.

[0017] Preferably, the IoT cloud platform processes the received vegetation remote sensing data and real-time environmental data, and evaluates irrigation information for different target areas based on the processed results, specifically: Based on the obtained water resource loss coefficients of vegetation in different target areas and theoretical water demand To assess irrigation data for different target areas.

[0018] The beneficial effects of this invention are as follows: The precision irrigation method for forestry based on the combination of satellite remote sensing and ground sensor networks provided by this invention, compared with traditional extensive irrigation methods such as flood irrigation and sprinkler irrigation, accurately assesses the irrigation volume and supplies the required amount of water only to the areas that need irrigation and at the required time, avoiding ineffective water loss and improving water resource utilization rate by more than 30%. It is especially suitable for forestry areas with scarce water resources such as mountainous and hilly areas. Moreover, the irrigation method provided by this invention, while ensuring normal vegetation growth, can precisely control the irrigation volume to avoid soil compaction, deep seepage, and water waste, while improving vegetation health and carbon sequestration capacity, achieving a synergistic improvement in forestry effectiveness and economic benefits. Attached Figure Description

[0019] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0020] Figure 1 This is a structural diagram of the precision irrigation system for forestry provided in an embodiment of the present invention.

[0021] Figure labels: Satellite remote sensing module 1; Ground sensor network module 2; Internet of Things cloud platform 3; Irrigation execution module 4; Meteorological station 5. Detailed Implementation

[0022] The present invention will be further described in conjunction with the following embodiments.

[0023] Figure 1 A precision irrigation system for forestry is illustrated, comprising: a satellite remote sensing module 1, a ground sensor network module 2, an Internet of Things (IoT) cloud platform 3, and an irrigation execution module 4. Based on this system, a precision irrigation method for forestry combining satellite remote sensing and ground sensor networks is provided, comprising the following steps: The total target area is divided into multiple target areas, i = 1, 2, 3, ..., I; i is the i-th target area, and I is the total number of target areas.

[0024] The satellite remote sensing module 1 acquires vegetation remote sensing data in different target areas and transmits the vegetation remote sensing data to the Internet of Things cloud platform 3. The ground sensor module 2 acquires real-time environmental data in different target areas and transmits the real-time environmental data to the Internet of Things cloud platform 3. The IoT cloud platform 3 processes the received vegetation remote sensing data and real-time environmental data, and evaluates the irrigation amount information in different target areas based on the processing results. The IoT cloud platform 3 also obtains meteorological data in different target areas through the meteorological station 5, and optimizes the irrigation amount information in different target areas obtained by the evaluation based on the meteorological data to obtain optimized irrigation amount information in different target areas. The IoT cloud platform 3 generates corresponding execution control instructions based on the optimized irrigation amount information, and sends the execution control instructions to the irrigation execution module 4; The irrigation execution module 4 generates corresponding equipment control commands based on the received execution control commands, and controls the valves and water pumps to perform irrigation operations, so as to achieve precise irrigation of vegetation in different target areas.

[0025] The precision irrigation method for forestry based on the combination of satellite remote sensing and ground sensor network provided by this invention, compared with traditional extensive irrigation methods such as flood irrigation and sprinkler irrigation, accurately assesses the irrigation volume and supplies the required amount of water only to the areas that need irrigation and at the time when irrigation is needed, thus avoiding the ineffective loss of water resources and improving the water resource utilization rate by more than 30%. It is especially suitable for forestry areas with scarce water resources such as mountains and hills.

[0026] Preferably, the acquisition of vegetation remote sensing data within different target areas via the satellite remote sensing module 1 specifically includes: Remote sensing data of vegetation in different target areas is acquired by using multispectral sensors, thermal infrared sensors, hyperspectral sensors and visible light cameras carried on artificial satellites. The vegetation remote sensing data includes: vegetation spectral index, canopy temperature, soil surface moisture, topography and landforms, and vegetation coverage.

[0027] Preferably, the ground sensor module 2 includes a wireless sensor network consisting of multiple sensor nodes deployed in different target areas and a base station device. The sensor nodes are used to collect real-time environmental data of their location and transmit it to the base station device. The base station device is deployed at the center of the total target area and aggregates the real-time environmental data collected by each sensor node and transmits it to the Internet of Things cloud platform 3.

[0028] Preferably, the sensor node includes one or more of the following: soil moisture sensor, soil temperature sensor, air temperature and humidity sensor, light sensor, wind speed sensor, and rainfall sensor.

[0029] Preferably, the IoT cloud platform 3 processes the received vegetation remote sensing data and real-time environmental data, specifically: Based on the received vegetation remote sensing data and the real-time environmental data, the water resource loss value of vegetation in different target areas is calculated using the following formula. , In the formula, Let i be the water resource loss value of the target area. Let be the soil thermal inertia coefficient of target region i, used to correct for the influence of soil type on heat conduction. Let be the surface albedo of target region i. Let be the amount of shortwave solar radiation received by target region i. Let be the amount of longwave solar radiation received by target region i. Let be the soil heat flux of target region i. This is the constant of the wet-bulb and dry-bulb thermometer; The real-time air temperature of target area i, in degrees Celsius; Let be the average surface wind speed in target area i; Let be the saturated water vapor pressure at the temperature corresponding to target region i; Let i be the actual water vapor pressure in the target region. Let be the slope of the saturated water vapor pressure versus temperature curve within the target region i. The actual soil volumetric water content of target region i; Let be the soil saturation water content of target area i; Let i be the soil moisture attenuation coefficient within the target region i. This is the slope correction function for the target region i; These are vegetation physiological state correction coefficients, dimensionless coefficients used to characterize the impact of the current vegetation health status in target area i on water demand, with values ​​ranging from 0 to 1. Let be the reflectance value in the near-infrared band of the remote sensing image of target region i. The reflectance value of the red band in the remote sensing image of target area i is used to ensure vegetation coverage and health.

[0030] The above formula allows for the calculation of water resource loss in vegetation within each target area, providing a precise understanding of water resource loss in each area. This facilitates adjustments to irrigation amounts for each target area based on its specific conditions, improving the scientific rigor and foresight of irrigation decisions. The formula integrates solar shortwave / longwave radiation, surface reflectivity, soil heat flux, air temperature, and wind speed, comprehensively reconstructing the energy exchange process between vegetation and soil in different target areas, and accurately quantifying water damage in each area. It also incorporates saturated vapor pressure difference and the ratio of soil volumetric water content to saturated water content, directly linking to the water supply capacity of vegetation transpiration and soil evaporation. Forestry scenarios are often located in hilly or mountainous areas, where uneven terrain leads to uneven distribution of sunlight and water, posing a challenge to precision irrigation. The formula innovatively incorporates a slope correction function. This function quantifies the impact of terrain slope on the angle of solar radiation incidence and gravitational water loss. On steep, sunny slopes, the system automatically increases... The system calculates the weights to provide early warnings of water loss risks; in low-lying, shady slopes, it appropriately reduces irrigation demand. This terrain-adaptive correction function effectively eliminates the interference of terrain on water distribution, fills the technological gap in precision irrigation under complex mountainous terrain, and ensures that every inch of land in the forest area receives water on demand.

[0031] Preferably, the slope correction function The effect of standard slope on soil moisture retention capacity can be calculated using the following formula: In the formula, This is the slope influence coefficient. This represents the maximum slope value for the total target area. Let be the average slope angle of the target area i.

[0032] This formula achieves non-linear quantification of the effect of slope. For vegetation with shallow root systems, a larger value can be set. The value is set to reflect its greater sensitivity to water loss caused by slope; for vegetation with deeper root systems, a smaller value can be set. Value. And by setting an appropriate slope correction function. This can more accurately reflect the impact of different terrains and vegetation on water loss in different target areas.

[0033] Preferably, the base station equipment is located at the center of the total target area, and each sensor node is randomly deployed in different target areas, ensuring that at least one sensor node is deployed in each target area. Sensor nodes, among which, The following formula is used to calculate: in, Let i be the number of sensors within the target region i. Let i be the area of ​​the target region. The effective sensing radius of the sensor node. This is the redundancy coefficient, and its value ranges from 1.1 to 1.2. This is the sensor attenuation coefficient in a ground sensor network, which is determined by the type of sensor used in the ground sensor network. The threshold of the ground sensor network is related to the accuracy that the ground sensor network needs to achieve. Its value ranges from [0,1]. The higher the accuracy, the smaller the value. Let be the slope influence factor of target region i. This is the floor function.

[0034] In a target area, deploying too few sensors will fail to accurately reflect the actual situation, while deploying too many will increase costs and waste resources. In the above embodiment, the minimum number of sensors required for each target area is calculated using the above formula, allowing staff to deploy the required number of sensors, achieving a balance between accurate measurement and cost. Simultaneously, the formula considers the area of ​​the current region and the effective sensing radius of the sensor nodes, breaking away from the drawbacks of traditional "experience-based" and "average" sensor deployment. It can accurately calculate the required number of sensors for target areas of different sizes, ensuring that the coverage of sensor nodes in each target area precisely matches the area size, fundamentally avoiding "sensing blind spots" and "duplication of coverage," and guaranteeing the integrity and accuracy of monitoring data. The influence of terrain is also considered. On steep slopes, signal propagation paths are longer and more susceptible to obstruction, while soil moisture loss is rapid, making monitoring crucial. Introducing a slope influence factor allows for increasing the number of sensor nodes deployed based on terrain conditions, while reducing the configuration in flat areas as needed. This achieves dynamic adaptation of sensor deployment to terrain features, ensuring the accuracy and effectiveness of monitoring data in complex terrain, supporting the scientific basis for subsequent decisions such as precision irrigation.

[0035] Preferably, after the sensor nodes and base station equipment are deployed, a clustered wireless sensor network is constructed according to a preset clustering mechanism, and the real-time environmental data collection and transmission in different target areas are completed through the wireless sensor network.

[0036] Preferably, after the sensor nodes and base station equipment are deployed, a clustered wireless sensor network is constructed according to a preset clustering mechanism, specifically: After the sensor nodes and base station equipment are deployed, all sensor nodes first send their own information to the base station equipment. After receiving the corresponding information, the base station equipment calculates the advantage value of each sensor node to be selected as a cluster head, and sorts the advantage values ​​in descending order. The base station equipment selects the top M sensor nodes from the sorted values ​​to be selected as cluster head nodes. The remaining sensor nodes are added to the cluster head with the closest distance to them and become the cluster member nodes of the corresponding cluster head, thus completing the clustering. The sensor node's own information includes: location information, energy information, etc.

[0037] The advantage value for each sensor node to be selected as the cluster head is calculated using the following formula: In the formula, Let sensor node j be the dominant value for being selected as the cluster head. These are the current remaining energy value and the initial energy value of sensor node j, respectively. The current available buffer capacity for sensor node j. Let j be the buffer capacity for the maximum data of sensor node j. Let be the distance from sensor node j to the base station device (base). Let be the average of the sum of the distances from all neighboring nodes of sensor node j to the base station device. Let be the sum of the distances between sensor node j and all its neighboring nodes. The number of neighboring nodes of sensor node j This represents the maximum number of neighboring nodes within the wireless sensor network. As a weighting factor, it satisfies In the above embodiments, the remaining energy of a node is evaluated in a coupled manner with the communication distance. Based on prioritizing nodes with high remaining energy, the distance from the node to the base station, the average distance to neighboring nodes, and the sum of the total distances to neighboring nodes are further introduced, achieving dual optimization of "energy endurance" and "communication energy consumption." This avoids network failure caused by low-energy nodes prematurely becoming cluster heads, and reduces the energy consumption of long-distance transmission by cluster heads through distance weighting, significantly extending the overall lifespan of the wireless sensor network. By introducing the ratio of a node's current buffer capacity to its maximum data buffer capacity as an election criterion, the selected sensor node as the cluster head is ensured to have sufficient data carrying and forwarding capabilities. This effectively avoids data congestion, packet loss, and delays caused by insufficient cluster head buffering capacity, ensuring the real-time and complete transmission of data such as soil moisture, temperature, and soil water content to the IoT cloud platform. By introducing the neighbor node criterion, sensor nodes with a large number of neighbor nodes and a wide coverage area are prioritized as cluster heads. This ensures stable communication between the cluster head and surrounding sensor nodes (i.e., cluster member nodes). Furthermore, a highly connected cluster head can efficiently aggregate data from its cluster member nodes, improving the anti-interference capability and fault redundancy of the wireless sensor network.

[0038] Preferably, the IoT cloud platform 3 processes the received vegetation remote sensing data and real-time environmental data, and evaluates irrigation information for different target areas based on the processed results, specifically: Based on the obtained water resource loss coefficients of vegetation in different target areas and theoretical water demand To assess irrigation data for different target areas, specifically by using the following formula to calculate the irrigation data for different target areas. Conduct an assessment: In the formula, Let i be the irrigation amount within the target area i. This refers to the theoretical water requirement within target area i, which is the basic water consumption for plant physiological growth within target area i. It is only related to inherent attributes such as vegetation type, tree age / seedling age, planting density, and vegetation cover within the target area. Let be the average slope angle of the target area i.

[0039] The above formula introduces This is an incremental compensation factor based on slope angle, which establishes a linear correlation between slope angle and water loss, ensuring that the results include not only basal losses due to the environment but also additional water loss due to gravity. As the coefficient increases, the amount of irrigation water increases linearly. This increase ensures that in areas with steeper slopes, irrigation levels will be increased to compensate for water loss caused by the terrain.

[0040] Preferably, in order to further and more accurately assess irrigation volume, the IoT cloud platform also obtains meteorological data from different target areas through the meteorological station, and optimizes the irrigation volume information in different target areas by combining the meteorological data to obtain optimized irrigation volume information in different target areas. The meteorological data includes, but is not limited to: real-time temperature, relative humidity, solar radiation intensity, wind speed, and precipitation probability.

[0041] Based on this real-time meteorological data, the IoT cloud platform 3 obtained irrigation data for different target areas. Dynamic adjustments are made to obtain optimized irrigation information for different target areas. Specifically, the irrigation amount for different target areas is calculated using the following formula. Make corrections: In the formula, Optimal irrigation information for target region i. Forecast precipitation (mm) for the target area i in the future time period. Let i be the solar radiation intensity within the target area. Let i be the relative humidity within the target area. Let i be the average surface wind speed in target area i. Let i be the area of ​​the target region. These are the precipitation compensation coefficient, solar radiation correction coefficient, humidity correction coefficient, and wind speed correction coefficient, respectively. They are all empirical constants between (0,1) and are calibrated based on factors such as climate, vegetation type, and soil type within the target area.

[0042] Precipitation data from weather forecasts This directly offsets irrigation demand, avoiding water waste caused by repeated irrigation, and simultaneously addresses rainfall timing in advance, enhancing the foresight of irrigation decisions. Furthermore, by comprehensively considering meteorological factors such as solar radiation, relative humidity, and wind speed, it precisely quantifies evaporation loss within the target area, ensuring a high degree of matching between irrigation volume and actual water consumption, thus solving the problem of the one-size-fits-all approach of traditional fixed irrigation models. This enables personalized irrigation for regions with different terrains, vegetation, and soil types, thereby improving the precision of forestry irrigation.

[0043] The beneficial effects of this invention are as follows: The precision irrigation method for forestry based on the combination of satellite remote sensing and ground sensor networks provided by this invention, compared with traditional extensive irrigation methods such as flood irrigation and sprinkler irrigation, accurately assesses the irrigation volume and supplies the required amount of water only to the areas that need irrigation and during the periods when irrigation is needed, avoiding the ineffective loss of water resources and improving water resource utilization rate by more than 30%. It is especially suitable for forestry areas with scarce water resources such as mountainous and hilly areas. The embodiments of this invention use a slope correction function... By incorporating a water resource depletion model, the dynamics of water availability under complex terrain can be more realistically reproduced. This correction term can dynamically adjust the soil moisture availability coefficient, so that when calculating the water demand of different target areas, not only vegetation growth and meteorological conditions are considered, but also the terrain-vegetation coupling effect is fully taken into account. This results in a more precise irrigation plan that meets actual needs, effectively avoiding insufficient irrigation or water waste caused by terrain factors.

[0044] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. A precision irrigation method for forestry based on the combination of satellite remote sensing and ground sensor networks, characterized in that, It is applied to a precision forestry irrigation system, which includes: a satellite remote sensing module, a ground sensor network module, an Internet of Things cloud platform, and an irrigation execution module; the precision forestry irrigation method includes the following steps: The overall target area is divided into multiple target areas; The satellite remote sensing module acquires vegetation remote sensing data in different target areas and transmits the vegetation remote sensing data to the Internet of Things cloud platform. The ground sensor module acquires real-time environmental data in different target areas and transmits the real-time environmental data to the Internet of Things cloud platform. The IoT cloud platform processes the received vegetation remote sensing data and real-time environmental data, and evaluates the irrigation amount information in different target areas based on the processing results. The IoT cloud platform also obtains meteorological data from different target areas through the meteorological station, and optimizes the irrigation amount information in different target areas by combining the meteorological data to obtain optimized irrigation amount information in different target areas. The IoT cloud platform generates corresponding execution control instructions based on the optimized irrigation amount information, and sends the execution control instructions to the irrigation execution module; The irrigation execution module generates corresponding equipment control commands based on the received execution control commands, and controls the valves and water pumps to perform irrigation operations, so as to achieve precise irrigation of vegetation in different target areas.

2. The precision irrigation method for forestry according to claim 1, characterized in that, The acquisition of vegetation remote sensing data in different target areas through the satellite remote sensing module specifically includes: Remote sensing data of vegetation in different target areas is acquired by using multispectral sensors, thermal infrared sensors, hyperspectral sensors and visible light cameras carried on artificial satellites. The vegetation remote sensing data includes: vegetation spectral index, canopy temperature, soil surface moisture, topography and landforms, and vegetation coverage.

3. The precision irrigation method for forestry according to claim 2, characterized in that, The ground sensor module includes a wireless sensor network consisting of multiple sensor nodes deployed in different target areas and a base station device. The sensor nodes are used to collect real-time environmental data of their location and transmit it to the base station device. The base station device is deployed at the center of the total target area and aggregates the real-time environmental data collected by each sensor node and transmits it to the Internet of Things cloud platform.

4. The precision irrigation method for forestry according to claim 3, characterized in that, The sensor nodes include one or more of the following: soil moisture sensor, soil temperature sensor, air temperature and humidity sensor, light sensor, wind speed sensor, and rainfall sensor.

5. The precision irrigation method for forestry according to claim 4, characterized in that, The IoT cloud platform processes the received vegetation remote sensing data and real-time environmental data, specifically: Based on the received vegetation remote sensing data and the real-time environmental data, the water resource loss value of vegetation in different target areas is calculated using the following formula. , In the formula, Let i be the water resource loss value of the target area. Let be the soil thermal inertia coefficient of target region i, used to correct for the influence of soil type on heat conduction. Let be the surface albedo of target region i. Let be the amount of shortwave solar radiation received by target region i. Let be the amount of longwave solar radiation received by target region i. Let be the soil heat flux of target region i. This is the constant of the wet-bulb and dry-bulb thermometer; The real-time air temperature of target area i, in degrees Celsius; Let be the average surface wind speed in target area i; Let be the saturated water vapor pressure at the temperature corresponding to target region i; Let i be the actual water vapor pressure in the target region. Let be the slope of the saturated water vapor pressure versus temperature curve within the target region i. The actual soil volumetric water content of target region i; Let be the soil saturation water content of target area i; Let i be the soil moisture attenuation coefficient within the target region i. This is the slope correction function for the target region i; These are the correction coefficients for vegetation physiological state. Let be the reflectance value in the near-infrared band of the remote sensing image of target region i. denoted as the reflectance value of the red band in the remote sensing image of target region i.

6. The precision irrigation method for forestry according to claim 3, characterized in that, The base station equipment is located at the center of the total target area, and the sensor nodes are randomly deployed in different target areas, ensuring that at least one sensor is deployed in each target area. Sensor nodes, among which, The following formula is used to calculate: in, Let i be the number of sensors within the target region i. Let i be the area of ​​the target region. The effective sensing radius of the sensor node. This is the redundancy coefficient, and its value ranges from 1.1 to 1.

2. This represents the sensor attenuation coefficient in a ground-based sensor network. For ground sensor network thresholds, Let be the slope influence factor of target region i. This is the floor function.

7. The precision irrigation method for forestry according to claim 6, characterized in that, After the sensor nodes and base station equipment are deployed, a clustered wireless sensor network is constructed according to a preset clustering mechanism, and real-time environmental data is collected and transmitted in different target areas through the wireless sensor network.

8. The precision irrigation method for forestry according to claim 7, characterized in that, The IoT cloud platform processes the received vegetation remote sensing data and real-time environmental data, and evaluates irrigation information for different target areas based on the processed results. Specifically, it evaluates the irrigation amount based on the obtained water resource loss coefficient of vegetation in different target areas. and theoretical water demand To assess irrigation data for different target areas.

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