CN117093813A - Composite drought index calculation method - Google Patents
Composite drought index calculation method Download PDFInfo
- Publication number
- CN117093813A CN117093813A CN202311141390.XA CN202311141390A CN117093813A CN 117093813 A CN117093813 A CN 117093813A CN 202311141390 A CN202311141390 A CN 202311141390A CN 117093813 A CN117093813 A CN 117093813A
- Authority
- CN
- China
- Prior art keywords
- index
- drought
- rainfall
- vegetation
- ndvi
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004364 calculation method Methods 0.000 title claims abstract description 24
- 239000002131 composite material Substances 0.000 title claims abstract description 14
- 238000000034 method Methods 0.000 claims abstract description 44
- 239000002689 soil Substances 0.000 claims abstract description 32
- 238000012544 monitoring process Methods 0.000 claims abstract description 29
- 230000008569 process Effects 0.000 claims abstract description 17
- 238000001556 precipitation Methods 0.000 claims abstract description 11
- 238000011156 evaluation Methods 0.000 claims abstract description 8
- 101000701853 Rattus norvegicus Serine protease inhibitor A3N Proteins 0.000 claims abstract description 7
- 238000010219 correlation analysis Methods 0.000 claims abstract description 4
- 230000002159 abnormal effect Effects 0.000 claims description 20
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 9
- 238000011161 development Methods 0.000 claims description 8
- 238000002310 reflectometry Methods 0.000 claims description 6
- 230000008020 evaporation Effects 0.000 claims description 3
- 238000001704 evaporation Methods 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 238000000131 plasma-assisted desorption ionisation Methods 0.000 claims description 3
- 239000011435 rock Substances 0.000 claims description 3
- 230000003442 weekly effect Effects 0.000 claims description 3
- 230000002950 deficient Effects 0.000 claims 4
- 238000011160 research Methods 0.000 abstract description 8
- 230000001186 cumulative effect Effects 0.000 abstract description 4
- 230000007547 defect Effects 0.000 description 7
- 230000006872 improvement Effects 0.000 description 4
- 230000004044 response Effects 0.000 description 4
- 230000015572 biosynthetic process Effects 0.000 description 2
- 238000007418 data mining Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/15—Correlation function computation including computation of convolution operations
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
The application relates to the technical field of climate research, in particular to a composite drought index calculation method, which calculates a Precipitation Condition Index (PCI), a soil humidity index (SMCI), a Temperature Condition Index (TCI) and a vegetation state index (VCI) by using precipitation, soil humidity, surface temperature, vegetation state and crop information data. Based on the method, 3 composite drought indexes including a Temperature Vegetation Drought Index (TVDI), an Optimized Vegetation Drought Index (OVDI) and a process-based cumulative drought index (PADI) are further calculated, and the applicability of the 3 indexes in drought prone areas of three major agricultural main areas of China is analyzed through linear correlation analysis and comparison of agricultural drought conditions with SPI-3 so as to provide reference for drought monitoring and evaluation work of typical agricultural areas of China.
Description
Technical Field
The application relates to the technical field of climate research, in particular to a composite drought index calculation method.
Background
Drought formation and development are determined by both moisture supply and moisture demand, and occur simultaneously under both global scale climate activity anomalies and local scale environmental conditions. To cope with increasingly severe drought conditions, monitoring and evaluation of drought is urgently needed to achieve dynamics, systemization, multisource and integration.
In order to efficiently cope with new normalcy of increasing sensor resources, a set of theory and method of satellite-ground multi-sensor cooperation is urgently needed to be established, the monitoring advantage of the multi-sensor cooperation is fully exerted, a novel drought index is further developed, and efficient drought monitoring and accurate disaster assessment are finally achieved.
The natural variables closely related to drought are: precipitation, snow, relative humidity, atmospheric temperature, surface temperature, soil moisture, surface runoff, vegetation status, and the like. At present, most of drought monitoring is in an index form, and natural variables are monitored, synthesized and evaluated, so that the characteristics of an index model are considered to be single-factor index, simple multi-factor comprehensive index and complex comprehensive index.
Single factor indices include simple single factor indices and complex single factor indices, where complex single factor indices also employ only a single drought variable, but the calculation process is generally more complex and scientific.
The simple multi-factor comprehensive index is generally used for simultaneously examining various drought variables, and the comprehensive expression of the drought state is obtained through simple calculation, so that the index is easy to understand, the calculation process is not difficult, and the business monitoring product can be formed rapidly. However, the drought index model and the threshold value thereof generally have specific regional applicability, and weight variables may be introduced in the calculation process, so that the universality of each region cannot be completely ensured.
The complex multi-factor comprehensive index is similar to the simple multi-factor comprehensive index, and meanwhile, various drought variables are considered, but the difference is that a calculation model of the index generally adopts a hydrothermal balance process, a data mining process or a connection function and the like, and because the states of various early-dry variables are fused, the complex multi-factor comprehensive index can provide more accurate drought information than the single-factor index and the simple multi-factor comprehensive index, but because the calculation process needs to be fused with various parameters and the calculated amount is larger, the calculation process needs to be checked and corrected for a longer time. In order to pursue more accurate drought monitoring results, current research on drought monitoring continues to introduce new and more drought indicating variables on one hand, and continues to discuss new models for multiple drought variable synthesis on the other hand.
The occurrence, development and impact of drought are extremely complex. At present, complex comprehensive indexes achieve good performance in early-dry monitoring, and mostly realize the coordination of various observation data through methods such as linear models, data mining, complex water balance equations or connection functions. And the current multi-sensor cooperation research is still in the research stage of single drought element, and can not answer how the multi-sensor cooperation is closely coupled with the multi-variable monitoring in the drought monitoring process, so that the deep integration problem is finally realized.
Therefore, a composite drought index calculation method needs to be designed, the observation capability of a sensor is systematically and completely analyzed, the defects of the existing research are overcome, a multi-sensor cooperation method is enriched, a satellite-ground cooperation soil moisture reconstruction method and a satellite-ground cooperation monitoring method facing the drought process realize capability cooperation of remote sensing and ground sensors theoretically, continuous monitoring and accurate evaluation of the drought occurrence and development process are realized based on multi-sensor cooperation, the drought cumulative influence degree is realized, the efficiency improvement level compared with the existing monitoring means is obtained, and technical support is provided for a decision maker in drought disaster emergency response.
Disclosure of Invention
The application aims to overcome the defects of the prior art, provides a composite drought index calculation method, systematically and completely analyzes the observation capability of a sensor, overcomes the defects of the prior study, enriches a multi-sensor cooperation method, a satellite-ground cooperation soil moisture reconstruction method and a satellite-ground cooperation monitoring method facing the drought process, theoretically realizes the capability cooperation of remote sensing and ground sensors, realizes continuous monitoring and accurate evaluation of the drought occurrence and development process based on the multi-sensor cooperation, realizes the drought cumulative influence degree, obtains the efficiency improvement level compared with the prior monitoring means, and provides technical support for a decision maker in the drought disaster emergency response.
In order to achieve the above purpose, the application provides a composite drought index calculation method, which calculates a precipitation condition index PCI, a soil humidity index SMCI, a vegetation index NDVI, a temperature condition index TCI and a vegetation state index VCI by using precipitation, soil humidity, surface temperature, vegetation state and crop information data, further calculates a temperature vegetation drought index TVDI on the basis, optimizes three composite drought indexes of a vegetation drought index OVDI and a cumulative drought index PADI based on a process, and compares the three composite drought indexes with the SPI-3 linear correlation analysis, the water conservancy department and the periodical statistics of agricultural drought to monitor and evaluate a drought easy-to-develop area.
The formula of the precipitation condition index PCI is:
P、P max and P min Respectively representing the current month rainfall of a certain pixel, and the historical maximum rainfall and the historical minimum rainfall; the historical maximum rainfall and the historical minimum rainfall are obtained from rainfall data of the same pixel position in the previous 30 years, the value range of PCI is 0-1, and the level of the rainfall is from very few to very many; before PCI calculation, outliers are removed from a time sequence consisting of monthly rainfall, and when the threshold value of PCI is 0.5, whether the rainfall is abnormal or not is effectively distinguished.
The formula of soil moisture index SMCI is:
SM，SM max and SM min The soil moisture value of a pixel in a month, the maximum value and the minimum value of the pixel in history, and the threshold value of SMCI is 0.5; if the total soil moisture content is less than 0.5, the soil moisture content is abnormal, and if the SMCI value is abnormal after the PCI value is abnormal, the soil moisture content is abnormal, and the weather drought is developed into the agricultural drought.
The formula of vegetation index NDVI is:
NIR is near infrared band reflectivity, R is red light band reflectivity, and NDVI ranges from-1 to 1; when NDVI is negative, it indicates that the ground has a covering that is highly reflective to visible light, when NDVI is 0, it indicates that the ground has bare earth rock, NIR is approximately equal to R, when NDVI is positive, it indicates that the ground has vegetation coverage, and the value increases with coverage.
The formula of the vegetation state index VCI is:
NDVI，NDVI max and NDVI min An NDVI value representing a pixel, and the maximum and minimum NDVI values for the pixel over a historical period; VCI is a weekly value, and the threshold of VCI is 0.6; when the VCI is lower than 0.6, the vegetation is influenced by drought, and the agricultural drought enters the development stage.
The calculation formula of the temperature condition index TCI is:
LST is the surface temperature, LST max And LST min Is the highest and lowest surface temperature.
The formula for optimizing the vegetation drought index OVDI is:
objective function:
wherein the method comprises the steps of
Wherein the constraint conditions:
the optimal weight combination of the OVDI on 4 variables of precipitation, soil humidity, vegetation state and surface temperature is different according to the weight combination obtained by different space differences;
x is a standardized rainfall index SPI-3 or a standardized rainfall evaporation index SPEI-3, Y is OVDI, and alpha, beta and gamma are optimization parameters;
f (x, y) represents the condition of maximum correlation between x and yCondition, sigma x Sum sigma y Standard deviation of x and y, mu x Sum mu y Is the mean of x and y, E is the mathematical expectation; and when the OVDI is calculated, unifying the spatial resolution of the single drought index calculation result.
The beneficial technical effects of the application are as follows:
theoretically, the observation capability of the sensor is systematically and completely analyzed, and the defects of the existing research are overcome.
A network foundation for multi-sensor cooperation is established, and several typical cooperation methods among satellite remote sensing sensors, among ground sensors and among ground and satellite remote sensing sensors are provided at the same time, so that a multi-sensor cooperation method system is enriched.
Especially, the method for reconstructing the satellite-ground cooperative soil moisture and the method for monitoring the satellite-ground cooperative facing the drought process realize the capability cooperation of remote sensing and a ground sensor in theory.
In application, aiming at the outstanding problems in the current drought monitoring and evaluating field, continuous monitoring and accurate evaluation of the drought occurrence and development process can be realized based on multi-sensor cooperation, the drought accumulation influence degree is realized, the efficiency improvement level compared with the existing monitoring means is obtained, and technical support is provided for a decision maker in drought disaster emergency response.
Meanwhile, the thought and the method of multi-sensor cooperation reflected by the research are not only suitable for drought monitoring, but also generate new elicitations for monitoring and evaluating flood and disaster chains.
Reference numerals illustrate:
FIG. 1 is a schematic diagram illustrating the algorithm index of the main model of the present application.
Detailed Description
Referring to fig. 1, the application provides a composite drought index calculation method, which calculates a rainfall condition index PCI, a soil humidity index SMCI, a vegetation index NDVI, a temperature condition index TCI and a vegetation state index VCI by using rainfall, soil humidity, surface temperature, vegetation state and crop information data, further calculates a temperature vegetation drought index TVDI on the basis, optimizes three composite drought indexes of a vegetation drought index OVDI and a process-based accumulated drought index PADI, and monitors and evaluates drought-prone areas by comparing with SPI-3 linear correlation analysis, water conservancy and periodical statistics of agricultural drought.
The formula of the precipitation condition index PCI is:
P、P max and P min Respectively representing the current month rainfall of a certain pixel, and the historical maximum rainfall and the historical minimum rainfall; the historical maximum rainfall and the historical minimum rainfall are obtained from rainfall data of the same pixel position in the previous 30 years, the value range of PCI is 0-1, and the level of the rainfall is from very few to very many; before PCI calculation, outliers are removed from a time sequence consisting of monthly rainfall, and when the threshold value of PCI is 0.5, whether the rainfall is abnormal or not is effectively distinguished.
The formula of soil moisture index SMCI is:
SM，SM max and SM min The soil moisture value of a pixel in a month, the maximum value and the minimum value of the pixel in history, and the threshold value of SMCI is 0.5; if the total soil moisture content is less than 0.5, the soil moisture content is abnormal, and if the SMCI value is abnormal after the PCI value is abnormal, the soil moisture content is abnormal, and the weather drought is developed into the agricultural drought.
The formula of vegetation index NDVI is:
NIR is near infrared band reflectivity, R is red light band reflectivity, and NDVI ranges from-1 to 1; when NDVI is negative, it indicates that the ground has a covering that is highly reflective to visible light, when NDVI is 0, it indicates that the ground has bare earth rock, NIR is approximately equal to R, when NDVI is positive, it indicates that the ground has vegetation coverage, and the value increases with coverage.
The formula of the vegetation state index VCI is:
NDVI，NDVI max and NDVI min An NDVI value representing a pixel, and the maximum and minimum NDVI values for the pixel over a historical period; VCI is a weekly value, and the threshold of VCI is 0.6; when the VCI is lower than 0.6, the vegetation is influenced by drought, and the agricultural drought enters the development stage.
The calculation formula of the temperature condition index TCI is:
LST is the surface temperature, LST max And LST min Is the highest and lowest surface temperature.
The formula for optimizing the vegetation drought index OVDI is:
objective function:
wherein the method comprises the steps of
Wherein the constraint conditions:
the optimal weight combination of the OVDI on 4 variables of precipitation, soil humidity, vegetation state and surface temperature is different according to the weight combination obtained by different space differences;
x is a standardized rainfall index SPI-3 or a standardized rainfall evaporation index SPEI-3, Y is OVDI, and alpha, beta and gamma are optimization parameters;
f (x, y) represents the case where the correlation between x and y is the largest, σ x Sum sigma y Standard deviation of x and y, mu x Sum mu y Is the mean of x and y, E is the mathematical expectation; and when the OVDI is calculated, unifying the spatial resolution of the single drought index calculation result.
The above is only a preferred embodiment of the present application, only for helping to understand the method and the core idea of the present application, and the scope of the present application is not limited to the above examples, and all technical solutions belonging to the concept of the present application belong to the scope of the present application. It should be noted that modifications and adaptations to the present application may occur to one skilled in the art without departing from the principles of the present application and are intended to be within the scope of the present application.
The application fundamentally solves the problems of drought monitoring and evaluation in the prior art, aims at the defects and defects of various index models and the defects of other influencing factors under the condition of increasing remote sensing sensor resources, simultaneously provides a plurality of typical collaborative methods among satellite remote sensing sensors, among ground sensors and among ground and satellite remote sensing sensors by establishing a multi-sensor collaborative network foundation, enriches the multi-sensor collaborative methods, realizes continuous monitoring and accurate evaluation, realizes drought accumulated influence degree, obtains efficiency improvement level compared with the existing monitoring means, and provides technical support for decision makers in drought disaster emergency response. Meanwhile, the thought and the method of multi-sensor cooperation are not only suitable for drought monitoring, but also can generate new elicitations for monitoring and evaluating flood and disaster chains.
Claims (7)
1. A composite drought index calculation method is characterized in that rainfall condition index PCI, soil humidity index SMCI, vegetation index NDVI, temperature condition index TCI and vegetation state index VCI are calculated by utilizing rainfall, soil humidity, surface temperature, vegetation state and crop information data, temperature vegetation drought index TVDI is further calculated on the basis, three composite drought indexes including vegetation drought index OVDI and accumulated drought index PADI based on the process are optimized, and the monitoring and evaluation of drought-prone areas are made by comparing with SPI-3 linear correlation analysis and agricultural drought of water conservancy and periodical statistics.
2. The method of claim 1, wherein the precipitation condition index PCI is formulated as:
said P, P max And P min Respectively representing the current month rainfall of a certain pixel, and the historical maximum rainfall and the historical minimum rainfall; the historical maximum rainfall and the historical minimum rainfall are obtained from rainfall data of the same pixel position in the previous 30 years, the value range of the PCI is 0-1, and the level of the representing rainfall is extremely high from extremely low; and before PCI calculation, outliers are removed from a time sequence consisting of monthly rainfall, and when the threshold value of the PCI is 0.5, whether the rainfall is abnormal or not is effectively distinguished.
3. The method of claim 1, wherein the soil moisture index SMCI is formulated as:
the SM, SM max And SM min The soil moisture value of a pixel in a month and the maximum value and the minimum value of the pixel in history are respectively, and the threshold value of the SMCI is 0.5; if the total quantity of the soil water is less than 0.5, the soil water is deficient and abnormal, and if the SMCI value is abnormal after the PCI value is abnormal, the soil water is deficient and abnormal, and the soil water is deficient and abnormal, the SMCI value is abnormal after the PCI value is abnormal, and the soil water is deficient and abnormal, and the SMCI value is abnormal, and represents weather drought to develop into agricultural drought.
4. The method of claim 1, wherein the vegetation index NDVI is formulated as:
the NIR is the reflectivity of a near infrared band, the R is the reflectivity of a red light band, and the range of the NDVI is-1 to 1; when the NDVI is negative, it indicates that the ground has a covering highly reflective of visible light, when the NDVI is 0, it indicates that the ground has bare earth rock, the NIR is approximately equal to the R, when the NDVI is positive, it indicates that the ground has vegetation coverage, and the value increases with coverage.
5. The method of claim 1, wherein the vegetation state index VCI is formulated as:
the NDVI, NDVI max And NDVI min An NDVI value representing a pixel, and the maximum and minimum NDVI values for the pixel over a historical period; the VCI is a weekly value, and the threshold of the VCI is 0.6; when the VCI is lower than 0.6, the vegetation is influenced by drought, and the agricultural drought enters a development stage.
6. The method of claim 1, wherein the temperature condition index TCI is calculated by the formula:
the LST is the surface temperature, the LST max And LST min At the highest and lowest surface temperaturesDegree.
7. The method of claim 1, wherein the formula for optimizing the vegetation drought index OVDI is:
objective function:
wherein the method comprises the steps of
Wherein the constraint conditions:
the optimal weight combination of the OVDI on 4 variables of precipitation, soil humidity, vegetation state and surface temperature is different according to the weight combination obtained by different space differences;
the X is a standardized rainfall index SPI-3 or a standardized rainfall evaporation index SPEI-3, the Y is OVDI, and the alpha, beta and gamma are optimization parameters;
the f (x, y) represents the case where the correlation between x and y is the largest, the sigma x And the sigma y Standard deviation of x and y, said μ x Sum mu y Is the mean of x and y, and E is the mathematical expectation; and unifying the spatial resolution of the single drought index calculation result when calculating the OVDI.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311141390.XA CN117093813A (en) | 2023-09-06 | 2023-09-06 | Composite drought index calculation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311141390.XA CN117093813A (en) | 2023-09-06 | 2023-09-06 | Composite drought index calculation method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117093813A true CN117093813A (en) | 2023-11-21 |
Family
ID=88780364
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311141390.XA Pending CN117093813A (en) | 2023-09-06 | 2023-09-06 | Composite drought index calculation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117093813A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117422313A (en) * | 2023-12-18 | 2024-01-19 | 中科星图智慧科技安徽有限公司 | Method for predicting forest fire risk level by combining meteorological factors and soil factors |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102103077A (en) * | 2009-12-16 | 2011-06-22 | 中国科学院沈阳应用生态研究所 | MODIS data-based agricultural drought monitoring method |
| CN103994976A (en) * | 2013-11-28 | 2014-08-20 | 江苏省水利科学研究院 | MODIS data-based agricultural drought remote sensing monitoring method |
| CN115062997A (en) * | 2022-06-27 | 2022-09-16 | 西南石油大学 | Agricultural drought monitoring method based on remote sensing technology and machine learning and suitable for Tibet region |
-
2023
- 2023-09-06 CN CN202311141390.XA patent/CN117093813A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102103077A (en) * | 2009-12-16 | 2011-06-22 | 中国科学院沈阳应用生态研究所 | MODIS data-based agricultural drought monitoring method |
| CN103994976A (en) * | 2013-11-28 | 2014-08-20 | 江苏省水利科学研究院 | MODIS data-based agricultural drought remote sensing monitoring method |
| CN115062997A (en) * | 2022-06-27 | 2022-09-16 | 西南石油大学 | Agricultural drought monitoring method based on remote sensing technology and machine learning and suitable for Tibet region |
Non-Patent Citations (2)
| Title |
|---|
| CUI HAO等: "Combination of multi-sensor remote sensing data for drought monitoring over Southwest China", 《INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION》, no. 35, 31 March 2015 (2015-03-31), pages 270 * |
| 王思琪等: "三种复合干旱指数在我国三大农业主产区的适用性分析", 《干旱地区农业研究》, vol. 38, no. 1, 31 January 2020 (2020-01-31), pages 243 - 254 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117422313A (en) * | 2023-12-18 | 2024-01-19 | 中科星图智慧科技安徽有限公司 | Method for predicting forest fire risk level by combining meteorological factors and soil factors |
| CN117422313B (en) * | 2023-12-18 | 2024-04-19 | 中科星图智慧科技安徽有限公司 | Method for predicting forest fire risk level by combining meteorological factors and soil factors |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109934515B (en) | Crop precision irrigation decision-making method and system | |
| CN107316095B (en) | Regional weather drought level prediction method coupled with multi-source data | |
| CN105760978B (en) | One kind being based on the agricultural drought disaster grade monitoring method of temperature vegetation drought index (TVDI) | |
| AU2021282487A1 (en) | Forecasting national crop yield during the growing season | |
| CN117093813A (en) | Composite drought index calculation method | |
| EP3719722A1 (en) | Forecasting national crop yield during the growing season | |
| CN114331023B (en) | Vegetation drought monitoring and evaluating method | |
| CN114266502A (en) | Flood disaster damage assessment method and simulation deduction system | |
| CN114297578A (en) | Grassland vegetation coverage estimation and prediction method based on remote sensing | |
| CN115099453A (en) | Multivariable rasterized satellite remote sensing comprehensive drought risk assessment method | |
| CN115575601A (en) | Vegetation drought index evaluation method and system based on water vapor flux divergence | |
| Gostein et al. | Local variability in PV soiling rate | |
| CN115691049A (en) | Convection birth early warning method based on deep learning | |
| Arshad et al. | Monitoring and forecasting drought impact on dryland farming areas. | |
| CN117648863B (en) | Regional water supply capacity acquisition method under special drought conditions based on water system communication | |
| CN113946796A (en) | Drought propagation time calculation method based on conditional probability high space-time resolution | |
| CN117494419A (en) | Multi-model coupling drainage basin soil erosion remote sensing monitoring method | |
| CN115810170A (en) | Method for monitoring drought by using multi-source remote sensing data | |
| CN116434138A (en) | Intelligent and real-time dynamic monitoring method applied to farmland in cloudy and foggy areas | |
| CN118014757B (en) | Intelligent planning method for intelligent agricultural park | |
| Horton et al. | Using snow depth observation to provide insight into the quality of regional-scale snowpack simulations for avalanche forecasting | |
| CN117033872A (en) | Leaf area index relative threshold value method for determining agricultural drought disaster area | |
| Berestov et al. | Assessment of Weather Risks for Agriculture using Big Data and Industrial Internet of Things Technologies | |
| CN117935491B (en) | Landslide rainfall threshold prediction method based on space-time probability | |
| Kyaw et al. | Validation of satellite datasets for the operation of flood and drought indicators in certai n regions of Myanmar |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |