CN117521951A - Method and system for determining resistance of green vegetation to climate change - Google Patents
Method and system for determining resistance of green vegetation to climate change Download PDFInfo
- Publication number
- CN117521951A CN117521951A CN202311480166.3A CN202311480166A CN117521951A CN 117521951 A CN117521951 A CN 117521951A CN 202311480166 A CN202311480166 A CN 202311480166A CN 117521951 A CN117521951 A CN 117521951A
- Authority
- CN
- China
- Prior art keywords
- climate
- vegetation
- accumulated
- resistance
- data
- 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
- 230000008859 change Effects 0.000 title claims abstract description 89
- 238000000034 method Methods 0.000 title claims abstract description 37
- 230000001186 cumulative effect Effects 0.000 claims abstract description 102
- 230000009471 action Effects 0.000 claims abstract description 40
- 238000009825 accumulation Methods 0.000 claims abstract description 36
- 230000000694 effects Effects 0.000 claims abstract description 28
- 238000007781 pre-processing Methods 0.000 claims abstract description 10
- 238000001556 precipitation Methods 0.000 claims description 43
- 230000005855 radiation Effects 0.000 claims description 37
- 238000012545 processing Methods 0.000 claims description 17
- 238000011160 research Methods 0.000 claims description 14
- 238000010276 construction Methods 0.000 claims description 6
- 238000010219 correlation analysis Methods 0.000 claims description 5
- 238000001308 synthesis method Methods 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims description 2
- 238000011425 standardization method Methods 0.000 claims description 2
- LHLMOSXCXGLMMN-CLTUNHJMSA-M [(1s,5r)-8-methyl-8-propan-2-yl-8-azoniabicyclo[3.2.1]octan-3-yl] 3-hydroxy-2-phenylpropanoate;bromide Chemical compound [Br-].C([C@H]1CC[C@@H](C2)[N+]1(C)C(C)C)C2OC(=O)C(CO)C1=CC=CC=C1 LHLMOSXCXGLMMN-CLTUNHJMSA-M 0.000 claims 6
- 238000004458 analytical method Methods 0.000 description 9
- 238000011835 investigation Methods 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 5
- 230000010485 coping Effects 0.000 description 3
- 238000009472 formulation Methods 0.000 description 3
- 239000000203 mixture Substances 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000012512 characterization method Methods 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 239000002689 soil Substances 0.000 description 2
- 238000003786 synthesis reaction Methods 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 241000282412 Homo Species 0.000 description 1
- 241000282414 Homo sapiens Species 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 229910002092 carbon dioxide Inorganic materials 0.000 description 1
- 239000001569 carbon dioxide Substances 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000006386 neutralization reaction Methods 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 230000029553 photosynthesis Effects 0.000 description 1
- 238000010672 photosynthesis Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 230000001932 seasonal effect Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
-
- 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
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Educational Administration (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Pure & Applied Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Mathematical Optimization (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Analysis (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Development Economics (AREA)
- Computational Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Primary Health Care (AREA)
- Health & Medical Sciences (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method and a system for determining the resistance of green vegetation to climate change, wherein the method comprises the following steps: acquiring vegetation index data and climate factor data, and performing data preprocessing on the vegetation index data and the climate factor data; setting an average cumulative action scenario and a change cumulative action scenario; acquiring an average accumulated climate factor time sequence data set based on the average accumulated action scenario and the preprocessed climate factor data; acquiring a change accumulation effect climate factor time sequence data set based on the change accumulation action scene and the preprocessed climate factor data; respectively constructing a vegetation resistance model under an average cumulative action scene and a change cumulative action scene based on the preprocessed vegetation index data and the preprocessed data set; and respectively acquiring the resistance characteristics of the green vegetation on the climate factors under the average accumulated action scene and the changed accumulated action scene based on the vegetation resistance model.
Description
Technical Field
The invention belongs to the technical field of coping with climate change, and particularly relates to a method and a system for determining resistance of green vegetation to climate change.
Background
The handling of climate change is a common problem that humans encounter the biggest regulation and the most complex mechanism of influence. The green vegetation is a natural tie for connecting soil, atmosphere and moisture, is a sensitive indicator of the influence degree of global climate change of a land ecosystem, and can absorb and fix carbon dioxide in the atmosphere into the vegetation and the soil to discharge oxygen through photosynthesis, so that the carbon neutralization of the land ecosystem is facilitated, and the environment-friendly vegetation plays a vital role in coping with global climate change of human beings. Under the climate change background, the research on how the green vegetation resists the change of different climate factors has important significance for formulating the strategy for coping with the climate change.
Studies have shown that historical periods, rather than current climatic conditions, are the primary limiting factor for current vegetation growth status, that is, historical periods of climatic conditions tend to have the strongest impact on current vegetation growth, and that such impact is not isolated, but rather cumulative and linked. For example, assuming that air temperature has a 3 month hysteresis effect on vegetation growth, current hysteresis-related studies only consider the current vegetation growth conditions in relation to the weather conditions 3 months ago, whereas weather conditions 2 months ago and 1 month ago are ignored. The weather conditions are proved to be constantly changing, and the weather conditions from 3 months ago to the current month can have non-negligible influence on the growth of vegetation, and the influence can gradually accumulate and finally appear on the growth and development of the vegetation with the lapse of time, so that the accumulated effect of the weather changes on the growth of the vegetation is formed. However, the vegetation resistance model constructed by the existing researches ignores the accumulated effect, so that the vegetation resistance model against the climate change is greatly simplified, and the uncertainty of the obtained result is necessarily increased.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method and a system for determining the resistance of green vegetation to climate change, and a model for the resistance of the green vegetation to climate change, which is coupled with the action of climate accumulation, is constructed, so that the characteristics of the green vegetation to climate change are better depicted, and scientific basis and decision reference are provided for future strategy formulation of the climate change.
To achieve the above object, in one aspect, the present invention provides a method for determining resistance of green vegetation to climate change, comprising:
acquiring vegetation index data and climate factor data, and performing data preprocessing on the vegetation index data and the climate factor data, wherein the climate factors comprise a month average air temperature TEM, a month total precipitation PRE and a month total incident short wave SOLAR radiation SOLAR;
setting an average cumulative action scenario and a change cumulative action scenario;
acquiring an average accumulated climate factor time sequence data set based on the average accumulated action scenario and the preprocessed climate factor data;
acquiring a change accumulation effect climate factor time sequence data set based on the change accumulation action scene and the preprocessed climate factor data;
respectively constructing a vegetation resistance model under the average accumulated action scene and the changed accumulated action scene based on the preprocessed vegetation index data, the average accumulated climate factor time sequence data set and the changed accumulated effect climate factor time sequence data set, wherein the vegetation resistance model comprises a comprehensive model, a space scale model and a time scale model;
and respectively acquiring the resistance characteristics of the green vegetation on the average accumulated action scene and the changed accumulated action scene to the climate factors based on the vegetation resistance model.
Optionally, the data preprocessing of the vegetation index data and the climate factor data includes:
processing the vegetation index data into a month value data set by adopting a maximum synthesis method;
performing space-time consistency processing on vegetation index data processed by a maximum synthesis method and the climate factor data processing by adopting Kriging space interpolation processing and mask extraction;
adopting a data standardization method to carry out climate factor data standardization treatment on the climate factor data subjected to the time-space consistency treatment;
and carrying out nonlinear trend processing on the vegetation index data after the time-space consistency processing and the climate factor data after the standardization processing based on a least square method.
Optionally, based on the average cumulative action scenario, obtaining the average cumulative climate factor time series dataset comprises:
under the average cumulative action scenario, acquiring an average cumulative climate factor time sequence data set by acquiring the sum of corresponding climate factor values higher than a preset threshold value in preset time based on the preprocessed climate factor data;
the average cumulative climate factor time series data set is:
wherein ACF represents cumulative climate factors, namely cumulative Air Temperature (ATEM), cumulative solar radiation (ASOLAR) and cumulative precipitation (APRE), respectively; CF represents time series data of the original climate factors corresponding to the ACF; CF (compact flash) base Representing a threshold value corresponding to the accumulated climate factor; m represents the current month time series; i represents an accumulated time period; k represents the difference between the current month and the accumulated month.
Optionally, based on the change cumulative action scenario and the preprocessed climate factor data, obtaining a change cumulative effect climate factor time series data set comprises:
obtaining the partial correlation of the climate factors and vegetation growth in a preset historical period by using a partial correlation analysis method;
calculating the influence contribution weight of the climate factors to the current vegetation growth state based on the partial correlation of the climate factors and vegetation growth, and obtaining weight factors;
multiplying the weight factors with weather factor data of a corresponding historical period and accumulating multiplication results to obtain a change accumulation effect weather factor time sequence data set.
Optionally, the change cumulative effect climate factor time series data set is:
wherein ACF represents accumulated climate factors, namely accumulated air temperature ATEM, accumulated solar radiation ASOLAR and accumulated precipitation APRE; CF represents time series data of the original climate factors corresponding to the ACF; beta k Weights for each historical period; CF (compact flash) base Representing a threshold value corresponding to the accumulated climate factor; m represents the current month time series; i represents an accumulated time period; k represents the difference between the current month and the accumulated month.
Optionally, calculating the influence contribution weight of the climate factor to the vegetation growth status, and obtaining the weight factor comprises the following steps:
wherein PCC is the partial correlation of a certain meteorological factor and vegetation growth, i represents the accumulated duration; k represents the difference between the current month and the accumulated month.
Optionally, the integrated model is:
NDVI＝a*ATEM(i)+b*ASOLAR(i)+c*APRE(i)+d
wherein, NDVI represents the time sequence value of vegetation index data; ATEM, APRE and ASOLAR represent the cumulative air temperature, cumulative precipitation and cumulative solar radiation time series values, respectively; i represents the cumulative duration of the climate factors; a. b and c are model coefficients, a representing the resistance of the vegetation to air temperature, b representing the resistance of the vegetation to precipitation, c representing the resistance of the vegetation to solar radiation; d is the intercept of the model;
the spatial scale model is as follows:
NDVI (x，y) ＝a (x，y) *ATEM (x，y) (i)+b (x，y) *ASOLAR (x，y) (i)+c (x，y) *APRE (x，y) (i)+d (x，y)
wherein (x, y) is the geographical coordinates of the pixel;
the time scale model is as follows:
NDVI t，m ＝a t，m *ATEM t，m (i)+b t，m *ASOLAR t，m (i)+c t，m *APRE t，m (i)+d t，m
wherein m is a time window; t represents the study period range.
Optionally, obtaining the resistance characteristic of the green vegetation to the climate factors includes:
based on the comprehensive model, comprehensively acquiring resistance characteristics of vegetation in a research area to different climatic factors;
acquiring resistance characteristics of vegetation in different spaces in a research area to different climate factors based on the space scale model;
and acquiring resistance characteristics of vegetation of different time scales in the research area to different climate factors based on the time scale model.
Optionally, based on the comprehensive model, comprehensively acquiring the resistance characteristics of the vegetation in the research area to different climatic factors includes:
constructing comprehensive models under different accumulation durations, and acquiring decisive coefficients of the comprehensive models under different accumulation durations;
and selecting an optimal comprehensive model based on the decisive coefficient to obtain the resistance characteristics of vegetation to different climate factors.
In another aspect, the present invention provides a method for determining resistance of green vegetation to climate change, comprising: the system comprises a data acquisition module, an accumulated action scene setting module, a vegetation resistance model construction module and a vegetation resistance determination module;
the data acquisition module is used for acquiring vegetation index data and climate factor data, and carrying out data preprocessing on the vegetation index data and the climate factor data, wherein the climate factors comprise a month average air temperature TEM, a month total precipitation PRE and a month total incident short wave SOLAR radiation SOLAR;
the cumulative action scene setting module is used for setting average cumulative action scenes and changed cumulative action scenes; acquiring an average cumulative climate factor time series data set based on the average cumulative action scenario; acquiring a change cumulative effect climate factor time sequence data set based on the change cumulative effect scenario;
the vegetation resistance model construction module is used for respectively constructing a vegetation resistance model under the average cumulative action scene and the change cumulative action scene based on the preprocessed vegetation index data, the average cumulative climate factor time sequence data set and the change cumulative effect climate factor time sequence data set, wherein the vegetation resistance model comprises a comprehensive model, a space scale model and a time scale model;
the vegetation resistance determining module is used for respectively acquiring the resistance characteristics of the green vegetation under the average cumulative action scene and the changed cumulative action scene to the climate factors based on the vegetation resistance model.
The invention has the technical effects that: the invention provides a method and a system for determining the resistance of green vegetation to climate change, which improve the resistance characterization capability of the vegetation to climate change through the coupling climate accumulation effect, provide a method with higher precision for better characterizing the green vegetation to climate change under the climate change background, and provide scientific basis and decision reference for future strategy formulation to climate change.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, illustrate and explain the application and are not to be construed as limiting the application. In the drawings:
FIG. 1 is a flow chart of a method for determining resistance of green vegetation to climate change according to an embodiment of the present invention;
FIG. 2 is a graph showing the spatial distribution of the resistance results of the pixels in the investigation region to the accumulated air temperature, the accumulated solar radiation and the accumulated precipitation, wherein the graph (a) is the spatial distribution of the resistance results of the pixels in the investigation region to the accumulated air temperature, the graph (b) is the spatial distribution of the resistance results of the pixels in the investigation region to the accumulated solar radiation, and the graph (c) is the spatial distribution of the resistance results of the pixels in the investigation region to the accumulated precipitation;
FIG. 3 is a graph showing the variation of resistance of vegetation to different meteorological factors in 1983-2014 according to an embodiment of the present invention.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
As shown in fig. 1, the present embodiment provides a method for determining resistance of green vegetation to climate change, which includes:
acquiring vegetation index data and climate factor data, and performing data preprocessing on the vegetation index data and the climate factor data;
setting an average cumulative action scenario and a change cumulative action scenario; acquiring an average accumulated climate factor time sequence data set based on the average accumulated action scenario and the preprocessed climate factor data; acquiring a change accumulation effect climate factor time sequence data set based on the change accumulation action scene and the preprocessed climate factor data;
respectively constructing vegetation resistance models under an average accumulated action scene and a change accumulated action scene based on the preprocessed vegetation index data, the average accumulated climate factor time sequence data set and the change accumulated effect climate factor time sequence data set, wherein the vegetation resistance models comprise a comprehensive model, a space scale model and a time scale model;
and respectively acquiring the resistance characteristics of the green vegetation on the climate factors under the average accumulated action scene and the changed accumulated action scene based on the vegetation resistance model.
1. Basic data preparation
The basic data utilized by the invention mainly comprise normalized vegetation index data and meteorological data.
Among them, normalized vegetation index data (NDVI) is from the third generation product (GIMMS NDVI3g v 1.0) manufactured by the global inventory monitoring and simulation research group (The Global Inventory Monitoring and Modelling Studies Group, GIMMS). The data set has a spatial resolution of 8 km, a temporal resolution of 15 days and a time span of 7 months from 1981 to 12 months from 2015, and spatially covers land areas other than antarctic.
Meteorological data employed in the present invention include month average air Temperature (TEM), month total Precipitation (PRE) and month total incident short wave SOLAR radiation (SOLAR). Wherein the month average gas temperature and month total precipitation are from time series data set version 3.24 (CRU TS v.3.24) provided by the university of eastern english university climate study center, the data time coverage range being 1901 to 2015. The month total incident short wave solar radiation data was driven with a global meteorological model manufactured by the university of prinston to drive a second version of the data set (The Princeton Global Meteorological Forcing Dataset, version2, PGMFD V2). The spatial resolution of the meteorological data is 0.5 degree, and the invention uses the data of the data sets from 1 month in 1982 to 12 months in 2014 in a unified way.
2. Data preprocessing
The invention aims to describe the resistance condition of green vegetation to climate change by constructing a multiple linear regression model on vegetation index and meteorological factor data. After two types of basic data are obtained, certain pretreatment is needed to be carried out on the basic data, and the method specifically comprises the following steps:
2.1 Vegetation index data maximum synthesis
To further remove errors from non-vegetation signals, the present embodiment uses a maximum synthesis (Maximum Value Composites, MVC) to process the raw data set into a month data set.
2.2 treatment of vegetation and climate data space-time consistency
In order to make the spatial resolution of the meteorological data and the vegetation data consistent, the invention also carries out Kerling spatial interpolation processing on the meteorological data.
And aiming at a specific research area, extracting a vegetation index average value in the area by utilizing a research area vector boundary, so that the vegetation index and climate factor time sequence spatial distribution data in the required research area and time period can be obtained.
And extracting the average value of the NDVI and the meteorological factor data of the whole area monthly to obtain the time sequence data of the vegetation index and the meteorological factor of the whole area time period.
2.3 climate factor data normalization
In order to eliminate the magnitude difference between different climate factors, avoid the influence of noise and abnormal values in data and improve the efficiency and accuracy of a model, the embodiment performs standardized processing on three types of climate factor data, and the method adopts a zscore (x) function in matlab, wherein the principle of the function is to perform centering and scaling processing on the data, so that the mean value of the function is 0, and the standard deviation is 1. The algorithm is as follows:
(1) Calculating the mean value of dataAnd standard deviation S:
where n is a number of data.
(2) And (3) standardization treatment:
wherein x' i I.e. new data after zscore normalization.
2.4 time series data De-linearities trend
To eliminate the influence of seasonal factors, all used NDVI and normalized time series data of climate factors are subjected to a linear trend treatment. The method adopts a detrend (x) function in matlab, and the principle of the function is that the best straight-line fitting line is removed from the original data x based on a least square method, so that the trend change of the data is eliminated. The algorithm comprises the following steps:
(1) Calculating the average value of the dataAnd average value of data number->The formula is as follows:
where n is a number of data.
(2) The slope a and intercept b of the best straight line fit line are calculated as follows:
obtaining the best straight line fitting line
(3) The linear trend is removed, and the formula is as follows:
y′ i ＝y i -(ax i +b) (8)
wherein y' i And the new data after the linear trend is removed.
3. Climate cumulative effect treatment
The invention obtains the weather accumulation effect intensity by calculating the accumulated weather factor data, including accumulated Air Temperature (ATEM), accumulated solar radiation (ASOLAR) and Accumulated Precipitation (APRE). For this purpose, two kinds of cumulative action calculation scenarios, an average cumulative action scenario and a change cumulative action scenario, respectively, are designed.
3.1 calculation of climate cumulative action in an average cumulative action scenario
The present scenario assumes that the effect of the climate conditions of different historic months on the current vegetation growth is the same. The intensity of the climate accumulation is in this case calculated as the sum of the corresponding climate factor values above a reference threshold value over a given time. When the climate factor data for a month is below the corresponding threshold, the cumulative value of the climate factors for the month defaults to not increasing. The climate factor accumulation (average accumulated climate factor time series data set) is processed and calculated as follows:
wherein ACF represents cumulative climate factors, namely cumulative Air Temperature (ATEM), cumulative solar radiation (ASOLAR) and cumulative climate factorsPrecipitation (APRE); CF represents time series data of the original climate factors corresponding to the ACF; CF (compact flash) base Representing a threshold value corresponding to the accumulated climate factor; m represents the current month time series; i represents an accumulated time period; k represents the difference between the current month and the accumulated month, and n represents the maximum accumulation period.
3.2 calculation of climate cumulative effect in a change cumulative effect scenario
The present scenario assumes that the impact of climate conditions of different historic months on current vegetation growth is different. In the scene, the invention obtains the weather accumulation action strength under the situation of changing the accumulation action by designing the accumulated weighted meteorological data set making thought and method, and the method comprises the following steps:
firstly, different influences of meteorological factors on vegetation growth status are obtained by using a partial correlation analysis method. Partial correlation analysis refers to a process of separately studying the degree of correlation between two variables while rejecting the effects of other variables that have a correlation with the two variables. The invention aims to obtain the partial correlation effect (PCC, formula (10)) between a certain meteorological factor and vegetation growth condition in a certain historical period through partial correlation analysis, and the partial correlation effect is used for representing the influence of the meteorological factor on the vegetation growth condition.
Assuming that the current vegetation growth is not affected by the climate factors of the historical period, calculating PCC 0 Assuming that the current vegetation growth condition is influenced by the climate factor of the previous month, calculating PCC 1 Assuming that the current vegetation growth condition is influenced by the climate factors of the first two months, calculating PCC 2 Assuming that the current vegetation growth condition is affected by the climate factors of the first three months, calculating PCC 3 And so on, as shown in table 1, which shows the climate accumulation effect calculation process, the arrow in table 1 shows that the historical climate data values exceeding the threshold value are accumulated into new climate data values.
Wherein X, Y and Z refer to independent, dependent and controlled variables, respectively; r is R X，Y|Z Is the bias correlation coefficient between X and Y after the influence of Z variable is removed; r is R XY ，R YZ R is R XZ Is the pearson correlation coefficient between X, Y and Z. For example, to calculate a Partial Correlation Coefficient (PCC) between vegetation NDVI and air temperature, PCC X，Y|Z Refers to the partial correlation coefficient between vegetation growth condition and air temperature, wherein the vegetation growth condition, air temperature and Z= [ solar radiation amount, rainfall amount are utilized]As independent, dependent and controlled variables.
TABLE 1
And calculating the influence contribution weight of the meteorological factors on the vegetation growth status. Determining the weight of the influence of the meteorological factors on the vegetation growth status by using PCC:
wherein PCC is the partial correlation of a certain meteorological factor and vegetation growth, i represents the accumulated duration; k represents the difference between the current month and the accumulated month; default the serial number of the current period is 0, and add 1 to the serial number of a period when the history is shifted; weight (beta) of each history period k ) The sum is equal to 1, n representing the maximum cumulative period.
A time series data set of varying cumulative effect climate factors is produced. Multiplying the weight factor (beta) with weather data (ClimateData) corresponding to historical periods to obtain the magnitude of the weather data which has an influence contribution to the vegetation growth status of the period, and then accumulating the results to obtain a time sequence data set of the change accumulated effect weather factor, which is coupled with the weather influence effect of each historical period:
wherein ACF represents cumulative climate factors, respectively cumulative Air Temperature (ATEM)Cumulative solar radiation (ASOLAR) and cumulative precipitation (APRE); CF represents time series data of the original climate factors corresponding to the ACF; beta k Weights for each historical period; CF (compact flash) base Representing a threshold value corresponding to the accumulated climate factor; m represents the current month time series; i represents an accumulated time period; k represents the difference between the current month and the accumulated month.
4. Vegetation resistance model coupled with climate accumulation effect
And constructing a vegetation resistance model coupled with the climate accumulation effect according to the processed vegetation index and the climate factor data. According to the invention, three forms of models are constructed according to different application situations.
4.1 comprehensive model
The comprehensive model is used as a most basic model and is suitable for describing the resistance characteristics of vegetation synthesized in a certain area to different climate factors.
The specific formula is as follows:
NDVI＝a*A TEM(i)+b*A SOLAR(i)+c*A PRE(i)+d (13)
wherein, NDVI represents vegetation NDVI data time sequence values; ATEM, APRE and ASOLAR represent the cumulative air temperature, cumulative precipitation and cumulative solar radiation time series values, respectively; i represents the cumulative duration of the climate factors; a. b and c are model coefficients, a representing resistance to air temperature, b representing resistance to precipitation, c representing resistance to solar radiation, the greater the absolute value of which represents the lower the resistance of the vegetation to the climatic factor and vice versa; positive a, b or c means higher temperature, stronger radiation or more precipitation may promote vegetation growth, whereas negative one may inhibit vegetation growth; d is the intercept of the model. Selecting a total of i+1 resistance models from 0 to i months to adjust R 2 The largest is the optimal model.
4.2 spatial scale model
In order to evaluate the vegetation resistance change of different spaces of the research area in detail, the comprehensive model can be applied to the space pixel by pixel, and a multiple regression model is built on each pixel to form a space scale model.
The specific formula is as follows:
NDVI (x，y) ＝a (x，y) *ATEM (x，y) (i)+b (x，y) *ASOLAR (x，y) (i)+c (x，y) *APRE (x，y) (i)+d (x，y) (14)
wherein (x, y) is the geographical coordinates of the pixel; the other parameters are as defined for formula (13).
4.3 time scale model
In order to evaluate the vegetation resistance strength change condition of different time scales in detail, the comprehensive model can be applied to the time scales, and a time scale multiple regression model can be constructed. At this time, a time window of a certain length needs to be introduced. The model formula constructed is as follows:
NDVI t，m ＝a t，m *ATEM t，m (i)+b t，m *ASOLAR t，m (i)+c t，m *APRE t，m (i)+d t，m (15)
where m is a time window (typically may be 2, 5, 10, 20, etc., typically no more than half of the study period); t represents a study period range, and 0.ltoreq.t-m.ltoreq.n, n being the maximum value of the study period; the other parameters are as defined for formula (13).
5. Model accuracy verification
The accuracy of the constructed model is analyzed based on adjusting decisive coefficients and the like.
6. Resistance results analysis
And (3) analyzing according to the result of the space-time distribution change of the resistance of the green vegetation, wherein the result comprises comprehensive performance characteristic analysis, time scale change characteristic analysis and space scale change characteristic analysis, so that the characteristic of the space-time distribution change of the resistance of the green vegetation is clear.
6.1 model accuracy analysis
According to the previous study, the threshold of the accumulated air temperature is set to be 0 ℃ and the thresholds of the accumulated solar radiation and the accumulated precipitation are respectively set to be 0W/m 2 And 0mm, the maximum cumulative length n is taken as 12.
Under the condition of changing and accumulating action according to the formula (13), the invention obtains 13 Yue harbor Australia days with the accumulating time period of 0 to 12 monthsThe model of resistance of green vegetation to climate change in the bay area from 1982 to 2014 is shown in table 1 as an adjustment R of the model of resistance of green vegetation to climate change in 13 guang-ling dawan areas from 1982 to 2014 with an accumulated period of 0 to 12 months 2 (decisive coefficient). From Table 2, it can be found that the accuracy of the resistance model varies from one accumulation period to another, where R is adjusted when the accumulation period reaches 3 months 2 Reaching a maximum.
TABLE 2
6.2, analysis of resistance characteristics of investigation region
Selectively adjust R from Table 1 2 The model of March with the largest absolute value is taken as the optimal model, and the model is as follows:
NDVI＝0.03222*ATEM (3) +0.01108*ASOLAR (3) -0.00076*APRE (3) -1.41185*10 -16
the investigation region had a resistance to cumulative air temperature of 0.03222, to cumulative solar radiation of 0.01108 and to cumulative precipitation of 0.00076. Since the larger the absolute value of the coefficient, the weaker the resistance, the vegetation of the investigation region is the most resistant to precipitation and the resistance to air temperature is the least. The coefficients of accumulated air temperature and accumulated solar radiation are positive and the coefficients of accumulated precipitation are negative, indicating that higher air temperature and stronger solar radiation promote the growth of the vegetation in the bay area and that more precipitation inhibits the growth thereof.
6.3, analysis of resistance of pixel dimensions in the investigation region
Under the situation of changing and accumulating actions according to the formula (14), coefficients of accumulated air temperature, accumulated solar radiation and accumulated precipitation on each pixel are obtained, wherein the coefficients are shown in fig. 2 and table 3, and the table 3 shows the positive and negative proportions of weather factor coefficients of vegetation resistance models of the pixels. The cumulative air temperature and cumulative solar radiation have most pixel coefficients (95.43% and 94.67%, respectively) positive, and the cumulative precipitation has 64.54% positive, indicating that higher air temperature and solar radiation have growth promoting effects on most vegetation in the gulf, and that increased precipitation inhibits vegetation growth in areas with more than three. Comparing the magnitude of the resistance, because the larger the absolute value of the coefficient, the smaller the resistance, the analysis can be: approximately half of the areas (49.6%) have the greatest resistance to precipitation, the least resistance to air temperature, and more than 80% of the areas have absolute coefficients of precipitation less than air temperature (82.2%) or solar radiation (80.8%), i.e. these areas have greater resistance to precipitation than air temperature or solar radiation, indicating that the resistance to precipitation is generally higher in all areas than in the other two climatic factors. In addition, the best cumulative durations for all pixel models are concentrated at 2 months (170), 3 months (160), 4 months (115), and 5 months (105).
TABLE 3 Table 3
6.4, analysis of resistance of the time-scale model in the study area
According to the formula (15), in order to observe dynamic changes of resistance under the situation of change accumulation, the invention calculates the resistance of every five years by utilizing a five-year time sliding window, wherein the first time window is in the beginning year of 1983, the ending year is in the ending year of 1987, the time window slides for 1 year in each step, and the last time window is in the 2010-2014 years, and the total number of the time windows is 28. Obtaining the coefficient change curve of the model of fig. 3, it can be seen that the resistance of the vegetation in the large bay area to the air temperature is smaller than that of solar radiation and precipitation before the 25 th time window (2007-2011), and then the resistance to the solar radiation and the precipitation starts to be smaller, and the resistance to the air temperature starts to be smaller and then larger; in the optimal model (adjusting R 2 Maximum), there are a maximum of 16 windows in march. The coefficients before the time window of the air temperature 2008-2012 are positive numbers, and the time window after the time window is negative, which shows that the coefficients of precipitation and solar radiation are continuously changed between positive and negative, and the effect on vegetation growth is also changed.
The present embodiment also provides a system for determining resistance of green vegetation to climate change, comprising: the system comprises a data acquisition module, an accumulated action scene setting module, a vegetation resistance model construction module and a vegetation resistance determination module;
the data acquisition module is used for acquiring vegetation index data and climate factor data, and carrying out data preprocessing on the vegetation index data and the climate factor data, wherein the climate factors comprise a month average air Temperature (TEM), a month total precipitation amount (PRE) and a month total incident short wave SOLAR radiation (SOLAR);
an accumulated action scene setting module for setting an average accumulated action scene and a changed accumulated action scene; acquiring an average cumulative climate factor time series data set based on the average cumulative action scenario; acquiring a change accumulation effect climate factor time sequence data set based on the change accumulation action scene;
the vegetation resistance model construction module is used for respectively constructing an average cumulative action scene and a vegetation resistance model under the changed cumulative action scene based on the preprocessed vegetation index data, the average cumulative climate factor time sequence data set and the changed cumulative effect climate factor time sequence data set, wherein the vegetation resistance model comprises a comprehensive model, a space scale model and a time scale model;
the vegetation resistance determining module is used for respectively acquiring the resistance characteristics of the green vegetation on the climate factors under the average accumulated action scene and the changed accumulated action scene based on the vegetation resistance model.
The invention provides a model and a system for determining the resistance of green vegetation to climate change, which improve the resistance characterization capability of the vegetation to climate change through the coupling climate accumulation effect, provide a method with higher precision for better characterizing the green vegetation to climate change under the climate change background, and provide scientific basis and decision reference for future strategy formulation to climate change.
The invention obtains the weather accumulation action intensity by calculating the accumulated meteorological factor data, comprising accumulated Air Temperature (ATEM), accumulated solar radiation (ASOLAR) and Accumulated Precipitation (APRE), and designs two accumulation action calculation scenes, namely an average accumulation action scene and a change accumulation action scene.
And constructing a vegetation resistance model coupled with the climate accumulation effect according to the processed vegetation index and the climate factor data. According to different application situations, the invention constructs three forms of models, including a comprehensive model, a space scale model and a time scale model.
The foregoing is merely a preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions easily conceivable by those skilled in the art within the technical scope of the present application should be covered in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of determining resistance of green vegetation to climate change, comprising:
acquiring vegetation index data and climate factor data, and performing data preprocessing on the vegetation index data and the climate factor data, wherein the climate factors comprise a month average air temperature TEM, a month total precipitation PRE and a month total incident short wave SOLAR radiation SOLAR;
setting an average cumulative action scenario and a change cumulative action scenario;
acquiring an average accumulated climate factor time sequence data set based on the average accumulated action scenario and the preprocessed climate factor data;
acquiring a change accumulation effect climate factor time sequence data set based on the change accumulation action scene and the preprocessed climate factor data;
respectively constructing a vegetation resistance model under the average accumulated action scene and the changed accumulated action scene based on the preprocessed vegetation index data, the average accumulated climate factor time sequence data set and the changed accumulated effect climate factor time sequence data set, wherein the vegetation resistance model comprises a comprehensive model, a space scale model and a time scale model;
and respectively acquiring the resistance characteristics of the green vegetation on the average accumulated action scene and the changed accumulated action scene to the climate factors based on the vegetation resistance model.
2. The method of determining resistance of green vegetation to climate change as claimed in claim 1 wherein data preprocessing the vegetation index data and the climate factor data comprises:
processing the vegetation index data into a month value data set by adopting a maximum synthesis method;
performing space-time consistency processing on vegetation index data processed by a maximum synthesis method and the climate factor data processing by adopting Kriging space interpolation processing and mask extraction;
adopting a data standardization method to carry out climate factor data standardization treatment on the climate factor data subjected to the time-space consistency treatment;
and carrying out nonlinear trend processing on the vegetation index data after the time-space consistency processing and the climate factor data after the standardization processing based on a least square method.
3. The method of determining resistance to climate change of green vegetation of claim 1, wherein obtaining an average cumulative climate factor time series dataset based on the average cumulative action scenario comprises:
under the average cumulative action scenario, acquiring an average cumulative climate factor time sequence data set by acquiring the sum of corresponding climate factor values higher than a preset threshold value in preset time based on the preprocessed climate factor data;
the average cumulative climate factor time series data set is:
wherein ACF represents accumulated climate factors, namely accumulated air temperature ATEM, accumulated solar radiation ASOLAR and accumulated precipitation APRE; CF represents the original climate factor corresponding to ACFTime-series data; CF (compact flash) base Representing a threshold value corresponding to the accumulated climate factor; m represents the current month time series; i represents an accumulated time period; k represents the difference between the current month and the accumulated month.
4. The method of determining resistance to climate change of green vegetation as claimed in claim 1 wherein obtaining a time series data set of change cumulative effect climate factors based on the change cumulative action scenario and the preprocessed climate factor data comprises:
obtaining the partial correlation of the climate factors and vegetation growth in a preset historical period by using a partial correlation analysis method;
calculating the influence contribution weight of the climate factors to the current vegetation growth state based on the partial correlation of the climate factors and vegetation growth, and obtaining weight factors;
multiplying the weight factors with weather factor data of a corresponding historical period and accumulating multiplication results to obtain a change accumulation effect weather factor time sequence data set.
5. The method of determining resistance to climate change of green vegetation as claimed in claim 4 wherein the time series data set of change cumulative effect climate factors is:
wherein ACF represents accumulated climate factors, namely accumulated air temperature ATEM, accumulated solar radiation ASOLAR and accumulated precipitation APRE; CF represents time series data of the original climate factors corresponding to the ACF; beta k Weights for each historical period; CF (compact flash) base Representing a threshold value corresponding to the accumulated climate factor; m represents the current month time series; i represents an accumulated time period; k represents the difference between the current month and the accumulated month.
6. The method for determining resistance of green vegetation to climate change according to claim 4, wherein the method for calculating the influence contribution weight of the climate factor to the current vegetation growth state and obtaining the weight factor comprises:
wherein PCC is the partial correlation of a certain meteorological factor and vegetation growth, i represents the accumulated duration; k represents the difference between the current month and the accumulated month.
7. The method for determining resistance of green vegetation to climate change according to claim 1, wherein the comprehensive model is:
NDVI＝a*ATEM(i)+b*ASOLAR(i)+c*APRE(i)+d
wherein, NDVI represents the time sequence value of vegetation index data; ATEM, APRE and ASOLAR represent the cumulative air temperature, cumulative precipitation and cumulative solar radiation time series values, respectively; i represents the cumulative duration of the climate factors; a. b and c are model coefficients, a representing the resistance of the vegetation to air temperature, b representing the resistance of the vegetation to precipitation, c representing the resistance of the vegetation to solar radiation; d is the intercept of the model;
the spatial scale model is as follows:
NDVI (x，y) ＝a (x，y) *ATEM (x，y) (i)+b (x，y) *ASOLAR (x，y) (i)+c (x，y) *APRE (x，y) (i)+d (x，y)
wherein (x, y) is the geographical coordinates of the pixel;
the time scale model is as follows:
NDVI t，m ＝a t，m *ATEM t，m (i)+b t，m *ASOLAR t，m (i)+c t，m *APRE t，m (i)+d t，m
wherein m is a time window; t represents the study period range.
8. The method of determining resistance of green vegetation to climate change as claimed in claim 1, wherein obtaining the resistance characteristic of the green vegetation to climate factors comprises:
based on the comprehensive model, comprehensively acquiring resistance characteristics of vegetation in a research area to different climatic factors;
acquiring resistance characteristics of vegetation in different spaces in a research area to different climate factors based on the space scale model;
and acquiring resistance characteristics of vegetation of different time scales in the research area to different climate factors based on the time scale model.
9. The method of claim 8, wherein comprehensively obtaining resistance characteristics of vegetation in the area of interest to different climate factors based on the comprehensive model comprises:
constructing comprehensive models under different accumulation durations, and acquiring decisive coefficients of the comprehensive models under different accumulation durations;
and selecting an optimal comprehensive model based on the decisive coefficient to obtain the resistance characteristics of vegetation to different climate factors.
10. A system for determining resistance of green vegetation to climate change, comprising: the system comprises a data acquisition module, an accumulated action scene setting module, a vegetation resistance model construction module and a vegetation resistance determination module;
the data acquisition module is used for acquiring vegetation index data and climate factor data, and carrying out data preprocessing on the vegetation index data and the climate factor data, wherein the climate factors comprise a month average air temperature TEM, a month total precipitation PRE and a month total incident short wave SOLAR radiation SOLAR;
the cumulative action scene setting module is used for setting average cumulative action scenes and changed cumulative action scenes; acquiring an average cumulative climate factor time series data set based on the average cumulative action scenario; acquiring a change cumulative effect climate factor time sequence data set based on the change cumulative effect scenario;
the vegetation resistance model construction module is used for respectively constructing a vegetation resistance model under the average cumulative action scene and the change cumulative action scene based on the preprocessed vegetation index data, the average cumulative climate factor time sequence data set and the change cumulative effect climate factor time sequence data set, wherein the vegetation resistance model comprises a comprehensive model, a space scale model and a time scale model;
the vegetation resistance determining module is used for respectively acquiring the resistance characteristics of the green vegetation under the average cumulative action scene and the changed cumulative action scene to the climate factors based on the vegetation resistance model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311480166.3A CN117521951A (en) | 2023-11-08 | 2023-11-08 | Method and system for determining resistance of green vegetation to climate change |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311480166.3A CN117521951A (en) | 2023-11-08 | 2023-11-08 | Method and system for determining resistance of green vegetation to climate change |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117521951A true CN117521951A (en) | 2024-02-06 |
Family
ID=89746974
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311480166.3A Pending CN117521951A (en) | 2023-11-08 | 2023-11-08 | Method and system for determining resistance of green vegetation to climate change |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117521951A (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113569409A (en) * | 2021-07-28 | 2021-10-29 | 生态环境部华南环境科学研究所 | Vegetation productivity model optimization method coupled with climate accumulation effect |
| CN113590689A (en) * | 2021-07-30 | 2021-11-02 | 生态环境部华南环境科学研究所 | Gradual multiple regression analysis method for vegetation growth change coupled with climate accumulation effect |
-
2023
- 2023-11-08 CN CN202311480166.3A patent/CN117521951A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113569409A (en) * | 2021-07-28 | 2021-10-29 | 生态环境部华南环境科学研究所 | Vegetation productivity model optimization method coupled with climate accumulation effect |
| CN113590689A (en) * | 2021-07-30 | 2021-11-02 | 生态环境部华南环境科学研究所 | Gradual multiple regression analysis method for vegetation growth change coupled with climate accumulation effect |
Non-Patent Citations (1)
| Title |
|---|
| YOUYUE WEN 等: ""Cumulative Effects of Climatic Factors on Terrestrial Vegetation Growth"", 《JGR BIOGEOSCIENCES》, 6 April 2019 (2019-04-06), pages 789 - 794 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Waongo et al. | Adaptation to climate change: The impacts of optimized planting dates on attainable maize yields under rainfed conditions in Burkina Faso | |
| CN112348812B (en) | Forest stand age information measurement method and device | |
| CN109711102B (en) | Method for rapidly evaluating crop disaster loss | |
| Nuarsa et al. | Relationship between rice spectral and rice yield using MODIS data | |
| CN110579186B (en) | Crop growth monitoring method based on inversion of leaf area index by inverse Gaussian process | |
| Chen et al. | Investigating rice cropping practices and growing areas from MODIS data using empirical mode decomposition and support vector machines | |
| CN111721714B (en) | Soil water content estimation method based on multi-source optical remote sensing data | |
| CN114821349B (en) | Forest biomass estimation method taking harmonic model coefficients and climatic parameters into consideration | |
| CN113642464B (en) | Time sequence remote sensing image crop classification method combining TWDTW algorithm and fuzzy set | |
| CN115018105A (en) | Winter wheat meteorological yield prediction method and system | |
| CN108537679B (en) | Remote sensing and crop model fused region scale crop emergence date estimation method | |
| Bolun et al. | Estimating rice paddy areas in China using multi-temporal cloud-free normalized difference vegetation index (NDVI) imagery based on change detection | |
| CN116665073A (en) | Corn yield remote sensing estimation method based on multi-source data | |
| CN117521951A (en) | Method and system for determining resistance of green vegetation to climate change | |
| Wu et al. | Evaluating the accuracy of ARMA and multi‐index methods for predicting winter wheat maturity date | |
| CN115855151A (en) | Crop drought monitoring method, device, storage medium and equipment | |
| CN114611699A (en) | Soil moisture downscaling method and device, electronic equipment and storage medium | |
| Bhatt et al. | Comparison of different regression models to predict mustard yield in central Punjab | |
| CN117910840A (en) | Evaluation method for influence factors of quality change of land ecological system | |
| Rossi et al. | Potential of MERIS fAPAR for drought detection | |
| CN117975293B (en) | Extraction method for rice planting area in multiple cloud and fog areas | |
| Claverie et al. | Spatialization of crop leaf area index and biomass by combining a simple crop model SAFY and high spatial and temporal resolutions remote sensing data | |
| CN114707412B (en) | SWAT model optimization method based on vegetation canopy time-varying characteristics | |
| Bouvet et al. | Assessment of Envisat ASAR alternating polarisation data for rice mapping in Jiangsu province, China | |
| US20230135643A1 (en) | Systems and Methods for Agricultural Optimization |
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 |