CN111160680A - Agricultural drought assessment method based on information assimilation and fusion - Google Patents
Agricultural drought assessment method based on information assimilation and fusion Download PDFInfo
- Publication number
- CN111160680A CN111160680A CN201910227582.XA CN201910227582A CN111160680A CN 111160680 A CN111160680 A CN 111160680A CN 201910227582 A CN201910227582 A CN 201910227582A CN 111160680 A CN111160680 A CN 111160680A
- Authority
- CN
- China
- Prior art keywords
- drought
- water
- information
- agricultural
- crops
- 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
- 238000000034 method Methods 0.000 title claims abstract description 51
- 230000004927 fusion Effects 0.000 title claims abstract description 24
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 150
- 238000012544 monitoring process Methods 0.000 claims abstract description 26
- 238000004088 simulation Methods 0.000 claims abstract description 23
- 238000005259 measurement Methods 0.000 claims abstract description 19
- 238000011156 evaluation Methods 0.000 claims description 48
- 238000004364 calculation method Methods 0.000 claims description 28
- 239000002689 soil Substances 0.000 claims description 26
- 238000012937 correction Methods 0.000 claims description 22
- 238000004458 analytical method Methods 0.000 claims description 16
- 238000001704 evaporation Methods 0.000 claims description 9
- 230000008020 evaporation Effects 0.000 claims description 9
- 238000003973 irrigation Methods 0.000 claims description 9
- 230000002262 irrigation Effects 0.000 claims description 9
- 239000010410 layer Substances 0.000 claims description 8
- 238000013210 evaluation model Methods 0.000 claims description 7
- 238000001556 precipitation Methods 0.000 claims description 7
- 239000002352 surface water Substances 0.000 claims description 7
- 239000003673 groundwater Substances 0.000 claims description 6
- 238000001308 synthesis method Methods 0.000 claims description 6
- 238000009736 wetting Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 3
- 230000009191 jumping Effects 0.000 claims description 3
- 230000005855 radiation Effects 0.000 claims description 3
- 239000000126 substance Substances 0.000 claims description 3
- 239000002344 surface layer Substances 0.000 claims description 3
- 230000005068 transpiration Effects 0.000 claims description 3
- 238000002310 reflectometry Methods 0.000 claims description 2
- 230000001360 synchronised effect Effects 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 abstract description 10
- 241000196324 Embryophyta Species 0.000 description 10
- 238000011161 development Methods 0.000 description 6
- 238000010521 absorption reaction Methods 0.000 description 4
- 230000006735 deficit Effects 0.000 description 4
- 241000209094 Oryza Species 0.000 description 2
- 235000007164 Oryza sativa Nutrition 0.000 description 2
- 239000007788 liquid Substances 0.000 description 2
- 235000009566 rice Nutrition 0.000 description 2
- 238000001228 spectrum Methods 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 208000005156 Dehydration Diseases 0.000 description 1
- 241001464837 Viridiplantae Species 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 230000024346 drought recovery Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000003621 irrigation water Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
Images
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
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C13/00—Surveying specially adapted to open water, e.g. sea, lake, river or canal
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3554—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for determining moisture content
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/24—Earth materials
- G01N33/246—Earth materials for water content
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
-
- 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
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/30—Assessment of water resources
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Strategic Management (AREA)
- General Health & Medical Sciences (AREA)
- Environmental & Geological Engineering (AREA)
- Chemical & Material Sciences (AREA)
- Economics (AREA)
- Biochemistry (AREA)
- General Business, Economics & Management (AREA)
- Remote Sensing (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Analytical Chemistry (AREA)
- Educational Administration (AREA)
- Theoretical Computer Science (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Tourism & Hospitality (AREA)
- Environmental Sciences (AREA)
- Ecology (AREA)
- Biodiversity & Conservation Biology (AREA)
- Atmospheric Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Hydrology & Water Resources (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Medicinal Chemistry (AREA)
- Food Science & Technology (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Agronomy & Crop Science (AREA)
- Animal Husbandry (AREA)
Abstract
The invention discloses an agricultural drought assessment method based on information assimilation and fusion, which adopts a simulation technology to simulate the growth process of crops, identify water demand and water shortage information of the crops, utilizes remote sensing to monitor drought information and actually-measured drought information of the crops to carry out assimilation and fusion so as to continuously update state variables and parameters of a farmland water circulation simulation model, and the assimilated model simulates the good fitting degree of the agricultural drought grade and the actual drought so as to realize the purpose of comprehensively monitoring and assessing the drought and the trend. The agricultural drought assessment model based on farmland water circulation simulation, remote sensing and actual measurement information assimilation is feasible, a new method is provided for timely and accurately identifying and assessing agricultural drought, and the method has important application and popularization values in the aspect of national drought monitoring and assessment.
Description
Technical Field
The invention relates to a drought assessment method, belonging to the technical field of drought assessment; in particular to an agricultural drought assessment method based on information assimilation and fusion.
Background
The agricultural drought is based on the water quantity actually stored in soil and available for crops, and the drought conditions of the crops are judged. Whether it is precipitation or irrigation, the water is firstly stored in the soil and then gradually absorbed and utilized by the growth of crops. In addition, the conditions of weather, hydrology, irrigation and the like of the area and the management condition of irrigation water can be well reflected by agricultural drought information. Therefore, the agricultural drought can be identified through soil moisture content information, crop water shortage information and remote sensing images.
How to judge the agricultural drought is the key of drought prediction and early warning, and various judging methods can be adopted to represent the generation and development processes of the agricultural drought. The simulation technology is adopted to simulate the crop growth process, the water demand and water shortage information of the crops is identified, and the remote sensing information is utilized for assimilation, so that the method is an effective method for judging agricultural drought, can identify and evaluate the agricultural drought timely and accurately, and has important application and popularization values in the aspect of drought monitoring and evaluation in China.
The drought assessment method mainly progresses through the following four stages:
① stage of analyzing drought strength at site, wherein the initial drought evaluation is mainly limited to site data, and the drought strength at some time at each site is calculated and analyzed by using drought indexes;
② in the site drought feature analysis stage, identifying the start-stop time, duration and intensity of the primary drought event by using the run-length theory, and calculating the occurrence frequency of the drought event by using a statistical analysis method;
③ drought space characteristic analysis stage, wherein the coverage area of each time in a drought event can be obtained with the development of 3S technology and the application in drought evaluation, and the drought evaluation is developed from site drought evaluation to regional drought evaluation;
④ drought space-time distribution characteristic analysis stage, in recent years, on the basis of calculating drought characteristic indexes such as drought intensity, duration, coverage area, frequency, etc., some scholars use statistical analysis method to comprehensively analyze multiple characteristics to obtain regional drought space-time distribution characteristics.
The multi-source information application is a trend of future water conservancy science development. Information assimilation is a technical means with great development prospect for applying remote sensing data to hydrological forecasting and is considered as a key technology for changing traditional hydrology and building a digital hydrological system. The application research of the information assimilation fusion technology in the drought aspect is just started, the data assimilation fusion technology is applied to drought monitoring evaluation and prediction, and the multi-source information assimilation fusion technology is developed to evaluate soil moisture content and agricultural drought content. The drought evaluation method does not fully utilize the monitored drought information, and the evaluation result has some deviation from the actual drought of the area. Therefore, the assimilation and fusion technology is used for regional agriculture drought evaluation, which is a development trend of the water conservancy subject.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems, the invention provides an agricultural drought assessment method based on information assimilation and fusion, which utilizes remote sensing to monitor drought, actually measured crop drought-suffering and disaster-formation data and an agricultural drought simulation result to carry out assimilation, continuously updates state variables and parameters of a farmland water circulation simulation model, and the assimilated model simulates the agricultural drought and has better fitting degree with the actual drought, thereby realizing the purposes of comprehensively monitoring and assessing the agricultural drought trend.
The technical scheme is as follows: the invention relates to an agricultural drought assessment method based on information assimilation and fusion, which comprises the following steps:
(1) and determining the agricultural drought evaluation range.
(2) And acquiring analysis data including weather, hydrology and soil moisture content information.
Further, the weather information includes: evaluating the precipitation, evaporation and air temperature in the range; the hydrologic information includes: estimating the river runoff, the reservoir water storage capacity and the groundwater level in the range; the soil moisture content information comprises: evaluating the water content of the soil and the soil moisture shortage of the crops within the range.
(3) Acquiring and processing level 1B data in MODIS source data synchronous with the analysis data in an evaluation range, wherein the MODIS source data are processed by radiation correction, geometric correction, atmospheric correction and cloud detection;
(4) according to a data set of normalized vegetation indexes NDWI day by day in a monitoring period, wherein the normalized vegetation indexes are the index of water shortage of leaf surfaces, a mean value synthesis method is adopted to calculate the index of the NDWI day by day, the relation between the index of the NDWI and the drought level is analyzed, and the drought information monitored by remote sensing day by day is obtained; the normalized vegetation index NDWI is calculated according to the following formula:
where ρ isnir、ρredRespectively representing the reflectivity of the vegetation on a near infrared band and a red light band; meanwhile, for MODIS, there are 2 nd and 1 st bands, respectively.
(5) According to the analysis data and the farmland water balance principle, establishing a farmland water circulation model by taking the farmland surface layer as a prototype, and simulating the farmland water circulation process in the crop growth period to obtain simulated drought information, including the water demand ET in ten days of cropsMThe water shortage in ten days of the crops is QET, and the daily water demand of the crops is ETMiWater shortage QET for day-by-day cropsi(ii) a Wherein i represents a date.
Further, the step (5) includes:
(5.1) calculating according to the water balance equation of the planned wet layerCalculating the daily actual water consumption ET of cropsi(ii) a The water balance equation is shown as follows:
Wi+1-Wi＝Pi+Gi+Isi+Igi+Ui-Esi-Eci-Qi-Rgi-Si
wherein, Wi、Wi+1The water content of the soil at the beginning and the end of the i period, Pi、Gi、Isi、Igi、UiPrecipitation, irrigation, surface water inflow, inflow of water in the soil and groundwater recharge, respectively, into the planned wetting zone at time i, Esi、Eci、Qi、Rgi、SiThe soil evaporation amount, the crop transpiration amount, the runoff generated on the surface of the farmland, the outflow amount of water in the soil and the deep layer leakage amount which flow out of the planned wetting layer in the ith time period are respectively;
actual daily water consumption ET for cropsiAs shown in the following equation:
ETi＝Esi+Eci
(5.2) calculating the daily crop water demand ETMi：
ETMi＝a×E601,i
Wherein, ETMiWater requirement for crops, α water requirement coefficient for crops, E601,iThe actually measured water surface evaporation capacity of the evaporator is obtained;
(5.3) according to daily crop water demand ETMiObtaining the water demand ET in ten days of the cropM；
(5.4) calculating the water shortage of the i-th day crop:
QETi＝ETMi-ETi；
(5.5) QET according to the daily crop water shortageiThe water shortage QET in ten days of the crop is obtained.
(6) Obtaining the water demand ET in ten days of crops according to the step (5)MAnd QET, calculating the water shortage rate theta in ten days of crops according to the following formula:
(7) and dividing the agricultural drought grade standard according to the calculated water shortage rate theta of the crops in ten days to judge the simulated agricultural drought grade.
Further, the agricultural drought levels in step (7) are divided according to the following rules:
for dry crops, the water shortage rate theta of the crop is less than or equal to 45 percent, the crop is in a no-drought state, and the drought grade is 0; the water shortage rate of the plants is more than 45 percent and less than or equal to 65 percent, the plants are in a light drought state, and the drought grade is 1; the water shortage rate of the plants is more than 65% and less than or equal to 85%, the plants are in a medium drought state, and the drought grade is 2; the water shortage rate of the crops is more than 85 percent and less than or equal to 95 percent, the crops are in a heavy drought state, and the drought grade is 3; the water shortage rate of the crops is more than 95 percent and is in an extremely drought state, and the drought grade is 4;
for paddy fields, the water shortage rate theta is less than or equal to 20 percent as an object, the paddy fields are in a drought-free state, and the drought grade is 0; the water shortage rate of the plants is more than 20 percent and less than or equal to 35 percent, the plants are in a light drought state, and the drought grade is 1; the water shortage rate of the plants is more than 35 percent and less than or equal to 50 percent, the plants are in a medium drought state, and the drought grade is 2; the water shortage rate of the plants is more than 50 percent and less than or equal to 60 percent, the plants are in a heavy drought state, and the drought grade is 3; the water shortage rate of the crops is more than 60 percent and theta, the crops are in an extremely drought state, and the drought grade is 4.
(8) And (4) acquiring assimilation-fused farmland water circulation simulation models by adopting a gradual iteration method according to the ten-day remote sensing monitoring drought information and the actually-measured drought information acquired in the step (4).
Further, the step (8) comprises:
(8.1) judging the agricultural drought level simulated by each calculating unit according to the step (7), and comparing the level values
As a zero iteration estimation value of the drought level;
(8.2) continuously inserting remote sensing monitoring drought information and actual measurement drought information by adopting a gradual iteration method shown in the following formula, and respectively using a ten-day remote sensing monitoring value, an actual measurement value and a paired sum within a given evaluation radius R
Performing correction once, simultaneously reducing the evaluation radius, and reducing the calculation unit of the most edge in the evaluation radius in each iteration; correcting in sequence until the error of the calculated values of the two times is less than a given error value:
wherein j is a calculation unit serial number, k is an observation point, and n is iteration times;
is the nth iteration estimation value of the agricultural drought level in the jth computing unit,
is the k observation, ε, in calculation unit j2Is an estimate of the ratio of the observed error variance to the simulated calculated error variance,
the total number of observation points within the range of the distance R from the central actual measurement point in the jth calculation unit; the calculation unit is a weight of a gradual iterative method in a county-level administrative district or a large and medium irrigation district within the drought evaluation range
As shown in the following equation:
weight of stepwise iterative method:
wherein the content of the first and second substances,
is the observation point r in the jth calculation unitkAnd actual measurement point rjThe square of the distance therebetween.
(9) Adjusting parameters of the farmland water circulation simulation model after assimilation and fusion to enable agricultural drought information to be assimilated until the expected value of the error series is 0 and the covariance is a given prior error, and obtaining an agricultural drought evaluation model; and the error series expected value is the difference value between the simulated agricultural drought grade and the actual drought grade in each ten days.
Further, the step (9) includes:
(9.1) adopting a farmland water circulation simulation model to calculate the water demand ET of the crops every ten daysMAnd the crop water shortage value QET, and the agricultural drought information with the time resolution of 1 ten days and the spatial resolution of each calculation unit in the evaluation range is used as an initial evaluation field;
(9.2) acquiring MODIS remote sensing data, and calculating the ten-day leaf surface water shortage index NDWI by adopting an average synthesis method to obtain the drought information monitored by remote sensing every ten days;
(9.3) counting and calculating the actually measured drought information of each calculation unit in the evaluation range, wherein the actually measured drought information comprises a drought exposure rate α and a disaster rate β;
(9.4) comparing the simulated agricultural drought information of each computing unit in the evaluation range with the remote sensing monitoring drought information and the actual measurement drought information, computing the error covariance of each computing unit, analyzing and adjusting farmland water circulation simulation model parameters, simulating the agricultural drought information in the next ten days, judging the corresponding agricultural drought grade standard value, taking the standard value as the initial value of the drought grade corrected in the next time, and correcting the standard value every ten days for the next time;
and (9.5) calculating a farmland moisture circulation analog value and actually-measured drought information to obtain an agricultural drought error series of each calculating unit, carrying out statistical calculation on samples of the error series, assimilating the agricultural drought information until an error series expected value is 0 and covariance is a given prior error, and obtaining an agricultural drought evaluation model.
(10) Judging whether the agricultural drought level simulated by the agricultural drought evaluation model is consistent with the actual drought level, if so, reserving the model, evaluating the agricultural drought at the next moment, outputting an agricultural drought level value, and finishing the evaluation; otherwise, jumping to step (8).
Has the advantages that: the invention has the following advantages:
1. the simulation technology is adopted to simulate the crop growth process, identify the water demand and water shortage information of crops, and can represent the generation and development process of agricultural drought so as to predict the agricultural drought;
2. utilizing remote sensing to monitor drought information and actually measured crop drought receiving and disaster formation information, carrying out information assimilation and fusion on an agricultural drought simulation result so as to continuously update state variables and parameters of a farmland water circulation simulation model, wherein the assimilated model simulates good fitting degree of an agricultural drought level and an actual drought;
3. the purpose of comprehensively monitoring and evaluating the drought and the trend is realized, and the method has important application and popularization values in the aspect of drought monitoring and evaluation.
Drawings
FIG. 1 is a flow chart of the agricultural drought assessment method of the present invention.
Detailed Description
The technical scheme of the present invention is further explained by taking mechanism experiments, model simulations, assimilation fusion technologies, etc. as key technical means around the key technical problems to be solved in the technical fields of drought disaster characteristics and disaster reduction, and by combining the accompanying drawings and embodiments.
As shown in FIG. 1, the agricultural drought assessment method based on information assimilation and fusion provided by the invention provides high-precision assessment information for agricultural drought prediction and early warning.
Step 1: determining an agricultural drought assessment range: nationwide, province (city, district), region (city), county, etc.
Step 2: and (4) sorting, analyzing and evaluating meteorological and hydrological information such as precipitation, evaporation capacity, air temperature, river runoff, reservoir water storage capacity, groundwater level and the like in the range.
And step 3: and (5) soil moisture content information such as soil moisture content, crop lack of moisture and the like in the range of the arrangement analysis evaluation.
And 4, step 4: and (3) obtaining and processing MODIS source data in the same period as the simulation analysis evaluation range, wherein the MODIS1B data processing process comprises radiation correction, geometric correction, atmospheric correction, cloud detection and the like. The spectral characteristics of the reflection spectrum of green plants are controlled by liquid water absorption in the 0.9-2.5 μm region and are also weakly influenced by the absorption of some other biochemical components. In the near infrared wave band, the absorption of vegetation liquid water can be ignored, in the short wave infrared wave band, the absorption of water is very weak to utilize the vegetation can be very sensitive reaction vegetation canopy water content in infrared wave band (NIR) and the spectrum of short wave infrared wave band. The water content of the leaf surface is directly related to the water stress. Thus, drought monitoring can be performed using the leaf surface water deficit index (NDWI). And obtaining the ten-day NDWI index by adopting an average synthesis method according to the day-by-day NDWI index data set product in the monitoring period, and analyzing the relation between the NDWI index and the drought level to obtain the drought information monitored by remote sensing day-by-day.
And 5: and (5) investigating and counting actual agricultural condition information such as drought areas, disaster areas, crop water shortage and the like within the evaluation range.
Step 6: and establishing an agricultural drought simulation model. Starting from the principle of farmland water balance, establishing a farmland water circulation model by taking a farmland surface layer as a prototype, and simulating the farmland water circulation process in the crop growth period and the ten-day crop water demand (ET)M) And the crop water shortage amount (QET) specifically comprises the following steps:
(6.1) calculating the daily actual water consumption ET of the crops according to the water balance equation of the planned wetting layeri(ii) a The water balance equation is shown as follows:
Wi+1-Wi＝Pi+Gi+Isi+Igi+Ui-Esi-Eci-Qi-Rgi-Si
wherein, Wi、Wi+1The water content of the soil at the beginning and end of day i, Pi、Gi、Isi、Igi、UiPrecipitation, irrigation, surface water inflow, inflow of water into the soil and groundwater recharge on day i, respectively, Esi、Eci、Qi、Rgi、SiThe soil evaporation amount, the crop transpiration amount, the runoff generated on the surface of the farmland, the outflow amount of water in the soil and the deep layer leakage amount of the planned wetting layer flowing out on the ith day are respectively;
the actual daily water consumption ET of the cropiAs shown in the following equation:
ETi＝Esi+Eci
(6.2) calculating the daily crop water demand ETMi：
ETMi＝a×E601,i
Wherein, ETMiWater requirement for crops, α water requirement coefficient for crops, E601,iThe actually measured water surface evaporation capacity of the evaporator is obtained;
(6.3) according to daily crop Water demand ETMiObtaining the water demand ET in ten days of the cropM；
(6.4) calculating the water shortage of the i-th day crop:
QETi＝ETMi-ETi；
(6.5) crop Water shortage QET on a daily basisiThe water shortage QET in ten days of the crop is obtained.
The water requirement ET in the crop ten days in the steps (6.3) and (6.5)MThe water demand and the water shortage of the crops day by day are respectively summed up in the ten days of the calendar according to the water shortage QET in the ten days, namely the initial calculation dates in the last, middle and last ten days are respectively 1 day, 11 days and 21 days per month.
And 7: crop water deficit rates are often used as an indicator for assessing agricultural drought. The crop water shortage rate refers to the ratio of the crop water shortage in a time period to the actual water demand of the crop in the time period. Calculating the water shortage rate theta in ten days of crops according to the following formula:
and 8: and dividing the agricultural drought grade standard. The invention determines the drought grade division standard of dry crops and rice because of different varieties of crops and different drought tolerance of the crops. As shown in tables 1 and 2 below.
TABLE 1 drought grading Standard for Dry crops
| Degree of drought | Without drought | Light drought | Zhonghan (middle drought) | Heavy drought | Extremely dry land |
| Grade number of drought | 0 | 1 | 2 | 3 | 4 |
| Crop water deficit rate theta (%) | θ≤45 | 45＜θ≤65 | 65＜θ≤85 | 85＜θ≤95 | 95＜θ |
TABLE 2 Rice drought Scale division Standard
| Degree of drought | Without drought | Light drought | Zhonghan (middle drought) | Heavy drought | Extremely dry land |
| Grade number of drought | 0 | 1 | 2 | 3 | 4 |
| Crop water deficit rate theta (%) | θ≤20 | 20＜θ≤35 | 35＜θ≤50 | 50＜θ≤60 | 60＜θ |
And step 9: the invention adopts a gradual correction method to carry out information assimilation and fusion. The gradual correction method adopts the simulated drought information as an initial estimation field, continuously inserts the remote sensing monitoring drought information and the statistical actual measurement drought information, respectively uses the remote sensing monitoring values, the actual measurement values and the given evaluation radius to correct the initial estimation field once, then uses the corrected analysis field as the initial estimation field for the next correction, and simultaneously reduces the evaluation radius to correct the next correction. Such a cyclic process constitutes four-dimensional data assimilation. Because the simulated drought information is close to the remote sensing monitoring drought information and the actual measurement drought information, the spatial continuity of the analysis field is better, and the time continuity is considered, the continuous correction is carried out on the basis, the influence radius is reduced in the correction process, the large-scale error of the initial estimation field can be removed, then the simulated drought information analysis field is more and more approximate to the actual drought information field, and the better effect is obtained compared with the direct interpolation. The method comprises the following specific steps:
(9.1) judging the agricultural drought level simulated by each calculating unit according to the step (8), and comparing the level values
As a zero iteration estimation value of the drought level;
(9.2) continuously inserting remote sensing monitoring drought information and actual measurement drought information by adopting a gradual iteration method shown in the following formula, and respectively using a ten-day remote sensing monitoring value, an actual measurement value and a paired sum within a given evaluation radius R
Performing correction once, simultaneously reducing the evaluation radius, and reducing the calculation unit of the most edge in the evaluation radius in each iteration; correcting in sequence until the error of the calculated values of the two times is less than a given error value:
wherein j is a calculation unit serial number, k is an observation point, and n is iteration times;
is the nth iteration estimation value of the agricultural drought level in the jth computing unit,
is the k observation, ε, in calculation unit j2Is an estimate of the ratio of the observed error variance to the simulated calculated error variance,
the total number of observation points within the range of the distance R from the central actual measurement point in the jth calculation unit; the calculation unit is a weight of a gradual iterative method in a county-level administrative district or a large and medium irrigation district within the drought evaluation range
As shown in the following equation:
weight of stepwise iterative method:
wherein the content of the first and second substances,
is the observation point r in the jth calculation unitkAnd actual measurement point rjThe square of the distance therebetween.
The final analysis value obtained by the stepwise correction method is actually a weighted average of the various available information. The stepwise correction method is simple and economical and can produce reasonable analysis.
Step 10: the farmland water circulation simulation model after assimilation and fusion can be used for agricultural drought assessment.
1) And (3) calculating the daily crop water demand and crop water shortage value by adopting a farmland water circulation simulation model, and taking the agricultural drought information of each calculation unit (county or irrigation district) with the time resolution of 1 ten days and the spatial resolution of the evaluation range as an initial evaluation field.
2) And (4) collecting remote sensing data of the MODIS, calculating the ten-day leaf surface water shortage index (NDWI) by adopting an average synthesis method, and obtaining the drought information monitored by remote sensing every ten days.
3) And analyzing the actually measured drought information drought receiving rate (α) and disaster rate (β) of each computing unit in the evaluation range.
4) Comparing the simulated agricultural drought information of each calculation unit with the remote sensing monitoring drought information and the actual measurement drought information in the evaluation range, calculating the error covariance of each calculation unit, analyzing and adjusting farmland water circulation simulation model parameters, simulating the agricultural drought information with the time step advancing by 1 ten days, serving as an initial estimation field for next correction, and performing the next correction by one ten days.
5) And calculating the farmland water circulation analog value and the actually-measured drought information to obtain an agricultural drought error field of each calculating unit, carrying out statistical calculation on a sample of the error field, and assimilating the agricultural drought information until the expected value of the error series is 0 and the covariance is a given prior error.
Step 11: and (4) evaluating the agricultural drought level. Judging whether the agricultural drought level simulated by the agricultural drought evaluation model is consistent with the actual drought level, if so, reserving the model, evaluating the agricultural drought at the next moment, judging and outputting an agricultural drought level value of an evaluation area according to an agricultural drought level standard value, and finishing the evaluation; otherwise, jumping to step (9).
Claims (6)
1. An agricultural drought assessment method based on information assimilation and fusion is characterized by comprising the following steps:
(1) determining an agricultural drought evaluation range;
(2) acquiring analysis data including weather, hydrology and soil moisture content information;
(3) acquiring and processing level 1B data in MODIS source data synchronous with the analysis data in an evaluation range, wherein the MODIS source data are processed by radiation correction, geometric correction, atmospheric correction and cloud detection;
(4) according to the monitoring time period day-by-day normalized vegetation index NDWI data set, calculating a ten-day NDWI index by adopting an average synthesis method, and analyzing the relation between the NDWI index and the drought level to obtain remote sensing monitoring drought information day-by-day; the normalized vegetation index NDWI is calculated according to the following formula:
where ρ isnir、ρredRespectively representing the reflectivity of the vegetation on a near infrared band and a red light band;
(5) according to the analysis data and the farmland water balance principle, establishing a farmland water circulation model by taking the farmland surface layer as a prototype, and simulating the farmland water circulation process in the crop growth period to obtain simulated drought information, including the water demand ET in ten days of cropsMAnd the water shortage in ten days of the crops is QET, and the water demand of the crops is ET day by dayMiWater shortage QET for day-by-day cropsi(ii) a Wherein i represents the date of the data;
(6) obtaining the water demand ET in ten days of crops according to the step (5)MAnd ten days of cropsThe water shortage amount QET, and the water shortage rate theta in ten days of the crops is calculated according to the following formula:
(7) dividing an agricultural drought grade standard value according to the calculated water shortage rate theta of the ten-day crops to judge the simulated agricultural drought grade;
(8) according to the ten-day-by-ten remote sensing monitoring drought information and the actually-measured drought information obtained in the step (4), obtaining an assimilation-fused farmland water circulation simulation model by adopting a gradual iteration method;
(9) adjusting parameters of the farmland water circulation simulation model after assimilation and fusion to enable agricultural drought information to be assimilated until the expected value of the error series is 0 and the covariance is a given prior error, and obtaining an agricultural drought evaluation model; wherein the error series expected value is the difference value between the simulated agricultural drought grade and the actual drought grade in each ten days;
(10) judging whether the agricultural drought level simulated by the agricultural drought evaluation model is consistent with the actual drought level, if so, reserving the model, evaluating the agricultural drought at the next moment, outputting an agricultural drought level value, and finishing the evaluation; otherwise, jumping to step (8).
2. The agricultural drought assessment method based on information assimilation and fusion of claim 1, wherein in the step (2), the meteorological information comprises: evaluating the precipitation, evaporation and air temperature in the range; the hydrologic information includes: estimating the river runoff, the reservoir water storage capacity and the groundwater level in the range; the soil moisture content information comprises: evaluating the water content of the soil and the soil moisture shortage of the crops within the range.
3. The agricultural drought assessment method based on information assimilation and fusion according to claim 1, wherein the step (5) comprises:
(5.1) calculating the daily actual water consumption ET of the crops according to the water balance equation of the planned wetting layeri(ii) a The water contentThe equilibrium equation is shown as follows:
Wi+1-Wi＝Pi+Gi+Isi+Igi+Ui-Esi-Eci-Qi-Rgi-Si
wherein, Wi、Wi+1The water content of the soil at the beginning and end of day i, Pi、Gi、Isi、Igi、UiPrecipitation, irrigation, surface water inflow, inflow of water into the soil and groundwater recharge on day i, respectively, Esi、Eci、Qi、Rgi、SiThe soil evaporation amount, the crop transpiration amount, the runoff generated on the surface of the farmland, the outflow amount of water in the soil and the deep layer leakage amount of the planned wetting layer flowing out on the ith day are respectively;
the actual daily water consumption ET of the cropiAs shown in the following equation:
ETi＝Esi+Eci
(5.2) calculating the daily crop water demand ETMi：
ETMi＝a×E601,i
Wherein, ETMiWater requirement for crops, α water requirement coefficient for crops, E601,iThe actually measured water surface evaporation capacity of the evaporator is obtained;
(5.3) according to daily crop water demand ETMiObtaining the water demand ET in ten days of the cropM；
(5.4) calculating the water shortage of the i-th day crop:
QETi＝ETMi-ETi；
(5.5) QET according to the daily crop water shortageiThe water shortage QET in ten days of the crop is obtained.
4. The agricultural drought assessment method based on information assimilation and fusion according to claim 1, wherein the standard value of the agricultural drought level in step (7) is divided according to the following rules:
for dry crops, the water shortage rate theta of the crop is less than or equal to 45 percent, the crop is in a no-drought state, and the drought grade is 0; the water shortage rate of the plants is more than 45 percent and less than or equal to 65 percent, the plants are in a light drought state, and the drought grade is 1; the water shortage rate of the plants is more than 65% and less than or equal to 85%, the plants are in a medium drought state, and the drought grade is 2; the water shortage rate of the crops is more than 85 percent and less than or equal to 95 percent, the crops are in a heavy drought state, and the drought grade is 3; the water shortage rate of the crops is more than 95 percent and is in an extremely drought state, and the drought grade is 4;
for paddy fields, the water shortage rate theta is less than or equal to 20 percent as an object, the paddy fields are in a drought-free state, and the drought grade is 0; the water shortage rate of the plants is more than 20 percent and less than or equal to 35 percent, the plants are in a light drought state, and the drought grade is 1; the water shortage rate of the plants is more than 35 percent and less than or equal to 50 percent, the plants are in a medium drought state, and the drought grade is 2; the water shortage rate of the plants is more than 50 percent and less than or equal to 60 percent, the plants are in a heavy drought state, and the drought grade is 3; the water shortage rate of the crops is more than 60 percent and theta, the crops are in an extremely drought state, and the drought grade is 4.
5. The agricultural drought assessment method based on information assimilation and fusion of claim 1, wherein the step (8) comprises the following steps:
(8.1) judging the agricultural drought level simulated by each calculating unit according to the step (7), and comparing the level values
As a zero iteration estimation value of the drought level;
(8.2) continuously inserting remote sensing monitoring drought information and actual measurement drought information by adopting a gradual iteration method shown in the following formula, and respectively using a ten-day remote sensing monitoring value, an actual measurement value and a paired sum within a given evaluation radius R
Performing correction once, simultaneously reducing the evaluation radius, and reducing the calculation unit of the most edge in the evaluation radius in each iteration; correcting in sequence until the error of the calculated values of the two times is less than a given error value:
wherein j is the calculationThe unit number, k is an observation point, and n is the iteration number;
is the nth iteration estimation value of the agricultural drought level in the jth computing unit,
is the k observation, ε, in calculation unit j2Is an estimate of the ratio of the observed error variance to the simulated calculated error variance,
the total number of observation points within the range of the distance R from the central actual measurement point in the jth calculation unit; the calculation unit is a weight of a gradual iterative method in a county-level administrative district or a large and medium irrigation district within the drought evaluation range
As shown in the following equation:
weight of stepwise iterative method:
wherein the content of the first and second substances,
is the observation point r in the jth calculation unitkAnd actual measurement point rjThe square of the distance therebetween.
6. The agricultural drought assessment method based on information assimilation and fusion of claim 1, wherein the step (9) comprises:
(9.1) adopting a farmland water circulation simulation model to calculate the water demand ET of the crops every ten daysMAnd the crop water shortage value QET, and the agricultural drought information with the time resolution of 1 ten days and the spatial resolution of each calculation unit in the evaluation range is used as an initial evaluation field;
(9.2) acquiring MODIS remote sensing data, and calculating the ten-day leaf surface water shortage index NDWI by adopting an average synthesis method to obtain the drought information monitored by remote sensing every ten days;
(9.3) counting and calculating the actually measured drought information of each calculation unit in the evaluation range, wherein the actually measured drought information comprises a drought exposure rate α and a disaster rate β;
(9.4) comparing the simulated agricultural drought information of each computing unit in the evaluation range with the remote sensing monitoring drought information and the actual measurement drought information, computing the error covariance of each computing unit, analyzing and adjusting farmland water circulation simulation model parameters, simulating the agricultural drought information in the next ten days, judging the corresponding agricultural drought grade standard value, taking the standard value as the initial value of the drought grade corrected in the next time, and correcting the standard value every ten days for the next time;
and (9.5) calculating a farmland moisture circulation analog value and actually-measured drought information to obtain an agricultural drought error series of each calculating unit, carrying out statistical calculation on samples of the error series, assimilating the agricultural drought information until an error series expected value is 0 and covariance is a given prior error, and obtaining an agricultural drought evaluation model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910227582.XA CN111160680A (en) | 2019-03-25 | 2019-03-25 | Agricultural drought assessment method based on information assimilation and fusion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910227582.XA CN111160680A (en) | 2019-03-25 | 2019-03-25 | Agricultural drought assessment method based on information assimilation and fusion |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111160680A true CN111160680A (en) | 2020-05-15 |
Family
ID=70555681
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910227582.XA Pending CN111160680A (en) | 2019-03-25 | 2019-03-25 | Agricultural drought assessment method based on information assimilation and fusion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111160680A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111681122A (en) * | 2020-06-05 | 2020-09-18 | 河南省气象科学研究所 | Construction and application of summer corn drought influence evaluation model based on soil humidity |
| CN112837169A (en) * | 2021-01-11 | 2021-05-25 | 黄河勘测规划设计研究院有限公司 | Gridding drought comprehensive monitoring, early warning and evaluating method in drought process |
| CN113052484A (en) * | 2021-04-08 | 2021-06-29 | 中国气象局公共气象服务中心(国家预警信息发布中心) | Agricultural product climate quality evaluation method and device based on lattice point climate data |
| CN113469440A (en) * | 2021-07-02 | 2021-10-01 | 河海大学 | Method for predicting monthly-scale water demand for life |
| CN117828906A (en) * | 2024-03-05 | 2024-04-05 | 长江水利委员会长江科学院 | Drought transmission process simulation method, system and medium based on crop growth model |
-
2019
- 2019-03-25 CN CN201910227582.XA patent/CN111160680A/en active Pending
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111681122A (en) * | 2020-06-05 | 2020-09-18 | 河南省气象科学研究所 | Construction and application of summer corn drought influence evaluation model based on soil humidity |
| CN112837169A (en) * | 2021-01-11 | 2021-05-25 | 黄河勘测规划设计研究院有限公司 | Gridding drought comprehensive monitoring, early warning and evaluating method in drought process |
| CN113052484A (en) * | 2021-04-08 | 2021-06-29 | 中国气象局公共气象服务中心(国家预警信息发布中心) | Agricultural product climate quality evaluation method and device based on lattice point climate data |
| CN113469440A (en) * | 2021-07-02 | 2021-10-01 | 河海大学 | Method for predicting monthly-scale water demand for life |
| CN113469440B (en) * | 2021-07-02 | 2023-11-14 | 河海大学 | Method for predicting monthly scale living water demand |
| CN117828906A (en) * | 2024-03-05 | 2024-04-05 | 长江水利委员会长江科学院 | Drought transmission process simulation method, system and medium based on crop growth model |
| CN117828906B (en) * | 2024-03-05 | 2024-05-17 | 长江水利委员会长江科学院 | Drought transmission process simulation method, system and medium based on crop growth model |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wang et al. | Satellite retrieval of actual evapotranspiration in the Tibetan Plateau: Components partitioning, multidecadal trends and dominated factors identifying | |
| Han et al. | Crop evapotranspiration prediction by considering dynamic change of crop coefficient and the precipitation effect in back-propagation neural network model | |
| CN111160680A (en) | Agricultural drought assessment method based on information assimilation and fusion | |
| Zhang et al. | A modified water demand estimation method for drought identification over arid and semiarid regions | |
| Li et al. | Impacts of land use change and climate variability on hydrology in an agricultural catchment on the Loess Plateau of China | |
| Maruyama et al. | Coupling land surface and crop growth models to estimate the effects of changes in the growing season on energy balance and water use of rice paddies | |
| Dutta et al. | Model development in DNDC for the prediction of evapotranspiration and water use in temperate field cropping systems | |
| Silva et al. | Estimating reference evapotranspiration (ETo) using numerical weather forecast data in central Chile | |
| CN109523175B (en) | Multi-time scale drought assessment method considering snow accumulation and melting process | |
| Liu et al. | Comparison of two methods to derive time series of actual evapotranspiration using eddy covariance measurements in the southeastern Australia | |
| Xu et al. | Exploring evapotranspiration changes in a typical endorheic basin through the integrated observatory network | |
| CN104077475B (en) | A kind of global land table based on many algorithm integrations evapotranspires estimating system and method | |
| Dong et al. | Comparison and analysis of data assimilation algorithms for predicting the leaf area index of crop canopies | |
| Huang et al. | Assessing grain crop water productivity of China using a hydro-model-coupled-statistics approach: Part I: Method development and validation | |
| CN113011372B (en) | Automatic monitoring and identifying method for saline-alkali soil | |
| Zhou et al. | Spatiotemporal variations of actual evapotranspiration over the Lake Selin Co and surrounding small lakes (Tibetan Plateau) during 2003–2012 | |
| CN106295865A (en) | A kind of Forecasting Methodology of rice yield | |
| CN115452822B (en) | Method and device for obtaining paddy field methane emission flux based on remote sensing and farmland information | |
| Nyolei et al. | Evapotranspiration simulation from a sparsely vegetated agricultural field in a semi-arid agro-ecosystem using Penman-Monteith models | |
| Feng et al. | Performance of the improved two-source energy balance model for estimating evapotranspiration over the heterogeneous surface | |
| CN115795819A (en) | Water utilization efficiency simulation method based on SEBS model and CASA model | |
| Sun et al. | Assessment and improvement of Noah-MP for simulating water and heat exchange over alpine grassland in growing season | |
| CN110377989B (en) | Non-uniformity two-variable design flood calculation method based on hydrothermal coupling balance | |
| López-Ramírez et al. | A comprehensive calibration and validation of SWAT-T using local datasets, evapotranspiration and streamflow in a tropical montane cloud forest area with permeable substrate in central Veracruz, Mexico | |
| Liu et al. | A simple framework for estimating the annual runoff frequency distribution under a non-stationarity condition |
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 |