CN116976516A - Early prediction method for single crop yield in arid region - Google Patents
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Abstract
The invention discloses a method for predicting single crop yield in drought areas at early stage, which comprises the steps of S1, obtaining a surface parameter data set of a long-time sequence, S2, estimating soil moisture in root areas, S3, calculating the effective moisture content percentage of soil, S4, estimating the water deficiency area proportion, S5, estimating the accumulated growth condition deviation of crops, S6, predicting early single crop yield in any crop growth period in a monitoring area; the method is used for jointly constructing the single-yield prediction model of the crops in the arid region by coupling the water deficit area proportion and the accumulated growth vigor deviation of the crops, and comprehensively considering the influences of the distribution condition of the available water and the growth state of the crops on the yield of the crops, so that the early prediction of the yields of the crops of different crop types in the arid region is realized.
Description
Technical Field
The invention relates to a crop unit yield prediction method, in particular to a drought area crop unit yield early prediction method.
Background
Estimation of grain yield is critical to aspects such as agricultural planning, purchasing, transportation, storage, export/import of grain, and the like.
In differentiation, crop growth in arid planting areas is very sensitive to moisture demand, low available soil moisture makes agricultural planting more susceptible to weather, resulting in frequent drought occurrences and increased uncertainty in crop yield; in a rain-fed planting area, the soil preparation and sowing of crops are completely dependent on the soil moisture provided by precipitation, the soil moisture in a root area directly influences the sowing time, and the sowing delay can lead to the reduction of the crop yield.
In this regard, the field is generally predicted by the following crop yield estimation methods:
the traditional field investigation method is always a common method for estimating early crop yield, but the method has the problems of large workload, long time consumption, high cost and the like, and meanwhile, the investigation result is also influenced by subjective factors and the number of samples, so that the possibility of deviation and error is high.
The statistical analysis method is another common crop yield estimation method, mainly based on historical data and model prediction, the applicability and reliability of the model of the method depend on the quality and sample number of the historical data, and the model is not sensitive enough to the change and interference of external factors, so that the prediction result deviation is easy to be larger.
With the rapid development of remote sensing technology, the remote sensing technology provides abundant data sources for crop growth and yield prediction by acquiring earth surface information collected by satellites, such as vegetation index, soil moisture and meteorological data, but the traditional yield prediction model at present adopts soil moisture to reflect the moisture condition in soil, and cannot directly represent the moisture availability of plant root systems, so that the prediction accuracy is affected;
in addition, the vegetation index is an important index for measuring vegetation condition and growth vigor, and the early crop unit yield prediction of pixel scale mostly adopts the means of vegetation index of crop growing period, so that the timeliness of the crop unit yield prediction can not be ensured.
Disclosure of Invention
In order to solve the defects of the technology, the invention provides a method for predicting the single crop early stage in arid regions.
In order to solve the technical problems, the invention adopts the following technical scheme: coupling the water deficit area ratio to the crop cumulative growth bias, the method comprising:
s1, acquiring a surface parameter data set of a long-time sequence,
the data set is closely related to the spatial distribution and variation of soil moisture,
the system specifically comprises a rainfall data set, a vapor emission, surface soil moisture, an enhanced vegetation index, a soil type, a crop distribution map and irrigation area data;
s2, estimating the soil moisture of the root zone,
the daily water quantity change is calculated by a water balance equation, the formula is as follows,
RZSM(t+1)＝RZSM(t)+PR(t+1)+IR(t+1)-ET(t+1)+GD(t+1)+SR(t+1) (1)，
wherein RZSM (t+1) represents the root zone soil moisture of the (t+1) th day calculated by remote sensing data driving, RZSM (t) represents the root zone soil moisture of the (t) th day calculated by remote sensing data driving, PR (t+1) represents the precipitation amount of the (t+1) th day, IR (t+1) represents the irrigation amount of the (t+1) th day, wherein the irrigation amount of the arid and anhydrous source region can be regarded as 0, ET (t+1) represents the evapotranspiration value of the (t+1) th day, GD (t+1) represents the water replenishment or downward leakage amount of the lower layer to the upper layer of the (t+1) th day, SR (t+1) represents the small-scale surface runoff of the (t+1) th day, wherein the precipitation intensity of the arid region is small, the water exchange mainly occurs in the vertical direction, the small-scale surface runoff is negligible,
for arid areas, the formula (1) is simplified to obtain the following formula (2),
wherein RZSM_RS (t+1) represents the root soil moisture of the t+1th day obtained by the remote sensing data, RZSM_RS (t) represents the root soil moisture of the t th day obtained by the remote sensing data, PR_RS (t+1) represents the precipitation of the t+1th day obtained by the remote sensing data, ET_RS (t+1) represents the evapotranspiration value of the t+1th day obtained by the remote sensing data, and FC represents the field water holding capacity of each soil type;
s3, calculating the percentage of the effective water content of the soil, namely PASM, wherein the formula is as follows:
wherein RZSM_RS represents the soil moisture in a root zone, FC represents the field water holding capacity of each soil type, WP represents the wilting coefficient of the soil, and the units of the RZSM_RS and the WP are m3/m3.
S4, estimating the water deficit area ratio, namely WDAP, according to the following formula:
in PASM lim Representing the percentage threshold of usable water, pixel (PASM < PASM) lim ) Representing a percentage of water below a threshold value PASM for the amount of water available in a certain area lim Pixel (all) represents the total number of all pixels in the region.
S5, estimating the accumulated growth deviation of crops, namely BIAS (Condition acc ) The formula is as follows:
BIAS(Condition acc )＝Condition acc /Condition(ave) acc (7)，
in the formula, BIAS (Condition acc ) Condition for accumulating growth bias acc To accumulate growth values Condition (ave) acc Is the cumulative growth average;
s6, predicting early single yield of any crop in the monitoring area,
polynomial fitting is carried out on the long time sequence data sets of the S4 and the S5 to construct an early-stage unit yield prediction model, and a specific model formula (8) is as follows:
Yield＝f(WDAP,BIAS(Condition acc ))＝μ 1 +μ 2 *WDAP+μ 3 *BIAS(Condition acc )+μ 4 *WDAP 2 +μ 5 *BIAS(Condition acc ) 2
wherein YIeld represents statistical unit Yield or actual measurement unit Yield data of the monitoring area, and f represents YIeld, WDAP, BIAS (Condition acc ) A prediction model constructed between the two, mu 1 、μ 2 、μ 3 、μ 4 、μ 5 The coefficients of the fitting model are respectively calculated,
the early single yield of any crop in the growing period of the monitored area can be predicted by the formula (8).
Further, in S1,
a precipitation data set, GR, which is used to initialize a water balance model, drive the model to calculate the soil effective moisture, and evaluate the variability of the precipitation in the monitored area;
evaporation, ET, the data is the primary input for calculating the effective moisture content of the soil;
surface soil moisture, TM, this data is the initial condition for driving the water balance model;
the enhanced vegetation index, namely EVI, is used for representing the growth condition of crops, calculating the accumulated growth condition information of the crops and is a main input of yield prediction;
soil type, SD, which is used to provide field water holding capacity and wilting coefficient for a particular soil type and a particular soil depth;
extracting crop distribution data of a monitoring area from a crop distribution map (CD) for covering a non-crop area;
irrigation area data, GM, is extracted from the irrigation distribution data of the monitored area for masking the irrigated area.
Further, when the root zone soil moisture is estimated in the arid region in the S2, each component in the water balance equation is replaced by remote sensing data, and then the traditional moisture balance model, namely the parameter of the formula (1), is driven and corrected by the remote sensing data, and a new model, namely the formula (2), is constructed to simulate the root zone soil moisture.
Further, in S3, the difference between rzms_rs and WP is the root zone water available, and the difference between FC and WP is the maximum water available.
Further, in S4, the cereal is processedArticle PASM lim 40 percent of fruit trees, rhizomes, vegetables and beans, 60 percent of leaf vegetables and 70 percent of beans.
Further, before estimating the crop accumulated growth bias in S5,
firstly, obtaining an enhanced vegetation index (more than or equal to 20 years) of a historical long-time sequence, and covering a non-crop area by using a crop distribution map;
secondly, extracting pixel enhanced vegetation index values of a plurality of times in the crop growing season, and calculating the average value in the monitoring areaAnd average value of years->A change curve.
Further, the percentile method is adopted to calculate 10% and 90% percentile of the whole growth curve so as to eliminate possible errors of contribution of the initial and final growth vigor of the crops to the yield, and the method is specifically as follows:
the accumulated growth value in the monitoring area is calculated as follows:
in the method, in the process of the invention,representing the mean value in the region;
secondly, the cumulative average growth value in the monitored area is calculated as follows:
in the method, in the process of the invention,mean values over a number of years in the region are indicated.
Further, at S6, μ 1 、μ 2 、μ 3 、μ 4 、μ 5 WDAP and BIAS (Condition) through historical long time series data sets acc ) And (3) obtaining YIeld by adopting least square fitting.
The invention discloses a method for predicting single crop yield in drought areas, which comprises the following steps:
(1) Aiming at the problem that the water consumption of plants in a specific depth range cannot be quantified by the soil moisture parameters in the traditional yield prediction model, the water consumption of crops in different soil types and depth root areas is simulated by utilizing the remote sensing data to drive the water balance model, the water deficiency area proportion index is provided, the space distribution condition of the water consumption of the whole area can be more comprehensively reflected, and the limiting factors of crop growth in arid areas are accurately reflected, so that early prediction of crop unit yield is realized;
(2) Aiming at the problem that the conventional early-stage crop unit yield prediction mainly depends on vegetation indexes in a growing period, a crop accumulated growth vigor deviation index is provided, and the index can accurately reflect the overall trend and change of crop growth, and gets rid of the limitation of prediction lag caused by single vegetation index information saturation, so that the timeliness of a crop unit yield prediction model is effectively improved;
(3) Aiming at the limitation that the traditional yield prediction model mostly adopts single parameter prediction, two new indexes of water deficiency area proportion and crop accumulated growth deviation are respectively provided, the influence of the water condition and the crop growth process on the crop yield can be comprehensively considered, the prediction model coupled with the two indexes can be used for early prediction of the yield of different types of crops in arid regions, and the prediction precision and timeliness of the crops in arid regions are effectively improved.
Drawings
FIG. 1 is a technical flow chart of the present invention.
Detailed Description
The invention will be described in further detail with reference to the drawings and the detailed description.
Aiming at the limitations of the existing crop single-yield early-stage prediction method, the embodiment discloses a drought region crop single-yield early-stage prediction method, in particular to a drought region crop single-yield early-stage prediction method for coupling a water deficit area proportion and crop accumulated growth potential deviation.
The method comprises the following steps:
s1, acquiring a surface parameter data set of a long-time sequence,
the data set is closely related to the spatial distribution and variation of soil moisture,
and specifically includes precipitation data sets, vapor emissions, surface soil moisture, enhanced vegetation index, soil type, crop distribution map, irrigation area data.
In this embodiment, the precipitation data set, that is, GR, specifically, the CHIRPS version 2.0, that is, the weather disaster group infrared precipitation site data, has a resolution of up to 0.05 degrees, and the data is used to initialize a water balance model, drive the model to calculate the soil effective moisture, and evaluate the variability of the precipitation in the monitored area;
evaporation, ET, can use medium resolution imaging spectrometer (MODIS) data, with a spatial resolution of 0.01 degrees and aggregate its resolution to 0.05 degrees, which is the primary input for calculating the effective moisture content of the soil;
surface soil moisture, i.e., TM, can use AMSR-E/AMSR-2 data, which is the initial condition for driving the water balance model;
enhanced vegetation index, EVI, using the MODIS data of TERRA and AQUA satellites to characterize crop growth conditions, calculate crop cumulative growth information, the primary input for yield prediction;
soil types, i.e., SD, derived from SoilGrids, which are global soil information data sets developed jointly by the International Soil Reference and Information Center (ISRIC) and the European Commission's Joint Research Center (JRC) to provide field water holding capacity and wilting coefficient for a particular soil type and a particular soil depth;
crop distribution map, i.e., CD, which is derived from the cropowatch platform, is raster data, with values of 1 and 0 representing a cut area and an unsorted area, respectively, and further, in this embodiment, it extracts crop distribution data of a monitored area for masking a non-crop area;
irrigation area data, i.e., GM, is used to extract irrigation distribution data of the monitored area for covering the irrigated area, and it is noted that GM is from the global irrigation area map of the united nations grain and agricultural organization (FAO) version 5 (2016), and the data shows irrigation areas before and after 2005, as well as irrigation proportions of groundwater, surface water, etc.
S2, estimating the soil moisture of the root zone,
the daily water quantity change is calculated by a water balance equation, the formula is as follows,
RZSM(t+1)＝RZSM(t)+PR(t+1)+IR(t+1)-ET(t+1)+GD(t+1)+SR(t+1) (1)，
wherein RZSM (t+1) represents the root zone soil moisture of the (t+1) th day calculated by remote sensing data driving, RZSM (t) represents the root zone soil moisture of the (t) th day calculated by remote sensing data driving, PR (t+1) represents the precipitation amount of the (t+1) th day, IR (t+1) represents the irrigation amount of the (t+1) th day, wherein the irrigation amount of the arid and anhydrous source region can be regarded as 0, ET (t+1) represents the evapotranspiration value of the (t+1) th day, GD (t+1) represents the water replenishment or downward leakage amount of the lower layer to the upper layer of the (t+1) th day, SR (t+1) represents the small-scale surface runoff of the (t+1) th day, wherein the precipitation intensity of the arid region is small, the water exchange mainly occurs in the vertical direction, the small-scale surface runoff is negligible,
and further simplifying the formula (1) aiming at arid areas to obtain the following formula (2),
wherein RZSM_RS (t+1) represents the root soil moisture of the t+1th day obtained by remote sensing data, RZSM_RS (t) represents the root soil moisture of the t th day obtained by remote sensing data, PR_RS (t+1) represents the precipitation of the t+1th day obtained by remote sensing data, CHIRPS2.0 version data with the highest resolution of 0.05 degrees is selected, ET_RS (t+1) represents the evapotranspiration value of the t+1th day obtained by remote sensing data, ET data released by the United states geological survey is preferred, FC represents the field water holding capacity of each soil type, and pixel FC values corresponding to soil types and depths can be obtained by grid soil attribute data in SoilGrids.
It should be noted that, when the root soil moisture estimation is performed on the arid area in S2, each component in the water balance equation is replaced by remote sensing data, and then the parameters of the traditional moisture balance model, i.e., the formula (1), are driven and corrected by the remote sensing data, and then a new model, i.e., the formula (2), is constructed to simulate the root soil moisture.
The principle of this embodiment S2 is:
and (3) selecting spring without crop growth, obvious water replenishment and exchange as initial time of model driving by utilizing the precipitation data set and the evapotranspiration data in the S1, then substituting surface soil moisture corresponding to the initial time into the formula (2) together to drive the model, covering a non-crop area and an irrigation area by using crop distribution map and irrigation area data respectively, and iteratively solving out a rain-fed crop planting area GD (t+1), namely the water replenishment or downward leakage amount of the lower layer to the upper layer in the t+1 day, thereby establishing a water balance model driven by the remote sensing model.
Based on the result of the soil moisture in the root zone on the t day, the precipitation data set and the evapotranspiration on the t+1 day are substituted into the formula (2), so that the soil moisture RZSM_RS (t+1) in the root zone on the t+1 day can be obtained through simulation.
S3, calculating the percentage of the effective water content of the soil, namely PASM, wherein the formula is as follows:
wherein RZSM_RS represents the soil moisture in a root zone, FC represents the field water holding capacity of each soil type, WP represents the wilting coefficient of the soil, and the units of the RZSM_RS and the WP are m3/m3.
In S3, the difference between the rzsm_rs and WP is the root zone usable water amount, and the difference between the FC and WP is the maximum usable water amount.
In this embodiment, S3 is to calculate the percentage PASM of the effective soil moisture in the root zone depth at t+1 by using the root zone soil moisture rzms_rs (t+1) at t+1 obtained in S2, where PASM represents the percentage of the effective soil moisture in the root zone depth as above.
S4, estimating the water deficit area ratio, namely WDAP, according to the following formula:
in PASM lim Representing the percentage threshold of usable water, pixel (PASM < PASM) lim ) Representing a percentage of water below a threshold value PASM for the amount of water available in a certain area lim Pixel (all) represents the total number of all pixels in the region.
In S4, cereal crop PASM lim 40 percent of fruit trees, rhizomes, vegetables and beans, 60 percent of leaf vegetables and 70 percent of beans.
S5, estimating the accumulated growth deviation of crops, namely BIAS (Condition acc ) The formula is as follows:
BIAS(Condition acc )＝Condition acc /Condition(ave) acc (7)，
in the formula, BIAS (Condition acc ) Condition for accumulating growth bias acc To accumulate growth values Condition (ave) acc Is the cumulative growth average.
Before estimating crop accumulation growth bias, firstly, obtaining a historical long-time sequence enhanced vegetation index (more than or equal to 20 years), and covering a non-crop area by using a crop distribution map; secondly, extracting pixel enhanced vegetation index values of a plurality of times in the crop growing season, and calculating the average value in the monitoring areaAnd average value of years->A change curve.
Preferably, the percentile method is used to calculate the 10% and 90% percentile of the whole growth curve so as to eliminate possible errors of contribution of the initial and final growth vigor of the crop to the yield, and the method is specifically as follows:
the accumulated growth value in the monitoring area is calculated as follows:
in the method, in the process of the invention,representing the mean value in the region;
secondly, the cumulative average growth value in the monitored area is calculated as follows:
in the method, in the process of the invention,mean values over a number of years in the region are indicated.
It will be appreciated that equation (7) can be calculated using equation (5) and equation (6).
S6, predicting early single yield of any crop in the monitoring area,
polynomial fitting is carried out on the long time sequence data sets of the S4 and the S5 to construct an early-stage unit yield prediction model, and a specific model formula (8) is as follows:
Yield＝f(WDAP,BIAS(Condition acc ))＝μ 1 +μ 2 *WDAP+μ 3 *BIAS(Condition acc )+μ 4 *WDAP 2 +μ 5 *BIAS(Condition acc ) 2
in the formula, YIeld represents the statistical unit Yield or the actual unit Yield of the monitoring areaData, f represents YIeld and WDAP, BIAS (Condition acc ) A prediction model constructed between the two, mu 1 、μ 2 、μ 3 、μ 4 、μ 5 Respectively, fitting model coefficients, it can be understood that WDAP and BIAS (Condition) are calculated by using the formula (4) and the formula (7), respectively acc ) And then the early single yield of any crop in the growth period of the monitored area can be predicted by the formula (8).
Preferably, mu 1 、μ 2 、μ 3 、μ 4 、μ 5 WDAP and BIAS (Condition) through historical long time series data sets acc ) And (3) obtaining YIeld by adopting least square fitting.
The method for predicting the single yield of the crops in the arid region disclosed by the invention is coupled with the water deficiency area proportion and the crop accumulated growth vigor deviation to jointly construct a single yield prediction model of the crops in the arid region, and can comprehensively consider the influences of the distribution condition of available water and the growth state of the crops on the crop yield, so that the early prediction of the crop yield of different crop types in the arid region is realized, specifically, the water balance model is driven by remote sensing data to simulate the change of the water quantity in the root region of different soil types and depths, the defect that the water content of plants can be extracted in a specific depth range in the traditional soil moisture is overcome, the water deficiency area proportion index is considered, the distribution condition of the available water quantity in the whole region can be comprehensively and accurately evaluated, and in addition, the accumulated growth vigor deviation index of the crops is also provided for aiming at the defect that the vegetation index can not reflect the integral trend and change of the crop growth, the accumulated growth condition of the crops can be accurately described, and the hysteresis problem of yield prediction caused by the vegetation information saturation can be solved.
The above embodiments are not intended to limit the present invention, and the present invention is not limited to the above examples, but is also intended to be limited to the following claims.
Claims (8)
1. A method for predicting early single crop yield in arid regions, characterized by coupling a water deficit area ratio with a cumulative crop growth bias, the method comprising:
s1, acquiring a surface parameter data set of a long-time sequence,
the data set is closely related to the spatial distribution and variation of soil moisture,
the system specifically comprises a rainfall data set, a vapor emission, surface soil moisture, an enhanced vegetation index, a soil type, a crop distribution map and irrigation area data;
s2, estimating the soil moisture of the root zone,
the daily water quantity change is calculated by a water balance equation, the formula is as follows,
RZSM(t+1)＝RZSM(t)+PR(t+1)+IR(t+1)-ET(t+1)+GD(t+1)+SR(t+1) (1)，
wherein RZSM (t+1) represents the root zone soil moisture of the (t+1) th day calculated by remote sensing data driving, RZSM (t) represents the root zone soil moisture of the (t) th day calculated by remote sensing data driving, PR (t+1) represents the precipitation amount of the (t+1) th day, IR (t+1) represents the irrigation amount of the (t+1) th day, wherein the irrigation amount of the arid and anhydrous source region can be regarded as 0, ET (t+1) represents the evapotranspiration value of the (t+1) th day, GD (t+1) represents the water replenishment or downward leakage amount of the lower layer to the upper layer of the (t+1) th day, SR (t+1) represents the small-scale surface runoff of the (t+1) th day, wherein the precipitation intensity of the arid region is small, the water exchange mainly occurs in the vertical direction, the small-scale surface runoff is negligible,
for arid areas, the formula (1) is simplified to obtain the following formula (2),
wherein RZSM_RS (t+1) represents the root soil moisture of the t+1th day obtained by the remote sensing data, RZSM_RS (t) represents the root soil moisture of the t th day obtained by the remote sensing data, PR_RS (t+1) represents the precipitation of the t+1th day obtained by the remote sensing data, ET_RS (t+1) represents the evapotranspiration value of the t+1th day obtained by the remote sensing data, and FC represents the field water holding capacity of each soil type;
s3, calculating the percentage of the effective water content of the soil, namely PASM, wherein the formula is as follows:
wherein RZSM_RS represents the soil moisture in a root zone, FC represents the field water holding capacity of each soil type, WP represents the wilting coefficient of the soil, and the units of the RZSM_RS and the WP are m3/m3.
S4, estimating the water deficit area ratio, namely WDAP, according to the following formula:
in PASM lim Representing the percentage threshold of usable water, pixel (PASM < PASM) lim ) Representing a percentage of water below a threshold value PASM for the amount of water available in a certain area lim Pixel (all) represents the total number of all pixels in the region.
S5, estimating the accumulated growth deviation of crops, namely BIAS (Condition acc ) The formula is as follows:
BIAS(Condition acc )＝Condition acc /Condition(ave) acc (7)，
in the formula, BIAS (Condition acc ) Condition for accumulating growth bias acc To accumulate growth values Condition (ave) acc Is the cumulative growth average;
s6, predicting early single yield of any crop in the monitoring area,
polynomial fitting is carried out on the long time sequence data sets of the S4 and the S5 to construct an early-stage unit yield prediction model, and a specific model formula (8) is as follows:
Yield＝f(WDAP,BIAS(Condition acc ))＝μ 1 +μ 2 *WDAP+μ 3 *BIAS(Condition acc )+μ 4 *WDAP 2 +μ 5 *BIAS(Condition acc ) 2
wherein YIeld represents the monitored areaStatistical or actual measurement of Yield per unit data, f represents Yield and WDAP, BIAS (Condition acc ) A prediction model constructed between the two, mu 1 、μ 2 、μ 3 、μ 4 、μ 5 The coefficients of the fitting model are respectively calculated,
the early single yield of any crop in the growing period of the monitored area can be predicted by the formula (8).
2. The method for predicting single crop yield in arid area according to claim 1, wherein:
in the step S1 of the process described above,
a precipitation data set, GR, which is used to initialize a water balance model, drive the model to calculate the soil effective moisture, and evaluate the variability of the precipitation in the monitored area;
evaporation, ET, the data is the primary input for calculating the effective moisture content of the soil;
surface soil moisture, TM, this data is the initial condition for driving the water balance model;
the enhanced vegetation index, namely EVI, is used for representing the growth condition of crops, calculating the accumulated growth condition information of the crops and is a main input of yield prediction;
soil type, SD, which is used to provide field water holding capacity and wilting coefficient for a particular soil type and a particular soil depth;
extracting crop distribution data of a monitoring area from a crop distribution map (CD) for covering a non-crop area;
irrigation area data, GM, is extracted from the irrigation distribution data of the monitored area for masking the irrigated area.
3. The method for predicting single crop yield in arid area according to claim 1, wherein: when the root zone soil moisture is estimated in the S2, each component in the water balance equation is replaced by remote sensing data, and then the traditional moisture balance model, namely the parameters of the formula (1), is driven and corrected by the remote sensing data, and a new model, namely the formula (2), is constructed to simulate the root zone soil moisture.
4. The method for predicting single crop yield in arid area according to claim 1, wherein: in the step S3, the difference value of RZSM_RS and WP is the root zone available water quantity, and the difference value of FC and WP is the maximum available water quantity.
5. The method for predicting single crop yield in arid area according to claim 1, wherein: in said S4, cereal crop PASM lim 40 percent of fruit trees, rhizomes, vegetables and beans, 60 percent of leaf vegetables and 70 percent of beans.
6. The method for predicting single crop yield in arid area according to claim 1, wherein: before estimating the crop cumulative growth bias in S5,
firstly, obtaining an enhanced vegetation index (more than or equal to 20 years) of a historical long-time sequence, and covering a non-crop area by using a crop distribution map;
secondly, extracting pixel enhanced vegetation index values of a plurality of times in the crop growing season, and calculating the average value in the monitoring areaAnd average value of years->A change curve.
7. The method for predicting early yield per crop in arid regions according to claim 6, wherein the percentile method is used to calculate 10% and 90% percentile of the whole growth curve so as to eliminate possible errors of contribution of early and final growth vigor to yield of crops, and the method is as follows:
the accumulated growth value in the monitoring area is calculated as follows:
in the method, in the process of the invention,representing the mean value in the region;
secondly, the cumulative average growth value in the monitored area is calculated as follows:
in the method, in the process of the invention,mean values over a number of years in the region are indicated.
8. The method for predicting single crop yield in arid area according to claim 1, wherein: at S6, mu 1 、μ 2 、μ 3 、μ 4 、μ 5 WDAP and BIAS (Condition) through historical long time series data sets acc ) And (3) obtaining YIeld by adopting least square fitting.
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