CEEMDAN-based method for screening and monitoring soil moisture stress in agricultural fields
The present invention discloses a CEEMDAN-based method for screening and monitoring soil moisture stress in farmland, characterised by the steps: preprocessing of remote sensing images, construction of NDVI long time series, CEEMDAN decomposition, calculation of statistical descriptors, screening of soil moisture stress sequences, ground data measurement, construction of soil moisture stress characteristic curves, fitting of soil moisture stress response characteristic curves and predicting the content of soil moisture stress. The invention adopts CEEMDAN decomposition, which solves the problems of noise residue and low reconstruction accuracy in the previous methods, and the high reconstruction accuracy of decomposed component data is more conducive to capturing the transient effects of soil moisture stress, and realizes the screening and extraction of soil moisture stress by combining with the ground measured data. The inverse model of soil moisture content is fitted by combining the effects of multiple indicators, and the CEEMDAN algorithm with remote sensing technology tools to achieve accurate monitoring of soil moisture in a large area of farmland.
1 . A CEEMDAN-based method for screening and monitoring soil moisture stress in agricultural fields, where the CEEMDAN refers to a algorithm of complete ensemble empirical mode decomposition with adaptive noise, comprising the steps as follows:
Step 1: preprocessing radiometric calibration, atmospheric correction and geometric correction on all N remote sensing images;
Step 2: calculating of NDVI long time series x(n), 1≤n≤N based on pre-processed remote sensing image data:
x
(
n
)
=
ρ
NIR
(
n
)
-
ρ
RED
(
n
)
ρ
NIR
(
n
)
+
ρ
RED
(
n
)
(
formula
1
)
in formula 1, ρ NIR (n) is a reflectance of a nth remote sensing image in a near infrared band and ρ RED (n) is the reflectance of the nth remote sensing image in a red band to construct a long time series of NDVI for crops in natural farmland ecosystems;
Step 3: decomposing the NDVI long time series based on CEEMDAN algorithm, a decomposition process thereof is as follows:
first add adaptive white noise Z (m) (n) to the NDVI long time series x(n), where m denotes the number of times the noise is added, 1≤m≤50, to obtain the first signal to be decomposed:
x
1
(
m
)
(
n
)
=
x
(
n
)
+
ε
m
Z
(
m
)
(
n
)
ε m is a standard deviation of a mth addition of white noise, a first IMF component obtained from the CEEMDAN decomposition is as follows:
IMF
1
=
1
50
∑
m
=
1
50
IMF
1
(
m
)
IMF
1
(
m
)
denotes a IMF component obtained by EMD decomposition of the first signal
x
1
(
m
)
(
n
)
to be decomposed, and the first residual term is s 1 (n)=x(n)−IMF 1 ;
a rth to-be-decomposed signal
x
r
(
m
)
(
n
)
=
S
r
-
1
(
n
)
+
ε
m
Z
(
m
)
(
n
)
is obtained by superimposing white noise on the (r−1)th residual term, and the rth to-be-decomposed signal
x
r
(
m
)
(
n
)
is decomposed again by EMD to obtain a IMF component
IMF
r
(
m
)
,
and the rth IMF component is then as follows:
IMF
r
=
1
50
∑
m
=
1
50
IMF
r
(
m
)
a rth residual term is S r (n)=S r−1 (n)−IMF r , r=1, . . . , R;
Step 4: calculating statistical descriptors for the first to Rth IMF component IMF r , r=1, . . . , R, the statistical descriptors including period of fluctuation (P r ), mean (M r ), variance (V r ), variance contribution margin (C r ) and pearson correlation coefficient (PS r ), where K r is the number of extreme value points of the rth eigenmodal component IMF r , r=1, . . . , R;
P
r
=
N
K
r
(
formula
2
)
M
r
=
1
N
∑
n
=
1
N
IMF
r
(
n
)
(
formula
3
)
V
r
=
1
N
∑
n
=
1
N
(
IMF
r
(
n
)
-
1
N
∑
n
=
1
N
IMF
r
(
n
)
)
2
(
formula
4
)
C
r
=
∑
n
=
1
N
(
IMF
r
(
n
)
-
1
N
∑
n
=
1
N
IMF
r
(
n
)
)
2
∑
n
=
1
N
(
x
(
n
)
-
1
N
∑
n
=
1
N
x
(
n
)
)
2
(
formula
5
)
PS
r
=
∑
n
=
1
N
(
IMF
r
(
n
)
-
1
N
∑
n
=
1
N
IMF
r
(
n
)
)
(
x
(
n
)
-
1
N
∑
n
=
1
N
x
(
n
)
)
∑
n
=
1
N
(
IMF
r
(
n
)
-
1
N
∑
n
=
1
N
IMF
r
(
n
)
)
2
∑
n
=
1
N
(
x
(
n
)
-
1
N
∑
n
=
1
N
x
(
n
)
)
2
(
formula
6
)
Step 5: screening of soil moisture stress sequences, wherein the soil moisture stress sequences are identified by combining the statistical descriptors described above with mechanistic characteristics of soil moisture stress, IMF components that meet characteristics of short-period soil moisture stress and identification conditions are summed and synthesized into the soil moisture stress sequences;
Step 6: determining Chlorophyll content of crop leaves through a hand-held chlorophyll meter by weighing a fresh biomass of a crop plant (FB), using a drying method at a predetermined killing temperature for a predetermined time, turning the temperature to a predetermined drying temperature and drying to a constant weight, then weighing its dry biomass (DB) and calculating a plant water content (PWC);
P
W
C
=
FB
-
DB
FB
×
100
%
(
formula
7
)
weighing the wet weight of the soil sample (FW), drying the soil sample to a constant weight using the drying method at a preset drying temperature, then weighing its dry weight (DW) and calculating a soil moisture content (SMC);
S
M
C
=
FW
-
DW
DW
×
100
%
(
formula
8
)
Step 7: constructing of soil moisture stress characteristic curves by taking two points n and n+1 of the soil moisture stress sequence including the measured time t and corresponding values y(n) and y(n+1), and fitting the corresponding value of the measured time t as the corresponding soil moisture stress content Y(t) at the measured time point;
Y ( t )=( t−n )( y ( n+ 1)− y ( n ))+ y ( n ) (formula 9)
Step 8: fitting of soil moisture stress response characteristic curves, where the moisture stress content is used as the independent variable and chlorophyll content is used as the dependent variable to construct a chlorophyll response index; the soil moisture stress is used as the independent variable and plant water content is used as the dependent variable to construct a wheat moisture content response index; the function of soil moisture stress and chlorophyll content and the function of soil moisture stress and plant water content are fitted; wherein a quadratic curve is used to fit chlorophyll content as a function of soil moisture stress and a composite curve is used to fit wheat moisture content as a function of soil moisture stress;
Step 9: predicting the content of soil moisture stress, where the model is constructed to predict the degree of soil moisture stress by inverting the soil moisture content between three indicators, soil moisture stress, chlorophyll response to soil moisture stress and wheat moisture content response to soil moisture stress.
2 . The method for screening and monitoring soil moisture stress in agricultural fields based on CEEMDAN according to claim 1 , characterized in that the conditions for screening soil moisture stress sequences in step 5 are that the fluctuation period P r is less than 7, the mean value M r is the smallest, and the variance V r , variance contribution C r and pearson correlation coefficient PS r are the largest.
3 . The method for screening and monitoring soil moisture stress in agricultural fields based on CEEMDAN according to claim 2 , characterized in that the model between soil moisture stress, chlorophyll response to soil moisture stress and wheat moisture response to soil moisture stress and soil moisture content in step 9 is as follows:
SMC=20.58−0.02 x 1 +10 −2 x 2 −1.76 x 3 −2×10 −3 x 1 x 2 −0.065 x 1 x 3 −0.045 x 2 x 3 −1.83×10 −4 x 1 2 +7.91×10 −5 x 2 2 +3.99 x 3 2
x 1 represents chlorophyll response to soil moisture stress, x 2 represents wheat moisture response to soil moisture stress and x 3 represents soil moisture stress content.
4 . The method for screening and monitoring soil moisture stress in agricultural fields based on CEEMDAN according to claim 1 , characterized in that screening out soil moisture stress sequences in step 5 synthesizes the first and second IMF components IMF 1 and IMF 2 cumulatively into a soil moisture stress sequence:
y
(
n
)
=
∑
n
=
1
N
(
IMF
1
(
n
)
+
IMF
2
(
n
)
)
.
(
formula
10
)
5 . The method for screening and monitoring soil moisture stress in agricultural fields based on CEEMDAN according to claim 1 , characterized in that the chlorophyll content as a function of the amount of soil moisture stress content in step 8 described is y1=58.241−19.917x−393.742x 2 and the plant water content as a function of the amount of soil moisture stress content is y2=68.121+0.404 x , wherein x represents the amount of soil moisture stress content, y1 represents the chlorophyll content, and y2 represents the plant water content.