| Type: | Package |
| Title: | Water Cloud Model (WCM) for the Simulation of Leaf Area Index (LAI) and Soil Moisture (SM) from Microwave Backscattering |
| Version: | 0.2.2 |
| Author: | Ujjwal Singh <ujjwalrsmt@gmail.com> Prashant K Srivastava <prashant.just@gmail.com)> Dharmendra Kumar Pandey <Dkp@sac.isro.gov.in> Sumit Kumar Chaudhary <sumit.mathe@gmail.com> Dileep Kumar Gupta <dileepgupta85@gmail.com> |
| Maintainer: | Ujjwal Singh <ujjwalrsmt@gmail.com> |
| Description: | Retrieval the leaf area index (LAI) and soil moisture (SM) from microwave backscattering data using water cloud model (WCM) model . The WCM algorithm attributed to Pervot et al.(1993) <doi:10.1016/0034-4257(93)90053-Z>. The authors are grateful to SAC, ISRO, Ahmedabad for providing financial support to Dr. Prashant K Srivastava to conduct this research work. |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.0.2 |
| Imports: | pracma,stats,raster |
| Suggests: | rmarkdown |
| NeedsCompilation: | no |
| Packaged: | 2020-04-01 12:51:25 UTC; Ujjwal Singh |
| Repository: | CRAN |
| Date/Publication: | 2020-04-01 14:00:08 UTC |
Inversion of LAI from look up table generated by WCM
Description
Inversion of LAI from look up table generated by WCM
Usage
lai_inversion_lut(img, lookuptable)
Arguments
img |
raster object |
lookuptable |
Look up table simulated from 'wcm_sim' function |
Value
a raster object (pixel value represents LAI)
Examples
radar <- raster::raster(ncol=10, nrow=10)
val <- seq(-12,-7, length.out=100)
radar[] <- val
A= -9.596695
B= -0.005331
C= -11.758309
D= 0.011344
lut <- lut_wcm(LAI=seq(1,6,0.1), SM=seq(0,.6,.01),coeff=c(A,B,C,D))
example(out_lai <- lai_inversion_lut(img = radar,lookuptable = lut))
Look up table of WCM
Description
Look up table of WCM
Usage
lut_wcm(LAI, SM, coeff)
Arguments
LAI |
one dimensional row vector or a range of LAI value |
SM |
one dimensionalrow vector or a range of SM value |
coeff |
Generated A, B, C, D fitted coefficient for WCM using non linear least square using in situ data |
Value
look up table for WCM for given range of LAI and SM
Examples
A= -9.596695
B=-0.005331
C=-11.758309
D=0.011344
lookuptable <- lut_wcm(LAI=seq(1,6,0.1), SM=seq(0,.6,.01),coeff=c(A,B,C,D))
Inversion of SM from look up table generated by WCM
Description
Inversion of SM from look up table generated by WCM
Usage
sm_inversion_lut(img, lookuptable)
Arguments
img |
raster object |
lookuptable |
Look up table simulated from 'wcm_sim' function |
Value
a raster object (pixel value represents SM)
Examples
radar1 <- raster::raster(ncol=10, nrow=10)
val <- seq(-12,-7, length.out=100)
radar1[] <- val
A= -9.596695
B= -0.005331
C= -11.758309
D= 0.011344
lut1 <- lut_wcm(LAI=seq(1,6,0.1), SM=seq(0,.6,.01),coeff=c(A,B,C,D))
example(out_sm <- sm_inversion_lut(img = radar1,lookuptable = lut1))
Simulate backscattering coefficient using WCM model
Description
This function can be used to simulate the backscattering coefficient using WCM. This function can be called in nls function for generation of model coefficients (A,B,C,D).
Usage
wcm_sim(X, Y, theta, A, B, C, D)
Arguments
X |
In situ LAI or vegetation descriptor |
Y |
In situ SM soil moisture |
theta |
incident angle of Satellite sensor |
A |
fitted coefficient for WCM using non linear least squre using in situ data |
B |
fitted coefficient for WCM using non linear least squre using in situ data |
C |
fitted coefficient for WCM using non linear least squre using in situ data |
D |
fitted coefficient for WCM using non linear least squre using in situ data |
wcm_sim |
is simulated backscattering coefficient |
Value
simulated backscattering coefficient
Examples
# For single value.
n <- wcm_sim(4,.3,48.9,-9.596695,-0.005331,-11.758309,0.011344)
#For list of value
X<-c(5.34, 4.34, 4.32, 4.12, 4.17, 3.58, 5.39, 5.66, 5.47, 5.73, 5.76, 5.93, 4.91, 5.36, 6.15,
4.56, 5.44, 6.54, 6.20, 6.34, 5.56, 5.88, 7.34, 5.74, 4.81, 5.73, 3.63, 4.61, 4.76, 4.02)
Y<-c(35.0, 26.0, 18.0, 13.0, 18.0, 22.0, 19.0, 16.5, 20.0, 24.0, 24.0, 21.0, 13.0, 22.0, 25.0,
24.0, 30.0, 23.0, 18.0, 17.6, 15.0, 17.0, 27.0, 22.0, 21.0, 15.0, 15.0, 18.0, 31.0, 10.0)
w<-c(-9.604, -11.648, -11.556, -11.556, -11.090, -10.444, -10.444, -10.042, -9.200, -9.750,
-9.200, -9.200, -9.812, -9.972, -8.938, -9.200, -8.198, -7.722, -7.348, -7.348,
-8.198, -10.082, -6.870, -8.104, -8.732, -7.830, -10.686, -10.964, -10.976, -10.976)
theta<-48.9
example(nlc<-nls.control(maxiter = 50000, tol = 1e-05, minFactor = 1/100000000000,
printEval = FALSE, warnOnly = FALSE))
example(k<-nls(w~wcm_sim(X,Y,theta,A,B,C,D),control=nlc,
start=list(A= 0.01,B=0.01,C=-21,D= 0.00014),trace = T))
example(y<-predict(k))
n <- wcm_sim(X,Y,theta,-9.596695,-0.005331,-11.758309,0.011344)