qcFUN: Initial weights according to qc

In phenofit: Extract Remote Sensing Vegetation Phenology

Initial weights according to qc

Description

• getBits: Extract bitcoded QA information from bin value

• qc_summary: Initial weigths based on Quality reliability of VI pixel, suit for MOD13A1, MOD13A2 and MOD13Q1 (SummaryQA band).

• qc_5l: Initial weights based on Quality control of five-level confidence score, suit for MCD15A3H(LAI, FparLai_QC), MOD17A2H(GPP, Psn_QC) and MOD16A2(ET, ET_QC).

• qc_StateQA: Initial weights based on StateQA, suit for MOD09A1, MYD09A1.

• qc_FparLai: For MODIS LAI

• qc_NDVI3g: For AVHRR NDVI3g

• qc_NDVIv4: For AVHRR NDVIv4

Usage

getBits(x, start, end = start)

qc_summary(QA, wmin = 0.2, wmid = 0.5, wmax = 1)

qc_StateQA(QA, wmin = 0.2, wmid = 0.5, wmax = 1)

qc_FparLai(QA, FparLai_QC = NULL, wmin = 0.2, wmid = 0.5, wmax = 1)

qc_5l(QA, wmin = 0.2, wmid = 0.5, wmax = 1)

qc_NDVIv4(QA, wmin = 0.2, wmid = 0.5, wmax = 1)

qc_NDVI3g(QA, wmin = 0.2, wmid = 0.5, wmax = 1)

qc_SPOT(QA, wmin = 0.2, wmid = 0.5, wmax = 1)

Arguments

x	Binary value
start	Bit starting position, count from zero
end	Bit ending position
QA	quality control variable
wmin	Double, minimum weigth (i.e. weight of snow, ice and cloud).
wmid	Dougle, middle weight, i.e. marginal
wmax	Double, maximum weight, i.e. good
FparLai_QC	Another QC flag of MCD15A3H

Details

If FparLai_QC specified, I_margin = SCF_QC >= 2 & SCF_QC <= 3.

Value

A list object with

• weigths: Double vector, initial weights.

• QC_flag: Factor vector, with the level of c("snow", "cloud", "shadow", "aerosol", "marginal", "good")

Note

qc_5l and qc_NDVIv4 only returns weight, without QC_flag.

References

https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13A1

https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD15A3H

Erwin Wolters, Else Swinnen, Carolien Toté, Sindy Sterckx. SPOT-VGT COLLECTION 3 PRODUCTS USER MANUAL V1.2, 2018, P47

See Also

qc_sentinel2()

Examples

set.seed(100)
QA <- as.integer(runif(100, 0, 2^7))

r1 <- qc_summary(QA, wmin = 0.2, wmid = 0.5, wmax = 1)
r2 <- qc_StateQA(QA, wmin = 0.2, wmid = 0.5, wmax = 1)
r_5l <- qc_5l(QA, wmin = 0.2, wmid = 0.5, wmax = 1)
r_NDVI3g <- qc_NDVI3g(QA, wmin = 0.2, wmid = 0.5, wmax = 1)
r_NDVIv4 <- qc_NDVIv4(QA, wmin = 0.2, wmid = 0.5, wmax = 1)

phenofit documentation built on Feb. 16, 2023, 6:21 p.m.