a prototype of global cropping intensity mapping using ......mapping using google earth engine...

Jun Xiong | Research Scientist

United States Geological SurveyWestern Geographic Science Center

A Prototype of Global Cropping Intensity

Mapping using Google Earth Engine

Global Food Security-Support Analysis Data at 30 m (GFSAD30) Workshop

Reston, VA from August 15-17, 2017

U.S. Department of the Interior

U.S. Geological Survey

Definition of “Cropping Intensity”

Overview

Cropping intensity (whether a field is single, double, or

continuous cropped in a given year)

➢ Cropping intensity can greatly affect net production

➢ Help to identify crop fallow, crop type information partly

“crop intensity / cropping intensification”, technically

defined as an increase in agricultural production per unit

of inputs (which may be labour, land, time, fertilizer, seed,

feed or cash).



Challenges of Cropping intensity MappingOverview

➢ Uncertainities in the NDVI Times series data

➢ Modules of pre-processing: gap-filling, fitting, smooth

➢ Algorithm for single and double crops

➢ Variance of fallow-land / continuous crops in large area

➢ Validation & evaluation approach



Overview

➢ GEE scripts of processing MODIS or Landsat/Sentinel-2

NDVI Times series on the fly

➢ Time-series-analyzed based algorithm to map single &

double crops

➢ Alternatives algorithm when time-series fails

➢ Setting of thresholds to identify fallow-land and continuous

crops

Global GEE Algorithm Components



GEE assets

➢ Source:

MODIS/006/MOD13Q1 and

MODIS/006/MYD13Q1

➢ Data Period: since Feb 2000

➢ Resolution: 250m

➢ Process: n/a

➢ Status: ready to use

ImageCollection: MODIS 16-day Time series

VHRI

MODIS NDVI



Modules

➢ Source:

LANDSAT/LC8_L1T_TOA

➢ Data Period: since Apr 2013

➢ Resolution: 30 m

➢ Process:

1. Cloud Mask (FMASK)

2. Gap-filling (16-day)

➢ Status: running on the flow,

moderate speed, scalable

ImageCollection: Landsat-8 16-day Time series

Landsat

VHRI



Modules

➢ Source:

LANDSAT/L*_L1T_TOA

➢ Data Period: since Jan 1984


➢ Process:

1. Cloud Mask (FMASK)



moderate speed, scalable,

time-out error

ImageCollection: Landsat-all-sensors 16-day Time series



Modules

➢ Source: Sentinel-2

➢ Data Period: since Jun 2015


➢ Process:

1. Cloud Mask (QA60)



slow speed, scalable, time-

out error

ImageCollection: Sentinel-2 10-day Time series

South Africa (25.637, -33.463)



Modules

➢ Data Period: since Jun

2015

➢ Resolution: 10-30 m

➢ Process:

1. Cloud Mask (QA60)


3. Harmonizer required

➢ Status: export-required,

very slow speed, scalable,

time-out error

ImageCollection: Landsat-Sentinel 16-day Time series

Landsat

Sentinel

South Africa

May 2016

25.637, -33.463



Modules

➢ Pro

very fast and stable; easy to use

➢ Con

fail when too much noise

Smoother: moving average



Modules

Savitzky Golay: polynomial-weighted moving average

➢ Pro

smooth without greatly distorting the signal

➢ Con

parameters tuning; time-consuming

Smoother: Savitzky Golay



Modules

implement by Justin

➢ Pro

more controls and powerful

➢ Con

expensive; time-out error

Smoother: 4253H twice smoothing



Modules

➢ Pro

good balance between

performance and speed

➢ Con

introduce pattern which

might not exists

Smoother: Harmonic model

Shumway and Stoffer (2017)



Methodology

pro

➢ no need of prior knowledge

➢ fast to implement

con

➢ depend on time series data quality

➢ uncertainty of smoother

1. Remove abnormal points

2. Smooth Time Series data

3. Locate the local max value (peak)

4. Filter out global low-value peaks

5. Counting the number of Peaks

Method 1: Peak Counting



Method #2: Time Series MatchingMethodology

Euclidean

MaySep

Jan

Feb

DTW

Sep May

➢ nonlinear matching

Dynamic time warping➢ linear matching

• Reference samples required; smoother required

• Linear approach: fast, fail when time series shifting

• Non-linear approach: accept given variance, very time-

consuming computing O(N2), PrunedDTW, SparseDTW,

FastDTW, MultiscaleDTW Demo



To identify if a pixel is cropped or

uncropped during each growing

season.

pro

easy to scale in large area, high

resolution

no need to smooth

computing for every season

con

need regional parameter

Methodology

Method #3: Growing Season Threshold



Harvested

Date

\

Methodology

Harvest Date: Sage Crop Calendar Dataset



Methodology

Flowchart of Cropping Intensity Mapping



Methodology

Global Cropping Intensity Map (MODIS, 2016, trial)

Xiong J, Thenkabail PS, Teluguntla, P., et al. Mapping Cropping Intensity of

Smallholder Farms Globally using Google Earth Engine. Journal TBD. 2017;(In

Preparation).



Methodology

Demo: MODIS vs. Sentinel-2



Future WorkTo-do

➢ Thresholds of mapping of fallow-land & continuous

crops

➢ Gap-filling in 30-m satellite time series

➢ More testing in typical sites globally

➢ Sampling of reference signatures

➢ Finalize global cropping intensity map

➢ Validation & area evaluation



Thank you!

a prototype of global cropping intensity mapping using ......mapping using google earth engine...

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