Crop Type & Yield Mapping:

Preparatory study for Sentinel 2

Sentinel 2 for Science Meeting - 20-22 May 2014

N.M. Knox; L.T. Tsoeleng; C. Adjorlolo; T. Newby

Free State Agricultural production • 2 million ha under agricultural production

• 40% of South Africa's total maize crop,

• 50% of wheat,

• 80% of sorghum,

• 33% of potatoes,

• 18% of red meat,

• 30% of groundnuts

• 15% of wool.

Crop Calendar

• Free State Province

Crop Type Modelling

Sentinel 2 for Science Meeting - 20-22 May 2014

Crop Type Modelling

• Focused on 5 primary crop types

• Maize, Sunflower, Soy beans, Pasture, Fallow

• Simple models based on NDVI and EVI time series

• Based on National Crop Estimates Committee annual aerial samples

• Calibration – 132 samples (balanced)

• Validation - 153 samples (heavily weighted on maize)

• 16 Cloud free BOA Spot4-Take5 scenes 31 Jan – 15 Jun

• Curve fitting and least squares fit

In-situ Data

• National Crop Estimates Committee Data

• Annual PICES aerial survey carried out. – Applicable to commercial not subsistence farming

– Approx 300 random sample points

• Only within field boundary layer

– Crop type identified

– Subset of the maize crop sites selected for yield estimation

– Yield estimation used to generate provincial yield estimate

• Annually updated field boundary layer

Maize

Sunflower

Soy Bean

Pasture

Fallow

NDVI

EVI

Sunflower 68% Maize

Maize 29% Pasture

Fallow 75 %Maize

Pasture 41 % Maize

30% SFlower

Soy bean 70% Maize

Limitations & Way Forward

• Late start in season prevents crucial sowing/green-up phase of crop

• Limited sample points of certain crops

• Result of random sampling and maize prevalence

• Heterogeneous in-field variability of crops

• Capability of expanding study to make use of greater spectral information

• Integration of climate data and crop calendar (restrict date inclusion past harvest)

Maize Yield Modelling

Sentinel 2 for Science Meeting - 20-22 May 2014

Maize Yield

• Small data set

• Maize yield derived from precision agriculture – during harvesting

• 3 Fields obtained through GrainSA Co-operative from a farmer in Bothaville region – hardcopy digistized and classified

• Yield ranged from 0-13 tonnes/ha

• Data set:

• Calibration data set – 314 stratified random sample

• Validation set – 155 random samples

• Applied to NDVI and EVI 16 date Spot4-Take5 layer stack

• Random forest modelling

SANSA’s role in earth observation R2+/-RMSE = 0.51+/-1.213

Measured yield (t/ha)

Pre

dic

ted y

ield

(t/

ha)

Variable Importance

Way Forward to Sentinel 2

• Present initial findings to farmers in region - July

• Request additional farm yield data in digital format

• Integrate data from the Automatic Weather Stations

• Evaluate effect of time series (5 vs 10 day intervals)

• Cloud masking

• Area mapping/field boundary discrimination

• Integrate spectral rather than simple indices for models

Thank you!

Questions

Sentinel 2 for Science Meeting - 20-22 May 2014