The objective of this research has been to investigate the accuracies of pixel and polygon based Random Forest classification using multi-temporal Sentinel data over an agricultural area. Four scenarios have been tested: i) Sentinel-1, ii) Sentinel-2, iii) Sentinel-2 and vegetation indices (VI), iv) Sentinel-1, Sentinel-2 and VI. The best accuracies (0.98) were obtained in the polygon based approach independently of the selected scenario. For the pixel based, the highest accuracy (0.84) was obtained using Sentinel-1, Sentinel-2, and VI.

Reliable information on land cover is required to help in the decision-making process needed to improve agricultural management. The Sentinel-1 and Sentinel-2 satellites can provide relevant information due to their spectral, spatial, and temporal resolution [1]. Amongst the analysis that can be done with remote sensing data, classification is one of the most popular. The Random Forest algorithm has received increasing attention over the last decade due to its performance [2,3]. This machine learning technique aggregates trees, learns the relation between the training and response dataset, and calculates the response variable [4].

ABSTRACT

• To determine the classification accuracy using Sentinel-1 and Sentinel-2 independently and in combination (including also vegetation indices).

• To analyze if the accuracy improves in a pixel or polygon based classification.

• To analyze the influence of multi-temporal data in classification accuracy.

S-2 Image (12) S-1 Image (10)

Convert to GeoTIFF

Mask to Study Area

Calculate Vegetation Index

Initial Crop

Radiometric Calibration

Terrain Correction

Speckle Filtering

Ratio VV/VH

Mask to Study Area

Pixel size > 10m

Resample to 10 m

Stack S1 Stack S2 Stack S2 &

VI Stack S1 & S2

& VI

RF Classification (pixel & polygon)

Accuracy test

No No Yes

[1] Inglada, J., Vincent, A., Arias, M., & Marais-Sicre, C. (2016). Improved early crop type identification by joint use of high temporal resolution SAR and optical image time series. Remote Sensing, 8(5). https://doi.org/10.3390/rs8050362 [2] Belgiu, M., & Drăgu, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011 [3] Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 93–104. https://doi.org/10.1016/j.isprsjprs.2011.11.002 [4] Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32, 45(1), 5–32.

• Best accuracies obtained with multi-temporal polygon based RF classification.

• In the pixel based approach, best result obtained with multi-temporal datasets of S-1, S-2 and VI.

• If large multi-temporal data is available, joining images from S-1 and S-2 is not mandatory to achieve high accuracy values.

• Benefits of joining S-1 and S-2 are more relevant in the pixel based approach and with limited multi-temporal data.

• The S-1 scenario in both approaches demonstrated its relevant performance for classification in cloudy conditions.

• Lowest accuracy in the S-1 pixel approach due to high heterogeneity of pixel values and low number of bands per image.

• The joint use of S-2 and VI in the pixel approach produced a slight increment since VI are just an arithmetic combination of S-2 spectral bands.

• The combination of S-1, S-2, and VI leaded to the highest accuracy increment due to the use non-related multi-source datasets.

• The polygon approach resulted in similar accuracy levels since the statistic calculation per polygon removed outliers and reduced variation.

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1 2 3 4 5 6 7 8 9 10 11 12

S1_po

S1_pi

S2_po

S2_pi

S2_VI_po

S2_VI_pi

S1_S2_VI_po

S1_S2_VI_pi

STUDY AREA The study site covers an agricultural area, where the major crops are wheat, barley, sugar beet and rape.

The study area is located in the south of Sweden (Skåne region – left figure) between the cities of Lund and Malmö.

öWinter Wheat Barley Rapeseed

Sugarbeet Built-up Others

S1 pixel RF classification – 1 image S1 pixel RF classification – 10 images

S1/S2/VI polygon RF classification – 1 image S1/S2/VI polygon RF classification – 12 images

2 km N

Flowchart of the methodology

JOINT USE OF SENTINEL-1 AND SENTINEL-2 FOR LAND COVER CLASSIFICATION: A MACHINE LEARNING APPROACH Miguel Castro Gómez - Dpt. of Physical Geography and Ecosystem Science - Lund University

castrog.miguelg@gmail.com www.linkedin.com/in/miguel-castrogomez/

Classification accuracies of the different scenarios in the pixel (‘pi’ - solid line) and polygon (‘po’ - dotted line) based approach.

INTRODUCTION

OBJECTIVES

METHOD

RESULTS

DISCUSSION

CONCLUSIONS

REFERENCES

Sentinel-1 (left) and Sentinel-2 (right) satellites.

STEP BY STEP

S1/S2/VI pixel RF classification – 1 image S1/S2/VI pixel RF classification – 12 images

WGS 84 / UTM zone 33N (EPSG:32633)