EARLY SEASON WINTER WHEAT IDENTIFICATION USING SENTINEL -1 

SYNTHETIC APERTURE RADAR (SAR) AND OPTICAL DATA

Claire Boryan, Zhengwei Yang, Patrick Willis and Avery Sandborn

National Agricultural Statistics Service (NASS)

United States Department of Agriculture (USDA)

ABSTRACT

Early season crop identification is important for food 

security and economic stability. The USDA NASS uses 

optical data to provide acreage estimates, each June, to the 

NASS Agricultural Statistics Board. However, early season 

crop identification is difficult using optical data alone,

because imagery is frequently cloudy during the spring. The 

purpose of this study is to determine whether using SAR and

SAR texture can improve early season winter wheat 

identification compared to optical data alone. Study areas in

the Missouri “Bootheel” (2017 growing season) and 

Northwest Texas (2018 growing season), United States 

(U.S.) are selected. The SAR data used in this study are

Sentinel-1. Optical data include: Landsat 8, Disaster 

Monitoring Constellation, and Sentinel-2. Study results

show that optical data with SAR achieved the highest winter 

wheat accuracies, 7.7% higher than optical data alone, in 

Missouri. Optical with SAR and SAR texture resulted in 

improved accuracies over optical alone, but only marginally, 

in Texas. These results indicate that optical and SAR, used

together, can potentially improve early season crop 

identification.

Index Terms— Sentinel-1, Synthetic Aperture Radar, 

Agricultural land cover classification, SAR texture analysis

1. INTRODUCTION

Accurate early season crop identification is important to 

NASS to provide reliable supplemental acreage estimates to 

the NASS Agricultural Statistics Board. Currently, only 

multi-temporal optical data are used to produce winter wheat

classifications and obtain subsequent crop area estimates.

Multi-temporal and multispectral remote sensing using 

optical data proved to be an effective approach to 

discriminate crop types [1-3]. However, the availability of 

optical data are sometimes limited due to clouds and

insufficient to conduct a multi-temporal crop analysis with 

optical data alone. The analysis of optical and SAR data for 

crop mapping, particularly in areas with cloud cover, has 

been investigated in multiple studies [3-8].

Early season crop identification presents challenges 

when compared with full season assessments conducted in 

the U.S. To identify the winter wheat crop, it is useful to 

include both late fall (previous season) and spring imagery 

in the classification process. Fall images capture winter 

wheat “emergence” and spring acquisitions capture 

“heading”. In many U.S. agricultural areas spring is the rainy

season, which poses challenges for crop identification using 

optical data. SAR, however, can acquire useful data through 

clouds, rain and darkness. Moreover, the availability of the 

ground reference training data are limited early in the 

growing season.

The purpose of this study is to determine whether using 

optical data with Sentinel-1 SAR and SAR texture improves 

early winter wheat identification in the Missouri “Bootheel” 

and Northwest Texas. This paper includes: 1) a description 

of the study areas and data 2) the classification method

based on a decision tree classifier, 3) an accuracy 

assessment for 17 test scenarios per study area, 4) test results

with a discussion and 5) a conclusion.

2. STUDY AREAS AND DATA

2.1. Study Areas 

The Missouri “Bootheel” (21,175 km2) and a region in 

Northwest Texas, U.S. (19,309 km2) (Fig. 1), are selected as 

study areas because they include large quantities of winter 

wheat, as well as, soybeans, corn, hay, cotton and rice. 

Fig. 1. Two study areas (highlighted in yellow) located in the 

Missouri “Bootheel” region and Northwestern Texas, U.S.
2.2. Sentinel-1 Synthetic Aperture Radar

The data used in this study include both SAR and optical. 

The SAR data are acquired from the European Space 

Agency (ESA) Sentinel-1 constellation which includes two 

polar-orbiting C-band SAR sensors (Sentinel-1A and 

Sentinel-1B). Sentinel-1 images have the following 

specifications: interferometric wide swath (250 km) Level-1 

products that are detected, multi-looked and projected to 

ground range, 5x20 meter spatial resolution and dual 

polarization (VV and VH). SAR images used in the 

Missouri (2017) assessment were acquired on October 24 

and November 17, 2016 and April 22, May 4, May 16 and 

May 28, 2017. SAR images used in the Texas (2018)

assessment were acquired on November 6, November 18 

and December 12, 2017 and March 30, April 11, April 23, 

May 5, May 17, May 29 and June 10, 2018.

Texture features are derived from SAR data and include

Mean Euclidean Distance, Homogeneity, Entropy and Grey 

Level Co-Occurrence (GLCM) Variance all at 5x5, 7x7 and 

9x9 window sizes. 

2.3. Optical Data 

Landsat 8 - 30 meter Operational Land Imager (OLI) Level1 images used for the Missouri assessment were acquired on 

October 5, October 21, October 30, November 6 and 

November 15, 2016 and April 15, April 24, May 10, May 26 

and June 2, 2017. Landsat 8 - 30 meter OLI images used for 

the Texas study were acquired on April 28 and May 30, 

2018. Bands used for this assessment include green, red, 

near infrared, short wave infrared – 1, cirrus and thermal 

infrared – TIRS-1. All Landsat 8 OLI Level 1 scenes are 

available at USGS Earth Explorer 

(https://earthexplorer.usgs.gov/).

The Disaster Monitoring Constellation (DMC) 

DEIMOS-1 and UK2 satellites operate in three spectral 

bands at a spatial resolution of 22 meters. All three spectral 

bands were used for all tests. The DEIMOS-1 images used 

in the Missouri assessment were acquired on April 19, May 

6, May 9, and May 16, 2017 and the UK2 images were 

acquired on April 19, May 6 and May 29, 2017. The 

DEIMOS images used in the Texas assessment were 

acquired on May 19 and May 29, 2018 and the UK2 images 

were acquired on May 3 and June 12, 2018.

ESA Sentinel-2 satellites have twelve spectral bands at 

10 to 60 meter resolution. Bands used for this study include

green (10 meter), red (10 meter), near infrared (10 meter), 

cirrus (60 meter), short wave infrared - 1 (20 meter), and 

short wave infrared - 2 (20 meter). Sentinel-2 images used in 

the Missouri assessment were acquired on April 24, May 14, 

and May 21, 2017. Sentinel 2 images used in the Texas

assessment were acquired on November 2, 2017 and April 

16, April 26, May 6, May 11, May 16 and May 13, 2018.

2.4. Ground Reference Data

USDA Farm Service Agency (FSA) Common Land Unit 

(CLU) data are used as ground reference training and 

validation data for crop categories in this study. This 

standardized GIS layer of U.S. crop fields supports

commodity and conservation programs. FSA CLU data are 

updated every growing season when farmers report crop 

type and acreage for their fields to FSA county offices. The 

vast majority of wheat planting intentions are reported to 

FSA due to subsidy programs. However, while wheat 

planting are reported early in the growing season, other crop 

planting intentions are reported later in the season (July and 

August), which adds to the complexity of separating wheat 

from other crops. The FSA CLU data are administratively 

confidential and not available for public dissemination [9].

The 2011 United States Geological Survey (USGS) National 

Land Cover Database (NLCD) is sampled to provide the 

ground reference training data for non-crop categories [10].

3. METHODOLOGY

3.1. Image Processing

All Sentinel-1 images are first preprocessed with calibration 

to sigma naught, Range Doppler terrain correction and despeckling (median 5x5 speckle filter) using the open source 

Sentinel Application Platform (SNAP) toolbox, which can 

be downloaded from the Copernicus Services Access Hub 

(https://scihub.copernicus.eu/). The Sentinel-1 data are then 

resampled to 30 meters using a nearest neighbor 

interpolation and rigorous transformation. The optical data 

are resampled to 30 meters using cubic convolution, 

rigorous transformation (Fig. 2).

3.2. Decision Tree Classification and Accuracy 

Assessment

A See5 decision tree classifier [11] (version 2.08), with the 

boosting option, was used to produce 17 early season winter 

wheat classifications for each study area. The identical 

training sample data set was used for all test scenarios in 

each study area. The only difference in the tests was the 

input imagery, SAR texture feature and window size. Crop

masks based on historic CDL data (2013 – 2016), USGS 

elevation, NLCD imperviousness, and NLCD canopy 

datasets were used as ancillary layers for all classifications. 

Seventeen individual classification tests were designed 

(Table 1) and replicated for each study area.

The classification test scenarios include optical data only

(1), SAR data only (2), SAR with same-date texture features 

with different window sizes (5x5, 7x7 and 9x9) (3 - 14), 

optical and SAR and texture (GLCM Variance) (15), optical 

and SAR only (16), and optical and GLCM Variance 

texture with a 9x9 window size (17).
Fig. 2. Method Flow Chart

For this study 70% of the available FSA CLU data are used

for training the classifier and 30% are used for validation. 

Winter Wheat accuracy measures include: producer 

accuracy, user accuracy, and a balanced accuracy. Producer 

accuracy indicates omission or false negative error and the 

user accuracy indicates commission error or false positive 

error [12]. The new balanced accuracy measure incorporates 

both false negative and false positive errors to truthfully 

reflect the accuracy of the targeted winter wheat class [13]. 

TABLE 1. Classification Tests

*Gray-Level Co-Occurrence Matrix (GLCM)

4. RESULTS AND DISCUSSION

The classification experiment results are summarized in 

Table 2. Fig. 3 illustrates the results of the Missouri winter 

wheat classification test (#16) which was produced using 

optical and SAR imagery. As shown in Table 2, 

classification test #16 in Missouri, which included optical 

and SAR without texture, achieved the highest winter wheat 

accuracies with a producer accuracy of 84.30%, a user 

accuracy of 72.80%, and a balanced winter wheat accuracy 

of 64.10%. This classification is only marginally improved 

over multi-sensory classification test #17 (optical and 

texture without the original SAR). While the 64.10% 

balanced accuracy is not as high as desired, it is 7.7% higher 

than optical alone (test #1). There is only a marginal 

accuracy difference between all of the Texas classification 

tests. In Texas, more cloud free images were available for

this study than were available in the Missouri “Bootheel” 

study area. These results indicate that if abundant cloud free 

optical data are available, SAR does not improve the results 

significantly. However, if the optical data are limited, due to 

a reduced number of acquisitions or cloudy conditions, SAR

can improve the early season winter wheat classifications. If 

the original SAR data are available for use with optical data, 

texture features provide little additional information.

 TABLE 2. Winter Wheat Accuracy Results
Fig. 3. Winter wheat classification in Missouri where winter wheat 

is highlighted in brown and other land cover in grey. 

5. CONCLUSION 

Early season crop identification is challenging due to limited 

cloud free optical data and limited ground reference training 

data. In the Missouri “Bootheel”, a 7.7% increase in 

accuracy is observed between optical alone (test #1) and the

optical and SAR combination (test #16) because the optical 

data are low quality due to heavy cloud cover. These results 

indicate that if sufficient cloud free optical data are not 

available, SAR data can improve classification results. 

Further, SAR with texture provides improved results over

SAR alone. Optical and SAR data without texture provides 

the highest accuracy results in Missouri. Therefore, the 

computationally intensive texture features are not necessary, 

when optical data are available. 

In Texas, optical with SAR resulted in improved 

accuracies over optical alone, but only marginally. 

Balanced winter wheat accuracies, for all 17 tests, were not 

significantly different. In this study, abundant optical data 

were available during the early season. However, SAR data 

produces comparable classification results. In fact, in Texas, 

the SAR only classification (test #2) was more accurate than 

the optical only classification (test #1). These results 

indicate that SAR can reasonably replace optical data, for 

early season crop identification.

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**Disclaimer: The findings and conclusions in this publication are 

those of the authors and should not be construed to represent any 

official USDA or U.S. Government determination or policy.