EVALUATION OF SENTINEL-1A C-BAND SYNTHETIC APERTURE RADAR FOR 

CITRUS CROP CLASSIFICATION IN FLORIDA, UNITED STATES

Claire Boryan1, Zhengwei Yang1, and Barry Haack2

1National Agricultural Statistics Service (NASS)

United States Department of Agriculture (USDA)

2George Mason University

ABSTRACT

Optical based remote sensing plays an important role in 

citrus crop change monitoring in Florida, United States 

(U.S). However, persistent cloud cover during the summer 

growing season in Florida often limits the application of 

optical sensors. Synthetic Aperture Radar (SAR) has the 

advantage over optical data by operating at wavelengths not 

impeded by cloud cover, rain or a lack of illumination. The 

objective of this study is to assess the effectiveness of using 

Sentinel-1A C-band SAR data for classifying citrus in 

Florida. Twelve individual citrus classifications produced 

using single date optical or SAR data, as well as multi-date 

optical and SAR data fusion, are designed and tested. It is 

found that the classification accuracies of Sentinel-1 C-band 

SAR data are slightly lower than those of multi-temporal 

cloud free optical data (approximately 2.5% difference). 

However, the relatively comparable classification accuracy

results indicate that the Sentinel-1 SAR is a useful 

alternative imagery source particularly in regions with 

persistent cloud cover.

Index Terms—Sentinel-1, Synthetic Aperture Radar, 

Agricultural land cover classification, Citrus classification

1. INTRODUCTION

Florida, United States (U.S.) citrus is a $3.34 billion 

industry, accounting for 49% of total U.S. citrus production.

However, the Florida citrus crop declined significantly over 

the past twenty years, from 329,859 ha in 1996 ~ 1997 to

176,150 ha in 2015 ~ 2016 [1]. This decline in citrus 

bearing areas is the result of numerous factors including: 1) 

urban expansion; 2) citrus greening, an incurable bacterial 

disease; and 3) the increased frequency of extreme weather 

events, which damage already weakened trees with high 

winds and flooding. Monitoring changes in the citrus crop 

over time is important for economic stability, agribusiness 

and agricultural decision makers. While annual field 

inspection surveys are conducted to monitor and estimate 

changes in citrus varieties and hectares going in and out of 

production, monitoring citrus with remote sensing provides 

an affordable and efficient method to support field 

inspection efforts. 

Optical data are generally used for most agricultural 

remote sensing applications including crop area and yield 

estimation and crop condition assessments. However, optical 

sensors, such as Landsat 8, cannot penetrate through clouds, 

which are pervasive in the summer over Florida. Synthetic 

Aperture Radar (SAR) is not impeded by clouds, rain or 

darkness and can acquire useful data during most weather 

conditions. Consequently, the freely available European 

Space Agency (ESA) Sentinel-1 C-band SAR provides the 

opportunity to improve citrus land cover mapping in Florida,

when combined with available cloud-free optical imagery.

Multi-temporal and multispectral remote sensing using 

optical data proved to be an effective approach to 

discriminate crop types [2-4]. However, the availability of 

optical data are sometimes very limited due to persistent 

cloud cover in Florida and insufficient to conduct a multitemporal crop analysis with optical data alone. The analysis 

of optical and SAR data for crop mapping, particularly in 

areas with persistent cloud cover, has been investigated in 

multiple studies [5-8]. 

This paper presents an assessment of Sentinel-1A Cband SAR for classifying the 2017 citrus crop in Florida. 

The study objective is to determine if the Sentinel-1A Cband SAR data can improve the identification of the citrus 

crop in Florida over optical data alone. This paper includes

1) the citrus classification methodology based on a decision 

tree classifier, 2) the citrus accuracy assessment for twelve 

classifications with a discussion, and 3) future research plans 

and conclusion.

2. DATA AND SCOPE

2.1. Study Area

A region within Florida, which is approximately 6357 km2

(Fig. 1), is selected as the study area for this assessment 

because the region includes most of the citrus in the state. 
The geospatial data used in this assessment include: 1) 

Sentinel 1-A C-band SAR images acquired on March 26, 

April 7, May 3 and July 24, 2017; 2) Landsat 8 Operational 

Land Imager (OLI) Level 1 images acquired on April 7 and 

May 9, 2017; 3) a citrus mask created using historic 

Cropland Data Layers (CDLs) [3]; 4) citrus polygon data 

provided by the USDA National Agricultural Statistics 

Service (NASS) Florida Field Office for training and 

validation of the resulting citrus classifications [9] and 5) the 

United States Geological Survey (USGS) National Land

Cover Data Set (NLCD) 2011 for training of non-citrus 

categories [10].

2.2. Sentinel-1 Synthetic Aperture Radar

The ESA Sentinel-1 constellation includes two polarorbiting C-band SAR satellites (Sentinel-1A and Sentinel1B). Sentinel-1A images used in this study have the 

following specifications: interferometric wide swath (250 

km), Level-1 products which have been detected, multilooked and projected to ground range, 5x20 meter spatial 

resolution and dual polarization (VV and HH). All Sentinel1A images were downloaded directly from the ESA 

Copernicus Open Access Hub < 

https://scihub.copernicus.eu/>.

2.3. Landsat 8

Landsat 8 30-meter OLI Level 1 images used for this 

assessment, with the same date and path, were mosaicked. 

The bands used for this assessment include: bands 3 (visible

green), 4 (visible red), 5 (near infrared), 6 (short wave 

infrared - 1), 9 (Cirrus) and 10 (Thermal Infrared – TIRS-1). 

All Landsat 8 OLI Level 1 scenes are available at USGS 

Earth Explorer < https://earthexplorer.usgs.gov/>,

2.4. Citrus Mask

A citrus mask derived from historic NASS Cropland Data 

Layers (2013 ~ 2016) [3] was used as an ancillary layer in 

the classifications. The 30m resolution pixels identified as 

citrus in all four years were included as citrus in the mask. 

2.5. Ground Reference Training and Validation data

Citrus field polygon data provided by the USDA NASS 

Florida Field Office were used for training and validation of 

the citrus classifications in this study. Fig. 2 shows a portion 

of the citrus field polygon data with blue boundaries, 

overlaid on aerial photography. All citrus groves are 

manually delineated and updated yearly based on field 

inspections which takes place from October through June 

each year. There are 26,536 citrus groves recorded in the 

2017 Citrus Geographic Information System Data Layer. A 

sample of 30,000 points was used for citrus training and the 

complete layer, including all polygons, was used for 

validation of the classifications in this study. The citrus field

polygon data are the most accurate, current and 

comprehensive delineation of the Florida citrus crop

available [9].

Fig. 1. Florida, U.S. – Study area (highlighted in red) for SAR 

citrus assessment. 

Fig. 2. Zoom of citrus field polygon data used for training and 

validation. All polygons are manually delineated and attributed, 

based on annual field inspections, in the USDA NASS Florida 

Field Office. Blue boundaries outline the citrus groves which are 

overlaid on aerial imagery. 

The USGS NLCD, 2011 was the source for training of all 

other categories of land cover [10]. The NLCD land cover 

data set has a 16-class land cover category classification 

scheme that is applied consistently across the United States 

at a 30 meter spatial resolution. NLCD 2011 is based 

primarily on a decision-tree classification of circa 2011 

Landsat satellite data.

3. METHODOLOGY

3.1. Sentinel-1A and Landsat 8 preprocessing 

All Sentinel 1-A images were first preprocessed with sigma 

naught calibration, Range Doppler terrain correction and despeckling (median 5x5 speckle filter) using the ESA open 

source Sentinel-1 toolbox. The preprocessed same date 

images were mosaicked, reprojected to Albers Conical 

Equal Area projection, resampled to 30 meter resolution and 
set to the map extent of the study area using Hexagon’s 

ERDAS Imagine 2016 software. 

All downloaded Landsat 8 OLI Level 1 scenes were

reprojected to Albers Conical Equal Area Projection, 

mosaicked (same date and path) and set to the extent of the 

study area. The six bands selected in this assessment were 

the bands identified as most useful in classifying crops by 

the NASS CDL program. 

3.2. See5 Decision Tree Classification 

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

boosting option, was used to produce twelve citrus 

classifications. A sampling ratio of 3% citrus and 97% 

“other” categories for a total sample size of 1,000,000 points

was used for all classifications. This sampling rate was 

selected because the citrus crop covers approximately 3.2% 

of the study area (Fig. 1). The identical training sample data 

set was used for all classifications with the only difference in 

the classifications being the imagery used as inputs to the 

decision tree classifier. A citrus mask based on historic CDL 

data (2013 – 2016) was used as an ancillary layer for all 

classifications. Twelve individual classification tests were 

designed (Table 1). They included single dates of optical 

and SAR images, and multi-date optical and SAR data 

fusion.

TABLE 1. Citrus Classification Tests with Single or 

Multi-Temporal Optical, SAR or Optical & SAR Data 

4. RESULTS AND DISCUSSION

The classification experiment results are summarized in 

Table 2. Results include: citrus producer accuracy, citrus 

user accuracy, Kappa coefficient and citrus total accuracy. 

The producer accuracy indicates the omission or False 

Negative error and the user accuracy indicates the 

commission error or False Positive error. The Kappa 

coefficient reflects the difference between actual agreement 

and the agreement expected by chance. The citrus total 

classification accuracy incorporates both False Negative and 

False Positive errors to truthfully reflect the accuracy of the

targeted citrus class.

Fig. 3 illustrates the results of one citrus classification 

test (#9) which was created using a multi-temporal optical 

(May 9, 2017) and SAR (May 13, 2017) combination. The 

citrus classification is overlaid over the Florida, U.S. County 

boundaries. Zooms show classification details with the 

“citrus” category in orange and the “other” category in tan. 

Fig. 3. Citrus classification test #9 created using multi-temporal 

optical (May 9, 2017) and SAR (May 13, 2017) overlaid over 

Florida, U.S. County boundaries. This classification achieved an 

88.40% citrus producer accuracy, a 77.80% citrus user accuracy, a 

Kappa of 0.8214 and a 70.55% citrus total accuracy.

As shown in Table 2, classification test #7, which

utilized multi-temporal optical data (April 7 and May 9, 

2017), achieved the highest citrus accuracy with a citrus 

producer accuracy of 88.80%, a citrus user accuracy of 

78.40%, a Kappa of 0.8270 and a citrus total accuracy of 

71.33%. This classification is only marginally improved 

over multi-sensory classifications #9 and #11. The single 

optical data classifications #1 and #2 with citrus total 

accuracies of 70.05% and 70.46% are approximately 2 – 3% 

improved over those of four single SAR classifications #3, 

#4, #5 and #6. There is no significant difference in the citrus 

total classification accuracy among the single date SAR 

classifications (#3 – #6). This indicates that there are no

significant changes detected in SAR signals from March to 

July. In addition, the multi-temporal SAR classifications (#8, 

#10 and #12) show no improvement in the citrus total

classification accuracy as compared with the single date

SAR classifications (#3 – #6). These results are consistent

with reality considering there are no significant changes in 

the citrus canopy from March - July. Therefore, multi-
temporal C-band SAR does not improve citrus classification 

accuracy over single date SAR.

As shown in Table 2, the classification results of multitemporal SAR and optical data are comparable to that of 

multi-temporal optical data for citrus classification in 

Florida. There is no improvement in citrus identification by 

adding additional SAR scenes as inputs. This indicates that 

SAR data can be an effective alternative to optical data for 

citrus classification in regions with persistent cloud cover, 

like the Florida citrus region. There will be no further 

improvement, using the SAR data, when multi-temporal 

optical data are available.

TABLE 2. Citrus Classification Accuracies

5. CONCLUSIONS

This study evaluates using Sentinel-1A C-band SAR data for

the classification of citrus in Florida, U.S. The results show 

that the highest citrus total classification accuracy (71.33%) 

was achieved using the multi-temporal optical data. This was 

followed very closely by the multi-temporal SAR and optical 

citrus classifications (70.55% and 70.54%). Classification 

accuracy of single date optical citrus classifications (around 

70.0%) is about 2.50% better than that of SAR only single 

date and multi-temporal citrus classifications (around 

67.50%). These results indicate that while the SAR data 

classification accuracies are slightly lower than those of

multi-temporal cloud free optical data, the Sentinel 1-A Cband SAR data can serve as a useful alternative imagery

source particularly in regions with persistent cloud cover 

where the availability of cloud free optical data is limited.

Future research will include 1) the evaluation of SAR 

texture bands at different window sizes to further improve 

the classification of citrus in Florida, and 2) assessments 

comparing optical and SAR data, with and without texture 

bands, to classify other crop types in U.S. agricultural

regions with persistent cloud cover such as the Texas Gulf 

Coast and Louisiana

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