OPERATIONAL AGRICULTURAL FLOOD MONITORING WITH

SENTINEL-1 SYNTHETIC APERTURE RADAR

Claire G. Boryan1, Zhengwei Yang1, Avery Sandborn1, 

Patrick Willis1, and Barry Haack2

1National Agricultural Statistics Service (NASS)

United States Department of Agriculture (USDA)

2George Mason University

ABSTRACT

Agricultural flood monitoring is important for food security

and economic stability. Synthetic Aperture Radar (SAR) has 

the advantage over optical data by operating at wavelengths 

not impeded by cloud cover or a lack of illumination. This 

characteristic makes SAR a potential alternative to optical 

sensors for agricultural flood monitoring during disasters.

The purpose of this study is to assess the effectiveness of 

using freely available Copernicus Sentinel-1 SAR data for

operational agricultural flood monitoring in the United 

States (U.S.). The operational detection of flood inundation

was tested during Hurricane Harvey in 2017, which resulted

in significant flooding over Texas and Louisiana, U.S. This 

paper presents 1) the agricultural flood monitoring method 

that utilizes Sentinel-1 SAR, the NASS 2016 Cultivated 

Layer, and the NASS 2016 and 2017 Cropland Data Layers;

2) flood detection validation results and 3) inundated 

cropland and pasture acreage estimates. The study shows 

that Sentinel-1 SAR is an effective and valuable data source 

for operational disaster assessment of agriculture.

Index Terms— Sentinel-1, Synthetic Aperture Radar, 

Agriculture Flood Monitoring, Flood Detection, Hurricane 

Harvey

1. INTRODUCTION

Agricultural flood monitoring is important for food security 

and economic stability and is of significant interest to the 

United States Department of Agriculture (USDA) National

Agricultural Statistics Service (NASS). In agricultural 

remote sensing applications, optical sensor data are 

traditionally used for acreage, yield and crop condition 

assessments. However, optical data are affected by cloud 

cover and cannot acquire useful data during the night. 

Cloud-free optical imagery is difficult to obtain during the 

flood event period. This limits the capability to assess the 

flood disaster event in a timely manner. Synthetic Aperture 

Radar (SAR), however, can penetrate cloud cover and

acquire imagery day or night, which makes it particularly 

useful for flood disaster monitoring. A variety of SAR 

techniques have been used successfully since the 1990s to

map flooding extent including visual interpretation [1-3], 

image thresholding [1, 4, 5], automated classification [6, 7], 

texture algorithms [5], change detection methods [8, 9] and 

principle components analysis [10]. The application of SAR 

data to map flooding extent has grown over the past decade 

which coincides with the launch of fine spatial resolution 

SAR sensors including Terra SAR-X, COSMO-Sky Med,

RadarSat 2 and Sentinel-1 A and B. 

The objective of this study is to assess the effectiveness

of using Copernicus C-band Sentinel-1 SAR data for 

operational agricultural flood monitoring in the United 

States (U.S.) by developing and implementing an 

operational flood detection procedure. Freely available 

Sentinel-1 SAR data offers a unique opportunity for 

affordable operational flood detection of agricultural areas 

in near real-time.

The Hurricane Harvey disaster event was selected as a 

case study for this assessment. Hurricane Harvey struck 

areas of Texas (TX) and Louisiana (LA), U.S. from August 

25 – August 31, 2017. Flooding was extensive with 132 cm

of rain recorded in Cedar Bayou, TX and 120 cm in

Beaumont, TX [11]. NASS was interested in quantifying the 

extent of flooding over cropland and pasture in near realtime. Consequently, a recently developed agricultural flood 

monitoring procedure based on Sentinel-1 SAR data was 

implemented in response to the flood event.

This paper describes a procedure for agricultural flood 

monitoring based on Sentinel-1 SAR data. The NASS 2016 

Cultivated Layer is used to define cropland and the NASS 

2017 Cropland Data Layer (CDL) is used to identify the 

specific crops planted in 2017. A region within TX and LA, 

which is approximately 8433 km2(Fig. 1), is selected as the 

study area for this assessment because this is the region most 

heavily impacted by the storm. Predominant crops in the 

area include corn, cotton, rice and sorghum. The inundated 
cropland, pasture hay and individual crop types are detected 

and the acreages are then estimated. The flood mapping 

results are validated with the manually derived reference 

data.

Fig. 1. Texas (TX) and Louisiana (LA) U.S. – Region impacted by 

Hurricane Harvey flooding and study area (bright yellow) for

agricultural flood monitoring assessment. 

2. DATA AND SCOPE

2.1. Sentinel-1 Synthetic Aperture Radar

The Sentinel-1 constellation includes two polar-orbiting Cband SAR satellites (Sentinel-1A and Sentinel-1B). Sentinel1A data, used in this study, are interferometric wide swath 

(250 km); Level-1 ground range detected products which 

have been multi-looked and projected to ground range; 5x20 

m spatial resolution and dual polarization (VV and HH). 

Pre-flood imagery acquired on July 31, August 5, August 19 

and August 22, 2017 and post-flood imagery acquired on 

August 29, August 31, September 3 and September 5, 2017 

were used to identify changes in inundation over agricultural 

land. Sentinel 1 data have a short latency with all data 

available for download within 24 hours of acquisition.

2.2. NASS Cropland Data Layers and Cultivated Layer

The Cropland Data Layer is an annual georeferenced, cropspecific land cover classification covering the continental 

U.S. at a 30 m resolution [12]. The Cultivated Layer is 

created using five years of historic CDLs [13-14]. The CDLs

are created using the decision tree classifier Rulequest 

See5.0 software, and ERDAS Imagine software, which was

also used in the pre- and post-processing of all raster data. 

Environmental Systems Research Institute (ESRI) ArcGIS 

software was used to prepare the vector-based training and 

validation data. Agricultural training and validation data 

were derived from the Farm Service Agency Common Land 

Unit Program. The United States Geological Survey 

National Land Cover Database (NLCD) was used as nonagricultural training and validation data.

2.3. Ground Reference Validation data

Flood inundation detection results were validated from 

manually-derived ground reference data which were defined 

using Sentinel-1 SAR, Landsat 7 ETM+ Level 1, Landsat 8 

TIRS Level 1 and Sentinel-2 imagery. Pre-flood imagery 

used in defining the ground reference data was acquired 

between July 1, 2017 and August 15, 2017. Post-flood 

imagery was acquired between August 27, 2017 and 

September 11, 2017 (Fig. 2). All imagery has between 0 –

15% cloud cover. 

Fig. 2. Sentinel-1A imagery acquired on August 29, 2017 over the 

city of Houston, TX and surrounding area. Manually-derived water 

validation data (red polygons) are overlaying the imagery. The top 

zoom is an area in Harris County, TX and the bottom zoom is an 

area in Brazoria County, TX. 

3. METHODOLOGY

3.1. Agricultural Flood Monitoring Procedure 

All Sentinel-1A images are first preprocessed with 

calibration to sigma naught, Range Doppler terrain 

correction and de-speckling (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 

preprocessed images are mosaicked using Hexagon’s 

ERDAS Imagine 2016 software. A log transformation is 

then applied to all individual scenes to enhance the lower

gray level value (dark) water body pixels and compress the 

higher pixel values of other land cover types as shown in 

Figure 3. The log image histogram is evaluated to determine 

the threshold for binary recoding into water and other 

categories. 
Fig. 3. Left – Original Sentinel-1 A image histogram before log 

transformation, Right - Image histogram after log transformation. 

Inundated areas or water bodies in the Sentinel-1 images

as shown in Fig. 4A and 4B, are segmented with the manual 

thresholding method for both pre- (August 5, 2017) and 

post-flood (August 29, 2017) event images. The

thresholding inundated areas or water bodies are represented 

in blue as shown in Fig. 4C and 4D. Fig. 4E shows cropland

(green), which is defined using the NASS Cultivated Layer,

and Pasture/Hay (light brown), which is defined using the 

NASS 2016 CDL. A change assessment between pre- and 

post-flood thresholded images is conducted to locate and 

identify inundated areas due to flooding as shown in Fig. 4F. 

Red indicates inundated cropland and yellow indicates 

inundated pasture/hay.

Fig. 4. (A) - Pre-flood Sentinel-1A image near Houston TX; (B) -

Post flood Sentinel 1-A image, (C) - Manual thresholding result of 

pre-flood image classification (water – blue, other – tan); (D) -

Manual thresholding result of post flood image classification; (E) -

Overlay NASS 2016 Cultivated Layer and Pasture/Hay; (F) Post 

flood cropland classification which identifies cropland inundated 

based on change detection between pre- and post-flood images.

Resulting crop inundation layers are intersected with the 

NASS 2017 CDL to identify specific crops that are 

inundated by flooding. Inundated cropland, pasture, and 

specific crop pixels, identified in the 2017 CDL, are counted 

to estimate the extent of flooding of agricultural land. 

4. RESULTS AND DISCUSSION

Results of the Sentinel-1 SAR agricultural flood monitoring

analysis show that approximately 290,314 hectares of 

cropland in the study area were inundated by flooding 

during Hurricane Harvey. This represents 10.16% of total 

cropland. Further, 140,763 hectares of pasture/hay or 3.68% 

of total pasture/hay were inundated. Table 1 shows the 

specific crop types of interest that were inundated. 

However, some fields may have already been harvested 

before Hurricane Harvey occurred. These estimates were 

derived by intersecting the Hurricane Harvey post flood 

cropland classification (shown in Figure 5) with the 2017 

CDL and converting the pixel size (30 meters) to hectares. 

The detected water areas in the pre- and post-flood crop 

classifications were validated using the manually derived 

ground reference validation data (Fig. 2). Across the entire 

study area, 329,631 hectares of water bodies were delineated 

in the pre-flood validation file and 354,598 hectares of water 

bodies were delineated in the post-flood validation file. 

Greater than 95% producer accuracies were recorded.

Table 1. Total hectares, inundated hectares and percent inundated 

of major crops and pasture hay in the study area, 

5. CONCLUSION

Sentinel-1 A C-band SAR is a valuable data source to 

conduct accurate, efficient, timely and affordable 

agricultural flood monitoring in an operational setting. 

Sentinel-1A SAR’s cloud penetration capability and short 

latency enabled us to effectively detect and estimate the 

extent of cropland, pasture hay and specific crops inundated

during and shortly after the Hurricane Harvey flood event. 

The operational process based on thresholding is effective 

and straightforward and provides accurate results with

accuracies over 95% as assessed using independent,

manually-derived reference data based on image 

interpretation of both SAR and optical data. Combining the 

flood classifications with the NASS 2016 Cultivated Layer 

and 2017 CDL enables the estimation of inundated cropland, 

pasture hay and specific crops in near real-time. The

preliminary estimates of inundated cropland, pasture and 

crops are delivered to USDA NASS within 48 hours of the 

flood event. This study shows that the Sentinel-1A based
agricultural flood monitoring is sufficiently accurate, 

affordable and efficient for operational use, and can provide 

decision makers with an accurate and timely estimate of the 

impact of flooding on agricultural land. 

Fig. 5. Hurricane Harvey post-flood cropland classification which 

identifies cropland and pasture that was inundated from Hurricane 

Harvey. 

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