ESTABLISHING CROP PRODUCTIVITY USING RADARSAT-2 
    
H. McNairn ?', J. Shang?, X. Jiao*, B. Deschamps” 
? Agriculture and Agri-Food Canada, 960 Carling Avenue, Ottawa, Canada K2K 2C2 — heather.mcnairn@agr.gc.ca 
® MDA Geospatial Services Inc., 57 Auriga Drive Suite 201, Ottawa, Canada K2E 8B2 - 
bdeschamps(a)mdacorporation.com 
Commission VI, WG VIII/6 
KEY WORDS: agriculture, crop, monitoring, SAR, RADARSAT, modelling, retrieval 
ABSTRACT: 
Crop productivity is influenced by a number of management and environmental conditions, and variations in crop growth can occur 
in-season due to, for example, unfavourable meteorological conditions. Consequently information on crop growth must be 
temporally frequent in order to adequately characterize crop productivity. Leaf Area Index (LAI) is a key indicator of crop 
productivity and a number of methods have been developed to derive LAI from optical satellite data. Integration of LAI estimates 
from synthetic aperture radar (SAR) sensors would assist in efforts to monitor crop production through the growing season, 
particularly during periods of persistent cloud cover. Consequently, Agriculture and Agri-Food Canada has assessed the capability of 
RADARSAT-2 data to estimate LAI. The results of a sensitivity analysis revealed that several SAR polarimetric variables were 
strongly correlated with LAI derived from optical sensors for small grain crops. As the growing season progressed, contributions 
from volume scattering from the crop canopies increased. This led to the sensitivity of the intensity of linear cross-polarization 
backscatter, entropy and the Freeman-Durden volume scattering component, to LAI. For wheat and oats, correlations above 0.8 were 
reported. Following this sensitivity analysis, the Water Cloud Model (WCM) was parameterized using LAI, soil moisture and SAR 
data. A look up table inversion approach to estimate LAI from SAR parameters, using the WCM, was subsequently developed. This 
inversion approach can be used to derive LAI from sensors like RADARSAT- to support the monitoring of crop condition 
throughout the cropping season. 
1. INTRODUCTION 
Monitoring crop productivity is critical in determining risks to 
regional and global food security. Gathering the necessary data 
to monitor productivity is challenging given the acreages 
involved and the variable nature of crop growth. Crop 
management applications during active crop growth, as well as 
ever changing meteorological conditions, mean that crop 
condition must be monitored continuously through the growing 
season. 
Crop descriptors such as leaf area index (LAI) are good 
indicators of crop condition and productivity. LAI is the total 
one-sided green leaf area per unit ground surface area and is a 
strong indicator of crop production. LAI can be linked with 
crop yield through process models. Derivation of these crop 
descriptors from remote sensing data can be used to drive these 
crop yield models, to validate model estimates and to update or 
adjust model predictions. Although LAI can be derived from 
optical sensors (Liu et al, 2010) the reliability of access to data 
to monitor continuously through the season is questionable due 
to cloud cover. Synthetic aperture radars (SARs) are thus an 
appropriate data source to build reliability into monitoring 
activities. However, the methods to estimate LAI from radar 
response are not as developed as those that use optical data and 
thus significant research is required. 
To address this knowledge gap, Agriculture and Agri-Food 
Canada (AAFC) has been investigating the sensitivity of 
polarimetric SAR data to LAI. This research has included the 
acquisition of numerous RADARSAT-2 and optical satellite 
*Corresponding author 
data sets over different cropping regions. In eastern Canada, 
where corn and soybean production dominates, results have 
proven the sensitivity of SAR response to LAI for these two 
broadleaf crops (Jiao et al., 2011). In 2009, the European Space 
Agency led the AgriSAR campaign under which fully 
polarimetric RADARSAT-2 data were acquired over three 
international agriculture research sites. One site was located in 
western Canada in a region of extensive production of small 
grains (wheat, oats and barley). The sensitivity of RADARSAT- 
2 to LAI for this class of crops, using the data acquired during 
AgriSAR, is presented here. The Water Cloud Model (WCM) is 
then used to model LAI from the radar scattering and a method 
is proposed to invert the WCM for LAI estimation. 
2. METHODOLOGY 
2. Study Sites and Data Collection 
In 2009, an extensive collection of both optical (RapidEye and 
Landsat TM) and polarimetric SAR (RADARSAT-2) data were 
acquired over a site in western Canada. The selected site was 
AAFC's precision farm located at Indian Head in southern 
Saskatchewan. 
These data were collected under a European Space Agency 
initiative called AgriSAR. One objective of this campaign was 
to develop a methodology to estimate crop condition from SAR 
data. In total, 57 quad-polarimetric ascending and descending 
RADARSAT-2 images were acquired as part of this AgriSAR 
initiative. Ground measurements of crop condition, including 
LAI, were acquired over several small grain (barley, oats,