International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
     
SYNTHETIC APERTURE RADAR (SAR) AND OPTICAL IMAGERY DATA FUSION: 
CROP YIELD ANALYSIS IN SOUTHEAST ASIA 
S. M. Parks“ 
* Exelis Visual Information Solutions, 4990 Pearl East Circle, Boulder, Colorado, 80304 
Working Group VII/6 
KEY WORDS: Remote Sensing, SAR, Radar, Data Fusion, LandSAT ETM+, ENVISAT ASAR, ENVI, SARscape 
ABSTRACT: 
With the expanding energy crisis and rising food prices, crop yield analysis in Southeast Asia is an increasingly important topic in 
this region. Rice is the most important food crop in Southeast Asia and the ability to accurately predict crop yields during a growing 
season is useful for decision-makers, aid providers, and commercial trade organizations. The use of optical satellite image data by 
itself is difficult due to the almost constant cloud in many parts of Southeast Asia. However, Synthetic Aperture Radar (SAR), or 
SAR data, which can image the Earth's surface through cloud cover, is suitable for many agricultural purposes, such as the detection 
of rice fields, and the identification of different crop species. Crop yield analysis is difficult in this region due to many factors. Rice 
cropping systems are often characterized by the type of rice planted, the size of rice field, the sowing dates for different fields, 
different types of rice cropping systems from one area to another, as well as cultural practices such as sowing and transplanting. This 
paper will discuss the use of SAR data fused with optical imagery to improve the ability to perform crop yield analysis on rice crops 
in Southeast Asia. 
1. INTRODUCTION 
Food source security is a major concern, particularly in Asia, 
due to the rapid population expansion happening in that 
region. Accurate evaluations of food crops can be difficult in 
many countries due to the lack of information available 
regarding yields. The regular usage of optical satellite image 
data for crop yield analysis is difficult due to the almost 
constant cloud in many parts of Southeast Asia. However, 
SAR data, which can image the Earth's surface through 
cloud cover, is suitable for many agricultural purposes, such 
as the detection of rice fields, and the identification of 
different crop species. Crop yield analysis is difficult in this 
region due to many factors. Rice cropping systems are often 
characterized by the type of rice planted, the size of rice 
field, the sowing dates for different fields, different types of 
rice cropping systems from one area to another, as well as 
cultural practices such as sowing and transplanting. 
Space-borne radar imagery has great potential for the 
delineation and monitoring of rice crop paddies. SAR 
images have proven to be suitable for many agricultural 
remote sensing purposes, for example, detection of 
agricultural land such as rice fields, and even identification 
of different crop species is possible using well-timed SAR 
images (Karjalainen, Kuittinen, Junnikkala, Karvonen, 
Nguyen, & Tran, 2010). Due to the abundance of cloud 
coverage in Southeast Asia, SAR data is often a better choice 
over optical data (Abu Bakar, Shaari, Chuah, & Ewe, 1997). 
Multi-temporal and multi-sensor data fusion has also been 
successfully used to identify irrigated rice fields. Rice is 
often planted in paddy fields and grows in distinct stages, 
including germination, emergence, tillering, heading, and 
maturing (Wang, 2009). Rice backscatter coefficients in 
SAR imagery display higher temporal variation than other 
types of land cover (Wang, 2009). Rice in the planting stage 
exhibits a lower backscatter coefficient from flooded water 
because the rice plant is short and sparse, whereas, in the 
tillering stage, the backscatter coefficient increases rapidly 
when more tillers emerge and develop into a denser canopy 
(Wang, 2009). The backscatter coefficient is slightly less in 
the late heading stage when the leaves start to dry up and 
mature (Wang, 2009). 
It is possible to monitor the rice growth stage during crop 
yield analysis, by measuring the backscattering coefficient 
from the plants as a function of time if radar images are 
acquired at appropriate time intervals during the growing 
season. Due to the nature of rice growth, there is a rapid 
increase in biomass during the 30-day vegetative phase for 
the short growth duration rice, so it is necessary to acquire 
time series data on a monthly basis in order to capture the 
changes in backscatter between the beginning of the planting 
cycle and the end of the reproductive stage (Wang, 2009). 
Generally, the beginning of a rice season would be identified 
by a low backscatter in the time series when the field was 
inundated while the end of the reproductive stage is 
characterized by a high backscatter (Wang, 2009). 
In this study, multi-temporal ENVISAT ASAR APS dual 
polarization (HH, HV) data from five different dates in 2011 
were used to estimate rice crop yields in the Ganges Delta of 
Bangladesh. This data corresponds to the growing season of 
Aman rice crops, which typically occurs from July through 
November. 
2. METHODS 
Ten ENVISAT ASAR APS datasets (5 HH and 5 HV 
datasets) in the original ESA format that were acquired over 
the same track and frame (i.e. same viewing geometry) were 
used in this example. The ENVISAT ASAR data were