SWAP crop model Parameter Identification using SPOT Vegetation
in Suphanburi, Thailand
Panithan Srinuandee, Kiyoshi Honda, Yann Chemin
School of Advanced Technologies, Asian Institute of Technology
P.O. Box 4 Klong Luang 12120 Pathumthani, Thailand
Tel: +66-2-524-6149 Fax: +66-2-524-5597.,
Amor V.M. Ines
International Research Institute for Climate Prediction
The Earth Institute at Columbia University
Palisades NY 10964 USA. Tel: +1-845-680-4501 Fax: +1-845-680-4864
Email: st100425@ait.ac.th,
honda@ait.ac.th,
yann.chemin@ait.ac.th
ABSTRACT
In this paper, the methodology to quantify the agriculture practice using remote sensing (RS) is
discussed. An approach for low spatial resolution RS data (LSRD) based on data assimilation
technique is discussed together with a case study for a large homogeneous agricultural area in
Suphanburi, Thailand.
In the data assimilation, the physically based simulation model called SWAP model (Van Dam,
2000) was used to represent the soil-water-atmosphere-plant system. Genetic algorithm (GA)
was used to optimize the output of SWAP (e.g. LAI) based on SPOT Vegetation data to
determine the date of sowing and the cropping intensity in the study area. The results show that
the reasonable parameters (sowing date and cropping intensity) were successfully estimated.
1.Introduction
To study the agricultural in regional scale requires a large amount of data both spatial and
temporal data. These data can be taken from High Spatial Resolution Remote Sensing Data
(HSRD) or from Low Spatial Resolution Remote Sensing Data (LSRD). In this study LSRD
was selected because of its high temporal resolution, but the problem is that agriculture areas are
highly possible to be mixed in one pixel, including some data that are non-observable from RS
data. Dr. Honda and Ines (2003) have developed a methodology to fix this problem by using the
combination of RS-SWAP-GA, data assimilation technique.
This technique is composed of 3 main parts. The first part is used to get data from remote
sensing; in this study, LAI (leaf area index) is calculated from NDVI (Normalized Difference
Vegetation Index) using SPOT Vegetation images, which were LMF processed. The second part
is physically based simulation model called SWAP model, used to simulate the interaction
between soil, water, atmosphere and plant. The last part is a genetic algorithm (GA), used to
assimilate LAI output of SWAP to LAI from SPOT Vegetation by finding the appropriate date
of planting and the cropping intensity. The process of this technique is, GA will evaluate the
parameter based on an objective function to minimize the difference of simulated and observed
LAI. SWAP model will be invoked repeatedly to evaluate the response of the system and to
obtain simulated LAI from the combination(s) of the candidates of parameters until the desired
solution is achieved.
This technique was tested with the synthetic data and the results show that this technique can
work effectively (Honda, K. and A.V.M. Ines., 2003). The aim of this study is to test the
approach with the actual data, but starting from a simple situation that is 2-crops/year in a large
continuous area.
