TITLE:
Irrigation Scheduling Using Remote Sensing Data Assimilation Approach
AUTHORS:
Baburao Kamble, Ayse Irmak, Kenneth Hubbard, Prasanna Gowda
KEYWORDS:
Artificial Neural Network; Genetic Algorithms; SEBAL; Remote Sensing; Groundwater; Crop Growth Modeling
JOURNAL NAME:
Advances in Remote Sensing,
Vol.2 No.3,
September
12,
2013
ABSTRACT: Remote sensing and crop growth models have
enhanced our ability to understand soil water balance in irrigated agriculture.
However, limited efforts have been made to adopt data assimilation methodologies
in these linked models that use stochastic parameter estimation with genetic
algorithm (GA) to improve irrigation scheduling. In this study, an innovative
irrigation scheduling technique, based on soil moisture and crop water
productivity, was evaluated with data from Sirsa Irrigation Circle of Haryana
State, India. This was done by integrating SEBAL (Surface Energy Balance
Algorithm for Land)-based evapotranspiration (ET) rates with the SWAP
(Soil-Water-Atmosphere-Plant), a process-based crop growth
model, using a GA. Remotely sensed ET and ground measurements from an
experiment field were combined to estimate SWAP model parameters such as sowing
and harvesting dates, irrigation scheduling, and groundwater levels to estimate
soil moisture. Modeling results showed that estimated sowing, harvesting, and
irrigation application dates were within ±10 days of observations and produced
good estimates of ET and soil moisture fluxes. The SWAP-GA model driven by the
remotely sensed ET moderately improved surface soil moisture estimates suggesting
that it has the potential to serve as an operational tool for irrigation
scheduling purposes.