Abstract
Abstract. Integration of remote sensing derived biophysical parameters with process based crop growth simulation model is an emerging technology with diversified application for crop insurance as well as precision farming. Basically the crop growth simulation models are point based which simulate crop growth and yield as a function of soil, weather and crop management factors at a daily time scale. The temporal dimension of the crop growth model is supplemented by the spatial information on crop coverage and condition generated from remote sensing satellite derived biophysical parameters. In the present study an attempt is made to simulate pixel wise rice crop growth, condition and yield using Sentinel1 SAR, AWiFS/Landsat8 OLI and MODIS data with CERES rice growth simulation model on DSSAT platform. Temporal SAR data provides pixel wise start of season for rice crop at a spatial resolution of 10 m. Spectral indices derived from Landsat8/AWiFS and MODIS optical images are used for characterisation of rice growth environment and at the same time these indices are used to generate crop management information like irrigation and sowing dates. Bhadrak district of Odisha in the eastern coast of India is selected as the study area based on the prevalence of diversified rice growing environments. Sufficient in season field data are collected for checking the accuracy of rice map as well as for calibration and validation of the crop growth model. In season rice growth is initialised using SAR derived staggered sowing dates along with daily weather data and soil inputs. As CERES crop growth models are running well on DSSAT environment, rice crop growth simulation is carried out using multi date SAR images on DSSAT platform. The output of this study is maps depicting the spatial variability in rice area, staggered sowing dates and irrigation in the study region. These in season information are crucial for decision making particularly for crop insurance related activities.
Highlights
The use of crop growth models on large areas for monitoring crop growing conditions or predicting crop production is limited by the lack of sufficient spatial information about model inputs
The broad objective of this study is to develop a methodology for integrating Synthetic Aperture Radar (SAR) derived biophysical parameters of rice crop with a process based crop growth model
Irrigation treatment is another sensitive and important parameters for crop growth simulation, LSWI is a potential tool for generating irrigation proxies
Summary
The use of crop growth models on large areas for monitoring crop growing conditions or predicting crop production is limited by the lack of sufficient spatial information about model inputs. Remote sensing and crop growth modelling are two distinct technologies that have been developed to address diverse agronomic issues at fieldlevel and regional scales (Moran et al, 1997; Batchelor et al.,2002; Xie et al, 2008). These technologies have often been developed independently, there is growing interest in utilizing information derived from remote sensing to update or drive cropping systems model simulations because the two technologies are naturally complementary (Maas, 1988a; Moulin et al, 1998; Inoue, 2003; Dorigo et al, 2007). The scale on which the processes are described by the model,
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