Abstract
Remote sensing is a useful technique to determine spatial variations in crop growth while crop modelling can reproduce temporal changes in crop growth. In this study, we formulated a hybrid system of remote sensing and crop modelling based on a random-effect model and the empirical Bayesian approach for parameter estimation. Moreover, the relationship between the reflectance and the leaf area index was incorporated into the statistical model. Plant growth and ground-based canopy reflectance data of paddy rice were measured at three study sites in South Korea. Spatiotemporal vegetation indices were processed using remotely-sensed data from the RapidEye satellite and the Communication Ocean and Meteorological Satellite (COMS). Solar insulation data were obtained from the Meteorological Imager (MI) sensor of the COMS. Reanalysis of air temperature data was collected from the Korea Local Analysis and Prediction System (KLAPS). We report on a statistical hybrid approach of crop modelling and remote sensing and a method to project spatiotemporal crop growth information. Our study results show that the crop growth values predicted using the hybrid scheme were in statistically acceptable agreement with the corresponding measurements. Simulated yields were not significantly different from the measured yields at p = 0.883 in calibration and p = 0.839 in validation, according to two-sample t tests. In a geospatial simulation of yield, no significant difference was found between the simulated and observed mean value at p = 0.392 based on a two-sample t test as well. The fabricated approach allows us to monitor crop growth information and estimate crop-modelling processes using remote sensing data from various platforms and optical sensors with different ground resolutions.
Highlights
Satellite-based remote sensing is a useful technique to acquire spatiotemporal data consisting of a large number of pixels, but a relatively small number of temporal data points, from an agricultural field
We present functional coupling of crop modelling and remote sensing using an updated GRAMI-rice model that uses remote sensing data, and a CIDS formulated to simulate geospatial rice growth and yield by adapting the GRAMI-rice model for use in this study
We presented that GRAMI-rice was successfully incorporated with optical satellite data with different geospatial resolutions
Summary
Satellite-based remote sensing is a useful technique to acquire spatiotemporal data consisting of a large number of pixels, but a relatively small number of temporal data points, from an agricultural field. Huang et al [11] and Zhao, Chen and Shen [12] empirically assimilated satellite-based remote sensing information into the WOFORST crop model to estimate regional wheat yield. The former used images combined from Moderate Resolution Imaging Spectroradiometer (MODIS) data and three Landsat TM images while the latter used MODIS images only. The crop modelling technique formulated in GRAMI was applied to assess and monitor crop conditions and yields at regional scales, using imagery from operational satellites [19,20,21,22,23] This within-season calibration methodology was used to estimate evaporation and biomass production [24,25]. The map (a) was produced using ArcGIS (ESRI, Inc., Redlands, CA, USA) and the RapidEye images were reproduced using ENVI (Harris Geospatial Solutions, Inc., Broomfield, CO, USA)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
