Mathematical Integration of Remotely-Sensed Information into a Crop Modelling Process for Mapping Crop Productivity

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.

Simulation of Wheat Productivity Using a Model Integrated With Proximal and Remotely Controlled Aerial Sensing Information

“…G1, nitrogen applications of 40 kg ha −1 at planting, 0 kg ha −1 at rejuvenation, and 0 kg ha −1 at initial reproduction (40-0-0); G2, 40-10-10; G3, 40-20-20; G4, 40-30-30; G5, 40-40-40-40; G6, 40-50-50; G7, 40-60-60. studies have introduced parameter estimation techniques for crop simulation modeling in order to reduce the model's complexity and investigate the best possible fit (Maas, 1993a;Ahuja et al, 2000). RUE, defined as the amount of dry biomass produced per unit of intercepted solar radiation (Monteith, 1977), is an important parameter employed in many crop models (Jones et al, 2003;Ko et al, 2010;Nguyen et al, 2019). Both k and SLA are also crucial parameters in simulating crop growth and development for most RUE-based crop models (Shawon et al, 2020a).…”

“…Early estimations of wheat productivity and timely information regarding seasonal growth conditions are expected to increase its productivity through cohesive crop management practices such as irrigation and nitrogen application regimes. Crop modeling and remote sensing (RS) are both conventional methodologies that, when used in conjunction, offer all the benefits of the techniques available for practical assessments of crop productivity and growth conditions (Nguyen et al, 2019). A crop model allows for sequential simulation, while RS enables consistency in the monitoring of geographic and spatial variations in crop conditions and productivity (Ko et al, 2015;Jeong et al, 2018b).…”

“…A crop model integrated with RS information can reinforce the advantages of RS and crop modeling, thereby circumventing the weaknesses of these approaches (Delécolle et al, 1992 ; Maas, 1992 ; Ko et al, 2015 ). This methodology can enable geospatial crop productivity monitoring and yield forecasting at different scales of croplands (Jeong et al, 2018b ; Nguyen et al, 2019 ). Previous studies have attempted to utilize this strategy of combining crop modeling and RS (Moulin et al, 1998 ; Cheng et al, 2016 ; Huang et al, 2018 , 2019 ; Jin et al, 2018 ; Nguyen et al, 2019 ).…”

“…This methodology can enable geospatial crop productivity monitoring and yield forecasting at different scales of croplands (Jeong et al, 2018b ; Nguyen et al, 2019 ). Previous studies have attempted to utilize this strategy of combining crop modeling and RS (Moulin et al, 1998 ; Cheng et al, 2016 ; Huang et al, 2018 , 2019 ; Jin et al, 2018 ; Nguyen et al, 2019 ). GRAMI (Maas, 1992 ), a gramineous crop model, was developed through such efforts; this model can use RS data.…”

Assessment of a Proximal Sensing-integrated Crop Model for Simulation of Soybean Growth and Yield

“…The parameters can affect each other as well, for example, a decreased RUE from 1.75 to 1.5 g MJ −1 to increase the light extinction coefficient from 0.3 to 1, according to Bell et al [49]. The scientific concept that we employed was that soybean growth could be calibrated for a given environment according to proximal sensing information using non-measurable but adjustable parameters employing the within-season calibration [18,50]. The non-measurable calibrated parameters include a, b, and c for which a and b control the magnitude and shapes of an LAI curve up to the maximum LAI, while c controls the reduced amount of LAI at the senescence stage.…”