Monitoring paddy productivity in North Korea employing geostationary satellite images integrated with GRAMI-rice model

Abstract

To meet the growing demands of staple crops with a strategy to develop amicable strategic measures that support efficient North Korean relief policies, it is a desirable task to accurately simulate the yield of paddy (Oryza sativa), an important Asian food commodity. We aim to address this with a grid-based crop simulation model integrated with satellite imagery that enables us to monitor the crop productivity of North Korea. Vegetation Indices (VIs), solar insolation, and air temperature data are thus obtained from the Communication Ocean and Meteorological Satellite (COMS), including the reanalysis data of the Korea Local Analysis and Prediction System (KLAPS). Paddy productivities for North Korea are projected based on the bidirectional reflectance distribution function-adjusted VIs and the solar insolation using the grid GRAMI-rice model. The model is calibrated on a 500-m grid paddy field in Cheorwon, and the model simulation performance accuracy is verified for Cheorwon and Paju, located at the borders of North Korea using four years of data from 2011 to 2014. Our results show that the paddy yields are reproduced reasonably accurately within a statistically significant range of accuracy, in comparison with observation data in Cheorwon (p = 0.183), Paju (p = 0.075), and NK (p = 0.101) according to a statistical t-test procedure. We advocate that incorporating a crop model with satellite images for crop yield simulations can be utilised as a reliable estimation technique for the monitoring of crop productivity, particularly in unapproachable, data-sparse regions not only in North Korea, but globally, where estimations of paddy productivity can assist in planning of agricultural activities that support regionally amicable food security strategies.

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

“…Secondly, optimising the model improves simulation performance. Last, RSCM can be integrated with proximal or remote sensing data from different platforms, for example, an unmanned aerial system [54] and operational optical satellites [14,15,55]. Meanwhile, RSCM has some limitations, such as the strong dependence on proximal or remote sensing data as well as the inapplicability of reproducing the effects of photoperiod and phenology, N fixation, and N dynamics in comparison with other conventional process-based crop models.…”

“…This integration approach likely improves estimation accuracy owing to advancement in both physical and empirical relationships between crop growth variables. Moreover, a crop simulation model incorporated with RS data can produce solid performances even with no data about the objective crop and field available except for RS information [13,14]. RS data assimilation in crop modelling has become widespread in crop growth and yield monitoring because of its improved simulation performance in crop growth and yield estimation and forecasting [15,16].…”

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

“…The current study was dedicated to the statistical hybrid approach of crop modelling and remote sensing, as well as the method to project spatiotemporal crop growth information using remote sensing data from various platforms and optical sensors with different geospatial resolutions. On the other hand, the previous studies using GRAMI-rice were focused on applying for different aspects of crop monitoring, most likely as case studies using images from a specific platform, e.g., an unmanned aerial system, UAS [55] or an optical satellite with either a high ground resolution [21] or a coarse ground resolution [23,56,57].…”

“…Second, the optimization method allows the model to advance the simulation performance. Third, the GRAMI model can be assimilated with remotely sensed information from various platforms, e.g., a UAS [55] and operational optical satellites on-board different ground resolution sensors [21,56,57]. Finally, once the model is applied to the satellite-based remote sensing, it is applicable for any region of interest on the Earth's surface.…”

“…The CIDS is designed to project geospatial information of crop growth and yield using the GRAMI model integrated with remote sensing images from various platforms of remote sensing from an UAS [18,55] to various operational optical satellites with different ground resolutions [21,56,57]. There have been similar practical efforts to assimilate a crop model with satellite images to improve the predictive performance of crop yields [11][12][13].…”