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

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 gridbased 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

High-Resolution Digital Soil Maps of Forest Soil Nitrogen across South Korea Using Three Machine Learning Algorithms

Reliable estimation of the forest soil nitrogen spatial distribution is necessary for effective forest ecosystem management. This study aimed to develop high-resolution digital soil maps of forest soil nitrogen across South Korea using three powerful machine learning methods to better understand the spatial variations of forest soil nitrogen and its environmental drivers. To achieve this, the study used national-level forest soil nitrogen data and environmental data to construct various geographic and environmental variables including geological, topographic, and vegetation factors for digital soil mapping. The results show that of the machine learning methods, the random forest model had the best performance at predicting total soil nitrogen in the A and B horizons, closely followed by the extreme gradient-boosting model. The most critical predictors were found to be geographic variables, quantitatively confirming the significant role of spatial autocorrelation in predicting soil nitrogen. The digital soil maps revealed that areas with high elevation, concave slopes, and deciduous forests had high nitrogen contents. This finding highlights the potential usefulness of digital soil maps in supporting forest management decision-making and identifying the environmental drivers of forest soil nitrogen distribution

Model validation based on R-square with cross validation methods.

<p>The dotted lines indicate the leave-one-out cross-validated result. 2f, 2-fold 50 repetitions; 5f, 5-fold 20 repetitions; 10f, 10-fold 10 repetitions; 20f, 20-fold 5 repetitions; N, nitrogen; P, phosphorus; o, organic layer; and a, A horizon.</p

Mean relative importance of predictors for nitrogen and phosphorus based on the increased mean square error (%incMSE) from random forest.

<p>N, nitrogen; P, phosphorus; o, organic layer; and a, A horizon.</p

Research area.

<p>(A) The Soyang watershed within South Korea. (B) The research area within the Soyang watershed. (C) The research area with the sampling points. (D) The tree species map (fgis.forest.go.kr/).</p

Maps of mean and coefficient of variation (CoV) of 100 models of phosphorus in the organic layer (P_o) with cross validation methods.

<p>2f50r, 2-fold 50 repetitions; 5f20r, 5-fold 20 repetitions; 10f10r, 10-fold 10 repetitions; 20f5r, 20-fold 5 repetitions.</p

Boxplots showing standard deviations of 100 predicted values for each raster cell with cross validation methods.

<p>2f, 2-fold 50 repetitions; 5f, 5-fold 20 repetitions; 10f, 10-fold 10 repetitions; 20f, 20-fold 5 repetitions; LOO, leave-one-out; N, nitrogen; P, phosphorus; o, organic layer; and a, A horizon.</p

Selected predictors using recursive feature elimination (RFE) based on repeated 10-fold cross validation.

<p>Selected predictors using recursive feature elimination (RFE) based on repeated 10-fold cross validation.</p