Mapping stocks of soil total nitrogen using remote sensing data: A comparison of random forest models with different predictors

Zhang, Yue; Sui, Biao; Shen, Haiou; Ouyang, Liang

doi:10.1016/j.compag.2019.03.015

Cited by 82 publications

(56 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the RF model can be recommended as the best model for the prediction of land suitability class for rain-fed wheat and barley in western Iran. This result is comparable to the findings of other researchers who demonstrated the reliable performance of RF [75][76][77][78][79].…”

Section: Comparison Of Different ML Modelssupporting

confidence: 90%

Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models

et al. 2020

View full text Add to dashboard Cite

Land suitability assessment is essential for increasing production and planning a sustainable agricultural system, but such information is commonly scarce in the semi-arid regions of Iran. Therefore, our aim is to assess land suitability for two main crops (i.e., rain-fed wheat and barley) based on the Food and Agriculture Organization (FAO) “land suitability assessment framework” for 65 km2 of agricultural land in Kurdistan province, Iran. Soil samples were collected from genetic layers of 100 soil profiles and the physical-chemical properties of the soil samples were analyzed. Topography and climate data were also recorded. After calculating the land suitability classes for the two crops, they were mapped using machine learning (ML) and traditional approaches. The maps predicted by the two approaches revealed notable differences. For example, in the case of rain-fed wheat, results showed the higher accuracy of ML-based land suitability maps compared to the maps obtained by traditional approach. Furthermore, the findings indicated that the areas with classes of N2 (≈18%↑) and S3 (≈28%↑) were higher and area with the class N1 (≈24%↓) was less predicted in the traditional approach compared to the ML-based approach. The major limitations of the study area were rainfall at the flowering stage, severe slopes, shallow soil depth, high pH, and large gravel content. Therefore, to increase production and create a sustainable agricultural system, land improvement operations are suggested.

show abstract

Section: Comparison Of Different ML Modelssupporting

confidence: 90%

Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models

et al. 2020

View full text Add to dashboard Cite

show abstract

“…For example, Wang et al [63] used the RF method to predict STN content in northeastern China, and found that areas with dense vegetation cover had higher STN. Similar findings were also reported in the STN mapping studies by Zhang et al [89] and Wang et al [90].…”

Section: Spatial Prediction Of Stn Contentsupporting

confidence: 90%

Mapping of Soil Total Nitrogen Content in the Middle Reaches of the Heihe River Basin in China Using Multi-Source Remote Sensing-Derived Variables

et al. 2019

View full text Add to dashboard Cite

Soil total nitrogen (STN) is an important indicator of soil quality and plays a key role in global nitrogen cycling. Accurate prediction of STN content is essential for the sustainable use of soil resources. Synthetic aperture radar (SAR) provides a promising source of data for soil monitoring because of its all-weather, all-day monitoring, but it has rarely been used for STN mapping. In this study, we explored the potential of multi-temporal Sentinel-1 data to predict STN by evaluating and comparing the performance of boosted regression trees (BRTs), random forest (RF), and support vector machine (SVM) models in STN mapping in the middle reaches of the Heihe River Basin in northwestern China. Fifteen predictor variables were used to construct models, including land use/land cover, multi-source remote sensing-derived variables, and topographic and climatic variables. We evaluated the prediction accuracy of the models based on a cross-validation procedure. Results showed that tree-based models (RF and BRT) outperformed SVM. Compared to the model that only used optical data, the addition of multi-temporal Sentinel-1A data using the BRT method improved the root mean square error (RMSE) and the mean absolute error (MAE) by 17.2% and 17.4%, respectively. Furthermore, the combination of all predictor variables using the BRT model had the best predictive performance, explaining 57% of the variation in STN, with the highest R2 (0.57) value and the lowest RMSE (0.24) and MAE (0.18) values. Remote sensing variables were the most important environmental variables for STN mapping, with 59% and 50% relative importance in the RF and BRT models, respectively. Our results show the potential of using multi-temporal Sentinel-1 data to predict STN, broadening the data source for future digital soil mapping. In addition, we propose that the SVM, RF, and BRT models should be calibrated and evaluated to obtain the best results for STN content mapping in similar landscapes.

show abstract

“…This study used a large amount of remote sensing image data and derivative indices as predictors, including terrain, climate, and other environmental factors, to predict the temporal and spatial distributions of the STN content in Shandong Province. Our study showed that B6_max, B6_mean, B5_mean and B7_mean are the most important predictors to explain the distribution of nitrogen in soils [36,38]. B6_max and B6_mean were calculated from MODIS short-wave infrared (SWIR) spectroscopy.…”

Section: Predictor Variables Importancementioning

confidence: 94%

“…With a spatial resolution of 30 m, the analysis and prediction accuracy of the STN content was the highest at 0.65. Zhang et al [38] used the Sentinel-2A Multispectral Instrument images to simulate the spatial distribution of the STN in Dehui City, Jilin Province in 2016 and built an RF model to compare the accuracy of different predictors, where the highest prediction accuracy reached 0.8. These studies analyzed STN in the region for a specific time and cannot reflect the change of STN content due to the impact of environmental factors and land management measures.…”

Section: Introductionmentioning

confidence: 99%

Multi-Temporal Mapping of Soil Total Nitrogen Using Google Earth Engine across the Shandong Province of China

Xiao

Chen

et al. 2020

Sustainability

View full text Add to dashboard Cite

Nitrogen plays an important role in improving soil productivity and maintaining ecosystem stability. Mapping and monitoring the soil total nitrogen (STN) content is the basis for modern soil management. The Google Earth Engine (GEE) platform covers a wide range of available satellite remote sensing datasets and can process massive data calculations. We collected 6823 soil samples in Shandong Province, China. The random forest (RF) algorithm predicted the STN content in croplands from 2002 to 2016 in Shandong Province, China on the GEE platform. Our results showed that RF had the coefficient of determination (R2) (0.57), which can predict the spatial distribution of the STN and analyze the trend of STN changes. The remote sensing spectral reflectance is more important in model building according to the variable importance analysis. From 2002 to 2016, the STN content of cropland in the province had an upward trend of 35.6%, which increased before 2010 and then decreased slightly. The GEE platform provides an opportunity to map dynamic changes of the STN content effectively, which can be used to evaluate soil properties in the future long-term agricultural management.

show abstract

Mapping stocks of soil total nitrogen using remote sensing data: A comparison of random forest models with different predictors

Cited by 82 publications

References 62 publications

Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models

Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models

Mapping of Soil Total Nitrogen Content in the Middle Reaches of the Heihe River Basin in China Using Multi-Source Remote Sensing-Derived Variables

Multi-Temporal Mapping of Soil Total Nitrogen Using Google Earth Engine across the Shandong Province of China

Contact Info

Product

Resources

About