Abstract
One of the core tasks in digital soil mapping (DSM) studies is the estimation of the spatial distribution of different soil variables. In addition, however, assessing the uncertainty of these estimations is equally important, something that a lot of current DSM studies lack. Machine learning (ML) methods are increasingly used in this scientific field, the majority of which do not have intrinsic uncertainty estimation capabilities. A solution to this is the use of specific ML methods that provide advanced prediction capabilities, along with innate uncertainty estimation metrics, like Quantile Regression Forests (QRF). In the current paper, the prediction and the uncertainty capabilities of QRF, Random Forests (RF) and geostatistical methods were assessed. It was confirmed that QRF exhibited outstanding results at predicting soil organic matter (OM) in the study area. In particular, R2 was much higher than the geostatistical methods, signifying that more variation is explained by the specific model. Moreover, its uncertainty capabilities as presented in the uncertainty maps, shows that it can also provide a good estimation of the uncertainty with distinct representation of the local variation in specific parts of the area, something that is considered a significant advantage, especially for decision support purposes.
Highlights
Digital soil mapping (DSM), known as predictive soil mapping or pedometric mapping, refers to the creation of digital maps that include spatial soil information, such as soil type or soil properties
DSM makes extensive use of geographic information systems (GIS), global positioning systems (GPS), remotely sensed spectral data, topographic data derived from digital elevation models (DEMs), predictive or inference models, and software for data analysis
Machine learning (ML) that emerged in the 1990s as a tool for DSM [2] is defined as the computer-assisted practice of using data-driven statistical models which resorts to a large amount of input data to learn a pattern and make a prediction [1]
Summary
Digital soil mapping (DSM), known as predictive soil mapping or pedometric mapping, refers to the creation of digital maps that include spatial soil information, such as soil type or soil properties. The RF is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time and is extensively used in DSM [4] It provided the best results in estimating soil OM [5], shortened the training time during the soil OM modeling process and improved the model’s accuracy and its predictive ability [6]. Vaysse and Lagacherie [11], for example, conducted an experiment in which they employed QRF in a temperate Mediterranean area with a comparable soil organic carbon (SOC) dataset in terms of areal extent, observation density, and distribution homogeneity They claim that QRF outperforms RK when it comes to interpreting uncertainty patterns and is better suited than other modeling methods when spatial sampling is sparse. The NDWI rate will drop during times of water stress [20]
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