Provision of soil information for biophysical modelling

This thesis is concerned with the generation of a framework for addressing soil data needs, specifically for biophysical modelling. The soil system is an important ecosystem actor, supporting most of the worlds' food production and being the major terrestrial carbon stocks, thereby information about it is crucial for management and policy making. To provide this information, it is important to deliver information of the highest possible quality; thus the need to define guidelines to standardise, not only the methodologies, but the minimum requirements that information must meet. In this project, providing soil data is addressed in two ways. The first scenario investigates the use of soil information to predict other soil properties, using pedotransfer function (PTFs). In the second scenario, it is assumed that the end-user does not have extra information about the soil properties at a specific location. In this case, the use of existing soil maps is a traditional solution, thus a framework for generating maps at national/continental scale, using digital soil mapping (DSM) techniques, is proposed

Machine learning to generate soil information

This thesis is concerned with the novel use of machine learning (ML) methods in soil science research. ML adoption in soil science has increased considerably, especially in pedometrics (the use of quantitative methods to study the variation of soils). In parallel, the size of the soil datasets has also increased thanks to projects of global impact that aim to rescue legacy data or new large extent surveys to collect new information. While we have big datasets and global projects, currently, modelling is mostly based on "traditional" ML approaches which do not take full advantage of these large data compilations. This compilation of these global datasets is severely limited by privacy concerns and, currently, no solution has been implemented to facilitate the process. If we consider the performance differences derived from the generality of global models versus the specificity of local models, there is still a debate on which approach is better. Either in global or local DSM, most applications are static. Even with the large soil datasets available to date, there is not enough soil data to perform a fully-empirical, space-time modelling. Considering these knowledge gaps, this thesis aims to introduce advanced ML algorithms and training techniques, specifically deep neural networks, for modelling large datasets at a global scale and provide new soil information. The research presented here has been successful at applying the latest advances in ML to improve upon some of the current approaches for soil modelling with large datasets. It has also created opportunities to utilise information, such as descriptive data, that has been generally disregarded. ML methods have been embraced by the soil community and their adoption is increasing. In the particular case of neural networks, their flexibility in terms of structure and training makes them a good candidate to improve on current soil modelling approaches

Using deep learning for digital soil mapping

Digital soil mapping (DSM) has been widely used as a cost-effective method for generating soil maps. However, current DSM data representation rarely incorporates contextual information of the landscape. DSM models are usually calibrated using point observations intersected with spatially corresponding point covariates. Here, we demonstrate the use of the convolutional neural network (CNN) model that incorporates contextual information surrounding an observation to significantly improve the prediction accuracy over conventional DSM models. We describe a CNN model that takes inputs as images of covariates and explores spatial contextual information by finding non-linear local spatial relationships of neighbouring pixels. Unique features of the proposed model include input represented as a 3-D stack of images, data augmentation to reduce overfitting, and the simultaneous prediction of multiple outputs. Using a soil mapping example in Chile, the CNN model was trained to simultaneously predict soil organic carbon at multiples depths across the country. The results showed that, in this study, the CNN model reduced the error by 30&thinsp;% compared with conventional techniques that only used point information of covariates. In the example of country-wide mapping at 100&thinsp;m resolution, the neighbourhood size from 3 to 9 pixels is more effective than at a point location and larger neighbourhood sizes. In addition, the CNN model produces less prediction uncertainty and it is able to predict soil carbon at deeper soil layers more accurately. Because the CNN model takes the covariate represented as images, it offers a simple and effective framework for future DSM models.</p

Pedotransfer functions in Earth system science: challenges and perspectives

Soil, through its various functions, plays a vital role in the Earth's ecosystems and provides multiple ecosystem services to humanity. Pedotransfer functions (PTFs) are simple to complex knowledge rules that relate available soil information to soil properties and variables that are needed to parameterize soil processes. In this paper, we review the existing PTFs and document the new generation of PTFs developed in the different disciplines of Earth system science. To meet the methodological challenges for a successful application in Earth system modeling, we emphasize that PTF development has to go hand in hand with suitable extrapolation and upscaling techniques such that the PTFs correctly represent the spatial heterogeneity of soils. PTFs should encompass the variability of the estimated soil property or process, in such a way that the estimation of parameters allows for validation and can also confidently provide for extrapolation and upscaling purposes capturing the spatial variation in soils. Most actively pursued recent developments are related to parameterizations of solute transport, heat exchange, soil respiration and organic carbon content, root density and vegetation water uptake. Further challenges are to be addressed in parameterization of soil erosivity and land use change impacts at multiple scales. We argue that a comprehensive set of PTFs can be applied throughout a wide range of disciplines of Earth system science, with emphasis on land surface models. Novel sensing techniques provide a true breakthrough for this, yet further improvements are necessary for methods to deal with uncertainty and to validate applications at global scale

GeoVec

Word embeddings for application in geosciences: development, evaluation and examples of soil-related concept

Multi-source data integration for soil mapping using deep learning

With the advances of new proximal soil sensing technologies, soil properties can be inferred by a variety of sensors, each having its distinct level of accuracy. This measurement error affects subsequent modelling and therefore must be integrated when calibrating a spatial prediction model. This paper introduces a deep learning model for contextual Digital Soil Mapping (DSM) using uncertain measurements of the soil property. The deep learning model, called Convolutional Neural Network (CNN), has the advantage that it uses as input a local representation of environmental covariates to leverage the spatial information contained in the vicinity of a location. Spatial non-linear relationships between covariate pixel values and measured soil properties are found by optimizing an objective function, which can be weighted with respect to a measurement error of soil observations. In addition, a single model can be trained to predict a soil property at different soil depths. This method is tested in mapping top- and subsoil organic carbon using laboratory analyzed and spectroscopically inferred measurements. Results show that CNNs significantly increased prediction accuracy as indicated by the coefficient of determination and concordance correlation coefficient, when compared to a conventional DSM technique. Deeper soil layer prediction error decreased, while preserving the interrelation between soil property and depths. The tests conducted using different window size of input covariates matrix to predict organic carbon suggest that CNN benefits from using local contextual information up to 260 to 360 metres. We conclude that CNN is a flexible, effective and promising model to predict soil properties at multiple depths while accounting for contextual covariates information and measurement error

Monitoring changes in global soil organic carbon stocks from space

Soils are under threat globally, with declining soil productivity and soil health in many places. As a key indicator of soil functioning, soil organic carbon (SOC) is crucial for ensuring food, soil, water and energy security, together with biodiversity protection. While there is a global effort to map SOC stock and status, SOC is a dynamic soil property and can change rapidly as a function of land management and land use. Here, we introduce a semi-mechanistic model to monitor SOC stocks at a global scale, underpinned by one of the largest worldwide soil database to date. Our model generates a SOC stock baseline for the year 2001, which is then propagated through time by keeping track of annual landcover changes obtained from remote sensing products with loss and gain dynamics dependent on temperature and precipitation, which finally define the magnitude, rate and direction of the SOC changes. We estimated a global SOC stock in the top 30~cm of around 793 Pg with annual losses due to landcover change of 1.9 Pg SOC/yr from 2001 to 2020, 20% larger than the annual production-based emissions of the United States in 2018. The biggest losses were found in the tropic and sub-tropical regions, accounting for almost 50% of the total global loss. This is a considerable contribution to greenhouse gas emissions but it also has a direct impact on agricultural production with more than 16 million hectares per year falling below critical SOC limits. The proposed modelling framework is flexible, allowing it to be updated as more remote sensing and soil data becomes available, offering a first-of-its-kind global spatio-temporal SOC stock assessment and monitoring system

Soil carbon sequestration potential in global croplands

Improving the amount of organic carbon in soils is an attractive alternative to partially mitigate climate change. However, the amount of carbon that can be potentially added to the soil is still being debated, and there is a lack of information on additional storage potential on global cropland. Soil organic carbon (SOC) sequestration potential is region-specific and conditioned by climate and management but most global estimates use fixed accumulation rates or time frames. In this study, we model SOC storage potential as a function of climate, land cover and soil. We used 83,416 SOC observations from global databases and developed a quantile regression neural network to quantify the SOC variation within soils with similar environmental characteristics. This allows us to identify similar areas that present higher SOC with the difference representing an additional storage potential. We estimated that the topsoils (0–30 cm) of global croplands (1,410 million hectares) hold 83 Pg C. The additional SOC storage potential in the topsoil of global croplands ranges from 29 to 65 Pg C. These values only equate to three to seven years of global emissions, potentially offsetting 35% of agriculture’s 85 Pg historical carbon debt estimate due to conversion from natural ecosystems. As SOC store is temperature-dependent, this potential is likely to reduce by 14% by 2040 due to climate change in a “business as usual” scenario. The results of this article can provide a guide to areas of focus for SOC sequestration, and highlight the environmental cost of agriculture

Comparison of Surface Water Volume Estimation Methodologies That Couple Surface Reflectance Data and Digital Terrain Models

Uncertainty about global change requires alternatives to quantify the availability of water resources and their dynamics. A methodology based on different satellite imagery and surface elevation models to estimate surface water volumes would be useful to monitor flood events and reservoir storages. In this study, reservoirs with associated digital terrain models (DTM) and continuously monitored volumes were selected. The inundated extent was based on a supervised classification using surface reflectance in Landsat 5 images. To estimate associated water volumes, the DTMs were sampled at the perimeter of inundated areas and an inverse distance weighting interpolation was used to populate the water elevation inside the flooded polygons. The developed methodology (IDW) was compared against different published methodologies to estimate water volumes from digital elevation models, which assume either a flat water surface using the maximum elevation of inundated areas (Max), and a flat water surface using the median elevation of the perimeter of inundated areas (Median), or a tilted surface, where water elevations are based on an iterative focal maximum statistic with increasing window sizes (FwDET), and finally a tilted water surface obtained by replacing the focal maximum statistic from the FwDET methodology with a focal mean statistic (FwDET_mean). Volume estimates depend strongly on both water detection and the terrain model. The Max and the FwDET methodologies are highly affected by the water detection step, and the FwDET_mean methodology leads to lower volume estimates due to the iterative smoothing of elevations, which also tends to be computationally expensive for big areas. The Median and IDW methodologies outperform the rest of the methods, and IDW can be used for both reservoir and flood volume monitoring. Different sources of error can be observed, being systematic errors associated with the DTM acquisition time and the reported volumes, which for example fail to consider dynamic sedimentation processes taking place in reservoirs. Resolution effects account for a fraction of errors, being mainly caused by terrain curvature