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

The location- and scale- specific correlation between temperature and soil carbon sequestration across the globe

Much research has been conducted to understand the spatial distribution of soil carbon stock and its temporal dynamics. However, an agreement has not been reached on whether increasing global temperature has a positive or negative feedback on soil carbon stocks. By analysing global maps of soil organic carbon (SOC) using a spherical wavelet analysis, it was found that the correlation between SOC and soil temperature at the regional scale was negative between 52° N and 40° S parallels and positive beyond this region. This was consistent with a few previous studies and it was assumed that the effect was most likely due to the temperature-dependent SOC formation (photosynthesis) and decomposition (microbial activities and substrate decomposability) processes. The results also suggested that the large SOC stocks distributed in the low-temperature areas might increase under global warming while the small SOC stocks found in the high-temperature areas might decrease accordingly. Although it remains unknown whether the potential increasing soil carbon stocks in the low-temperature areas can offset the loss of carbon stocks in the high-temperature areas, the location- and scale- specific correlations between SOC and temperature should be taken into account for modeling SOC dynamics and SOC sequestration management

Grassland Carbon Sequestration Ability in China: A New Perspective from Terrestrial Aridity Zones

Current climate change (e.g., temperature and precipitation variations) profoundly influences terrestrial vegetation growth and production, ecosystem respiration, and nutrient circulation. Grasslands are sensitive to climate change, and the carbon sequestration ability is closely related to water availability. However, how the terrestrial water budget influences regional carbon sequestration by the grassland ecosystem is still unclear. In this study, we modified a terrestrial biogeochemical model to investigate net ecosystem productivity (NEP) of Chinese grasslands under different aridity index (AI) levels from 1982 to 2008. The results showed that Chinese grasslands acted as a carbon sink of 33.7 TgC. yr-1, with a clear decrease in the spatial distribution from the humid end (near-forest) to the arid end (near-desert). During these 27 years, gross primary productivity (GPP) and net primary productivity (NPP) significantly increased with regional warming over the entire range of the AI, but no significant tendency was found for NEP. Meanwhile, only NPP in the arid zone (AR) and the semiarid zone (SAR) were significantly correlated with mean annual precipitation (MAP), and no significant correlation was found between heterotrophic respiration (Rh and MAP; NPP and Rh were both positively correlated with mean annual temperature (MAT) in all AI zones except for NPP in AR; no significant correlation between NEP and MAP or MAT was found. These results revealed that the grasslands with different AI levels keep different response patterns to temperature and precipitation variations. On the basis of these results, we predicted that the gap of carbon sequestration ability between humid and arid grassland will expand. The total carbon sink in Chinese grasslands will continue to fluctuate, but there is a danger that it might shrink in the future because of a combination of climatic and human factors, although CO2 fertilization and N deposition might partly mitigate this reduction. © 2016 Society for Range Management. Published by Elsevier Inc. All rights reserved.The Rangeland Ecology & Management archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information

A proposal for the assessment of soil security: Soil functions, soil services and threats to soil

Human societies face six existential challenges to their sustainable development. These challenges have been previously addressed by a myriad of concepts such as soil conservation, soil quality, and soil health. Yet, of these, only soil security attempts to integrate the six existential challenges concurrently through the five biophysical and socio-economic dimensions of capacity, condition, capital, connectivity and codification. In this paper, we highlight past and existing concepts, and make a proposal for a provisional assessment of soil security. The proposal addresses three roles of soil: soil functions, soil services and threats to soil. For each identified role, we indicate a potential, but not exhaustive, list of indicators that characterise the five dimensions of soil security. We also raise issues of quantification and combination of indicators briefly. We found that capacity and condition are theoretically easier to measure and quantify than connectivity and codification. The dimension capital might be conveniently assessed using indicators that relate to the economic value of soils. The next step is to test this proposal for which we make recommendations on potential study cases and examples. We conclude that the five dimensions of soil security can potentially be assessed quantitatively and comprehensively using indicators that characterise each role, but also found that there is need for further work to devise an operational measurement methodology to estimate connectivity of people to soil

Global soil organic carbon assessment

Soil carbon is a key component of functional ecosystems and crucial for food, soil, water and energy security. Climate change and altered land-use are having a great impact on soils. The influence of these factors creates a dynamic feedback between soil and the environment. There is a crucial need to evaluate the responses of soil to global environmental change at large spatial scales that occur along natural environmental gradients over decadal timescales. This work provides a suite of new data on global soil change which will uniquely utilize the world’s prior investment in soil data infrastructure. Here we attempt a comprehensive global space–time assessment of soil carbon dynamics in different ecoregions of the world accounting for impacts of climate change and other environmental factors

Modeling Soil Processes: Review, Key Challenges, and New Perspectives

The remarkable complexity of soil and its importance to a wide range of ecosystem services presents major challenges to the modeling of soil processes. Although major progress in soil models has occurred in the last decades, models of soil processes remain disjointed between disciplines or ecosystem services, with considerable uncertainty remaining in the quality of predictions and several challenges that remain yet to be addressed. First, there is a need to improve exchange of knowledge and experience among the different disciplines in soil science and to reach out to other Earth science communities. Second, the community needs to develop a new generation of soil models based on a systemic approach comprising relevant physical, chemical, and biological processes to address critical knowledge gaps in our understanding of soil processes and their interactions. Overcoming these challenges will facilitate exchanges between soil modeling and climate, plant, and social science modeling communities. It will allow us to contribute to preserve and improve our assessment of ecosystem services and advance our understanding of climate-change feedback mechanisms, among others, thereby facilitating and strengthening communication among scientific disciplines and society. We review the role of modeling soil processes in quantifying key soil processes that shape ecosystem services, with a focus on provisioning and regulating services. We then identify key challenges in modeling soil processes, including the systematic incorporation of heterogeneity and uncertainty, the integration of data and models, and strategies for effective integration of knowledge on physical, chemical, and biological soil processes. We discuss how the soil modeling community could best interface with modern modeling activities in other disciplines, such as climate, ecology, and plant research, and how to weave novel observation and measurement techniques into soil models. We propose the establishment of an international soil modeling consortium to coherently advance soil modeling activities and foster communication with other Earth science disciplines. Such a consortium should promote soil modeling platforms and data repository for model development, calibration and intercomparison essential for addressing contemporary challenges