Hand-feel soil texture observations to evaluate the accuracy of digital soil maps for local prediction of soil particle size distribution: A case study in Central France

Richer-de-Forges, Anne C.; Arrouays, Dominique; Poggio, Laura; CHEN, Songchao; Lacoste, Marine; Minasny, Budiman; Libohova, Zamir; Roudier, Pierre; Mulder, Vera Leatitia; NÉDÉLEC, Hervé; Martelet, Guillaume; Lemercier, Blandine; Lagacherie, Philippe; Bourennane, Hocine

doi:10.1016/j.pedsph.2022.07.009

Cited by 10 publications

(4 citation statements)

References 65 publications

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“…SoilGrids [60] is the result of global predictions for standard soil properties based on ML methods, using several remote sensingbased soil covariates, mostly MODIS land products, and an extensive database of soil profiles for training purposes. The distribution of soil profiles greatly affects the accuracy of the derived predictions, as reported by a recent study [61] in Central FR, concerning the accuracy of SoilGrids at the local level. The study area of Flanders has a very high density coverage of soil profiles (228 profiles per 1000 km 2 ) [62], with more than 7000 profiles covering the whole of BE.…”

Section: Data-related Implicationsmentioning

confidence: 98%

Analysis of Land Suitability for Maize Production under Climate Change and Its Mitigation Potential through Crop Residue Management

Karapetsas,

Gobin,

Bilas

et al. 2024

Land

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Land Suitability Analysis (LSA), under the impact of climate change, is a fundamental approach to the design of appropriate land management strategies for sustainable crop production and food security. In this study, the FAO framework was used to assess the impact of climate change on land suitability for maize in Flanders, Belgium. The current LSA revealed the marginal suitability for maize cultivation, characterizing most of the agricultural land in Flanders and identifying precipitation as the most limiting factor for maize suitability. The LSA, under two climate change scenarios, was based on climate projections from several CMIP5 Global Circulation Models, transformed into future land suitability projections and assembled into a multi-model ensemble (MME) of projected suitability changes. The results indicate an average reduction in projected suitability of approximately 7% by 2099 under the high-emission scenario. The potential of the Soil-Improving Cropping System (SICS) to mitigate the impacts of climate change on land suitability was statistically significant under both low- and high-emission scenarios. This research provides valuable insights into the MME modeling of climate change impacts on land suitability and its associated uncertainty, with the application of SICS as a potential long-term mitigation measure to promote sustainable agricultural practices.

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Section: Data-related Implicationsmentioning

confidence: 98%

Analysis of Land Suitability for Maize Production under Climate Change and Its Mitigation Potential through Crop Residue Management

Karapetsas,

Gobin,

Bilas

et al. 2024

Land

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“…However, it will also cause the extreme value to not be verified. In addition, some studies have shown that SoilGrids2.0 may produce some bias in the local area [69,70], but the addition of this data can improve the generalization ability of the model to a certain extent [71]; of course, we will also try to use regional data in the future [36].…”

Section: Limitations and Potential Improvementsmentioning

confidence: 99%

Prediction of Soil Organic Carbon Content in Complex Vegetation Areas Based on CNN-LSTM Model

Dong,

Yao,

Bao

et al. 2024

Land

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Synthesizing bare soil pictures in regions with complex vegetation is challenging, which hinders the accuracy of predicting soil organic carbon (SOC) in specific areas. An SOC prediction model was developed in this study by integrating the convolutional neural network and long and short-term memory network (CNN-LSTM) algorithms, taking into consideration soil-forming factors such as climate, vegetation, and topography in Hainan. Compared with common algorithmic models (random forest, CNN, LSTM), the SOC prediction model based on the CNN-LSTM algorithm achieved high accuracy (R2 = 0.69, RMSE = 6.06 g kg−1, RPIQ = 1.96). The model predicted that the SOC content ranged from 5.49 to 36.68 g kg−1, with Hainan in the central and southern parts of the region with high SOC values and the surrounding areas with low SOC values, and that the SOC was roughly distributed as follows: high in the mountainous areas and low in the flat areas. Among the four models, CNN-LSTM outperformed LSTM, CNN, and random forest models in terms of R2 accuracy by 11.3%, 23.2%, and 53.3%, respectively. The CNN-LSTM model demonstrates its applicability in predicting SOC content and shows great potential in complex areas where obtaining sample data is challenging and where SOC is influenced by multiple interacting factors. Furthermore, it shows significant potential for advancing the broader field of digital soil mapping.

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“…− The lack of field data is often one of the main limiting factors of RS and DSM prediction performances (e.g., [13,21,[138][139][140][141][142][143]). − Soil knowledge acquired in the field is useful to improve and assess the RS and DSM prediction performances (e.g., [17,[19][20][21]127,138,139,[144][145][146][147][148]).…”

Section: Use Of Rs Data As a Substitute For Soil Properties' Measurementmentioning

confidence: 99%

“…The idea of an article gathering the specific contributions of RS to DSM from French research originates from a scientific working group founded in 2015 and named Theia "CNS for Cartographie Numérique des Sols" (DSM for Digital Soil Mapping). This French working group aims to federate the efforts of French research laboratories developing digital mapping approaches for perennial soil properties [113,114] under the supervision of Centre National d'Etudes Spatiales (French Space Agency). We believe that such an approach can be useful not only on a national scale, but that it can also be considered as the first step to implement a similar approach on a global scale.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Data for Digital Soil Mapping in French Research—A Review

et al. 2023

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Soils are at the crossroads of many existential issues that humanity is currently facing. Soils are a finite resource that is under threat, mainly due to human pressure. There is an urgent need to map and monitor them at field, regional, and global scales in order to improve their management and prevent their degradation. This remains a challenge due to the high and often complex spatial variability inherent to soils. Over the last four decades, major research efforts in the field of pedometrics have led to the development of methods allowing to capture the complex nature of soils. As a result, digital soil mapping (DSM) approaches have been developed for quantifying soils in space and time. DSM and monitoring have become operational thanks to the harmonization of soil databases, advances in spatial modeling and machine learning, and the increasing availability of spatiotemporal covariates, including the exponential increase in freely available remote sensing (RS) data. The latter boosted research in DSM, allowing the mapping of soils at high resolution and assessing the changes through time. We present a review of the main contributions and developments of French (inter)national research, which has a long history in both RS and DSM. Thanks to the French SPOT satellite constellation that started in the early 1980s, the French RS and soil research communities have pioneered DSM using remote sensing. This review describes the data, tools, and methods using RS imagery to support the spatial predictions of a wide range of soil properties and discusses their pros and cons. The review demonstrates that RS data are frequently used in soil mapping (i) by considering them as a substitute for analytical measurements, or (ii) by considering them as covariates related to the controlling factors of soil formation and evolution. It further highlights the great potential of RS imagery to improve DSM, and provides an overview of the main challenges and prospects related to digital soil mapping and future sensors. This opens up broad prospects for the use of RS for DSM and natural resource monitoring.

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Hand-feel soil texture observations to evaluate the accuracy of digital soil maps for local prediction of soil particle size distribution: A case study in Central France

Cited by 10 publications

References 65 publications

Analysis of Land Suitability for Maize Production under Climate Change and Its Mitigation Potential through Crop Residue Management

Analysis of Land Suitability for Maize Production under Climate Change and Its Mitigation Potential through Crop Residue Management

Prediction of Soil Organic Carbon Content in Complex Vegetation Areas Based on CNN-LSTM Model

Remote Sensing Data for Digital Soil Mapping in French Research—A Review

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