scholarly journals Added value of geophysics-based soil mapping in agro-ecosystem simulations

SOIL ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 125-143
Author(s):  
Cosimo Brogi ◽  
Johan A. Huisman ◽  
Lutz Weihermüller ◽  
Michael Herbst ◽  
Harry Vereecken
Keyword(s):  
High Resolution ◽  
Growth Models ◽  
Geophysical Methods ◽  
General Purpose ◽  
Soil Mapping ◽  
Added Value ◽  
Soil Maps ◽  
Area Index ◽  
Soil Map ◽  
Soil Information

Abstract. There is an increased demand for quantitative high-resolution soil maps that enable within-field management. Commonly available soil maps are generally not suited for this purpose, but digital soil mapping and geophysical methods in particular allow soil information to be obtained with an unprecedented level of detail. However, it is often difficult to quantify the added value of such high-resolution soil information for agricultural management and agro-ecosystem modelling. In this study, a detailed geophysics-based soil map was compared to two commonly available general-purpose soil maps. In particular, the three maps were used as input for crop growth models to simulate leaf area index (LAI) of five crops for an area of ∼ 1 km2. The simulated development of LAI for the five crops was evaluated using LAI obtained from multispectral satellite images. Overall, it was found that the geophysics-based soil map provided better LAI predictions than the two general-purpose soil maps in terms of correlation coefficient R2, model efficiency (ME), and root mean square error (RMSE). Improved performance was most apparent in the case of prolonged periods of drought and was strongly related to the combination of soil characteristics and crop type.

scholarly journals Added value of geophysics-based soil mapping in agro-ecosystem simulations

2020 ◽  
Author(s):  
Cosimo Brogi ◽  
Johan A. Huisman ◽  
Lutz Weihermüller ◽  
Michael Herbst ◽  
Harry Vereecken
Keyword(s):  
High Resolution ◽  
Growth Models ◽  
Geophysical Methods ◽  
General Purpose ◽  
Soil Mapping ◽  
Added Value ◽  
Soil Maps ◽  
Area Index ◽  
Soil Map ◽  
Soil Information

Abstract. Developments in agricultural applications have led to an increased demand for quantitative high-resolution soil maps that enable within-field management. Commonly available soil maps are generally not suited for this purpose, but digital soil mapping and geophysical methods in particular allow to obtain soil information with unprecedented level of detail. However, it is often difficult to quantify the added value of such high-resolution soil information for agricultural management and crop modelling. In this study, a detailed geophysics-based soil map was compared to two commonly available general-purpose soil maps. In particular, the three maps were used as input for crop growth models to simulate leaf area index (LAI) of five crops for an area of ~1 km2. The simulated development of LAI for the five crops was evaluated using LAI obtained from multispectral satellite images. Overall, it was found that the geophysics-based soil map provided better LAI predictions than the two general-purpose soil maps in terms of correlation coefficient R2, model efficiency (ME), and root mean square error (RMSE). Improved performance was most apparent in case of prolonged periods of drought and was strongly related to the combination of soil characteristics and crop type.

scholarly journals Unveiling the 3D undercover structure of a Precambrian intrusive complex by integrating airborne magnetic and gravity gradient data into 3D quasi-geology model building

2020 ◽  
Vol 8 (4) ◽  
pp. SS15-SS29 ◽  
Author(s):  
Jiajia Sun ◽  
Aline Tavares Melo ◽  
Jae Deok Kim ◽  
Xiaolong Wei
Keyword(s):  
High Resolution ◽  
Model Building ◽  
Country Rock ◽  
Joint Inversion ◽  
Sedimentary Cover ◽  
Geophysical Methods ◽  
Added Value ◽  
Gravity Gradient ◽  
Airborne Magnetic

Mineral exploration under a thick sedimentary cover naturally relies on geophysical methods. We have used high-resolution airborne magnetic and gravity gradient data over northeast Iowa to characterize the geology of the concealed Precambrian rocks and evaluate the prospectivity of mineral deposits. Previous researchers have interpreted the magnetic and gravity gradient data in the form of a 2D geologic map of the Precambrian basement rocks, which provides important geophysical constraints on the geologic history and mineral potentials over the Decorah area located in the northeast of Iowa. However, their interpretations are based on 2D data maps and are limited to the two horizontal dimensions. To fully tap into the rich information contained in the high-resolution airborne geophysical data, and to further our understanding of the undercover geology, we have performed separate and joint inversions of magnetic and gravity gradient data to obtain 3D density contrast models and 3D susceptibility models, based on which we carried out geology differentiation. Based on separately inverted physical property values, we have identified 10 geologic units and their spatial distributions in 3D which are all summarized in a 3D quasi-geology model. The extension of 2D geologic interpretation to 3D allows for the discovery of four previously unidentified geologic units, a more detailed classification of the Yavapai country rock, and the identification of the highly anomalous core of the mafic intrusions. Joint inversion allows for the classification of a few geologic units further into several subclasses. We have demonstrated the added value of the construction of a 3D quasi-geology model based on 3D separate and joint inversions.

scholarly journals Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation

2019 ◽  
Author(s):  
Clément Albergel ◽  
Emanuel Dutra ◽  
Bertrand Bonan ◽  
Yongjun Zheng ◽  
Simon Munier ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Western Europe ◽  
Initial Conditions ◽  
Added Value ◽  
Area Index ◽  
Long Time ◽  
Land Data Assimilation System ◽  
The Impact

This study aims to assess the potential of the LDAS-Monde a land data assimilation system developed by Météo-France to monitor the impact of the 2018 summer heatwave over western Europe vegetation state. The LDAS-Monde is forced by the ECMWF’s (i) ERA5 reanalysis, and (ii) the Integrated Forecasting System High Resolution operational analysis (IFS-HRES), used in conjunction with the assimilation of Copernicus Global Land Service (CGLS) satellite derived products, namely the Surface Soil Moisture (SSM) and the Leaf Area Index (LAI). Analysis of long time series of satellite derived CGLS LAI (2000-2018) and SSM (2008-2018) highlights marked negative anomalies for July 2018 affecting large areas of North Western Europe and reflects the impact of the heatwave. Such large anomalies spreading over a large part of the considered domain have never been observed in the LAI product over this 18-yr period. The LDAS-Monde land surface reanalyses were produced at spatial resolutions of 0.25°x0.25° (January 2008 to October 2018) and 0.10°x0.10° (April 2016 to December 2018). Both configuration of the LDAS-Monde forced by either ERA5 or HRES capture well the vegetation state in general and for this specific event, with HRES configuration exhibiting better monitoring skills than ERA5 configuration. The consistency of ERA5 and IFS HRES driven simulations over the common period (April 2016 to October 2018) allowed to disentangle and appreciate the origin of improvements observed between the ERA5 and HRES. Another experiment, down-scaling ERA5 to HRES spatial resolutions, was performed. Results suggest that land surface spatial resolution is key (e.g. associated to a better representation of the land cover, topography) and using HRES forcing still enhance the skill. While there are advantages in using HRES, there is added value in down-scaling ERA5, which can provide consistent, long term, high resolution land reanalysis. If the improvement from LDAS-Monde analysis on control variables (soil moisture from layers 2 to 8 of the model representing the first meter of soil and LAI) from the assimilation of SSM and LAI was expected, other model variables benefit from the assimilation through biophysical processes and feedbacks in the model. Finally, we also found added value of initializing 8-day land surface HRES driven forecasts from LDAS-Monde analysis when compared with model only initial conditions.

scholarly journals Potential and limitations of using soil mapping information to understand landscape hydrology

2011 ◽  
Vol 15 (12) ◽  
pp. 3895-3933 ◽  
Author(s):  
F. Terribile ◽  
A. Coppola ◽  
G. Langella ◽  
M. Martina ◽  
A. Basile
Keyword(s):  
Case Studies ◽  
Quantitative Methods ◽  
Transfer Functions ◽  
Soil Mapping ◽  
Land Evaluation ◽  
Data Availability ◽  
Detailed Knowledge ◽  
Full Potential ◽  
Soil Maps ◽  
Soil Map

Abstract. This paper addresses the following points: how can whole soil data from normally available soil mapping databases (both conventional and those integrated by digital soil mapping procedures) be usefully employed in hydrology? Answering this question requires a detailed knowledge of the quality and quantity of information embedded in and behind a soil map. To this end a description of the process of drafting soil maps was prepared (which is included in Appendix A of this paper). Then a detailed screening of content and availability of soil maps and database was performed, with the objective of an analytical evaluation of the potential and the limitations of soil data obtained through soil surveys and soil mapping. Then we reclassified the soil features according to their direct, indirect or low hydrologic relevance. During this phase, we also included information regarding whether this data was obtained by qualitative, semi-quantitative or quantitative methods. The analysis was performed according to two main points of concern: (i) the hydrological interpretation of the soil data and (ii) the quality of the estimate or measurement of the soil feature. The interaction between pedology and hydrology processes representation was developed through the following Italian case studies with different hydropedological inputs: (i) comparative land evaluation models, by means of an exhaustive itinerary from simple to complex modelling applications depending on soil data availability, (ii) mapping of soil hydrological behaviour for irrigation management at the district scale, where the main hydropedological input was the application of calibrated pedo-transfer functions and the Hydrological Function Unit concept, and (iii) flood event simulation in an ungauged basin, with the functional aggregation of different soil units for a simplified soil pattern. In conclusion, we show that special care is required in handling data from soil databases if full potential is to be achieved. Further, all the case studies agree on the appropriate degree of complexity of the soil hydrological model to be applied. We also emphasise that effective interaction between pedology and hydrology to address landscape hydrology requires (i) greater awareness of the hydrological community about the type of soil information behind a soil map or a soil database, (ii) the development of a better quantitative framework by the pedological community for evaluating hydrological features, and (iii) quantitative information on soil spatial variability.

Impact of regional climate model input and soil map resolution on projected winter wheat production in SW Germany

2020 ◽  
Author(s):  
Sebastian Gayler ◽  
Rajina Bajracharya ◽  
Tobias Weber ◽  
Thilo Streck
Keyword(s):  
High Resolution ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Scale ◽  
Grid Cell ◽  
Small Scale ◽  
Climate Projections ◽  
Soil Map ◽  
Soil Information ◽  
The Impact

<p>Agricultural ecosystem models, driven by climate projections and fed with soil information and plausible management scenarios are frequently used tools to predict future developments in agricultural landscapes. On the regional scale, the required soil parameters must be derived from soil maps that are available in different spatial resolutions, ranging from grid cell sizes of 50 m up to 1 km and more. The typical spatial resolution of regional climate projections is currently around 12 km. Given the small-scale heterogeneity in soil properties, using the most accurate soil representation could be important for predictions of crop growth. However, simulations with very highly resolved soil data requires greater computing time and higher effort for data organization and storage. Moreover, the higher resolution may not necessarily lead to better simulations due to redundant information of the land surface and because the impact of climate forcing could dominate over the effect of soil variability. This leads to the question if the use of high-resolution soil data leads to significantly different predictions of future yields and grain protein trends compared to simulations in which soil data is adapted to the resolution of the climate input.</p><p>This study investigated the impact of weather and soil input on simulated crop growth in an intensively used agricultural region in Southwest Germany. For all areas classified as ‘arable land’ (CLC10), winter wheat growth was simulated over a 44-year period (2006 to 2050) using weather projections from three regional climate models and soil information at two spatial resolutions. The simulations were performed with the model system Expert-N 5.0, where the crop model Gecros was combined with the Richards equation and the CN turnover module of the model Daisy. Soil hydraulic parameters as well as initial values of soil organic matter pools were estimated from BK50 soil map information on soil texture and soil organic matter content, using pedo-transfer functions and SOM pool fractionation following Bruun and Jensen (2002). The coarser soil map is derived from BK50 soil map (50m x 50m) by selecting only the dominant soil type in a 12km × 12km grid to be representative for that grid cell. The crop model was calibrated with field data of crop phenology, leaf area, biomass, yield and crop nitrogen, which were collected at a research station within the study area between 2009 and 2018.</p><p>The predicted increase in temperatures during the growing season correlated with earlier maturity, lower yields and a higher grain protein content. The regional mean values varied by +/- 0.5 t/ha or +/-0.3 percentage points of protein content depending to the climate model used. On the regional scale, the simulated trends remained unchanged using high-resolution or coarse resolution soil data. However, there are strong differences in both the forecasted averages and the distribution of forecasts, as the coarser resolution captures neither the small-scale heterogeneity nor the average of the high-resolution results.</p>

scholarly journals A QUESTIONNAIRE TO USERS OF SOIL MAPS IN BRITISH COLUMBIA: RESULTS AND IMPLICATIONS FOR DESIGN AND CONTENT

1981 ◽  
Vol 61 (1) ◽  
pp. 123-135 ◽  
Author(s):  
K. W. G. VALENTINE ◽  
W. C. NAUGHTON ◽  
MARTHA NAVAI
Keyword(s):  
British Columbia ◽  
Water Content ◽  
Soil Maps ◽  
Large Area ◽  
Soil Map ◽  
Common Scale ◽  
Map Symbol ◽  
Soil Information ◽  
Pattern Information ◽  
A Site

A total of 70 people (30%) replied to a questionnaire about the design and content of soil maps from British Columbia. Most people are using their maps in the office. The most common scale used is between 1:40 000 and 1:80 000 as a result of the general availability of 1:50 000 maps, but maps of a larger scale would be preferred. Thirty-two percent of the respondents usually wanted soil information about a site, whereas 23% wanted pattern information about a large area (46% wanted both). This has an implication on the way the legend should be constructed. Texture, slope and soil water content were the properties ranked of highest importance by respondents regardless of their occupation. There was a slight preference for a complex connotative map symbol as opposed to a simple one. This preference was expressed more by those with experience in using soil maps. Those will less experience preferred a simple symbol. A colored soil map with a topographic base map was what most respondents wanted. Fully 51% of the respondents had some difficulties in getting the information they needed from a soil map. They either found the information inaccurate, they could not understand the legend or the symbol, or they had difficulty locating themselves on the map. These difficulties most often arose from the lack of any statements about mapping procedures and reliability, complicated and non-standard legends and symbols and unfamiliar terminology (usually soil taxonomic names). As a result of these responses, a number of suggestions are offered for the improvement of soil maps.

scholarly journals Comparison between detailed digital and conventional soil maps of an area with complex geology

2013 ◽  
Vol 37 (5) ◽  
pp. 1136-1148 ◽  
Author(s):  
Osmar Bazaglia Filho ◽  
Rodnei Rizzo ◽  
Igo Fernando Lepsch ◽  
Hélio do Prado ◽  
Felipe Haenel Gomes ◽  
...  
Keyword(s):  
Great Influence ◽  
Soil Formation ◽  
Principal Component ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Mapping Technique ◽  
Topographic Map ◽  
Soil Maps ◽  
Soil Map ◽  
Terrain Attributes

Since different pedologists will draw different soil maps of a same area, it is important to compare the differences between mapping by specialists and mapping techniques, as for example currently intensively discussed Digital Soil Mapping. Four detailed soil maps (scale 1:10.000) of a 182-ha sugarcane farm in the county of Rafard, São Paulo State, Brazil, were compared. The area has a large variation of soil formation factors. The maps were drawn independently by four soil scientists and compared with a fifth map obtained by a digital soil mapping technique. All pedologists were given the same set of information. As many field expeditions and soil pits as required by each surveyor were provided to define the mapping units (MUs). For the Digital Soil Map (DSM), spectral data were extracted from Landsat 5 Thematic Mapper (TM) imagery as well as six terrain attributes from the topographic map of the area. These data were summarized by principal component analysis to generate the map designs of groups through Fuzzy K-means clustering. Field observations were made to identify the soils in the MUs and classify them according to the Brazilian Soil Classification System (BSCS). To compare the conventional and digital (DSM) soil maps, they were crossed pairwise to generate confusion matrices that were mapped. The categorical analysis at each classification level of the BSCS showed that the agreement between the maps decreased towards the lower levels of classification and the great influence of the surveyor on both the mapping and definition of MUs in the soil map. The average correspondence between the conventional and DSM maps was similar. Therefore, the method used to obtain the DSM yielded similar results to those obtained by the conventional technique, while providing additional information about the landscape of each soil, useful for applications in future surveys of similar areas.

scholarly journals Digital soil mapping: Predicting soil classes distribution in large areas based on existing soil maps from similar small areas

2021 ◽  
Vol 45 ◽  
Author(s):  
Thaís Gabriela Gonçalves ◽  
Nívea Adriana Dias Pons ◽  
Eliane Guimarães Pereira Melloni ◽  
Marcelo Mancini ◽  
Nilton Curi
Keyword(s):  
Large Scale ◽  
Kappa Coefficient ◽  
Parent Material ◽  
Soil Maps ◽  
Promising Alternative ◽  
Small Areas ◽  
Soil Map ◽  
Local Soil ◽  
Soil Information ◽  
Global Accuracy

ABSTRACT There is an ever-growing need for soil maps, since detailed soil information is directly related to agricultural activities, urbanization and environmental protection. However, there is a lack of large-scale soil maps in developing tropical countries such as Brazil. Albeit there are soil maps for small areas, large regions usually have undetailed maps. Considering the importance of finding low-cost alternatives to overcome the lack of detailed soil information, the main objective of this work was to manually create a local soil map and extrapolate it to similar larger areas that lack detailed soil information. The Anhumas River Basin, in the municipality of Itajubá, southeast Brazil, was manually mapped and this map was used to predict soils distribution for the entire municipality. First, the prediction model was tested in the same basin and provided sufficient results, achieving 67% global accuracy and 0.62 Kappa coefficient. Second, the resulting map was used together with the soil map of the larger José Pereira Basin to map the entire municipality, achieving 54% global accuracy and 0.40 Kappa coefficient. Low resolution parent material information was found to confuse models; maps showed better results when this variable was removed. The Minas Gerais soil map presents general mapping units only for the Acrisol class and its associations with other soil classes in the area. The soil map predicted by this work identified more soil classes. Mapping representative areas and extrapolating these maps to larger similar areas constitute a promising alternative to overcome the lack of detailed soil maps.

scholarly journals Spatial distribution of selected soil features in Hajdú-Bihar county represented by digital soil maps

2016 ◽  
Vol 10 (3-4) ◽  
pp. 203-213 ◽  
Author(s):  
László Pásztor ◽  
Annamária Laborczi ◽  
Katalin Takács ◽  
Gábor Szatmári ◽  
Gábor Illés ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Data ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Maps ◽  
Information Requirements ◽  
Soil Features ◽  
Soil Information ◽  
Mapping Techniques ◽  
Digital Soil Maps

With the ongoing DOSoReMI.hu project we aimed to significantly extend the potential, how soil information requirements could be satisfied in Hungary. We started to compile digital soil maps, which fulfil optimally general as well as specific national and international demands from the aspect of thematic, spatial and temporal accuracy. In addition to relevant and available auxiliary, spatial data themes related to soil forming factors and/or to indicative environmental elements we heavily lean on the various national soil databases. The set of the applied digital soil mapping techniques is gradually broadened. In our paper we present some results in the form of brand new soil maps focusing on the territory of Hajdú-Bihar county.

scholarly journals Digital soil mapping: strategy for data pre-processing

2012 ◽  
Vol 36 (4) ◽  
pp. 1083-1092 ◽  
Author(s):  
Alexandre ten Caten ◽  
Ricardo Simão Diniz Dalmolin ◽  
Luis Fernando Chimelo Ruiz
Keyword(s):  
Decision Tree ◽  
Predictive Performance ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Maps ◽  
Soil Map ◽  
Mapping Strategy

The region of greatest variability on soil maps is along the edge of their polygons, causing disagreement among pedologists about the appropriate description of soil classes at these locations. The objective of this work was to propose a strategy for data pre-processing applied to digital soil mapping (DSM). Soil polygons on a training map were shrunk by 100 and 160 m. This strategy prevented the use of covariates located near the edge of the soil classes for the Decision Tree (DT) models. Three DT models derived from eight predictive covariates, related to relief and organism factors sampled on the original polygons of a soil map and on polygons shrunk by 100 and 160 m were used to predict soil classes. The DT model derived from observations 160 m away from the edge of the polygons on the original map is less complex and has a better predictive performance.

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