Regional Characterisation of soil properties by combining remote sensing, geophysical and pedological methods

2021 ◽  
Author(s):  
Lars Konen ◽  
Richard Mommertz ◽  
Daniel Rückamp ◽  
Malte Ibs-von Seht ◽  
Andreas Möller
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Thermal Analyses ◽  
Arable Land ◽  
Geophysical Methods ◽  
Regional Level ◽  
Soil Survey ◽  
Digital Information ◽  
Sustainable Food ◽  
Research Concept

<p>Knowing our soils well, is the base for a sound land use management, and thus for a worldwide sustainable food production and safe drinking water supply. Especially in countries of the Global South, high quality digital information on soil properties on regional level are rare. While conventional soil inventories are time consuming, digital mapping of soil properties is a promising approach to close the gap more quickly. For this purpose, a reliable method is developed within the BGR project “ReCharBo” (Regional Characterisation of Soil Properties) to minimize field and laboratory work by combining remote sensing techniques like hyperspectral and thermal analyses as well as geophysical methods (e.g. gamma spectrometry) with conventional soil survey from different scales.  At local and field-scale the data acquisition is done by drones, portable equipment and soil sampling, complemented at regional level by helicopter and satellite supported methods. In a corresponding talk in the same session Mommertz et al. (2021) give a detailed technical overview of the selected methods and the research concept of the project. To deploy the method including the concept of ground-truthing on arable land, areas in Germany were selected from Soil Maps of Germany at scale 1:1.000.000 (BÜK1000), 1:200.000 (BÜK200) and 1:50.000 (BK50) depending on representative soil types and region. In a first attempt, the research concept was  carried out with simultaneous field and air borne analyses at two sites  in autumn 2020. The results of this first attempt will be presented at the conference.</p>

Regional characterisation of soil properties by combining soil science and hyperspectral and thermal remote sensing: A technical overview of lab and field remote sensing methods

2021 ◽  
Author(s):  
Richard Mommertz ◽  
Lars Konen ◽  
Martin Schodlok
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Regional Scale ◽  
Arable Land ◽  
Soil Mapping ◽  
Soil Science ◽  
Near Surface ◽  
Soil Parameter ◽  
The Impact ◽  
Parameter Retrieval

<p>Soil is one of the world’s most important natural resources for human livelihood as it provides food and clean water. Therefore, its preservation is of huge importance. For this purpose, a proficient regional database on soil properties is needed. The project “ReCharBo” (Regional Characterisation of Soil Properties) has the objective to combine remote sensing, geophysical and pedological methods to determine soil characteristics on a regional scale. Its aim is to characterise soils non-invasive, time and cost efficient and with a minimal number of soil samples to calibrate the measurements. Konen et al. (2021) give detailed information on the research concept and first field results in a presentation in the session “SSS10.3 Digital Soil Mapping and Assessment”. Hyperspectral remote sensing is a powerful and well known technique to characterise near surface soil properties. Depending on the sensor technology and the data quality, a wide variety of soil properties can be derived with remotely sensed data (Chabrillat et al. 2019, Stenberg et al. 2010). The project aims to investigate the effects of up and downscaling, namely which detail of information is preserved on a regional scale and how a change in scales affects the analysis algorithms and the possibility to retrieve valid soil parameter information. Thus, e.g. laboratory and field spectroscopy are applied to gain information of samples and fieldspots, respectively. Various UAV-based sensors, e.g. thermal & hyperspectral sensors, are applied to study soil properties of arable land in different study areas at field scale. Finally, airborne (helicopter) hyperspectral data will cover the regional scale. Additionally forthcoming spaceborne hyperspectral satellite data (e.g. Prisma, EnMAP, Sentinel-CHIME) are a promising outlook to gain detailed regional soil information. In this context it will be discussed how the multisensor data acquisition is best managed to optimise soil parameter retrieval. Sensor specific properties regarding time and date of acquisition as well as weather/atmospheric conditions are outlined. The presentation addresses and discusses the impact of a multisensor and multiscale remote sensing data collection regarding the results on soil parameter retrieval.</p><p> </p><p>References</p><p>Chabrillat, S., Ben-Dor, E. Cierniewski, J., Gomez, C., Schmid, T. & van Wesemael, B. (2019): Imaging Spectroscopy for Soil Mapping and Monitoring. Surveys in Geophysics 40:361–399. https://doi.org/10.1007/s10712-019-09524-0</p><p>Stenberg, B., Viscarra Rossel, R. A., Mounem Mouazen, A. & Wetterlind, J. (2010): Visible and Near Infrared Spectroscopy in Soil Science. In: Donald L. Sparks (editor): Advances in Agronomy. Vol. 107. Academic Press:163-215. http://dx.doi.org/10.1016/S0065-2113(10)07005-7</p>

Effect of Slope Mesorelief on the Spatial Variability of Soil Properties and Vegetation Index Based on Remote Sensing Data

2019 ◽  
Vol 55 (9) ◽  
pp. 1329-1337
Author(s):  
N. V. Gopp ◽  
T. V. Nechaeva ◽  
O. A. Savenkov ◽  
N. V. Smirnova ◽  
V. V. Smirnov
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Soil Properties ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Natural and Managed Grasslands Productivity during Multiyear in Ex-Arable Lands (in the Context of Climate Change)

Agriculture ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 215
Author(s):  
Liudmila Tripolskaja ◽  
Asta Kazlauskaite-Jadzevice ◽  
Virgilijus Baliuckas ◽  
Almantas Razukas
Keyword(s):  
Climate Change ◽  
Soil Properties ◽  
Dry Matter ◽  
Arable Land ◽  
Rainfall Amount ◽  
Rainfall Variation ◽  
Global Issue ◽  
Term Change ◽  
The Impact

Ex-arable land-use change is a global issue with significant implications for climate change and impact for phytocenosis productivity and soil quality. In temperate humid grassland, we examined the impact of climate variability and changes of soil properties on 23 years of grass productivity after conversion of ex-arable soil to abandoned land (AL), unfertilized, and fertilized managed grassland (MGunfert and MGfert, respectively). This study aimed to investigate the changes between phytocenosis dry matter (DM) yield and rainfall amount in May–June and changes of organic carbon (Corg) stocks in soil. It was found that from 1995 to 2019, rainfall in May–June tended to decrease. The more resistant to rainfall variation were plants recovered in AL. The average DM yield of MGfert was 3.0 times higher compared to that in the AL. The DM yields of AL and MG were also influenced by the long-term change of soil properties. Our results showed that Corg sequestration in AL was faster (0.455 Mg ha−1 year−1) than that in MGfert (0.321 Mg ha−1 year−1). These studies will be important in Arenosol for selecting the method for transforming low-productivity arable land into MG.

scholarly journals Using geostatistical and remote sensing approaches for mapping soil properties

2005 ◽  
Vol 23 (3) ◽  
pp. 279-289 ◽  
Author(s):  
F. López-Granados ◽  
M. Jurado-Expósito ◽  
J.M. Peña-Barragán ◽  
L. García-Torres
Keyword(s):  
Remote Sensing ◽  
Soil Properties

Application of geophysical methods to evaluate hydrology and soil properties in urban areas

Urban Water ◽  
2001 ◽  
Vol 3 (3) ◽  
pp. 205-216 ◽  
Author(s):  
Larisa Pozdnyakova ◽  
Anatoly Pozdnyakov ◽  
Renduo Zhang
Keyword(s):  
Soil Properties ◽  
Urban Areas ◽  
Geophysical Methods

scholarly journals Geostatistical prediction of clay percentage based on soil survey data

2002 ◽  
Vol 11 (4) ◽  
pp. 381-390
Author(s):  
A. TALKKARI ◽  
L. JAUHIAINEN ◽  
M. YLI-HALLA
Keyword(s):  
Soil Properties ◽  
Spatial Variation ◽  
Survey Data ◽  
Clay Content ◽  
Spherical Model ◽  
Simulation Method ◽  
Soil Survey ◽  
Management Zones ◽  
Additional Information ◽  
Clay Percentage

In precision farming fields may be divided into management zones according to the spatial variation in soil properties. Clay content is an important soil characteristic, because it is associated with other soil properties that are important in management. Soil survey data from 150 sampling sites taken from an area of 218 ha were used to predict the spatial variation of clay percentage geostatistically in an agricultural soil in Jokioinen, Finland. The exponential and spherical models with a nugget component were fitted to the experimental variogram. This indicated that the medium-range pattern could be modelled, but the short-range variation could not, due to sparsity of sample points at short distances. The effect of sampling density on the kriging error was evaluated using the random simulation method. Kriging with a spherical model produced a map with smooth variation in clay percentage. The standard error of kriging estimates decreased only slightly when the density of samples was increased. The predictions were divided into three classes based on the clay percentage. Areas with clay content below 30%, between 30% and 60% and over 60% belong to non-clay, clay and heavy clay zones, respectively. With additional information from the soil samples on the contents of nutrients and organic matter these areas can serve as agricultural management zones.;

Deep Learning-based Soil Property Prediction for Remediation of Radioactive Contamination in Agriculture

2021 ◽  
Author(s):  
Franck Albinet ◽  
Gerd Dercon ◽  
Tetsuya Eguchi
Keyword(s):  
Deep Learning ◽  
Soil Properties ◽  
Radioactive Contamination ◽  
The United States ◽  
Soil Survey ◽  
United States Department ◽  
Least Squares Regression ◽  
Transfer Factors ◽  
Wide Range ◽  
Plant Transfer

<p>The Joint IAEA/FAO Division of Nuclear Techniques in Food and Agriculture, through its Soil and Water Management & Crop Nutrition Laboratory (SWMCNL), launched in October 2019, a new Coordinated Research Project (D15019) called “Monitoring and Predicting Radionuclide Uptake and Dynamics for Optimizing Remediation of Radioactive Contamination in Agriculture''. Within this context, the high-throughput characterization of soil properties in general and the estimation of soil-to-plant transfer factors of radionuclides are of critical importance.</p><p>For several decades, soil researchers have been successfully using near and mid-infrared spectroscopy (MIRS) techniques to estimate a wide range of soil physical, chemical and biological properties such as carbon (C), Cation Exchange Capacities (CEC), among others. However, models developed were often limited in scope as only small and region-specific MIR spectra libraries of soils were accessible.</p><p>This situation of data scarcity is changing radically today with the availability of large and growing library of MIR-scanned soil samples maintained by the National Soil Survey Center (NSSC) Kellogg Soil Survey Laboratory (KSSL) from the United States Department of Agriculture (USDA-NRCS) and the Global Soil Laboratory Network (GLOSOLAN) initiative of the Food Agency Organization (FAO). As a result, the unprecedented volume of data now available allows soil science researchers to increasingly shift their focus from traditional modeling techniques such as PLSR (Partial Least Squares Regression) to classes of modeling approaches, such as Ensemble Learning or Deep Learning, that have proven to outperform PLSR on most soil properties prediction in a large data regime.</p><p>As part of our research, the opportunity to train higher capacity models on the KSSL large dataset (all soil taxonomic orders included ~ 50K samples) makes it possible to reach a quality of prediction for exchangeable potassium so far unsurpassed with a Residual Prediction Deviation (RPD) around 3. Potassium is known for its difficulty of being predicted but remains extremely important in the context of remediation of radioactive contamination after a nuclear accident. Potassium can help reduce the uptake of radiocaesium by crops, as it competes with radiocaesium in soil-to-plant transfer.</p><p>To ensure informed decision making, we also guarantee that (i) individual predictions uncertainty is estimated (using Monte Carlo Dropout) and (ii) individual predictions can be interpreted (i.e. how much specific MIRS wavenumber regions contribute to the prediction) using methods such as Shapley Additive exPlanations (SHAP) values.</p><p>SWMCNL is now a member of the GLOSOLAN network, which helps enhance the usability of MIRS for soil monitoring worldwide. SWMCNL is further developing training packages on the use of traditional and advanced mathematical techniques to process MIRS data for predicting soil properties. This training package has been tested in October 2020 with thirteen staff members of the FAO/IAEA Laboratories in Seibersdorf, Austria.</p>

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