Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals

Schönauer, Marian; Prinz, Robert; Väätäinen, Kari; Astrup, Rasmus; Pszenny, Dariusz; Lindeman, Harri; Jaeger, Dirk

doi:10.1016/j.jag.2022.102730

Cited by 12 publications

(13 citation statements)

References 34 publications

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“…Current research demonstrated already how openly available geospatial and temporal data can be used to improve predictions of soil moisture and trafficability. Recent findings of Schönauer et al [103] showed a method how information of different origins and spatial resolutions was fused, in order to achieve a spatiotemporal prediction of soil moisture on different forest sites in Europe. Moreover, spatial predictive systems can be merged with operationspecific information, captured in real-time through forestry machine-based sensors itself [17, 45•, 46••].…”

Section: Discussionmentioning

confidence: 99%

“…Harvester-mounted technical instruments, i.e., a controlling area network (CAN) bus device, can also be used to measure the rolling resistance of the soil, and provide live data regarding actual soil bearing capacity . Integrating these types of field-measured live data or frequently updated satellite-based soil moisture measurements (Schönauer et al 2022) in GIS-based decision support systems may facilitate creation of a more dynamic prediction of risks for rutting.…”

Section: Rutting Measurementsmentioning

confidence: 99%

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GIS-based decision support systems to minimise soil impacts in logging operations

Mohtashami¹

2022

Acta Universitatis Agriculturae Sueciae

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Mechanised logging operations can leave negative impacts, like ruts, on forest soils. To avoid this, forestry planners and machine operators need decision support systems that can estimate soil trafficability and help to minimise soil impacts. The main objective of this thesis was to evaluate whether or how different data, stored in a geographic information system (GIS), can contribute to improved estimation of soil trafficability. Requirements for implementation of soil trafficability maps in forestry GIS applications were also described. A soil trafficability map, based on several GIS data using multi-criteria decision analysis (MCDA), was proposed in Paper I. Availability and implementation of soil trafficability maps, mainly depth-to-water (DTW) maps, in some European countries, was reviewed in Paper II. Effect of DTW map resolutions to predict soil moisture was evaluated in Paper IV, and the study showed that a spatial resolution of 1–2 m was sufficient. Risk for rutting was analysed in relation to field-measured and GIS data in Papers III, V and VI. GIS data included digital elevation models, DTW maps, hydrological data, soil type, and clay content maps. The results showed that planning forwarder trails and evaluating different alternatives can be improved by using a soil trafficability map. GIS data of high quality is required to achieve acceptable results. Easy or free access to soil trafficability maps facilitate their application in forestry operations. DTW maps, together with other data, can be used to estimate risk for rutting. Clay content maps and hydrological data, at current resolution, need further development but showed potential to predict risk for rutting. More studies are required to estimate temporal and spatial variability of soil trafficability maps. In conclusion, GIS-based decision support systems should be used for planning of logging operations to minimise risk for rutting.

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Section: Discussionmentioning

confidence: 99%

Section: Rutting Measurementsmentioning

confidence: 99%

GIS-based decision support systems to minimise soil impacts in logging operations

Mohtashami¹

2022

Acta Universitatis Agriculturae Sueciae

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“…In method 1, GLASS LAI, TRIMS LST, and SMAP MPDI were used to downscale the coarse microwave SMC data. The performance of the downscaled SMC is affected by the accuracy of these auxiliary data [84,85]. The uncertainties of input auxiliary data can also introduce other sources of errors in SMC downscaling [42].…”

Section: A Influence Of Downscaling Methods On 1-km Smc Estimationmentioning

confidence: 99%

Investigating the Potential of Downscaling Approaches for SMAP Radiometer Soil Moisture in Agroforestry Areas, China

Cui

Jiang

Wu³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Accurate high-spatial-resolution soil moisture content (SMC) datasets are crucial for applications such as erosion modelling, flood forecasting, and agricultural production. Downscaling is an effective way to convert coarse satellite observations to a finer spatial resolution. Two downscaling approaches were proposed to improve the spatial resolution of the Soil Moisture Active Passive (SMAP) radiometer SMC. A downscaling method (method 1) based on the triangular feature space concept was developed to express SMAP L3 SMC as a polynomial function of the Global Land Surface Satellite (GLASS) leaf area index (LAI), preprocessed synthetic land surface temperature (LST), and microwave polarization difference index (MPDI). A second downscaling method (method 2) based on the simulated datasets was developed to express high-resolution SMC as a function of coarse-resolution SMC and Sentinel-1 synthetic aperture radar (SAR) observations. Downscaled SMC (1 km) was evaluated by the in situ measurements and compared by the SMAP L2 active and passive SMC product in agroforestry areas, China. The results showed that the two downscaling methods could effectively capture the spatial variability of soil moisture at 1-km spatial scales. The RMSE of downscaled SMC for grass, shrub, and forestland is 0.052~0.055 cm 3 cm -3 , 0.063~0.069 cm 3 cm -3 , and 0.067~0.073 cm 3 cm -3 , respectively. The accuracies of method 1, method 2, and SMAP L2 SMC in the grassland were higher than those in the shrubland and forestland. Overall, the R and RMSE between the downscaled soil moisture from method 1, method 2, and SMAP L2 SMC were 0.613, 0.626, and 0.619 and 0.051 cm 3 cm -3 , 0.041 cm 3 cm -3 , and 0.45 cm 3 cm -3 , respectively. The active and SMAP passive microwave combination method has great potential for soil moisture downscaling in agroforestry areas in China.

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“…Furthermore, we calculate several estimates of the peatland coverage of the Swedish forest landscape which allows us to constrain the estimates (Table 3). Schönauer et al (2022), recently showed that by combining airborne laser data with other map sources and AI models, they could produce accurate soil moisture maps for six study areas in Finland, Germany, and Poland. By applying an XGBoost machine learning model for predicting soil moisture, they predicted 74 % of wet values correctly, a significant improvement compared to depth-to-water maps that predicted 38 % of wet values correctly.…”

Section: The Novelty Of the Developed Mapsmentioning

confidence: 99%

“…By applying an XGBoost machine learning model for predicting soil moisture, they predicted 74 % of wet values correctly, a significant improvement compared to depth-to-water maps that predicted 38 % of wet values correctly. As the number of countries that have wide-area public lidar datasets are increasing in the northern boreal zone (Cohen et al, 2020) and new methods of mapping soil moisture using machine learning from a combination of data sources (Schönauer et al, 2022;Ågren et al, 2021) are being developed, this study can provide a benchmark for new and improved peatland maps of the northern boreal zone at a nationwide scale. This can bridge an important research gap between global-scale mapping using satellites on a coarse scale, and detailed field-scale mapping (Minasny et al, 2019).…”

Section: The Novelty Of the Developed Mapsmentioning

confidence: 99%

Delineating the distribution of mineral and peat soils at the landscape scale in northern boreal regions

Ågren

Hasselquist

Stendahl

et al. 2022

SOIL

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Abstract. To meet the sustainable development goals and enable sustainable management and protection of peatlands, there is a strong need for improving the mapping of peatlands. Here we present a novel approach to identify peat soils based on a high-resolution digital soil moisture map that was produced by combining airborne laser scanning-derived terrain indices and machine learning to model soil moisture at 2 m spatial resolution across the Swedish landscape. As soil moisture is a key factor in peat formation, we fitted an empirical relationship between the thickness of the organic layer (measured at 5479 soil plots across the country) and the continuous SLU (Swedish University of Agricultural Science) soil moisture map (R2= 0.66, p < 0.001). We generated categorical maps of peat occurrence using three different definitions of peat (30, 40, and 50 cm thickness of the organic layer) and a continuous map of organic layer thickness. The predicted peat maps had a higher overall quality (MCC = 0.69–0.73) compared to traditional Quaternary deposits maps (MCC = 0.65) and topographical maps (MCC = 0.61) and captured the peatlands with a recall of ca. 80 % compared to 50 %–70 % on the traditional maps. The predicted peat maps identified more peatland area than previous maps, and the areal coverage estimates fell within the same order as upscaling estimates from national field surveys. Our method was able to identify smaller peatlands resulting in more accurate maps of peat soils, which was not restricted to only large peatlands that can be visually detected from aerial imagery – the historical approach of mapping. We also provided a continuous map of the organic layer, which ranged 6–88 cm organic layer thickness, with an R2 of 0.67 and RMSE (root mean square error) of 19 cm. The continuous map exhibits a smooth transition of organic layers from mineral soil to peat soils and likely provides a more natural representation of the distribution of soils. The continuous map also provides an intuitive uncertainty estimate in the delineation of peat soils, critically useful for sustainable spatial planning, e.g., greenhouse gas or biodiversity inventories and landscape ecological research.

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Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals

Cited by 12 publications

References 34 publications

GIS-based decision support systems to minimise soil impacts in logging operations

GIS-based decision support systems to minimise soil impacts in logging operations

Investigating the Potential of Downscaling Approaches for SMAP Radiometer Soil Moisture in Agroforestry Areas, China

Delineating the distribution of mineral and peat soils at the landscape scale in northern boreal regions

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