Soil Surface Roughness Research Articles

Simultaneously estimating surface soil moisture and roughness of bare soils by combining optical and radar data

Both radar and optical signals are sensitive to the change of surface soil moisture (SSM) and surface roughness properties (such as root mean squared height- RMSH), and the accuracy of retrieved SSM from single radar and optical remote sensing data is influenced by the spatiotemporal change of surface roughness. Here, we attempt to explore a method to simultaneously estimate SSM and RMSH of bare soil by combining optical and radar data, so as to weaken the effect of surface roughness on SSM inversion results. To achieve this goal, two satellite synchronous ground experiments were carried out, collecting 88 sampling plots each with an area of 50 m × 50 m. Radar backscattering coefficient and spectral reflectance are uniformly corrected to a fixed observation direction and solar incident direction respectively, which can eliminate the difference of satellite signal resulted from various sun-satellite geometry. Combining radar backscattering and optical reflectance model, Sentinel-1 and Sentinel-2 data are used to simultaneously retrieve SSM and RMSH of bared soils, and some conclusions are given as below: 1) a strong correlation is observed for (radar and optical) satellite signals and soil surface parameters (SSM and RMSH); 2) a higher accuracy was obtained by the combined use of optical and radar data, indicated by the decreased root mean squared error of retrieved SSM (~0.045 cm3/cm3) and RMSH (~0.8 cm); 3) the further improvement of retrieved SSM and RMSH was achieved by introducing their initial values, revealing that the prior knowledge of soil properties is also beneficial to improve the retrieval accuracy. This study proposed an framework for simultaneous estimation of SSM and RMSH by combining optical and radar data, and its feasibility is verified by experimental data.

Machine learning inversion approach for soil parameters estimation over vegetated agricultural areas using a combination of water cloud model and calibrated integral equation model

Estimating volumetric soil moisture (Mv) and surface roughness (S) are the key parameters for numerous agricultural and hydrological applications. Although these two parameters can be effectively retrieved from synthetic aperture radar (SAR) data, the presence of vegetation can negatively affect the results. A method was proposed to accurately estimate Mv and S over vegetated agricultural areas. The method was based on applying the machine learning inversion approach along with SAR data to invert a combination of the parameterized water cloud model (PWCM) and the calibrated integral equation model (CIEM). The soil backscattered component in water cloud model (WCM) was generated by CIEM to be applied to the WCM parameterization and dataset simulation. Three machine learning algorithms, including the support vector regression (SVR), multi-output SVR (MSVR), and artificial neural network (ANN), were employed to model the relationship between the simulated dataset variables. The genetic algorithm was also applied to optimize the models’ parameters. The inversion technique results demonstrated that the MSVR and ANN had the highest accuracy in estimating Mv and S due to their better structures. The SMAPVEX-16 in situ dataset, along with three Sentinel-1 images, was applied to evaluate the accuracy of the WCM parameterization and the proposed method for Mv and S estimation. The accuracies of the PWCM in the VV and VH polarizations of Sentinel-1 C-band data were reasonable for VWC < 2.5 kg / m2 [root-mean-square error (RMSE) = 1.44 and 1.77 dB, respectively]. Additionally, it was observed that the trained SVR, MSVR, and ANN had similar results for different VWC values. In summary, the proposed method had high potential in vegetated agricultural areas with VWC < 2.5 kg / m2, for which the RMSEs were 4 to 7 vol. % and 0.35 to 0.46 cm depending on the VWC values in retrieving Mv and S, respectively.

Towards Mapping of Soil Crust Using Multispectral Imaging.

Soil crusts and surface roughness are properties which are highly dynamic in both space and time that change in response to biotic processes, meteorological conditions and farming operations. These factors, however, are difficult to quantify and are usually described using simplified expert-based classes. This hampers a clear identification of the controlling factors and their relation to soil erosion and sediment generation processes. The availability of new small portable multispectral cameras offers the potential to study soil surface dynamics at a high spatial and temporal resolution. The objective of this study was to analyse the relationship between soil crusting, represented by cumulative rainfall kinetic energy, and soil surface reflectance, as derived from vis-NIR multispectral imaging. We designed a series of rainfall-soil surface experiments to disentangle the effects of soil crusting on spectral reflectance factors from those related to surface micro-scale roughness. Partial least squared regression (PLSR) models were developed to predict both kinetic energy and roughness from multispectral images. We evaluated different roughness removal methods which were based on the transformation of reflectance through standard normal variate (SNV) and roughness thresholding using high resolution digital elevation models. Furthermore, we assigned the light scattering effect related to roughness in the multispectral spatial domain by calculating the inter-quantile range of the reflectance values in a kernel. Our experiments and workflow demonstrate that it is possible to model crust development, using rainfall kinetic energy as a proxy, from vis-NIR based multispectral imaging.

Evolution of surface roughness of single sand grains with normal loading

The surfaces of soil grains are not perfectly smooth, especially examined at small scale. In geotechnical engineering, surface roughness has been found to be able to influence the inter-particle friction angle at micro scale and small-strain stiffness at macro scale. However, the quantity and quality of the studies on surface roughness of natural soils are still limited. In this study, the evolution of surface roughness of natural sand grains with increasing normal load was investigated by a single-particle compression apparatus. Thirty Leighton Buzzard sand (LBS) grains coarser than 2·36 mm were tested, and the surface roughness was measured before and after compression by an optical interferometer. The deformations of the asperities and of the bulk of the sand grains in the vicinity of the contact were mapped. Three stages were identified as the normal load increased: (a) plastic deformation of the asperities; (b) asperities and bulk plastic deformation; and (c) bulk only plastic deformation. At very small normal load, only the asperities were found to deform plastically, and the surface roughness of the sand grains decreases due to the flattening of the asperities. Within this regime, the load–displacement relationship of LBS grains under compression could be simulated by the modified Hertz model, which takes surface roughness into consideration. With increasing normal load, the bulk of the sand grains began to yield near the contact. The geometry of the surfaces of LBS grains in contact with the loading platen is the main factor that influences the plastic deformation of the bulk. Differently from the plastic deformation of the asperities, the plastic deformation of the bulk could both smoothen and roughen the surfaces. When plastic deformation of the bulk occurred, both Hertz and modified Hertz theory could not predict the load and displacement relationship of sand grains. Through analysing the cumulative distributions of surface roughness of 30 LBS grains at different normal loads by the Weibull function, the surface roughness was found to decrease dramatically with increasing normal load at first and then tended to be constant.

Quantifying Shallow Overland Flow Patterns Under Laboratory Simulations Using Thermal and LiDAR Imagery

AbstractDesertification processes pose a global environmental threat, impacting 61 × 106 km2 of the terrestrial land area. Changes in overland flow patterns and consequent rainwater redistribution in drylands present a potential pathway to desertification, because vegetation often relies on water inputs from runoff to sustain growth under insufficient rainfall conditions. Of particular importance are the very shallow overland flows that redistribute water, nutrients, and biological matter within arid landscapes. However, characterizing overland flow patterns remains challenging, due to their very shallow depths, their distributed nature, and the poor understanding of how these flows interact with the underlying rough soil surface. This paper describes how coupling thermal images of shallow overland flows with light detection and ranging (LiDAR) scanning of the underlying soil surface in 1 m2 experimental trays allows spatial patterns of shallow overland flow to be quantified. Laboratory experiments were used to explore the behaviors of shallow overland flow as mean slope gradients and soil roughness were varied. The results show that these imaging techniques are able to capture differences in flow patterns arising across soil surfaces with varying slope, roughness, and spatial variation in infiltration properties. Several spatial indices characterizing overland flow patterns were found to correlate with runoff volume. The presence of high permeability soil patches substantially regulated overland flow. A next logical step would be to apply the thermal and LiDAR measurement techniques to the hillslope scale.

Validation and analysis of Terra and Aqua MODIS, and SNPP VIIRS vegetation indices under zero vegetation conditions: A case study using Railroad Valley Playa

Spectral vegetation index (VI) time series data from coarse resolution satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), have been utilized in studying vegetation dynamics. Numerous studies have evaluated how well VI products capture variations in vegetation biophysical or physiological conditions. Equally important is to evaluate VI products over “zero vegetation” surfaces consisting of soils, litters, and/or rocks, as they define the lower bound for vegetation detection. VIs, however, vary over zero vegetation surfaces as a function of soil moisture content and surface roughness. In this study, we evaluated the behavior of VIs from Terra MODIS (T-MODIS), Aqua MODIS (A-MODIS), and Suomi-National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (S-VIIRS) at Railroad Valley Playa, Nevada for a period from April 2013 to September 2019. The playa is a dried lakebed devoid of vegetation throughout the year. Long-term in situ reflectance measurements acquired over the 1 km-by−1 km Radiometric Calibration Test Site (RadCaTS) located on the playa were obtained from the Radiometric Calibration Network (RadCalNet) portal and used as a reference. Three VIs were analyzed, including the normalized difference VI (NDVI), enhanced VI (EVI), and two-band EVI (EVI2). RadCaTS NDVI, EVI, and EVI2 of the playa surface increased and decreased occasionally for the time period examined in this study, and the satellite NDVIs, EVIs, and EVI2s had comparable temporal signatures to the RadCaTS counterparts. T-MODIS and A-MODIS NDVI and EVI2 values were comparable to the RadCaTS counterparts, whereas T-MODIS and A-MODIS EVI values were lower than the RadCaTS counterparts by ~0.006 and ~ 0.01 EVI units, respectively. All the three VIs of S-VIIRS were consistently higher than their RadCaTS counterparts by ~0.008 VI units, due to the higher near-infrared (NIR) reflectances of S-VIIRS than the RadCaTS NIR reflectance. The red and NIR, and red and blue reflectances each formed linear relationships (i.e., soil lines) for each of the three sensors. Variations in reflectance due to surface conditions and observation geometries all appeared as variations along these soil lines. The satellite red-NIR soil lines were comparable to the RadCaTS counterparts, whereas the satellite red-blue soil lines had steeper slopes than the RadCaTS counterparts due to a negative bias in the satellite blue reflectances. This translated into the T-MODIS and A-MODIS EVI behaviors different from those depicted by RadCaTS EVI, and the satellite NDVI and EVI2 behaving more comparably with the RadCaTS counterparts and across the three sensors than the satellite EVI.

Potential and Accurate Evaluation of Unmanned Aerial Vehicle Remote Sensing for Soil Surface Roughness Measurement

Soil surface roughness (SSR) plays an important role in the physical and hydrological processes of soil surfaces. In order to achieve nondestructive, fast, and large area measurement of SSR, unmanned aerial vehicle (UAV) photogrammetry method was used to take digital images at the altitude of 10 m on three plots and generated the digital elevation model for calculating SSR. From the results of UAV-based SSR, the following conclusions were obtained. First, the domain of soil surface height was consistent with the designed height of the three plots: smooth (all pixels: −5.5–6.5 cm and 80% pixels: −2.3–2.3 cm) < medium (all pixels: −8.5–8.5 cm and 80% pixels: −3.4–3.4 cm) < rough (all pixels: −16.0–13.0 cm and 80% pixels: −6.8–6.8 cm). Second, UAV-based SSR can represent their differences among the three plots, indicated by a consistent root-mean-square height (rmsh) and correlation length (cl) with the pin-profiler results. Third, UAV-based SSR results can reveal the anisotropy of SSR, and for the medium plot, the maximum variation of rmsh and cl with observed azimuth angle is 0.77 cm and 14.35 cm, respectively. The UAV-based SSR method has the advantages of low cost, high efficiency, and all-directional measurement, and can be used in remote sensing model and hydrological simulation.

Measuring Soil Surface Roughness with the RealSense D435i

Measuring Soil Surface Roughness with the RealSense D435i

EFFECT OF SOIL MOISTURE AND PULVERIZATION IMPLEMENTS ON TILLAGE APPEARANCE AND SOIL PROPERTIES

The experiment was conducted at the fields of Agriculture College-University of Baghdad Al- Jadiriyah in 2015 in sandy loam soil in order to evaluate the effect of soil moisture level and pulverization implement on tillage appearance and some soil physical properties. Two levels of soil moisture included fist moisture level 14-16% and second moisture level 18-20% and pulverization implements included: disc harrow, Spring Spike tooth harrow and rotary harrow were used. Soil surface roughness, number of clods with diameter larger than 5 cm/m2, disturbed soil volume, and soil total porosity were measured. Split plot design under randomized complete block design (RCBD) with three replicates was used. The results were showed that Rotary harrow and 14-16% moisture level superior in obtained the best soil surface roughness, less number of clods &gt; 5cm/m2 which was 7.56 clod/m2, while Spring Spike tooth Harrow and 18-20% moisture level was superior in record highest disturbed soil volume which was 761.56 m3/ hr and soil porosity of 55.78 %. Using locally manufactured ultrasonic device for measuring soil surface roughness was done successfully in this experiment with high performance efficiency.

Surface Soil Moisture Retrievals Under Forest Canopy for $L$-Band SAR Observations Across a Wide Range of Incidence Angles by Inverting a Physical Scattering Model

Surface soil moisture retrievals were performed by inverting physical scattering models for forests over $\text{30}^\circ$ to $\text{50}^\circ$ incidence angle range and 0.05 to 0.40 m $^3$ /m $^3$ soil moisture range using $L$ -band airborne synthetic aperture radar (SAR) data during a 28-day period. The forward models implemented single-scattering of discrete elements of trees and were validated at $F$ 2 site within 1.5 dB rmse of observation for VV-pol, which was enabled by introducing the gaps between trees. The physical forward models were inverted using the time-series SAR data to retrieve soil moisture and soil surface roughness, which were validated with in situ data at four sites $F$ 1, $F$ 2, $F$ 3, and $F$ 5. Retrievals using VV input over the full dynamic ranges of wetness are accurate to 0.044 m $^3$ /m $^3$ unbiased rmse with correlations of 0.71 to 0.84, which is very encouraging for retrieval under forest canopy. The conditions of these results are the vegetation water content varied from 7.3 to 25.6 kg/m $^2$ and the sensitivity of VV to soil moisture changes ranged from 0.64 to 2.27 dB/(0.1 m $^3$ /m $^3$ ). When both HH and VV were used as inputs to retrieval, the performance did not improve because the benefit of the multichannels was offset by the larger uncertainties in HH modeling. Due to the short duration of in situ data, the efficacy of commonly used relative change index retrieval is diminished. In comparison, the inversion of the scattering model is found to be an effective way to estimate forest soil moisture, it is capable of systematic correction of vegetation effect, and offers accurate retrieval of the dynamic ranges of soil moisture values in terms of unbiased rmse. The retrieval with the physical forward model provides a first step toward global application for the future NISAR satellite.

Effects of water tension and surface roughness on soil hyperspectral reflectance

Hyperspectral imaging techniques have been successfully employed to characterize soil chemical and physical parameters. However, such approaches can lack utility due to noise sources such as soil moisture (SM) and soil surface roughness (SSR). Moreover, studies dealing with SSR and SM lack comparability and standardized methods. To quantify these noise sources, reflectance patterns of different SM and SSR were measured in the visible-near (VNIR, 350–1000 nm), short-wave infra-red (SWIR, 1000–2500 nm) and longwave infrared (LWIR, 7500–13000 nm). For SSR analysis, three defined particle sizes were analyzed to represent different roughness states. For SM analysis, water tension (WT) was used to achieve a homogenous water pressure in the sample. At different WT stages changes in SSR were observed macro- and microscopically. We show that SSR and WT both regulate reflectance, in varying amounts. For both SSR and WT texture plays an important role. Different SSRs change the reflectance intensity. Hereby, quantitative analysis of soil components is not affected. For different WT, the surface roughness did not change visibly, but a formation of a microscopically fine-grained surface layer was observed. High WT (~pF 4.2) have shown to change reflectance and spectral feature geometry considerably. A correlation between WT and reflectance/relative band depth becomes apparent. WT thus influences characteristic spectral signals of soil constituents. For retrieving soil properties by remote sensing techniques, SSR, relatively speaking, is of minor importance. In this context, the WT of investigated samples is recommended to be addressed to avoid its impact on the estimation of soil properties.

Quantitative Assessment of Satellite L-Band Vegetation Optical Depth in the U.S. Corn Belt

Satellite L-band vegetation optical depth (L-VOD) contains new information about terrestrial ecosystems. However, it has not been evaluated against the geophysical variable that it represents, plant water, the mass of liquid water contained within vegetation tissue per ground area. We quantitatively assess the seasonal variation of three L-VOD products at the South Fork Core Validation Site in the Corn Belt state of Iowa where L-VOD is directly proportional to crop plant water. We use three satellite-scale crop plant water estimates: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements; a normalized difference water index (NDWI) calibrated with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements; and a crop model. We find that overall the L-VOD satellite products are 0.02–0.09 Np (0.4– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${1.7} \,\,\text {kg} \cdot \text {m}^{-2}$ </tex-math></inline-formula> ) lower than the three estimates. We show that overestimation of L-VOD can be attributed to dynamic soil surface roughness, and hypothesize that crop plant water observations will require the incorporation of this effect into retrieval algorithms.

Optimizing the observation time for bare arable land to determine mean diurnal albedo in relation to roughness

ABSTRACT It is assumed that the approximation of the albedo values of the Earth’s surface components using satellite data obtained at the optimal time (T o) when these areas’ albedo reach mean diurnal values (α d), rather than these instantaneous values, allows Earth’s overall climate change to be modelled with greater accuracy. The T o values were investigated using examples of arable land areas in the 33 most extensive agricultural regions of the world, on given days of the year when the largest sections of these land areas become bare after major crops were planted under conventional tillage. This study is based on processed data obtained from nearly 900 soil reflectance spectra, representing 24 major soil groupings on 6 continents, assuming that these soils, which were either shaped by a plough (Pd) or smoothing harrow (Hs), were air-dried. It was found that in the morning the T o, expressed in solar local time, was expected to be earlier for rough bare soil surfaces than smooth ones. In the afternoon this relationship was reversed. The differences in the T o values that resulted from the different surface roughness of the same soils (formed by Pd and Hs) were 5–10 minutes. Deviations of about 5–20 minutes from T o in the observations of the studied land areas generated errors of ±2% in the approximations of their α d values.

A Semiempirical Modeling of Soil Moisture, Vegetation, and Surface Roughness Impact on CYGNSS Reflectometry Data

Data from the Cyclone Global Navigation Satellite System (CYGNSS) mission augmented with a physical surface scattering model were analyzed to develop a semiempirical model, which consists of three main modeling components for soil moisture, vegetation, and surface roughness. CYGNSS data collected from March 2017 to March 2020 were collocated with the soil moisture data from the Soil Moisture Active Passive (SMAP) mission and climatology vegetation water content (VWC) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data. The matchup data were binned as a function of soil moisture, VWC, and incidence angle. The CYGNSS data were calibrated using a coherent reflection equation to obtain an effective reflectivity. The response of CYGNSS effective reflectivity to soil moisture changes is consistent with the change of the Fresnel reflectivity based on Mironov’s soil dielectric constant model used by the SMAP and Soil Moisture Ocean Salinity (SMOS) missions for soil moisture retrieval. The CYGNSS effective reflectivity decreases approximately linearly (in dB) with respect to the NDVI-VWC. The estimated values of vegetation attenuation parameter ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula> ) agree with values published in the literature and are corroborated with the estimated values of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula> using the SMAP dual-polarized channel algorithm based on land cover types. A CYGNSS surface scattering map has been derived and reveals a mixed contribution of coherent and incoherent scattering effects and the effects of topography. The semiempirical model, leveraging two of the key modeling functions used by microwave radiometry, will pave the way for a synergistic use of reflectometry and radiometry data for multiparameter retrieval and development of consistent soil moisture products.

Tillage-induced surface soil roughness controls the chemistry and physics of eroded particles at early erosion stage

Intensive row crop agriculture on loess mantled hillslopes in the upper Midwest, USA, accelerates soil erosion and the loss of organic matter by increasing soil aggregate breakdown, changing surface roughness patterns, and leaving soil exposed to rainfall and wind for much of the year. There is a knowledge gap, however, in relating how the dynamics of soil erosion on hillslopes control landscape heterogeneity in terms of particle size, surface soil organic chemistry and reactivity. Simulated rainfall experiments on intensively managed hillslopes in the Clear Creek, IA watershed, USA, revealed that tillage-induced, oriented roughness controls the amount and organic geochemistry of soil particles mobilized downhill. The average discharge rate of mobilized sediment was nearly 30-fold higher when the tillage orientation was parallel to the runoff direction compared to when the tillage orientation (i.e. contour tillage) was perpendicular to the runoff direction. Mobilized particles under contour tillage were higher in organic carbon (OC) content and were relatively less decomposed, based on lignin chemistry, compared to analogous particles under the parallel tillage orientation. Contour tillage increased deposition of denser particles within the furrows and increased the release of the finer, lighter, and organic-rich material during over-spilling and contour breach. Our findings have important implications for understanding how the interaction of tillage practices and event-based erosion influences the spatial heterogeneity of soil geochemistry and potentially soil health across agricultural landscapes as well as understanding what controls the geochemical signature of soil erosion into streams.

Simultaneous Retrieval of Surface Roughness Parameters for Bare Soils From Combined Active–Passive Microwave SMAP Observations

An active–passive microwave retrieval algorithm for simultaneous determination of soil surface roughness parameters [vertical root-mean-square (RMS) height ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${s}$ </tex-math></inline-formula> ) and horizontal correlation length ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${l}$ </tex-math></inline-formula> )] is presented for bare soils. The algorithm is based on active–passive microwave covariation, including the improved Integral Equation Method (I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> EM), and is tested with global soil moisture active passive (SMAP) observations. The estimated retrieval results for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${s}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${l}$ </tex-math></inline-formula> are overall consistent with values in the literature, indicating the validity of the proposed algorithm. Sensitivity analyses showed that the developed roughness retrieval algorithm is independent of permittivity for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\varepsilon }_{s} &gt; 10$ </tex-math></inline-formula> [-]. Furthermore, the physical model basis of this approach (I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> EM) allows the application of different autocorrelation functions (ACF), such as Gaussian and exponential ACFs. Global roughness retrieval results confirm bare areas in deserts such as Sahara or Gobi. However, the type of ACF used within roughness parameter estimation is important. Retrieval results for the Gaussian ACF describe a rougher surface than retrieval results for the exponential ACF. No correlations were found between roughness results and the amount of precipitation or the soil texture, which could be due to the coarse spatial resolution of the SMAP data. The extension of this approach to vegetated soils is planned as an add-on study.

Calibrated Integral Equation Model for Bare Soil Moisture Retrieval of Synthetic Aperture Radar: A Case Study in Linze County

Soil moisture plays a significant role in surface energy balance and material exchange. Synthetic aperture radar (SAR) provides a promising data source to monitor soil moisture. However, soil surface roughness is a key difficulty in bare soil moisture retrieval. To reduce the measurement error of the correlation length and improve the inversion accuracy, we used the surface roughness (Hrms, root mean surface height) and empirical correlation length lopt as proposed by Baghdadi to introduce analytical equations of the backscattering coefficient using the calibrated integral equation model (CIEM). This empirical model was developed based on analytical equations to invert soil moisture for Hrms between 0.5 and 4 cm. Experimental results demonstrated that when the incidence angle varied from 33.5° to 26.3°, R2 of the retrieved and measured soil moisture decreased from 0.67 to 0.57, and RMSE increased from 2.53% to 5.4%. Similarly, when the incidence angle varied from 33.5° to 26.3°, R2 of the retrieved and measured Hrms decreased from 0.64 to 0.51, and RMSE increased from 0.33 to 0.4 cm. Therefore, it is feasible to use the empirical model to invert soil moisture and surface roughness for bare soils. In the inversion of the soil moisture and Hrms, using Hrms and the empirical correlation length lopt as the roughness parameters in the simulations is sufficient. The empirical model has favorable validity when the incidence angle is set to 33.5° and 26.3° at the C-band.

Monitoring soil surface roughness under growing winter wheat with low‐altitude UAV sensing: Potential and limitations

AbstractSoil surface roughness (SSR) is an important factor in controlling sediment and runoff generation, influencing directly a wide spectrum of erosion parameters. SSR is highly variable in time and space under natural conditions, and characterizing SSR to improve the parameterization of hydrological and erosion models has proved challenging. Our study uses recent technological and algorithmic developments in capturing and processing close aerial sensing data to evaluate how high‐resolution imagery can assist the temporally and spatially explicit monitoring of SSR. We evaluated the evolution of SSR under natural rainfall and growing vegetation conditions on two arable fields in Denmark. Unmanned aerial vehicle (UAV) photogrammetry was used to monitor small field plots over 7 months after seeding of winter wheat following conventional and reduced tillage treatments. Field campaigns were conducted at least once a month from October until April, resulting in nine time steps of data acquisition. Structure from motion photogrammetry was used to derive high‐resolution point clouds with an average ground sampling distance of 2.7 mm and a mean ground control point accuracy of 1.8 mm. A comprehensive workflow was developed to process the point clouds, including the detection of vegetation and the removal of vegetation‐induced point cloud noise. Rasterized and filtered point clouds were then used to determine SSR geostatistically as the standard deviation of height, applying different kernel sizes and using semivariograms. The results showed an influence of kernel size on roughness, with a value range of 0.2–1 cm of average height deviation during the monitoring period. Semivariograms showed a measurable decrease in sill variance and an increase in range over time. This research demonstrated multiple challenges to measuring SSR with UAV under natural conditions with increasing vegetation cover. The proposed workflow represents a step forward in tackling those challenges and provides a knowledge base for future research. © 2020 John Wiley &amp; Sons, Ltd.

Global Soil Moisture Retrievals From the Chinese FY-3D Microwave Radiation Imager

The FengYun-3 (FY-3) series satellite is the second generation of Chinese polar-orbiting meteorological satellite missions. The FY-3D satellite was launched on November 2017 and has been providing valuable data for meteorological applications, including brightness temperature ( T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> ) data from the MicroWave Radiation Imager (MWRI). In this study, we developed a global soil moisture retrieval algorithm, based on the radiative transfer equation (RTE) for using the FY-3D MWRI T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> to continue the soil moisture record from FY-3 satellites. We adopted a new empirical model to compute vegetation water content (VWC) based on the leaf area index (LAI) and canopy height ( H) for vegetation effects correction. The Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> model, which addresses the soil surface roughness effects using dual-polarization information, is then used for soil moisture retrieval. Validation of the FY-3D soil moisture was conducted with the in-situ data and the validation results showed encouraging accuracy over a variety of landcovers, with bias and unbiased root-mean-squared difference (ubRMSE) at or below the level of 0.06 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ·m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> . Monthly averaged soil moisture products generated from FY-3D could represent the seasonal changes in soil moisture and show reasonable spatial distribution of soil moisture at a global scale.

土壤表面粗糙度数据采集及分形特性研究

It is of great significance to acquire the soil surface roughness accurately for the study of the interaction between tractors and soil. Based on the laser sensor, this paper proposed the non-contact measuring instrument of the soil surface roughness with the data acquiring system by using Lab-View software. By using W-M theory, three commonly used fractal dimension calculation methods are compared and analyzed.. The result showed that the Root-mean-square method has the highest accuracy and clear physical meaning, which is ideal method to calculate the soil surface roughness characteristics. When the fractal dimension is between 1.4 and 1.6, the acquired data is analysed by the Root-mean-square method to obtain the fractal features of the soil surface roughness. The experiment results indicated that the fractal dimension of the ploughed surface is 1.39, that of disc harrow surface is 1.550, and that of rolled surface is 1.46-1.54. Obviously, the fractal dimension can accurately distinguish the soil surface roughness with the different treatments. However, the fractal dimension selected from different scales showed an obvious instability during calculations. The surface roughness index combined with the two parameters can effectively represent the soil surface roughness, and the larger the surface roughness index is, the greater the surface roughness is.