Comparison of different soil dielectric models for microwave soil moisture retrievals

ABSTRACT

Soil dielectric model is an essential part of the microwave soil moisture retrieval process. This study compared the performance of four widely used dielectric models, the Wang-Schmugge model (WS), Dobson model, generalized refractive mixing dielectric model (GRMDM), and multi-relaxation generalized refractive mixing dielectric model (MRGRMDM), and investigated the effects of the uncertainties of each model on soil moisture retrievals. Furthermore, the simulated soil dielectric constants were evaluated by measured dielectric data at the P/L/C/X bands. The results showed that the uncertainties induced in soil moisture retrievals by an alternative dielectric model exceeded 0.09 m³ m^?3 in the worst case. The Dobson model is sensitive to the sand content. WS, GRMDM, and MRGRMDM model are sensitive to the clay content. The measured dielectric data further verified that the applicability of each dielectric model depends on the soil texture type and soil moisture condition. Compared with Dobson model, WS showed better performance at dry soil. GRMDM and MRGRMDM provided better results under lower clay content soil. Especially, MRGRMDM has better simulation accuracy than GRMDM in the low-frequency range (< 1 GHz).     

Influence of soil moisture content on spectral reflectance of bare soils in the 0.4–14 μm domain

A new semi-empirical soil model simulating the spectral signatures of bare soils in the optical domain 0.4–14 μm according to surface moisture content variation is presented and applied to several databases. The model specification is based on laboratory spectral reflectance measurements of many bare soils at different moisture contents. The measurement analysis leads to the definition of groups of bare soil samples according to their spectral behaviours. These laboratory measurements are made also to characterize the impact of soil moisture on spectral signatures (reflectance levels increasing with moisture content) and to give information on absorption peaks related to soil mineral components (hydroxyl, carbonate, and quartz). The procedure of modelling the spectral signatures of bare soil groups according to moisture content is discussed. The model is applied to a laboratory reflectance database and to the data available in the literature. The spectral reflectances, estimated with a semi-empirical model, compare favourably with reflectance observations.     

Technical note Remote sensing of soil moisture content over bare soil at microwave frequencies

Abstract

Results of radiometric measurements over bare soil obtained with horizontally polarized microwave radiometers at 1·55 and 19·1 GHz are presented. The observed normalized brightness temperatures were used to estimate the soil moisture content using the radiative transfer model. It is found that the r.m.s. difference between observed and estimated soil moisture content is comparable to the standard deviation found in ground measurement of soil moisture content.     

Water content estimation in vegetation with MODIS reflectance data and model inversion methods

Statistical and radiative-transfer physically based studies have previously demonstrated the relationship between leaf water content and leaf-level reflectance in the near-infrared spectral region. The successful scaling up of such methods to the canopy level requires modeling the effect of canopy structure and viewing geometry on reflectance bands and optical indices used for estimation of water content, such as normalized difference water index (NDWI), simple ratio water index (SRWI) and plant water index (PWI). This study conducts a radiative transfer simulation, linking leaf and canopy models, to study the effects of leaf structure, dry matter content, leaf area index (LAI), and the viewing geometry, on the estimation of leaf equivalent water thickness from canopy-level reflectance. The applicability of radiative transfer model inversion methods to MODIS is studied, investigating its spectral capability for water content estimation. A modeling study is conducted, simulating leaf and canopy MODIS-equivalent synthetic spectra with random input variables to test different inversion assumptions. A field sampling campaign to assess the investigated simulation methods was undertaken for analysis of leaf water content from leaf samples in 10 study sites of chaparral vegetation in California, USA, between March and September 2000. MODIS reflectance data were processed from the same period for equivalent water thickness estimation by model inversion linking the PROSPECT leaf model and SAILH canopy reflectance model. MODIS reflectance data, viewing geometry values, and LAI were used as inputs in the model inversion for estimation of leaf equivalent water thickness, dry matter, and leaf structure. Results showed good correlation between the time series of MODIS-estimated equivalent water thickness and ground measured leaf fuel moisture (LFM) content (r²=0.7), demonstrating that these inversion methods could potentially be used for global monitoring of leaf water content in vegetation.     

Experimental data for separation of vegetation and soil and estimation of soil moisture using passive microwaves

Abstract

Microwave radiometer measurements of soil moisture content were made over bare and vegetated fields with dual polarized microwave radiometers at 1·55GHz (L-band) and 19·1 GHz (K.-band). Two typical Indian crops Bazra and Gawar have been studied. The bare field measurements were used to investigate the effect of soil texture on sensitivity of a radiometer to soil moisture and for soil moisture sampling depth. It is found that expression of soil moisture as available moisture content in the soil can minimize the texture effect. The estimated soil moisture sampling depth for L-band is 2-5 cm, while for K-band it is less than 2 cm. The vegetation cover affects the sensitivity of the radiometer to soil moisture. This effect is more pronounced the denser the vegetation and higher the frequency of observation. The measured polarization factor over a vegetated field at L-band was found to be appreciably reduced compared to that over a bare field. The difference between normalized brightness temperature from L-band and K-band is sensitive to vegetation type. The soil moisture under vegetation cover at L-band can be predicted well using Jackson's parametric model.     

Soil adjusted vegetation water content retrievals in grasslands

Soil contamination of canopy reflectance over grasslands can cause errors in empirical vegetation water content (VWC) retrievals using the NDII (Normalized Difference Infrared Index, [ρ_0.86?ρ_1.64]/[ρ_0.86+ρ_1.64]). Minimization of soil contamination by NDII relies on the existence of a quasi straight soil line and quasi straight VWC isolines (lines of equal VWC) in the 1.64–0.86 µm reflectance space. Further the VWC isolines are expected to meet at the origin of the 1.64–0.86 µm reflectance space. Considering soil moisture as the primary determinant of soil reflectance variation at a given location, this study investigates the effect of soil moisture on the nature of soil lines and VWC isolines under grassland conditions. Reflectance simulations from coupled soil‐leaf‐canopy reflectance models under grassland conditions show that soil lines and VWC isolines are expected to be curved and may not converge at the origin. This behaviour is attributed to disproportionate soil moisture related absorption processes operating at 1.64 µm and 0.86 µm. A new technique that accounts for these inconsistencies in NDII assumptions is proposed for VWC retrievals. The technique consists of using separate regression relationships between VWC and a Soil Adjusted NDII (SANDII) based on the volumetric soil moisture category of the background. SANDII, based on the idea borrowed from the Soil Adjusted Vegetation Index (SAVI) is an origin shifted transformation of NDII. The optimum origin that reduces VWC retrieval errors is shown to be soil moisture category specific. The proposed technique requires categorical soil moisture information in order to decide which regression relationship to apply for VWC retrievals. Climatology, meteorological models or microwave observations are expected to be reliable resources for such categorical soil moisture information. Evaluations of the proposed technique using simulated reflectances showed that absolute errors in VWC retrievals were reduced by an average 20% as compared to the traditional NDII regression method. Such improvements are expected to be significant for fire‐risk applications. Finally supporting evidence for the need of an origin translated NDII is provided using data collected over pastures during the Soil Moisture Experiment 2003 (SMEX03) field campaign.     

Effects of sub-pixel heterogeneity on the retrieval of soil moisture from passive microwave radiometry

A simple formulation relating the L-band microwave brightness temperature detected by a passive microwave radiometer to the near surface soil moisture was developed using MICRO-SWEAT, a coupled microwave emission model and soil-vegetation-atmosphere-transfer (SVAT) scheme. This simple model provides an ideal tool with which to explore the impact of sub-pixel heterogeneity on the retrieval of soil moisture from microwave brightness temperatures. In the case of a bare soil pixel, the relationship between apparent emissivity and surface soil moisture is approximately linear, with the clay content of the soil influencing just the intercept of this relationship. It is shown that there are no errors in the retrieved soil moisture from a bare soil pixel that is heterogeneous in soil moisture and texture. However, in the case of a vegetated pixel, the slope of the relationship between apparent emissivity and surface soil moisture decreases with increasing vegetation. Therefore for a pixel that is heterogeneous in vegetation and soil moisture, errors can be introduced into the retrieved soil moisture. Generally, under moderate conditions, the retrieved soil moisture is within 3% of the actual soil moisture. Examples illustrating this discussion use data collected during the Southern Great Plains '97 Experiment (SGP97).     

A review of: “The Earth's Surface Studied From Space”, Edited by S. G,UNGAR. (Oxford: Pergamon Press,Advances in Space Research,Vol. 5, No.5, 1985.) [Pp. 120.] Price £31·00.

Abstract

In the interpretation of measured reflectance data it is important to consider those surface radiation effects which make a significant contribution to the overall irradiation pattern. A model was constructed for furrowed bare soil surfaces which includes the direct cross-radiation effect between the surface elements. According to the model computations performed the direct cross-radiation plays a significant role in the measured, reflected signal intensity. The computational method developed is suitable for including the direct cross-radiation effect in surface radiation models in the optical region.     

Comparison of ERS-2 SAR and Landsat TM imagery for monitoring agricultural crop and soil conditions

Studies over the past 25 years have shown that measurements of surface reflectance and temperature (termed optical remote sensing) are useful for monitoring crop and soil conditions. Far less attention has been given to the use of radar imagery, even though synthetic aperture radar (SAR) systems have the advantages of cloud penetration, all-weather coverage, high spatial resolution, day/night acquisitions, and signal independence of the solar illumination angle. In this study, we obtained coincident optical and SAR images of an agricultural area to investigate the use of SAR imagery for farm management. The optical and SAR data were normalized to indices ranging from 0 to 1 based on the meteorological conditions and sun/sensor geometry for each date to allow temporal analysis. Using optical images to interpret the response of SAR backscatter (σ^o) to soil and plant conditions, we found that SAR σ^o was sensitive to variations in field tillage, surface soil moisture, vegetation density, and plant litter. In an investigation of the relation between SAR σ^o and soil surface roughness, the optical data were used for two purposes: (1) to filter the SAR images to eliminate fields with substantial vegetation cover and/or high surface soil moisture conditions, and (2) to evaluate the results of the investigation. For dry, bare soil fields, there was a significant correlation (r²=.67) between normalized SAR σ^o and near-infrared (NIR) reflectance, due to the sensitivity of both measurements to surface roughness. Recognizing the limitations of optical remote sensing data due to cloud interference and atmospheric attenuation, the findings of this study encourage further studies of SAR imagery for crop and soil assessment.     

Soil and Sun angle interactions on partial canopy spectra

Abstract

The spectral behaviour of an incomplete cotton canopy was analysed in relation to solar zenith angle and soil background variations. Soil and vegetation spectral contributions towards canopy response were separated using a first-order interactive model and consequently used to compare the relative sensitivity of canopy spectra to soil background and solar angle differences. Canopy reflectance behaviour with solar angle increased, decreased or remained invariant depending on the reflectance properties of the underlying soil. Sunlit and shaded soil contributions were found to alter vegetation index behaviour significantly over different Sun angles.     

The estimation of the surface moisture of a vegetated soil using aerial infrared photography

The bidirectional reflectance of near infrared wavelengths of electromagnetic radiation from a vegetation canopy is primarily determined by the relative area and reflectance of the canopy and canopy dependent components: leaves, non-green vegetation, soil and shadow. It has been shown that when the percentage cover of leaves and non-green vegetation are both known and constant and the effect of shadow is minimal, then the near infrared bidirectional reflectance from the-canopy is negatively related to surface soil moisture.

This study was based on the above observation to estimate surface soil moisture of a vegetated soil from remotely sensed measurements of near infrared bidirectional reflectance.

The near infrared bidirectional reflectance, surface soil moisture and vegetation cover were measured at 10 heathland sites on 18 dates. The surface soil moisture was significantly related (at better than the 1 per cent level) to the Y axis intercept, when near infrared bidirectional reflectance (Y) was regressed against the percentage cover of green vegetation (X). This relationship between soil moisture and canopy reflectance was then used to enable the surface soil moisture of vegetated heathland soil to be estimated by means of five flights of black and white infrared aerial photography. It proved possible to estimate the surface soil moisture of the vegetated soil with an accuracy of ±18·4 percent at the 95 percent confidence limits. Possible improvements to the technique are discussed.     

Visible and near — infrared reflectance of beach sands: A study on the spectral reflectance/ grain size relationship

The general theory which relates how the grain sizes of particulates influences spectral reflectance has been extended to account for such environmental factors as variable moisture content and iron staining. Utilizing regression analysis, a preliminary grain size predictor model has been developed from the visible and near-infrared reflectance of iron stained quartz beach samples. The model achieved a multiple correlation coefficient squared ($\text{R}{}^2$) of 0.950. It was found to be independent of moisture content and was in complete agreement with the theory. When samples from a non-iron stained beach were used, regression analysis failed to find any significant correlations. This failure can be partly attributed to the lack of an adequate grain size distribution for the non-iron stained samples collected.     

A MODIS-based perpendicular moisture index to retrieve leaf moisture content of forest canopies

Moisture dictates vegetation susceptibility to fire ignition and propagation. Various spectral indices have been proposed for the estimation of equivalent water thickness (EWT), which is defined as the mass of liquid water per unit of leaf surface. However, fire models use live fuel moisture content (LFMC) as a measure of vegetation moisture. LFMC is defined as the ratio of the mass of the liquid water in a leaf over the mass of dry matter, and traditional spectral indices are not as effective as with EWT in capturing LFMC variability. The aim of this research was to explore the potential of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra and Aqua satellites in retrieving LFMC from top of the canopy reflectance, and to develop a new spectral index sensitive to this parameter. All the analyses were based on synthetic canopy spectra constructed by coupling the PROSPECT (leaf optical properties model) and SAIL (Scattering by Arbitrarily Inclined Leaves) radiative transfer models. Simulated top of the canopy spectra were then convolved to MODIS ‘land’ channels 1–7 spectral response functions. All band pairs were evaluated to determine the subspace of MODIS measurements where the separability of points based on their value of LFMC was the highest. This led to the identification of isolines of LFMC in the plane defined by MODIS reflectance measurements in channels 2 and 5; the isolines are straight and parallel, and ordered from lower to higher values of LFMC. This observation allowed the construction of a novel spectral index that is directly related to LFMC – the perpendicular moisture index (PMI). This index measures the distance of a point in the plane spanned by reflectance measurements in MODIS channels 2 and 5 from a reference line, that of completely dry vegetation. Validation against simulated data showed that PMI exhibits a linear relationship with LFMC. When the vegetation cover is dense, the LFMC explains most of the variability in the PMI (R² = 0.70 when LAI > 2; R² = 0.87 when LAI > 4). When the LAI is lower, the contribution of soil background to the measured reflectance increases, and the index underestimates LFMC. The PMI was also validated against the LOPEX93 (Leaf Optical Properties Experiment 1993) data set of leaf optical and biophysical measurements, scaled to canopy reflectance with SAIL, showing acceptable results (R² = 0.56 when LAI > 2; R² = 0.63 when LAI > 4).     

Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semiarid environment

Two different configurations of a shortwave infrared water stress index (SIWSI) are derived from the MODIS near- and shortwave infrared data. A large absorption by leaf water occurs in the shortwave infrared wavelengths (SWIR) and the reflectance from plants thereby is negatively related to leaf water content. Two configurations of a water stress index, SIWSI(6,2) and SIWSI(5,2) are derived on a daily basis from the MODIS satellite data using the information from the near infrared (NIR) channel 2 (841-876 nm) and the shortwave infrared channel 5 (1230-1250 nm) or 6 (1628-1652 nm), respectively, which are wavelength bands at which leaf water content influence the radiometric response. The indices are compared to in situ top layer soil moisture measurements from the semiarid Senegal 2001 and 2002, serving as an indicator of canopy water content. The year 2001 rainfall in the region was slightly below average and the results show a strong correlation between SIWSI and soil moisture. The SIWSI(6,2) performs slightly better than the SIWSI(5,2) (r²=0.87 and 0.79). The fieldwork in 2002 did not verify the results found in 2001. However, year 2002 was an extremely dry year and the vegetation cover apparently was too sparse to provide information on the canopy water content. To test the robustness of the SIWSI findings in 2001, soil moisture has been modelled from daily rainfall data at 10 sites in the central and northern part of Senegal. The correlations between SIWSI and simulated soil moisture are generally high with a median r²=0.72 for both configurations of the SIWSI. It is therefore suggested that the combined information from the MODIS near- and shortwave infrared wavelengths is useful as an indicator of canopy water stress in the semiarid Sahelian environment.     

Sensitivity studies of the moisture effects on MODIS SWIR reflectance and vegetation water indices

The effects of soil moisture and leaf water content on canopy reflectance of MODIS shortwave infrared (SWIR) bands 5, 6, and 7 and water‐related indices are studied quantitatively using the coupled soil–leaf–canopy reflectance model. Canopy spectra simulations under various input conditions show that soil moisture has a large effect on each SWIR reflectance at low leaf area index (LAI) values, among which band 5 is the most sensitive to soil moisture variations, while band 7 responds strongest to dry soil conditions. Band 5 is also better suited to measure leaf water content change, since it obtains a higher variation when leaf water content changes from dry to wet. In general, each SWIR band responds to soil moisture and leaf water content differently. By using the normalized calculation between the water absorption‐sensitive band and insensitive band, the Normalized Difference Water Index shows the most capability to remove the soil background effect and enhance the sensitivity to leaf water content. These two moisture variables may be separated by combining multiple rather than one SWIR band with a near‐infrared band considering that each SWIR band has a different response to soil moisture and leaf water content.     

A simple model to predict soil moisture: Bridging Event and Continuous Hydrological (BEACH) modelling

This paper introduces a simple two-layer soil water balance model developed to Bridge Event And Continuous Hydrological (BEACH) modelling. BEACH is a spatially distributed daily basis hydrological model formulated to predict the initial condition of soil moisture for event-based soil erosion and rainfall–runoff models. The latter models usually require the spatially distributed values of antecedent soil moisture content and other input parameters at the onset of an event. BEACH uses daily meteorological records, soil physical properties, basic crop characteristics and topographical data. The basic processes incorporated in the model are precipitation, infiltration, transpiration, evaporation, lateral flow, vertical flow and plant growth. The principal advantage of this model lies in its ability to provide timely information on the spatially distributed soil moisture content over a given area without the need for repeated field visits. The application of this model to the CATSOP experimental catchment showed that it has the capability to estimate soil moisture content with acceptable accuracy. The root mean squared error of the predicted soil moisture content for 6 monitored locations within the catchment ranged from 0.011 to 0.065 cm³ cm^?3. The predicted daily discharge at the outlet of the study area agreed well with the observed data. The coefficient of determination and Nash–Sutcliffe efficiency of the predicted discharge were 0.824 and 0.786, respectively. BEACH has been developed within freely available GIS and programming language, PCRaster. It is a useful teaching tool for learning about distributed water balance modelling and land use scenario analysis.     

A novel ground-based experimental observation method for determining multiband microwave radiative properties of forest litter

The layer of litter covering the forest floor attenuates microwave radiation coming from soil. In satellite remote-sensing data, this reduces the sensitivity of brightness temperature to land surface parameters (e.g. soil moisture, snow depth, and snow water equivalent), resulting in poorer inversion accuracy. To quantify the effects of microwave radiative properties of litter at different frequencies, and especially the impact on transmissivity, a novel approach was developed for modelling radiative transfer (RT) through litter. This approach is based on a zero-order RT model that accounts for scattering effects (the τ–ω model, τ is the optical thickness; ω is the single scattering albedo). Controlled ground-based experiments were conducted to obtain brightness temperatures at several frequencies (1.4, 18.7, and 36.5 GHz) as affected by the thickness and weight moisture content of the litter. The effects of measurement errors on transmissivity were then evaluated. This novel method, which is not only based on sound theory but also prevents calibration errors, can be used to obtain parameters such as the extinction coefficient and transmissivity. The results of this study provide new insights into the microwave RT theory of forest systems, allowing for more appropriate brightness temperatures corrections for satellites data, and providing a guide for controlled experiments.     

Atmospheric optical depth effects on angular anisotropy of plant canopy reflectance

Abstract

A field experiment was conducted to determine whether changes in atmospheric aerosol optical depth would effect changes in bi-directional reflectance distributions of vegetation canopies. Measurements were made of the directionally reflected radiance distributions of two pasture grass canopies (same species, different growth forms) and one soya bean plant canopy under different sky irradiance distributions, which resulted from a variation in aerosol optical depth. The reflected radiance data were analysed in the solar principal plane in two narrow spectral bands, one visible (662 nm) and one infrared (826 nm). The observed changes in reflectance for both wavelengths from irradiance distribution variation is interpreted to be due largely to changes in the percentage of shadowed area viewed by the sensor for the incomplete canopies (pasture grass). For the complete coverage vegetation canopy (soya bean) studied, the effects of specular reflection and the increased diffuse irradiance penetration into the canopy are concluded to be primary physical mechanisms responsible for reflectance changes. Observed reflectivities were found to be lower on a hazy day (higher optical depth with a greater diffuse fraction) than on a clear day, with solar zenith angles at about 58^° on both days, for full-coverage soya bean canopies. The reduced reflectance most likely results from a diminished specular reflection and a greater diffuse radiation penetration into the canopy, which effects an increased energy absorption at large solar zenith angles. The opposite was true for fractional coverage grass canopies at solar zenith angles of about 56^° since the shadowing was less on the hazy day and, therefore, the soil/litter background was more fully illuminated. In the near-infrared waveband the changes in reflectance are much less than in the visible and, therefore, normalized difference vegetation index values differ substantially under clear and hazy sky conditions for the same vegetation canopy conditions. Thus, the influence of atmospheric optical depth must be considered for accurate remote sensing and in situ data interpretation.     

Spectral unmixing of vegetation,soil and dry carbon cover in arid regions: Comparing multispectral and hyperspectral observations

Remote measurements of the fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil are critical to understanding climate and land-use controls over the functional properties of arid and semi-arid ecosystems. Spectral mixture analysis is a method employed to estimate PV, NPV and bare soil extent from multispectral and hyperspectral imagery. To date, no studies have systematically compared multispectral and hyperspectral sampling schemes for quantifying PV, NPV and bare soil covers using spectral mixture models. We tested the accuracy and precision of spectral mixture analysis in arid shrubland and grassland sites of the Chihuahuan Desert, New Mexico, USA using the NASA Airborne Visible and Infrared Imaging Spectrometer (AVIRIS). A general, probabilistic spectral mixture model, Auto-MCU, was developed that allows for automated sub-pixel cover analysis using any number or combination of optical wavelength samples. The model was tested with five different hyperspectral sampling schemes available from the AVIRIS data as well as with data convolved to Landsat TM, Terra MODIS, and Terra ASTER optical channels. Full-range (0.4-2.5 w m) sampling strategies using the most common hyperspectral or multispectral channels consistently over-estimated bare soil extent and under-estimated PV cover in our shrubland and grassland sites. This was due to bright soil reflectance relative to PV reflectance in visible, near-IR, and shortwave-IR channels. However, by utilizing the shortwave-IR2 region (SWIR2; 2.0-2.3 w m) with a procedure that normalizes all reflectance values to 2.03 w m, the sub-pixel fractional covers of PV, NPV and bare soil constituents were accurately estimated. AVIRIS is one of the few sensors that can provide the spectral coverage and signal-to-noise ratio in the SWIR2 to carry out this particular analysis. ASTER, with its 5-channel SWIR2 sampling, provides some means for isolating bare soil fractional cover within image pixels, but additional studies are needed to verify the results.     

Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data

The aim of this study was to estimate soil moisture from RADARSAT-2 Synthetic Aperture Radar (SAR) images acquired over agricultural fields. The adopted approach is based on the combination of semi-empirical backscattering models, four RADARSAT-2 images and coincident ground measurements (soil moisture, soil surface roughness and vegetation characteristics) obtained near Saskatoon, Saskatchewan, Canada during the summer of 2008. The depolarization ratio (χ_v), the co-polarized correlation coefficient (ρ_vvhh) and the ratio of the absolute value of cross polarization to crop height (Λ_vh) derived from RADARSAT-2 data were analyzed with respect to changes in soil surface roughness, crop height, soil moisture and vegetation water content. This sensitivity analysis allowed us to develop empirical relationships for soil surface roughness, crop height and crop water content estimation regardless of crop type. The latter were then used to correct the semi-empirical Water-Cloud model for soil surface roughness and vegetation effects in order to retrieve soil moisture data. The soil moisture retrieved algorithm is evaluated over mature crop fields (wheat, pea, lentil, and canola) using ground measurements. Results show average relative errors of 19%, 10%, 25.5% and 32% respectively for the retrieval of crop height, soil surface roughness, crop water content and soil moisture.