# A Preliminary Investigation of the Potential of Sentinel-1 Radar to Estimate Pasture Biomass in a Grazed Pasture Landscape

^{1}

^{2}

^{3}

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## Abstract

**:**

^{2}= 0.71), and significant exponential relationships between polarimetric entropy and LAI and AGB (R

^{2}= 0.53 and 0.45, respectively). Similarly, the mean scattering angle showed a significant exponential relationship with LAI and AGB (R

^{2}= 0.58 and R

^{2}= 0.83, respectively). The study also found a significant quadratic relationship between the mean scattering angle and pasture height (R

^{2}= 0.72). Despite a relatively small dataset and single season, the mean scattering angle in conjunction with a generalised additive model (GAM) explained 73% of variance in the AGB estimates. The GAM model estimated AGB with a root mean square error of 392 kg/ha over a range in pasture AGB of 443 kg/ha to 2642 kg/ha with pasture LAI ranging from 0.27 to 1.87 and height 3.25 cm to 13.75 cm. These performance metrics, while indicative at best owing to the limited datasets used, are nonetheless encouraging in terms of the application of S1 data to evaluating pasture parameters under conditions which may preclude use of traditional optical remote sensing systems.

## 1. Introduction

^{TM}programme used NDVI derived from LANDSAT, SPOT, and MODIS satellites to estimate pasture quantity and quality [7]. Edirisinghe et al. [8] subsequently used NDVI estimated from both LANDSAT TM and SPOT XS to estimate AGB of annual pasture grasses in Western Australia with a root mean square error (RMSE) of prediction of 315 kg/ha. Less precision was found in a mountainous region of Spain where the RMSE for the AGB of meadow and pasture grass derived from vegetation indices from multi-temporal Landsat-5 TM images was 950 kg/ha for mid-summer growth and 1280 kg/ha for end of summer growth [9]. Other studies have used higher spatial resolution optical satellite data such as WorldView to estimate AGB [10,11]. Recently, a study conducted in South Africa combined red-edge and textural metrics of WorldView-3 image to achieve an RMSE of 2000 kg/ha when predicting AGB of native grasses that were subjected to different management practices [10].

## 2. Materials and Methods

#### 2.1. Description of Study Site and Selection of Sampling Sites

#### 2.2. Field Data Collection and Processing

#### 2.3. Theory of Eigenvector Scattering Mechanism

_{1}≥ λ

_{2}are eigenvalues and $\left[U\right]$ is orthogonal unitary matrix while $\ast \mathrm{and}T$ represent complex conjugate and transpose matrices, respectively.

_{1}(λ

_{2}= 0) to imply only one scattering mechanism exists. On the contrary, H = 1 is a complete random scattering mechanism. The entropy is indicative of the number of dominant scattering mechanisms and is thus proportional to the degree of depolarisation. Anisotropy, A, provides additional information on the entropy by illustrating the contributions of secondary scattering mechanism, calculated as:

#### 2.4. Pre-Processing of Sentinel-1 Data

#### 2.5. Statistical Analysis

^{2}) was selected and reported on. Furthermore, a linear regression was conducted to investigate the significance level of the influence of soil moisture (volumetric water content) and roughness (depolarisation ratio) on the SAR signal. A generalised additive model (GAM) was built using the mean scattering angle as the only predictor. Given the univariate character of the model, a cubic regression spline was selected as the basis function for GAM [57]. The model was inspected to confirm normality of residuals and homoscedasticity using the Breusch and Pagan test [58]. Since the data for model calibration was limited in size, the GAM model was validated using a leave-one-out cross-validation (LOOCV) method as it has been assessed by earlier work to be appropriate under such constraints [59,60]. The performance of the GAM model was evaluated using adjusted R

^{2}, deviance and RMSE. All statistical analyses were conducted in R [61] with a particular use of the ‘mixed GAM computation vehicle’ (mgcv) package [62].

## 3. Results

#### 3.1. Exploratory Analysis of Field Data

#### 3.2. Spatial Characterisation of SAR Polarimetric Measures

#### 3.3. Regression Analyses

#### 3.3.1. Field Measured Biophysical Variables and Influence of Soil Moisture and Roughness on SAR

^{2}= 0.84) as was the relationship between pasture height and AGB (R

^{2}= 0.85). Similar results have been reported elsewhere [4].

^{2}= 0.35; p > 0.05) (Figure 7c,d).

#### 3.3.2. SAR Polarisation against Biophysical Variables

^{2}= 0.29 to R

^{2}= 0.71. The strongest correlation values were observed for the 1 m DEM analysis in that the LAI, pasture height and AGB measured against the backscattering coefficients produced R

^{2}values of 0.51, 0.49 and 0.71, respectively. However, it was only the relationship between AGB and VH for the 1 m DEM anlysis that proved to be statistically significant. The convexity of the observed parabola in Figure 8e suggests lower values of AGB have an inverse linear relationship with the backscattering coefficient up until an AGB value of ~1750 kg/ha. At higher levels of AGB, the relationship is positive. For co-polarisation, the correlation values varied between R

^{2}= 0.003 and R

^{2}= 0.35, but none of the relationships is statistically significant. There was no statistically significant relationship observed between VSI and any of the biophysical variables.

#### 3.3.3. The Biophysical Variables against Polarimetric Scattering Parameters

^{2}values between the scattering entropy and the LAI, pasture height and AGB were 0.53, 0.54 and 0.45, respectively (Figure 9a–c). The R

^{2}values associated with mean scattering angle and the LAI, pasture height and AGB were 0.58, 0.72 and 0.83, respectively. The relationships between these polarimetric measures and the biophysical variables were exponential or quadratic and with exception of pasture height, were monotonically decreasing (scattering entropy) or increasing (mean scattering angle) with increasing parameter values. There were significant relationships between the scattering entropy and LAI and AGB only (p < 0.05). On the other hand, the relationships between mean scattering angle and all the biophysical variables were statistically significant.

#### 3.4. Generalised Additive Model for AGB Estimation

## 4. Discussion

#### 4.1. The Influence of Topography on SAR Measures

#### 4.2. Relationship between the Biophysical Variables and Polarimetric SAR Parameters

#### 4.3. Estimation of Pasture Grass AGB

^{2}= 0.66) (Figure 10). Such a result should be interpreted with caution given the sparsity of data and the consequent use of a leave one out cross validation process instead of separate calibration and validation datasets. Moreover, the data were collected in just a single season which does not enable conclusions to be made around seasonal translatability. However, the results of this preliminary investigation are encouraging given the fact that the S1 system is satellite based and offers advantages over optical satellite systems such as ability to collect data under observation conditions that preclude the use of optical satellite sensors. The use of comparable spatial resolution optical systems such as Landsat TM and SPOT XS which have been observed to yield pasture biomass estimations with an RMSE of 315 kg DM/ha [8] could be augmented through the use of radar systems such as S1, not only if/when constrained by target visibility but also when the target comprises of senesced grasses which is typically beyond the sensitivity of pigment-based, spectro-optical indices such as the NDVI.

^{2}= 0.66) (Figure 10). Such a result should be interpreted with caution given the sparsity of data and the consequent use of a leave one out cross validation process instead of separate calibration and validation datasets. Moreover, the data were collected in just a single season which does not enable conclusions to be made around seasonal translatability. However, the results of this preliminary investigation are encouraging given the fact that the S1 system is satellite based and offers advantages over optical satellite systems such as ability to collect data under observation conditions that preclude the use of optical satellite sensors. The use of comparable spatial resolution optical systems such as Landsat TM and SPOT XS which have been observed to yield pasture biomass estimations with an RMSE of 315 kg DM/ha [8] could be augmented through the use of radar systems such as S1, not only if/when constrained by target visibility but also when the target comprises of senesced grasses which is typically beyond the sensitivity of pigment-based, spectro-optical indices such as the NDVI.

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**True colour (RGB) Sentinel-2A image (24 February 2017 acquisition) of study site with some photo insets, and the central location point of the ten sampling sites (indicated by green circles and are not drawn to scale). Each sampling site was 900 m

^{2}.

**Figure 2.**Field sampling set up. The red triangles are demarcations for site central position and transect radii (North, East, South, and West). The grey dots are mid-positions of transect radii from the site’s central location and provide sampling locations (s1, s2, s3 and s4).

**Figure 3.**The work flow for Sentinel-1 image processing. The broken rectangles represent input or output data. The input data are single look complex (SLC) and ground range detected (GRD). The solid rectangles depict the processes conducted, the solid connected arrows indicate the processing steps for SLC while broken arrows connect processes performed over GRD. The output data for SLC were polarimetric scattering entropy, scattering anisotropy and mean scattering alpha angle while that of the GRD are backscattering coefficients in gamma nought for both polarisations.

**Figure 4.**The field measured; (

**a**) leaf area index (LAI), pasture height and aboveground biomass (AGB) and (

**b**) volumetric water content for each site.

**Figure 5.**True colour composite images for backscattering coefficients processed with digital elevation model of varied spatial resolutions; (

**a**) 90 m, (

**b**) 30 m and (

**c**) 1 m. The red colour in the image denotes the VH cross-polarisation, the green colour denotes the VV co-polarisation while the blue colour represents the volume scattering index (VSI). The image is the study site without non-herbaceous land cover types. Pixels in white colour represent masked land cover types or image background.

**Figure 6.**True colour composite images for polarimetric decomposition parameters. The images represent scattering entropy (H), scattering anisotropy (A) and mean scattering angle (α) processed with digital elevation model (DEM) of varied spatial resolutions; (

**a**) 90 m, (

**b**) 30 m and (

**c**) 1 m. The red colour in the image denotes the scattering entropy, green colour denotes the scattering anisotropy while blue colour represents the mean scattering angle. Areas with non-herbaceous land cover types have been masked from the image (pixels in white colour represent masked land cover types or image background).

**Figure 7.**Correlation between: (

**a**,

**b**) leaf area index (LAI), pasture height and aboveground biomass (AGB), (

**c**) volumetric water content and backscattering coefficients, and (

**d**) mean scattering angle and depolarisation ratio.

**Figure 8.**Relationship between the VH cross-polarisation and VV co-polarisation and biophysical variables; (

**a**,

**b**) leaf area index, (

**c**,

**d**) pasture height and (

**e**,

**f**) aboveground biomass. The parabola is a representation of the relationships observed for the 1 m DEM analysis only. The parabolas for 30 m and 90 m DEM analyses were not shown to preserve clarity of figures. The R

^{2}values are for the 1 m, 30 m and 90 m DEM analyses, respectively. * denotes significant relationship at 95% confidence level.

**Figure 9.**Scatter plots of polarimetric scattering entropy, H (

**a**–

**c**) and mean scattering angle, α (

**d**–

**f**) as functions of and leaf area index, pasture height (LAI) and aboveground biomass (AGB).

**Figure 10.**Relationship between observed and estimated aboveground biomass (AGB) from the generalised additive model.

Feature | Description |
---|---|

pass | descending |

antenna direction | right facing |

near incidence angle | 36° |

far incidence angle | 42° |

azimuth bandwidth | 327 Hz |

range bandwidth | 56.5 MHz |

pulse repetition frequency | 1717 Hz |

pixel spacing (azimuth) | 14.07 m |

pixel spacing (range) | 3.71 m |

**Table 2.**Summary statistics of a generalised additive model (GAM) to predict AGB. The model is characterised by standard error (se), p-value at 95% confidence level and adjusted R

^{2}(adj. R

^{2}). The spline function over the mean scattering angle is further explained by the estimated degrees of freedom (edf) and percentage of unexplained deviance in the model (deviance).

Model | Coefficient | Estimate | se | p-Value | adj. R^{2} | edf | Deviance (%) |
---|---|---|---|---|---|---|---|

GAM | intercept | 1445.3 | 115.5 | 1.55e-06 | |||

smooth | 7.96e-04 | 0.73 | 1 | 24.3 | |||

post-model statistic | |||||||

Breusch-Pagan | 0.336 |

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**MDPI and ACS Style**

Crabbe, R.A.; Lamb, D.W.; Edwards, C.; Andersson, K.; Schneider, D.
A Preliminary Investigation of the Potential of Sentinel-1 Radar to Estimate Pasture Biomass in a Grazed Pasture Landscape. *Remote Sens.* **2019**, *11*, 872.
https://doi.org/10.3390/rs11070872

**AMA Style**

Crabbe RA, Lamb DW, Edwards C, Andersson K, Schneider D.
A Preliminary Investigation of the Potential of Sentinel-1 Radar to Estimate Pasture Biomass in a Grazed Pasture Landscape. *Remote Sensing*. 2019; 11(7):872.
https://doi.org/10.3390/rs11070872

**Chicago/Turabian Style**

Crabbe, Richard Azu, David William Lamb, Clare Edwards, Karl Andersson, and Derek Schneider.
2019. "A Preliminary Investigation of the Potential of Sentinel-1 Radar to Estimate Pasture Biomass in a Grazed Pasture Landscape" *Remote Sensing* 11, no. 7: 872.
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