Application of Repeat-Pass TerraSAR-X Staring Spotlight Interferometric Coherence to Monitor Pasture Biophysical Parameters: Limitations and Sensitivity Analysis

Ali, Iftikhar; Barrett, Brian; Cawkwell, Fiona; Green, Stuart; Dwyer, Edward; Neumann, Maxim

doi:10.1109/jstars.2017.2679761

Cited by 15 publications

(18 citation statements)

References 23 publications

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“…As earlier studies show, in the case of COSMO-Skymed interferometric pairs with a one-day temporal baseline, short vegetation after mowing results in high coherence [10]. A similar effect is present on TerraSAR-X Staring Spotlight coherence images with a temporal baseline of 11 days [11]. A comprehensive effort by [12] in relating Sentinel-1 12-day coherence to mowing events demonstrated the value of Sentinel-1 coherence for agricultural applications.…”

Section: Introductionmentioning

confidence: 56%

Separability of Mowing and Ploughing Events on Short Temporal Baseline Sentinel-1 Coherence Time Series

et al. 2020

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Short temporal baseline regular Synthetic Aperture Radar (SAR) interferometry is a tool well suited for wide area monitoring of agricultural activities, urgently needed in European Union Common Agricultural Policy (CAP) enforcement. In this study, we demonstrate and describe in detail, how mowing and ploughing events can be identified from Sentinel-1 6-day interferometric coherence time series. The study is based on a large dataset of 386 dual polarimetric Sentinel-1 VV/VH SAR and 351 Sentinel-2 optical images, and nearly 2000 documented mowing and ploughing events on more than 1000 parcels (average 10.6 ha, smallest 0.6 ha, largest 108.5 ha). Statistical analysis revealed that mowing and ploughing cause coherence to increase when compared to values before an event. In the case of mowing, the coherence increased from 0.18 to 0.35, while Sentinel-2 NDVI (indicating the amount of green chlorophyll containing biomass) at the same time decreased from 0.75 to 0.5. For mowing, there was virtually no difference between the polarisations. After ploughing, VV-coherence grew up to 0.65 and VH-coherence to 0.45, while NDVI was around 0.2 at the same time. Before ploughing, both coherence and NDVI values were very variable, determined by the agricultural management practices of the parcel. Results presented here can be used for planning further studies and developing mowing and ploughing detection algorithms based on Sentinel-1 data. Besides CAP enforcement, the results are also useful for food security and land use change detection applications.

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Section: Introductionmentioning

confidence: 56%

Separability of Mowing and Ploughing Events on Short Temporal Baseline Sentinel-1 Coherence Time Series

et al. 2020

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“…For this purpose, HH-polarized COSMO-Sky-Med 1-day repeat-pass SAR pairs were used to inversely relate temporal interferometric coherence to vegetation height and fresh above-ground biomass. In addition, Ali et al (2017a) showed that TerraSAR-X interferometric coherence data can be used successfully to predict agricultural grassland height (R 2 = 0.55) and biomass (R 2 = 0.75) at the paddock scale. Thus, further research is required to investigate the potential contribution of Sentinel-1 coherence data to predict semi-natural grassland forage quality and quantity indicators.…”

Section: Sentinel-1 Data For Grassland Forage Quantity and Quality Prmentioning

confidence: 99%

Target‐oriented habitat and wildlife management: estimating forage quantity and quality of semi‐natural grasslands with Sentinel‐1 and Sentinel‐2 data

Raab

Riesch

Tonn

et al. 2020

Remote Sens Ecol Conserv

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Semi‐natural grasslands represent ecosystems with high biodiversity. Their conservation depends on the removal of biomass, for example, through grazing by livestock or wildlife. For this, spatially explicit information about grassland forage quantity and quality is a prerequisite for efficient management. The recent advancements of the Sentinel satellite mission offer new possibilities to support the conservation of semi‐natural grasslands. In this study, the combined use of radar (Sentinel‐1) and multispectral (Sentinel‐2) data to predict forage quantity and quality indicators of semi‐natural grassland in Germany was investigated. Field data for organic acid detergent fibre concentration (oADF), crude protein concentration (CP), compressed sward height (CSH) and standing biomass dry weight (DM) collected between 2015 and 2017 were related to remote sensing data using the random forest regression algorithm. In total, 102 optical‐ and radar‐based predictor variables were used to derive an optimized dataset, maximizing the predictive power of the respective model. High R2 values were obtained for the grassland quality indicators oADF (R2 = 0.79, RMSE = 2.29%) and CP (R2 = 0.72, RMSE = 1.70%) using 15 and 8 predictor variables respectively. Lower R2 values were achieved for the quantity indicators CSH (R2 = 0.60, RMSE = 2.77 cm) and DM (R2 = 0.45, RMSE = 90.84 g/m²). A permutation‐based variable importance measure indicated a strong contribution of simple ratio‐based optical indices to the model performance. In particular, the ratios between the narrow near‐infrared and red‐edge region were among the most important variables. The model performance for oADF, CP and CSH was only marginally increased by adding Sentinel‐1 data. For DM, no positive effect on the model performance was observed by combining Sentinel‐1 and Sentinel‐2 data. Thus, optical Sentinel‐2 data might be sufficient to accurately predict forage quality, and to some extent also quantity indicators of semi‐natural grassland.

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“…There has been some progress using satellite data to estimate pasture biomass using synthetic-aperture radar. One study of Irish pasture found that grass biomass could be predicted with an R 2 of up to 0.75 based on this approach (Ali et al, 2017). However, predictions with that level of accuracy were only achieved periodically when weather conditions were clear.…”

Section: Pasture Specific Ptsmentioning

confidence: 99%

A review of precision technologies in pasture-based dairying systems

Shalloo

Byrne

Leso

et al. 2021

Irish Journal of Agricultural and Food Research

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The promised benefits of precision technologies (PTs) include improved efficiency, quality, animal health and welfare and reduced environmental impacts. To date, PTs (including sensors, algorithms, big data, decision-support tools, etc.) have had a relatively modest impact in pasture-based dairying systems in comparison with other agricultural sectors such as arable production. The areas animals roam and graze in pasture-based systems and the associated connectivity challenges may, in part at least, explain the comparatively reduced use of PTs in those systems. Thus, there are very few technologies designed specifically to increase pasture utilisation with the exception of global positioning systems (GPS) and Bluetooth-enabled Plate Meters. Terrestrial and satellite-based spectral analysis of pasture biomass and quality is still in the development phase. Therefore, one of the key drivers of efficiency in pasture-based systems has only been marginally impacted by PTs. In contrast, technological development in the area of fertility and heat detection has been important and offers significant potential value to dairy farmers. In general PTs can be described as good at measurement, data collection and storage but fall down around interpretation and providing useful outputs to end users. As a result, it is unclear if farm management is being sufficiently improved to justify widespread adoption of PTs. A needs-driven development of PTs and decision-support tools are required for the succesful integration within agriculture. Further cost/benefit analysis is also required to determine the efficiency of investing in PTs and what, if any, factors affect the variation in the returns.

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Application of Repeat-Pass TerraSAR-X Staring Spotlight Interferometric Coherence to Monitor Pasture Biophysical Parameters: Limitations and Sensitivity Analysis

Cited by 15 publications

References 23 publications

Separability of Mowing and Ploughing Events on Short Temporal Baseline Sentinel-1 Coherence Time Series

Separability of Mowing and Ploughing Events on Short Temporal Baseline Sentinel-1 Coherence Time Series

Target‐oriented habitat and wildlife management: estimating forage quantity and quality of semi‐natural grasslands with Sentinel‐1 and Sentinel‐2 data

A review of precision technologies in pasture-based dairying systems

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