Sentinel-1 Cross Ratio and Vegetation Optical Depth: A Comparison over Europe

Vreugdenhil, Mariëtte; Navacchi, Claudio; Bauer-Marschallinger, Bernhard; Hahn, Sebastian; Steele‐Dunne, Susan; Pfeil, Isabella; Dorigo, Wouter; Wagner, Wolfgang

doi:10.3390/rs12203404

Cited by 54 publications

(28 citation statements)

References 29 publications

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“…in Noah-MP, suggesting that observed σ 0 VH might also contain much more information about irrigation (via the influence of the vegetation change due to irrigation) than that contained in σ 0 VV which is normally used for SSM retrieval (Vreugdenhil et al 2020). We believe that the cause of this is related to a comparatively larger σ 0 of vegetation with respect to that of the soil when the crops are well developed.…”

Section: Wcm Backscatter Simulationmentioning

confidence: 91%

“…Following Vreugdenhil et al (2018) and Vreugdenhil et al (2020), Noah-MP LAI and PROBA-V LAI were also compared with the Sentinel-1 σ 0 VH/ σ 0 VV cross ratio (CR), which was demonstrated to have a high agreement with the vegetation signal. Though the σ 0 VH was demonstrated to increase with the vegetation signal (Macelloni et al, 2001), the CR will be more sensitive to vegetation changes as the ratio is less sensitive to changes in soil moisture and soil-vegetation interaction (Veloso et al, 2017;Vreugdenhil et al, 2020).…”

Section: Noah-mp Lsm and Wcm Evaluationsmentioning

confidence: 99%

“…The limitations found in simulating LAI and vegetation by Noah-MP even when irrigation was simulated could potentially be overcome by assimilating Sentinel-1 σ 0 data. To explore this potential, we compared the LAI from both model runs, and from PROBA-V, with the observed Sentinel-1 σ 0 CR (VH/VV), which should provide information about the vegetation dynamics (Vreugdenhil et al 2018;Vreugdenhil et al 2020). We found that the correlation between σ 0 CR and LAI from PROBA-V was much higher than that between σ 0 CR and the simulated LAI by Noah-MP (see Figure 7) suggesting that Sentinel-1 σ 0 DA could help in correcting poor LAI model simulations.…”

Section: Noah-mp Irrigation Modellingmentioning

confidence: 99%

“…However, to our knowledge, none of these studies considered the joint updating of soil moisture and vegetation, and none specifically focussed on the performance over irrigated areas. The σ 0 from Sentinel-1 contains information on both soil moisture (Zribi et al, 2011;Liu and Shi, 2016;Li and Wang, 2018;Bauer-Marschallinger et al, 2018) and vegetation (Vreugdenhil et al, 2018;Vreugdenhil et al, 2020) and assimilating this data could allow us to update both soil moisture and vegetation in a land data assimilation system and, in doing so, correct for missed irrigation events.…”

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confidence: 99%

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Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land

Modanesi

Massari

Alexander

et al. 2021

Preprint

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Abstract. Worldwide, the amount of water used for agricultural purposes is rising and the quantification of irrigation is becoming a crucial topic. Because of the the limited availability of in situ observations, an increasing number of studies is focusing on the synergistic use of models and satellite data to detect and quantify irrigation. The parameterization of irrigation in large scale Land Surface Models (LSM) is improving, but it is still hampered by the lack of information about dynamic crop rotations or the extent of irrigated areas, and the mostly unknown timing and amount of irrigation. On the other hand, remote sensing observations offer an opportunity to fill this gap as they are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining LSMs and satellite information through data assimilation can offer the optimal way to quantify the water used for irrigation. The aim of this study is to optimize a land modeling system, consisting of the Noah-MP LSM, coupled with a backscatter observation operator, over irrigated land in order to simulate backscatter predictions. This is a first step towards building a reliable data assimilation system to ingest level-1 Sentinel-1 observations. In this context, we tested how well modeled soil moisture and vegetation estimates from the Noah-MP LSM running within the NASA Land Information System (LIS), with or without irrigation simulation, are able to capture the signal of high-resolution Sentinel-1 backscatter observations over the Po river Valley, an important agricultural area in Northern Italy. Next, aggregated 1-km Sentinel-1 backscatter observations were used to calibrate a Water Cloud Model (WCM) as observation operator using simulated soil moisture and Leaf Area Index estimates. The WCM was calibrated with and without activating an irrigation scheme in Noah-MP and considering two different cost functions. Results demonstrate that activating an irrigation scheme provides the optimal calibration of the WCM, even if the irrigation estimates are inaccurate. The Bayesian optimization is shown to result in the best unbiased calibrated system, with minimal chance of having error cross correlations between the model and observations. Our time series analysis further confirms that Sentinel-1 is able to track the impact of human activities on the water cycle, highlighting its potential to improve irrigation, soil moisture and vegetation estimates via future data assimilation.

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Section: Wcm Backscatter Simulationmentioning

confidence: 91%

Section: Noah-mp Lsm and Wcm Evaluationsmentioning

confidence: 99%

Section: Noah-mp Irrigation Modellingmentioning

confidence: 99%

mentioning

confidence: 99%

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Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land

Modanesi

Massari

Alexander

et al. 2021

Preprint

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“…5 below). The latter may be caused by the relatively stronger scattering contribution from forests (and vegetation in general) in VH-polarization (Vreugdenhil et al, 2020), decreasing the sensitivity to the underlying snowpack. Therefore, two change detection indices (SI1 and SI2; in dB) are calculated for each location i and weekly time step t:…”

Section: Snow Depth Retrievalsmentioning

confidence: 99%

Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps

Lievens

Brangers

Marshall

et al. 2021

Preprint

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Abstract. Seasonal snow in mountain regions is an essential water resource. However, the spatio-temporal variability in mountain snow depth or snow water equivalent (SWE) from regional to global scales is not well understood due to the lack of high-resolution satellite observations and robust retrieval algorithms. We demonstrate the ability of the Sentinel-1 mission to monitor weekly snow depth at sub-kilometer (100 m, 300 m and 1 km) resolutions over the European Alps, for 2017–2019. Sentinel-1 backscatter observations, especially for the cross-polarization channel, show a high correlation with regional model simulations of snow depth over Austria and Switzerland. The observed changes in radar backscatter with the accumulation or ablation of snow are used in a change detection algorithm to retrieve snow depth. The algorithm includes the detection of dry and wet snow conditions. For dry snow conditions, the 1 km Sentinel-1 retrievals have a spatio-temporal correlation (R) of 0.87 and mean absolute error (MAE) of 0.17 m compared to in situ measurements across 743 sites in the European Alps. A slight reduction in performance is observed for the retrievals at 300 m (R = 0.85 and MAE = 0.18 m) and 100 m (R = 0.79 and MAE = 0.21 m). The results demonstrate the ability of Sentinel-1 to provide regional snow estimates at an unprecedented resolution in mountainous regions, where satellite-based estimates of snow mass are currently lacking. The retrievals can improve our knowledge of seasonal snow mass in areas with complex topography and benefit a number of applications, such as water resources management, flood forecasting and numerical weather prediction.

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Satellite-Based Light Use Efficiency Model for Sugarcane Yield Estimation at Field Scale

Nihar

Patel

Singh

et al. 2023

J Indian Soc Remote Sens

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Sentinel-1 Cross Ratio and Vegetation Optical Depth: A Comparison over Europe

Cited by 54 publications

References 29 publications

Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land

Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land

Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps

Satellite-Based Light Use Efficiency Model for Sugarcane Yield Estimation at Field Scale

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