scholarly journals Analyzing an InSAR short-term systematic phase bias with regards to soil moisture and landcover

2021 ◽  
Author(s):  
Paloma Saporta ◽  
Giorgio Gomba ◽  
Francesco De Zan
Keyword(s):  
Soil Moisture ◽  
Surface Deformation ◽  
Phase Error ◽  
Region Of Interest ◽  
Sar Interferometry ◽  
Systematic Bias ◽  
Short Term ◽  
Sample Covariance ◽  
Phase Bias ◽  
Eigen Decomposition

<p>This work investigates a systematic phase bias affecting Synthetic Aperture Radar interferograms, in particular at short-term, causing biases in displacement velocity estimates that can reach several mm per year ([1]).<br>The analysis relies on the processing of a stack of Single Look Complex SAR images; in our case, the stack consists in 184 Sentinel-1 images acquired regularly between 2014 and 2018 and covering the Eastern part of Sicily. A reference phase history is derived using the EMI method (Eigen-decomposition-based Maximum-likelihood estimator of Interferometric phase), which takes advantage of the full sample covariance matrix built out of all the SAR acquisitions at a given pixel. This phase history has been shown to be equivalent to a persistent scatterer’s phase history over our region of interest. We use it to calibrate the direct multilooked interferograms built out of consecutive acquisitions. The short-term phase bias signal thus obtained is analyzed in time and space, making use in addition of ASCAT soil moisture variations and landcover information from the CORINE dataset.<br>We observe that for certain land classes, the high-frequency part of the signal is correlated with soil moisture variations in both dry and wet seasons. The low-pass trend exhibits strongly seasonal variations, with maxima of comparable value in spring (April-May) of each year. Areas with similar landcover types (forests, vegetated areas, agricultural areas) witness similar phase biases behavior, indicating a physical contribution associated with vegetation effects.<br>By investigating the behavior of the bias, this study contributes towards a future mitigation of this phase error in deformation estimates, or the exploitation of the bias itself as a physically relevant signal.</p><p>[1] H. Ansari, F. De Zan and A. Parizzi, "Study of Systematic Bias in Measuring Surface Deformation With SAR Interferometry," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3003421.</p>

scholarly journals Study of Systematic Bias in Measuring Surface Deformation with SAR Interferometry

2020 ◽  
Author(s):  
Homa Ansari ◽  
Francesco De Zan ◽  
Alessandro Parizzi
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Sar Interferometry ◽  
Systematic Bias ◽  
Earth Surface ◽  
Phase Component ◽  
Measuring Surface ◽  
Deformation Velocity ◽  
Temporal Decorrelation ◽  
Efficient Processing

<div>This paper investigates the presence of a new interferometric signal in multilooked Synthetic Aperture Radar (SAR) interferograms which cannot be attributed to atmospheric or earth surface topography changes. The observed signal is short-lived and decays with temporal baseline; however, it is distinct from the stochastic noise usually attributed to temporal decorrelation. The presence of such fading signal introduces a systematic phase component, particularly in short temporal baseline interferograms. If unattended, it biases the estimation of Earth surface deformation from SAR time series. <br></div><div>The contribution of the mentioned phase component is quantitatively assessed. For short temporal baseline interferograms, we quantify the phase contribution to be in the regime of 5 rad at C-band. The biasing impact on deformation signal retrieval is further evaluated. As an example, exploiting a subset of short temporal baseline interferograms which connects each acquisition with the successive 5 in the time series, a significant bias of -6.5 mm/yr is observed in the estimation of deformation velocity from a four-year Sentinel-1 data stack. A practical solution for mitigation of this physical fading signal is further discussed; special attention is paid to the efficient processing of Big Data from modern SAR missions such as Sentinel-1 and NISAR. Adopting the proposed solution, the deformation bias is shown to decrease to -0.24 mm/yr for the Sentinel-1 time series.</div>Based on these analyses, we put forward our recommendations for efficient and accurate deformation signal retrieval from large stacks of multilooked interferograms.

scholarly journals Comment on “Study of Systematic Bias in Measuring Surface Deformation With SAR Interferometry” by Ansari et al. (2021)

2021 ◽  
Author(s):  
Claudio De Luca ◽  
Francesco Casu ◽  
Michele Manunta ◽  
Giovanni Onorato ◽  
Riccardo Lanari
Keyword(s):  
Surface Deformation ◽  
Sar Interferometry ◽  
Systematic Bias ◽  
Phase Errors ◽  
Measuring Surface ◽  
Systematic Biases ◽  
Small Baseline ◽  
Differential Sar Interferometry ◽  
Long Baselines ◽  
Baseline Approach

<p>In a recent publication Ansari et al. (2021) [1] claim (see, in particular, the Discussion and Recommendation Section in their article) that the advanced differential SAR interferometry (InSAR) algorithms for surface deformation retrieval, based on the small baseline approach, are affected by systematic biases in the generated InSAR products. Therefore, to avoid such biases, they recommend a strategy primarily focused on excluding “the short temporal baseline interferograms and using long baselines to decrease the overall phase errors”. In particular, among various techniques, Ansari et al. (2021) [1] identify the solution presented by Manunta et al. (2019) [2] as a small baseline advanced InSAR processing approach where the presence of the above-mentioned biases (referred to as a fading signal) compromises the accuracy of the retrieved InSAR deformation products. We show that the claim of Ansari et al. (2021) [1] is not correct (at least) for what concerns the mentioned approach discussed by Manunta et al. (2019) [2]. In particular, by processing the Sentinel-1 dataset relevant to the same area in Sicily (southern Italy) investigated by Ansari et al. (2021) [1], we demonstrate that the generated InSAR products do not show any significant bias.</p>

scholarly journals Comment on “Study of Systematic Bias in Measuring Surface Deformation With SAR Interferometry” by Ansari et al. (2021)

2021 ◽  
Author(s):  
Claudio De Luca ◽  
Francesco Casu ◽  
Michele Manunta ◽  
Giovanni Onorato ◽  
Riccardo Lanari
Keyword(s):  
Surface Deformation ◽  
Sar Interferometry ◽  
Systematic Bias ◽  
Phase Errors ◽  
Measuring Surface ◽  
Systematic Biases ◽  
Small Baseline ◽  
Differential Sar Interferometry ◽  
Long Baselines ◽  
Baseline Approach

<p>In a recent publication Ansari et al. (2021) [1] claim (see, in particular, the Discussion and Recommendation Section in their article) that the advanced differential SAR interferometry (InSAR) algorithms for surface deformation retrieval, based on the small baseline approach, are affected by systematic biases in the generated InSAR products. Therefore, to avoid such biases, they recommend a strategy primarily focused on excluding “the short temporal baseline interferograms and using long baselines to decrease the overall phase errors”. In particular, among various techniques, Ansari et al. (2021) [1] identify the solution presented by Manunta et al. (2019) [2] as a small baseline advanced InSAR processing approach where the presence of the above-mentioned biases (referred to as a fading signal) compromises the accuracy of the retrieved InSAR deformation products. We show that the claim of Ansari et al. (2021) [1] is not correct (at least) for what concerns the mentioned approach discussed by Manunta et al. (2019) [2]. In particular, by processing the Sentinel-1 dataset relevant to the same area in Sicily (southern Italy) investigated by Ansari et al. (2021) [1], we demonstrate that the generated InSAR products do not show any significant bias.</p>

scholarly journals Comments on ``Study of Systematic Bias in Measuring Surface Deformation With SAR Interferometry''

2021 ◽  
pp. 1-5
Author(s):  
Claudio De Luca ◽  
Francesco Casu ◽  
Michele Manunta ◽  
Giovanni Onorato ◽  
Riccardo Lanari
Keyword(s):  
Surface Deformation ◽  
Sar Interferometry ◽  
Systematic Bias ◽  
Measuring Surface

scholarly journals Study of Systematic Bias in Measuring Surface Deformation with SAR Interferometry

2020 ◽  
Author(s):  
Homa Ansari ◽  
Francesco De Zan ◽  
Alessandro Parizzi
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Sar Interferometry ◽  
Systematic Bias ◽  
Earth Surface ◽  
Phase Component ◽  
Measuring Surface ◽  
Deformation Velocity ◽  
Temporal Decorrelation ◽  
Efficient Processing

<div>This paper investigates the presence of a new interferometric signal in multilooked Synthetic Aperture Radar (SAR) interferograms which cannot be attributed to atmospheric or earth surface topography changes. The observed signal is short-lived and decays with temporal baseline; however, it is distinct from the stochastic noise usually attributed to temporal decorrelation. The presence of such fading signal introduces a systematic phase component, particularly in short temporal baseline interferograms. If unattended, it biases the estimation of Earth surface deformation from SAR time series. <br></div><div>The contribution of the mentioned phase component is quantitatively assessed. For short temporal baseline interferograms, we quantify the phase contribution to be in the regime of 5 rad at C-band. The biasing impact on deformation signal retrieval is further evaluated. As an example, exploiting a subset of short temporal baseline interferograms which connects each acquisition with the successive 5 in the time series, a significant bias of -6.5 mm/yr is observed in the estimation of deformation velocity from a four-year Sentinel-1 data stack. A practical solution for mitigation of this physical fading signal is further discussed; special attention is paid to the efficient processing of Big Data from modern SAR missions such as Sentinel-1 and NISAR. Adopting the proposed solution, the deformation bias is shown to decrease to -0.24 mm/yr for the Sentinel-1 time series.</div>Based on these analyses, we put forward our recommendations for efficient and accurate deformation signal retrieval from large stacks of multilooked interferograms.

scholarly journals Interactive effects of ambient ozone and climate measured on growth of mature loblolly pine trees

1996 ◽  
Vol 26 (4) ◽  
pp. 670-681 ◽  
Author(s):  
S.B. McLaughlin ◽  
D.J. Downing
Keyword(s):  
Soil Moisture ◽  
Loblolly Pine ◽  
Moisture Stress ◽  
Growth Patterns ◽  
Forest Growth ◽  
Interactive Effects ◽  
Forest Trees ◽  
Short Term ◽  
Data Set ◽  
Ambient Ozone

Seasonal growth patterns of mature loblolly pine (Pinustaeda L.) trees over the interval 1988–1993 have been analyzed to evaluate the effects of ambient ozone on growth of large forest trees. Patterns of stem expansion and contraction of 34 trees were examined using serial measurements with sensitive dendrometer band systems. Study sites, located in eastern Tennessee, varied significantly in soil moisture, soil fertility, and stand density. Levels of ozone, rainfall, and temperature varied widely over the 6-year study interval. Regression analysis identified statistically significant influences of ozone on stem growth patterns, with responses differing widely among trees and across years. Ozone interacted with both soil moisture stress and high temperatures, explaining 63% of the high frequency, climatic variance in stem expansion identified by stepwise regression of the 5-year data set. Observed responses to ozone were rapid, typically occurring within 1–3 days of exposure to ozone at ≥40 ppb and were significantly amplified by low soil moisture and high air temperatures. Both short-term responses, apparently tied to ozone-induced increases in whole-tree water stress, and longer term cumulative responses were identified. These data indicate that relatively low levels of ambient ozone can significantly reduce growth of mature forest trees and that interactions between ambient ozone and climate are likely to be important modifiers of future forest growth and function. Additional studies of mechanisms of short-term response and interspecies comparisons are clearly needed.

Sign in / Sign up

Export Citation Format

Share Document