C band radar crops monitoring at high temporal frequency: first results of the MOCTAR campaign

Author(s):  
P.L. Frison ◽  
A. Chakir ◽  
J. Ezzahar ◽  
P. Fanise ◽  
L. Villard ◽  
...  
2020 ◽  
Author(s):  
Pierre-Louis Frison ◽  
Adnane Chakir ◽  
Jamal Ezzahar ◽  
Pascal Fanise ◽  
Ludovic Villard ◽  
...  

<p>This work deals with crops monitoring in a semi-arid environment, the Mediterranean region, where up to 90% of available water is used for irrigation. In addition to help for yield predictions, temporal monitoring at a regular time basis can help for the optimization of water use. We focused on the daily cycle of the backscattering radar coefficient over two different crop Mediterranean types: olive trees and wheat. With a six-day period between two consecutive acquisitions, the Sentinel-1 mission improves significantly the potential of SAR data for seasonal monitoring of earth surfaces. The available temporal frequency allows for the first time the temporal monitoring of natural surfaces in relation with seasonal changes. However, they are still many issues for better understanding Sentinel-1 temporal signatures and the full potential of these data over crop fields. Indeed, crop fields are characterized by contrasted surface states between bare soils and densely vegetated, with sudden changes due to field works (changing dramatically soil roughness or moisture) or harvests.  The MOCTAR experiment consists in the acquisitions of radar fully polarimetric interferometric C-band data acquired continuously at 10 min time step from the top of a tower. The study site is located in the Haouz plain, near the city of Marrakech, in the Chichaoua region, in Morocco. The region is characterized by a semi-arid Mediterranean climate, with an average of 250 mm of yearly precipitation. The region is characterized by two main seasons: wet and dry, extended from October to April and from May to September respectively. Maximum temperatures occur in July-August (average of 27.2 °C) and minimum in January (10.8° C). The study site is composed of two plots of 2.50 ha each, one consisting in olive trees, the other in wheat (Fig. 1). Both are irrigated with drip technique. The study site is documented for more than 10 years, and in situ measurements such as soil moisture, biomass, sapflow sensors (thermal dissipation method) and a micrometric dendrometer are regularly collected.</p><p>The radar antennas are fixed on a 20 m height tower, in a similar way than the TropiScat experiment They have been installed in May 2019. Four L-band antennas, two emitting and two receiving, one in H and the other in V polarizations, are visible on the bottom row. Above, six antennas operating at C band are mounted on two rows: four on the bottom one (two emitting and two receiving in H and V pol.) and above two receiving antennas in H and V pol. This configuration allows for interferometric fully polarimetric acquisitions also called PolInSAR. The acquisitions are made continuously with a 10 min time step.</p><p>First results show pronounced daily cycles, with amplitude of about 2 dB. These cycles are likely correlated to diurnal variations of tree water content and sap flow, but need to be further investigated sap flows and dielectric constant measurements made on the trunks. These results will be analyzed by comparison with Sentinel-1 temporal profiles.</p>


Neuroscience ◽  
2010 ◽  
Vol 166 (2) ◽  
pp. 482-490 ◽  
Author(s):  
Y. Shigihara ◽  
M. Tanaka ◽  
N. Tsuyuguchi ◽  
H. Tanaka ◽  
Y. Watanabe

Author(s):  
Peter Cawley

Abstract Permanently installed SHM systems are now a viable alternative to traditional periodic inspection (NDT). However, their industrial use is limited and this paper reviews the steps required in developing practical SHM systems. The transducers used in SHM are fixed in location, whereas in NDT they are generally scanned. The aim is to reach similar performance with high temporal frequency, low spatial frequency SHM data to that achievable with conventional high spatial frequency, low temporal frequency NDT inspections. It is shown that this can be done via change tracking algorithms such as the Generalized Likelihood Ratio (GLR) but this depends on the input data being normally distributed, which can only be achieved if signal changes due to variations in the operating conditions are satisfactorily compensated; there has been much recent progress on this topic and this is reviewed. Since SHM systems can generate large volumes of data, it is essential to convert the data to actionable information, and this step must be addressed in SHM system design. It is also essential to validate the performance of installed SHM systems, and a methodology analogous to the model assisted POD (MAPOD) scheme used in NDT has been proposed. This uses measurements obtained from the SHM system installed on a typical undamaged structure to capture signal changes due to environmental and other effects, and to superpose the signal due to damage growth obtained from finite element predictions. There is a substantial research agenda to support the wider adoption of SHM and this is discussed.


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