Irrigated Grassland Monitoring Using a Time Series of TerraSAR-X and COSMO-SkyMed X-Band SAR Data

Hajj, Mohammad El; Baghdadi, Nicolas; Belaud, Gilles; Zribi, Mehrez; Cheviron, Bruno; Courault, Dominique; Hagolle, Olivier; Charron, François

doi:10.3390/rs61010002

Cited by 74 publications

(78 citation statements)

References 86 publications

(100 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The decrease in the radar signal sensitivity to the soil moisture when the vegetation cover increases was reported in the studies of Baghdadi et al [30] and El Hajj et al [44]. In Baghdadi et al [30], the sensitivity of the radar signal at the C-band (VV polarization and high incidence angle) is 0.11 dB/[vol %] and 0.05 dB/[vol %] for a grassland biomass lower and higher than 1 kg/m 2 , respectively.…”

Section: Sar Signal Sensitivity Analysismentioning

confidence: 90%

Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas

et al. 2017

Self Cite

View full text Add to dashboard Cite

Abstract:Soil moisture mapping at a high spatial resolution is very important for several applications in hydrology, agriculture and risk assessment. With the arrival of the free Sentinel data at high spatial and temporal resolutions, the development of soil moisture products that can better meet the needs of users is now possible. In this context, the main objective of the present paper is to develop an operational approach for soil moisture mapping in agricultural areas at a high spatial resolution over bare soils, as well as soils with vegetation cover. The developed approach is based on the synergic use of radar and optical data. A neural network technique was used to develop an operational method for soil moisture estimates. Three inversion SAR (Synthetic Aperture Radar) configurations were tested: (1) VV polarization; (2) VH polarization; and (3) both VV and VH polarization, all in addition to the NDVI information extracted from optical images. Neural networks were developed and validated using synthetic and real databases. The results showed that the use of a priori information on the soil moisture condition increases the precision of the soil moisture estimates. The results showed that VV alone provides better accuracy on the soil moisture estimates than VH alone. In addition, the use of both VV and VH provides similar results, compared to VV alone. In conclusion, the soil moisture could be estimated in agricultural areas with an accuracy of approximately 5 vol % (volumetric unit expressed in percent). Better results were obtained for soil with a moderate surface roughness (for root mean surface height between 1 and 3 cm). The developed approach could be applied for agricultural plots with an NDVI lower than 0.75.

show abstract

Section: Sar Signal Sensitivity Analysismentioning

confidence: 90%

Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several studies showed that the correlation between the NDVI and the vegetation parameters (LAI, VWC, biomass, etc.) is strong when the NDVI is less than 0.7-0.8 [21][22][23]. Beyond 0.7-0.8, the NDVI saturates and does not vary with the increase of the vegetation parameters.…”

Section: Introductionmentioning

confidence: 88%

“…Beyond 0.7-0.8, the NDVI saturates and does not vary with the increase of the vegetation parameters. For example, the logarithmic relationship observed between NDVI and LAI saturates when the LAI exceeded 2-3 m 2 /m 2 [6,[21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Calibration of the Water Cloud Model at C-Band for Winter Crop Fields and Grasslands

et al. 2017

Self Cite

View full text Add to dashboard Cite

Abstract:In a perspective to develop an inversion approach for estimating surface soil moisture of crop fields from Sentinel-1/2 data (radar and optical sensors), the Water Cloud Model (WCM) was calibrated from C-band Synthetic Aperture Radar (SAR) data and Normalized Difference Vegetation Index (NDVI) values collected over crops fields and grasslands. The soil contribution that depends on soil moisture and surface roughness (in addition to SAR instrumental parameters) was simulated using the physical backscattering model IEM (Integral Equation Model). The vegetation descriptor used in the WCM is the NDVI because it can be directly calculated from optical images. A large dataset consisting of radar backscattered signal in Vertical transmit and Vertical receive (VV) and Vertical transmit and Horizontal receive (VH) polarizations with wide range of incidence angle, soil moisture, surface roughness, and NDVI-values was used. It was collected over two agricultural study sites. Results show that the soil contribution to the total radar backscattered signal is lower in VH than in VV because VH is more sensitive to vegetation cover. Thus, the use of VH alone or in addition to VV for retrieving the soil moisture is not advantageous in presence of well-developed vegetation cover.

show abstract

“…A similar outcome has been observed in studies focused on the use of SAR backscatter for agricultural monitoring. Hajj et al [43] reported that the X-band HV polarisation is more sensitive to grasslands parameters than HH. Additionally, Baghdadi et al [16] concluded that the co-pol channels (HH and VV) have a slightly lower potential for the detection of the sugarcane harvest.…”

Section: Groupmentioning

confidence: 99%

Relating Sentinel-1 Interferometric Coherence to Mowing Events on Grasslands

et al. 2016

View full text Add to dashboard Cite

Abstract:In this study, the interferometric coherence calculated from 12-day Sentinel-1 image pairs was analysed in relation to mowing events on agricultural grasslands. Results showed that after a mowing event, median VH (vertical transmit, horizontal receive) and VV (vertical transmit, vertical receive) polarisation coherence values were statistically significantly higher than those from before the event. The shorter the time interval after the mowing event and the first interferometric acquisition, the higher the coherence. The coherence tended to stay higher, even 24 to 36 days after a mowing event. Precipitation caused the coherence to decrease, impeding the detection of a mowing event. Given the three analysed acquisition geometries, it was concluded that afternoon acquisitions and steeper incidence angles were more useful in the context of this study. In the case of morning acquisitions, dew might have caused a decrease of coherence for mowed and unmowed grasslands. Additionally, an increase of coherence after a mowing event was not evident during the rapid growth phase, due to the 12-day separation between the interferometric acquisitions. In future studies, six-day pairs utilising Sentinel-1A and 1B acquisitions should be considered.

show abstract

Irrigated Grassland Monitoring Using a Time Series of TerraSAR-X and COSMO-SkyMed X-Band SAR Data

Cited by 74 publications

References 86 publications

Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas

Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas

Calibration of the Water Cloud Model at C-Band for Winter Crop Fields and Grasslands

Relating Sentinel-1 Interferometric Coherence to Mowing Events on Grasslands

Contact Info

Product

Resources

About