Sensitivity of X-Band (<i>σ</i><sup>0</sup>, <i>γ</i>) and Optical (NDVI) Satellite Data to Corn Biophysical Parameters

Abstract

The objective of this work was to evaluate the sensitivity of three different satellite signals (interferometric coherence (γ), backscattering coefficient (σ 0 ) and NDVI) to corn biophysical parameters (leaf area index, height, biomass and water content) throughout its entire vegetation cycle. All of the satellite and in situ data were collected during the Multi-spectral Crop Monitoring (MCM'10) experiment conducted in 2010 by the CESBIO Laboratory over eight different agricultural sites located in southwestern France. The results demonstrated that the NDVI is well adapted for leaf area index monitoring, whereas γ27.3° is much more suited to the estimation of the three other Biophysical Parameters throughout the entire crop cycle, with a coefficient of determination ranging from 0.83 to 0.99, using non-linear relationships. Moreover, contrary to the use of the NDVI or backscattering coefficients, the use of coherence exhibited a low sensitivity to the changes in vegetation and soil moisture occurring during senescence, offering interesting perspectives in the domain of applied remote sensing.

Evaluation of Multiorbital SAR and Multisensor Optical Data for Empirical Estimation of Rapeseed Biophysical Parameters

“…Regarding the potential use of the main microwave bands (X-, C-and L-bands), many authors have highlighted the interest of the X-and L-bands for the monitoring of wheat [30], [31], corn [27], barley [21] or rice [32]. However, the lack of dense temporal satellite data series acquired at L-band and/or their high cost (acquired for example by Alos-2, Terrasar-X, Tandem-X or Cosmoskymed constellation) do not permit their use in fine temporal approaches for crop monitoring.…”

“…Nonetheless, most of the few studies that focused on the synergy of optical and SAR data for crop monitoring suffer from the unavailability of a sufficient dense dataset containing concomitant in situ and satellite data [27], [34]. Such shortcomings intrinsically weaken the statistical robustness of the relationships established between satellite indicators and in situ biophysical parameters.…”

Estimation of Crop Water Deficit in Lower Bari Doab, Pakistan Using Reflection-Based Crop Coefficient

“…The crop specific water requirements are expressed in terms of Kc and have been quantified for different crops. Water requirements can then be found through multiplying crop coefficients times the reference evapotranspiration [22][23][24][25][26]. Using modeled potential evapotranspiration (PET) and the relationship between NDVI and Kc, NDVI differences between ideal and monitored crop can be translated into irrigation water needs.…”

Temporal Evolution of Corn Mass Production Based on Agro-Meteorological Modelling Controlled by Satellite Optical and SAR Images

“…The limits encountered by the use of optical reflectance/indices are inherent to the image specificities, with, for instance, saturation of the indices when the vegetation becomes dense, or with a lack of observation during cloudy periods. The synthetic aperture radar (SAR) data then constitute an alternative, providing information about substitution by targeting the same ( [32,33]) or complementary ( [16,34]) variables as those derived from optical imagery. The combined use of optical and SAR images to constraint models remains original and stays confined to a limited number of studies; however, they have all demonstrated the interest of this kind of approach for improving crop monitoring [16,32,33,35,36].…”