Characterizing Olive Grove Canopies by Means of Ground-Based Hemispherical Photography and Spaceborne RADAR Data

Abstract

Abstract:One of the main strengths of active microwave remote sensing, in relation to frequency, is its capacity to penetrate vegetation canopies and reach the ground surface, so that information can be drawn about the vegetation and hydrological properties of the soil surface. All this information is gathered in the so called backscattering coefficient (σ 0 ).The subject of this research have been olive groves canopies, where which types of canopy biophysical variables can be derived by a specific optical sensor and then integrated into microwave scattering models has been investigated. This has been undertaken by means of hemispherical photographs and gap fraction procedures. Then, variables such as effective and true Leaf Area Indices have been estimated. Then, in order to characterize this kind of vegetation canopy, two models based on Radiative Transfer theory have been applied and analyzed. First, a generalized two layer geometry model made up of homogeneous layers of soil and vegetation has been considered. Then, a modified version of the Xu and Steven Water Cloud Model has been assessed integrating the canopy biophysical variables derived by the suggested optical procedure. The backscattering coefficients at various polarized channels have been acquired from RADARSAT 2 (C-band), with 38.5° incidence angle at the scene center. For the soil simulation, the best results have been reached using a Dubois scattering model and the VV polarized channel (r 2 = 0.88). In turn, when effective LAI OPEN ACCESSSensors 2011, 11 7477(LAI eff ) has been taken into account, the parameters of the scattering canopy model are better estimated (r 2 = 0.89). Additionally, an inversion procedure of the vegetation microwave model with the adjusted parameters has been undertaken, where the biophysical values of the canopy retrieved by this methodology fit properly with field measured values.

The Role of Remote Sensing in Olive Growing Farm Management: A Research Outlook from 2000 to the Present in the Framework of Precision Agriculture Applications

“…To our best knowledge, there are only three studies involving the use of satellite imagery to calculate biophysical parameters of the canopy [176][177][178]. Gómez et al [176] evaluated the potentialities of using airborne hyperspectral sensors and Quickbird satellite imagery for determining and mapping tree canopy size, canopy transmittance, and LAI of single olive trees.…”

“…Active microwave remote sensing imagery has also been employed, taking advantage of its ability to penetrate vegetation canopy and reach the ground surface, providing vegetation information. Molina et al [177] assessed a methodology for characterizing vegetation canopies using optical imagery and microwave scattering models. In particular, the authors integrated biophysical variables of olive tree canopies, derived from optical images, into microwave (RADARSAT 2 imagery) scattering models.…”

“…Assessment of the performance of low-cost image UAV sensors for the estimation of olive crown parameters [137] UAV -Sony NEX 7 (RGB) Evaluation of UAV data in the phenotyping of canopy traits [176] Satellite Quickbird PAN-MS Evaluating the potential of CASI and QuickBird, for mapping the tree crown size, crown transmittance and LAI * Aircraft -CASI (HY) [173] Aircraft -Tetracam MCA-6 (MS) Estimation of the fraction of Intercepted Photosynthetically Active Radiation [159] UAV DJI Matrice 210 Sony Alpha 7 RIII (RGB) MS image mapping on point cloud and the multi-temporal analysis of morphological and spectral traits Parrot Sequoia (MS) [177] Satellite RADARSAT 2 SAR Characterizing olive grove canopies using radar data Automatic procedure for a high-throughput 3D monitoring of olive trees by using UAV imagery and OBIA * Olympus PEN E-PM1 (RGB) [162] UAV MD4-1000…”

Twenty Years of Remote Sensing Applications Targeting Landscape Analysis and Environmental Issues in Olive Growing: A Review

“…[106] and temporal variations in the olive canopy associated with Xylella fastidiosa's symptoms [107,108]. In addition to Sentinel-1 radar images, Radarsat 2, COSMO-SkyMed, and TerraSAR-X satellites were exploited to map olive groves and characterize canopy biophysical parameters [109][110][111]. The main characteristics of satellites used in olive-growing applications (from 2000 to date) are summarized in Table 2.…”