Assessment of different methods for shadow detection in high-resolution optical imagery and evaluation of shadow impact on calculation of NDVI, and evapotranspiration

Abstract

Significant efforts have been made recently in the application of high-resolution remote sensing imagery (i.e., sub-meter) captured by unmanned aerial vehicles (UAVs) for precision agricultural applications for high-value crops such as wine grapes. However, at such high resolution, shadows will appear in the optical imagery effectively reducing the reflectance and emission signal received by imaging sensors. To date, research that evaluates procedures to identify the occurrence of shadows in imagery produced by UAVs is limited. In this study, the performance of four different shadow detection methods used in satellite imagery was evaluated for high-resolution UAV imagery collected over a California vineyard during the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) field campaigns. The performance of the shadow detection methods was compared and impacts of shadowed areas on the normalized difference vegetation index (NDVI) and estimated evapotranspiration (ET) using the Two-Source Energy Balance (TSEB) model are presented. The results indicated that two of the shadow detection methods, the supervised classification and index-based methods, had better performance than two other methods. Furthermore, assessment of shadowed pixels in the vine canopy led to significant differences in the calculated NDVI and ET in areas affected by shadows in the highresolution imagery. Shadows are shown to have the greatest impact on modeled soil heat flux, while net radiation and sensible heat flux are less affected. Shadows also have an impact on the modeled Bowen ratio (ratio of sensible to latent heat) which can be used as an indicator of vine stress level.

A Review of Current and Potential Applications of Remote Sensing to Study the Water Status of Horticultural Crops

“…Generally, multispectral sensors are used to compute normalised indices such as NDVI. The normalised indices are relatively less influenced, although significant, by the change in illumination conditions which affect the entire spectrum proportionally [29,101]. In this regard, radiometric calibration of the multispectral camera has used a range of stringent to simplified, and vicarious approaches [123,125,171,173,[178][179][180].…”

“…In the absence of accurate ET c measurements, k c is an easy and practical means of getting reliable estimates of ET c using ET 0 [255]. In this regard, studies have focused on the use of remote sensing to study spatial variability in k c and ET c [101,[256][257][258]. Thermal and NIR imagery can be used to compute k c and ET c as transpiration rate is closely related to canopy temperature [259][260][261] and k c has been shown to correlate with canopy reflectance [101,255].…”

“…In this regard, studies have focused on the use of remote sensing to study spatial variability in k c and ET c [101,[256][257][258]. Thermal and NIR imagery can be used to compute k c and ET c as transpiration rate is closely related to canopy temperature [259][260][261] and k c has been shown to correlate with canopy reflectance [101,255]. Various thermal indices, such as CWSI, canopy temperature ratio, canopy temperature above non-stressed, and canopy temperature above canopy threshold, can be used to estimate ET c , where CWSI-based ET c was found to be the most accurate [24].…”

“…UASs are being increasingly utilised to acquire multi-spectral and thermal imagery to compute ET at an unprecedented spatial resolution [270,271]. Using high-resolution images, filtering the shadowed-pixel is possible, which showed significant improvement in the estimation of ET in grapevine [101]. Using high-resolution thermal and/or multispectral imagery, ET has been derived for horticultural crops, such as grapevines [270] and olives [271].…”

Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspiration Models

“…The 3D point cloud is a useful source of information about the size, position, and orientation of an object that can be combined with UAV multi-spectral or hyper-spectral imagery to explore relationships between an object's 3D geometry information and its spectral information. Several classification methods, such as supervised and unsupervised machine learning algorithms, have been developed to generate a classified map of aerial imagery based on the similarities in spectral signatures [39]. While these algorithms fail to distinguish objects having similar spectral signatures (e.g., differentiating between water and shadows [40] in optical bands), point cloud would be a useful and an additional source to combine with multi-spectral imagery in order to improve the accuracy of classification methods.…”