Remote sensing can provide good alternatives for traditional in situ water status measurements in orchard crops, such as stem water potential (Ψ_stem). However, the heterogeneity of these cropping systems causes significant differences with regards to remote sensing products within one orchard and between orchards. In this study, robust spectral indicators of Ψ_stem were sought after, independent of sensor viewing geometry, orchard architecture and management. To this end, Ψ_stem was monitored throughout three consecutive growing seasons in (deficit) irrigated and rainfed pear orchards and related to spectral observations of leaves, canopies and WorldView-2 imagery. On a leaf and canopy level, high correlations were observed between the shortwave infrared reflectance and in situ measured Ψ_stem. Additionally, for canopy measurements, visible and near-infrared wavelengths (R₅₃₀/R₆₀₀, R₅₃₀/R₇₀₀ and R₇₂₀/R₈₀₀) showed significant correlations. Therefore, the Red-edge Normalized Difference Vegetation Index (ReNDVI) was applied on fully sunlit satellite imagery and found strongly related with Ψ_stem (R² = 0.47; RMSE = 0.36 MPa), undoubtedly showing the potential of WorldView-2 to monitor water stress in pear orchards. The relationship between ReNDVI and Ψ_stem was independent of management, irrigation setup, phenology and environmental conditions. In addition, results showed that this relation was also independent of off-nadir viewing angle and almost independent of viewing geometry, as the correlation decreased after the inclusion of fully shaded scenes. With further research focusing on issues related to viewing geometry and shadows, high spatial water status monitoring with space borne remote sensing is achievable.

Yield and quality estimations provide vital information to fruit growers, yet require accurate monitoring throughout the growing season. To this end, the temporal dependency of fruit yield and quality estimations through spectral vegetation indices was investigated in irrigated and rainfed pear orchards. Both orchards were monitored throughout three consecutive growing seasons, including spectral measurements (i.e., hyperspectral canopy reflectance measurements) as well as yield determination (i.e., total yield and number of fruits per tree) and quality assessment (i.e., fruit firmness, total soluble solids and fruit color). The results illustrated a clear association between spectral vegetation indices and both fruit yield and fruit quality (|r| > 0.75; p < 0.001). However, the correlations between vegetation indices and production variables varied throughout the growing season, depending on the phenological stage of fruit development. In the irrigated orchard, index values showed a strong association with production variables near time of harvest (|r| > 0.6; p < 0.001), while in the rainfed orchard, index values acquired during vegetative growth periods presented stronger correlations with fruit parameters (|r| > 0.6; p < 0.001). The improved planning of remote sensing missions during (rainfed orchards) and after (irrigated orchards) vegetative growth periods could enable growers to more accurately predict production outcomes and improve the production process.

Stress-related biophysical variables of capital intensive orchard crops can be estimated with proxies via spectral vegetation indices from off-nadir viewing satellite imagery. However, variable viewing compositions affect the relationship between spectral vegetation indices and stress-related variables (i.e., chlorophyll content, water content and Leaf Area Index (LAI)) and could obstruct change detection. A sensitivity analysis was performed on the estimation of biophysical variables via vegetation indices for a wide range of viewing geometries. Subsequently, off-nadir viewing satellite imagery of an experimental orchard was analyzed, while all influences of background admixture were minimized through vegetation index normalization. Results indicated significant differences between nadir and off-nadir viewing scenes (∆R² > 0.4). The Photochemical Reflectance Index (PRI), Normalized Difference Infrared Index (NDII) and Simple Ratio Pigment Index (SRPI) showed increased R² values for off-nadir scenes taken perpendicular compared to parallel to row orientation. Other indices, such as Normalized Difference Vegetation Index (NDVI), Gitelson and Merzlyak (GM) and Structure Insensitive Pigment Index (SIPI), showed a significant decrease in R² values from nadir to off-nadir viewing scenes. These results show the necessity of vegetation index selection for variable viewing applications to obtain an optimal derivation of biophysical variables in all circumstances.

The suitability of high resolution satellite imagery to provide the water status in orchard crops, i.e. stem water potential (Ψ_stem) was evaluated in [1]. However, the contribution of a number of collaborators was not properly acknowledged. Pieter Janssens, Wendy Odeurs, Hilde Vandendriessche and Tom Deckers all provided a substantial contribution to the conception and the design of the work. They furthermore had a leading role in the acquisition, processing, analysis, and interpretation of the reference evapotranspiration (ETo) and Ψ_stem data. The article [1] would not have been possible without their valuable input, and the authors would like to correct the authors list as follows.