- freely available
Remote Sensing 2013, 5(12), 6647-6666; doi:10.3390/rs5126647
Published: 4 December 2013
Abstract: Remote sensing can provide good alternatives for traditional in situ water status measurements in orchard crops, such as stem water potential (Ψstem). 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 Ψstem were sought after, independent of sensor viewing geometry, orchard architecture and management. To this end, Ψstem 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 Ψstem. 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 Ψstem (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 Ψstem 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.
In capital intensive cropping systems, such as pear orchards, prolonged periods of water deficiencies can lead to significant yield loss , making water status monitoring crucial to optimize irrigation scheduling . Traditional in situ measurements of plant water status are time consuming and destructive, allowing only limited samples and repetitions. Furthermore, the point measurements are insufficient to account for the high spatial variability present in orchards . Technological advances in remote sensing provide non-destructive, time efficient and cost beneficial alternatives that visualize the spatial variability in water status at a wide range of temporal scales .
Various vegetation indices have been related to leaf and canopy water content (see review by ). In fruit trees, however, leaf water potential (Ψleaf) has been found a more robust indicator of plant water status compared to plant water content [6,7]. Leaf water content is normally measured on tissue samples , which are subject to some variability, while Ψleaf provide an integrated measurement of environmental conditions on water availability within the leaf. Moreover, while large changes in leaf water content only occur at advanced stages of dehydration, Ψleaf measurements were able to detect the onset of water deficiency, allowing a reaction before permanent damage occurs . Several studies have linked Ψleaf in fruit orchards with vegetation indices [9,10]. The down-side of Ψleaf is the high variability within one tree. Therefore, most researchers prefer stem water potential (Ψstem) as an indicator of plant water status in fruit orchards [7,11,12]. The Ψstem is the water status of non-transpiring leaves, which is related to the water availability and transpiration. As opposed to plant water status and Ψleaf, the potential of remote monitoring of Ψstem is not yet fully explored nor widely adopted (but see examples for olives  and citrus [7,14]).
Due to high cloud cover in many of the pear (and apple) growing areas of the world , irrigation scheduling through remote sensing requires a near-to-daily revisit time to provide the necessary information . Furthermore, due to the large spatial variation within one management block , a high spatial resolution is required. Currently, this combination of both high spatial and temporal resolution is only feasible with high spatial resolution satellite sensors having off-nadir viewing capabilities, such as WorldView-2 (DigitalGlobe). However, most of the developed techniques and indices to monitor water status (as mentioned above) are based on reflectance information from the shortwave infrared (SWIR; 1,300–2,500 nm; ) and thermal infrared (3,000–35,000 nm; ) region of the electromagnetic spectrum. Yet very high spatial resolution satellites lack spectral bands in these regions of the spectrum. Furthermore, the highly variable off-nadir viewing angle influences spectral observations, which in turn complicates plant status monitoring . Additionally, different management practices cause significant differences in orchard and tree architecture again influencing spectral observations and complicating accurate remote monitoring of plant water status .
This study aims at investigating the potential of WorldView-2 satellite imagery for the monitoring of Ψstem. The goal is to identify robust spectral indicators of Ψstem, independent of orchard parameters (i.e., phenology, tree structure and irrigation setup), environmental conditions (i.e., soil conditions) and sensor parameters (i.e., off-nadir viewing angle). To this end, Ψstem was monitored throughout three consecutive growing seasons in a (deficit) irrigated and non-irrigated (rainfed) pear orchard and related to spectral observations of leaves, canopies and spectral vegetation indices derived from WorldView-2 imagery.
2. Materials and Methods
2.1. Study Area and Experimental Setup
Experiments were conducted in a (deficit) irrigated and a non-irrigated orchard, which were monitored throughout the 2011, 2012 and 2013 growing seasons.
The (deficit) irrigated orchard, planted with Conference pear trees (Pyrus communis L. cv. ′Conference’) on Quince C rootstock, was situated in Bierbeek, Belgium (50°49′34.59″N, 4°47′42.83″E). The 2.5 m high trees were planted in 2000 in a 3.5 by 1 meter grid. They were trained in a V-system with four fruiting branches on one central stem . The orchard was situated on a south-east facing slope (3.5°) with a loamy soil and two dominant row azimuths, namely 41 and 131°. The trees received 100% of the reference evapotranspiration (ETo) , throughout most of the growing season. In the second phenological phase, characterized by vegetative growth , two irrigation treatments were applied. One treatment was fully irrigated (100% ETo), while the other treatment received no irrigation from Day of Year (DOY) 150–213, to create deficit irrigation. Each treatment was applied to four adjacent rows, with all measurements performed on both center rows of each treatment to minimize the influence of adjacent rows. In each of the center rows, four plots of four trees were selected on fixed intervals (30 m) within the row. As a result, 16 plots were monitored throughout three consecutive growing seasons. Additionally, 16 fully irrigated plots with a similar management system were selected throughout the orchard and monitored throughout the growing season in 2012 and 2013. The additional plots comprise trees with different age groups, soil types and row orientation. During the experiment, all management practices were carried out without interference to the experiment.
The non-irrigated or rainfed orchard, situated in Kerkom, Belgium (50°46′24.25″N, 5°09′27.05″E), was planted in 2000 with Conference pear trees on Quince A rootstock. The 3.5 m high trees, planted in a 3.75 by 1.75 meter grid, were trained in a Spindle bush system . The orchard was situated on a south facing slope (1.1°) with a loamy soil and a row azimuth of 197°. The trees were rainfed and received no additional water input. Two adjacent rows were selected and divided into eight plots of six trees, each plot consisting of four central experimental trees and two outside buffer trees. To ensure differences in water availability, root pruning was applied on one side of the canopy in the beginning of the growing season (March). In each row, a root-pruned plot was alternated with a non-treated plot.
In both orchards, the soil under the trees was kept weed free for about 0.3 m from the trunk and grass was sown in between the tree rows.
2.2.1. In Situ Water Status
Midday stem water potential (Ψstem) was acquired for one tree per plot, on three leaves per tree, on sunny days throughout the 2011, 2012 and 2013 growing seasons (see Table 1) with a pressure chamber . Prior to measurement, the leaves were covered with aluminum foil and enclosed in plastic bags to equilibrate leaf water with stem water potential .
Reference evapotranspiration (ETo; mm/day) was calculated according to  for the (deficit) irrigated and rainfed orchard from weather data obtained from nearby weather stations in Beauvechain, Belgium (50°46′49.86″N, 4°46′26.44″E) and Bierset, Belgium (50°39′18.69″N, 5°27′03.34″E), respectively.
2.2.2. Leaf Level Reflectance
Throughout the 2011, 2012 and 2013 growing seasons, leaf spectra were gathered for one tree per plot. Table 1 summarizes the exact dates of measurement. The measurements were performed with an ASD spectroradiometer (Analytic Spectral Devices, Boulder, CO, USA), conforming to , with a contact plant probe and an attached light source. For each tree, 20 undamaged and mature leaves were randomly selected from the sunlit part of the canopy, sampled 10 times in a basal position on the adaxial side of the leaf and averaged per leaf. All measurements were performed on sunny days within 1.5 h of local solar noon. All spectra were smoothed using a 2nd order Savitsky-Golay filter with a window size of 21 . In this study, only leaf measurements coinciding with in situ water status measurements were considered, i.e., within one day before or after a Ψstem measurement.
2.2.3. Canopy Level Reflectance
Throughout the 2011, 2012 and 2013 growing seasons, canopy reflectance measurements were collected on cloud-free days (see Table 1), using a full range (350–2,500 nm) HR-1024 spectroradiometer (Spectra Vista Corporation, New York, NY, USA). The canopy spectra were taken from an elevated position between the rows at an average height of one meter above the top of the canopy. For each plot, eight sunlit canopy spectra were taken and averaged. All measurements were performed within 1.5 h from local solar noon, to minimize differences with regards to solar geometry and illumination. All spectra were smoothed using a 2nd order Savitsky-Golay filter with a window size of 21  and atmospheric water absorption features were removed. In this study, only canopy measurements coinciding with in situ water status measurements were considered, i.e., within one day before or after a Ψstem measurement.
2.2.4. Satellite Level Reflectance
WorldView-2 multispectral iages were acquired under different off-nadir viewing angles, with a resampled ground sampling distance of 2.0 m and a spectral resolution complying eight bands: Coastal (400–450 nm), Blue (450–510 nm), Green (510–580 nm), Yellow (585–625 nm), Red (630–690 nm), Red Edge (705–745 nm), NIR1 (Near InfraRed 1; 770–895 nm) and NIR2 (860–1,040 nm). The acquisition details for the WorldView-2 images are shown in Table 2. All images were radiometrically , atmospherically  and geometrically corrected . Additionally, the position of each plot was determined with a differential global positioning system (Trimble RTK 58000).
Because of the unnatural hedge shape of capital intensive pear orchards, a viewing azimuth opposite the solar azimuth would imply that only shaded area was visible. Therefore, in the first part of this study, only plots acquired on the sunlit side of the canopy were considered.
The hyperspectral leaf and canopy measurements (Sections 2.2.2 and 2.2.3) were related with the measured Ψstem values. A normalized difference ratio (Equation (1)) was calculated for each combination of wavelengths to investigate the predictive power of all wavelengths for water status estimation.
3.1. Water Status
The temporal profile of the ETo (mm/day), the measured Ψstem (MPa) and its standard deviation are shown in Figure 1.
Before the experimental water treatment (DOY 150), the small standard deviations indicated similar Ψstem values within one day. Moreover, the measured Ψstem values were similar for all growing seasons and orchards pre-treatment, although a warm April and May in 2011 caused an exception.
During the water treatment, the average Ψstem values decreased and the standard deviations increased, indicative of water deficiency accumulation and differences between treatments. This effect was more pronounced in the (deficit) irrigated orchard, as a result of the larger differences between treatments. In the rainfed orchard, differences in Ψstem values throughout the growing season were less variable and more dependent on weather conditions. In general, the lowest average Ψstem values were encountered near the end of the water treatment period in the (deficit) irrigated orchard, as a result of the prolonged period of water deficiency. The extended use of drip irrigation caused small root systems [30,31], which were more sensitive to longer periods of lack of water, causing lower Ψstem values in the deficit irrigation treatment. In 2012, however, during the later stages of the experimental water treatment (DOY 180–220), ETo dropped to 2 mm/day for an extended period. Together with high rainfall (data not shown here), this caused low standard deviations and high average Ψstem values for both the (deficit) irrigated and rainfed orchard.
After the water treatment was finished, full irrigation (100% ETo) was again applied and the average Ψstem values restored to the level prior to the treatment.
As a result of the differences in experimental treatments, a wide range of Ψstem values were present in the dataset, with a slightly skewed distribution towards the lower Ψstem values. The overall average Ψstem was −1.20 MPa with a standard deviation of 0.43 MPa, a minimum value of −3.07 MPa and a maximum value of −0.42 MPa. The overall average standard deviation within each tree was 0.09 MPa (1,172 observations).
3.2. Leaf and Canopy Reflectance
Figure 2 summarizes the significant R2 values between measured Ψstem and the normalized difference ratios (Equation (1)) of all wavelength combinations for both leaf (above diagonal; 395 observations; Section 2.2.2) and canopy (below diagonal; 167 observations; Section 2.2.3) reflectances.
Overall, the highest R2 values between Ψstem and both leaf and canopy reflectance spectra (R2 > 0.45) were located in the SWIR region. The R1400–R1800 nm and R2000–R2400 nm regions correlated strongly with Ψstem (Figure 2). Normalized difference band combinations between SWIR and other regions resulted in low(er), yet significant R2 values. On the other hand, significant and high R2 values were also found in the VIS-NIR region (Visible-Near Infrared; 350–1,300 nm). As the spectral range of the WorldView-2 satellite only compromised the VIS-NIR region, this was further investigated.
In the VIS-NIR region, the canopy reflectance spectra were better correlated with the Ψstem values compared to the leaf reflectance spectra (Figure 2). More specifically, several bands in the VIS-NIR region showed high R2 values (R2 > 0.35), i.e., the combination of green to red (550–650 nm) or red-edge (700 nm) with blue (500–530 nm). Additionally, the combination of red-edge (700–750 nm) with NIR (800–900 nm) reflectance correlated relatively well with Ψstem (R2 > 0.3). These high R2 values were further highlighted in Figure 3, through scatterplots between Ψstem and three normalized difference ratio indices (Equation (1)); namely, a ratio of two SWIR bands (R1480 and R2230), a ratio of two VIS bands (R520 and R700), and a red-edge/NIR band combination (R800 and R722).
Similar to Figure 2, the SWIR band combination showed a high correlation with Ψstem on both leaf (R2 = 0.51; RMSE = 0.34 MPa; Figure 3a) and canopy level (R2 = 0.39; RMSE = 0.34 MPa; Figure 3d). The VIS combination showed a good correlation on canopy level (R2 = 0.48; RMSE = 0.31 MPa; Figure 3e) but no relation on leaf level (R2 = 0.02; RMSE = 0.49 MPa; Figure 3b), caused by the small range of the normalized difference ratio. Similarly, the red-edge/NIR combination showed a good correlation on canopy level (R2 = 0.20; RMSE = 0.39 MPa; Figure 3f), but was incapable to estimate Ψstem on the leaf level (R2 = 0.04; RMSE = 0.49 MPa; Figure 3c).
In addition to the SWIR band combinations, the canopy level showed high R2 values in areas of the spectral domain for which the WorldView-2 sensor was also sensitive (Figures 2, 3e and 3f). The narrow regions in the VIS with high R2 values, such as combinations between R600/R550 and R700/R550 (Figures 2 and 3e), might not transfer to the broad satellite bands (40–70 nm bandwidth; Section 2.2.4). In this perspective, the red-edge/NIR combination showed more potential (Figure 3f), as a result of the uniformly high R2 values for a large region of the NIR wavelengths (750–1,000 nm), which should transfer better to broad satellite bands (Figure 2). However, both regions in the VIS/NIR with high R2 values indicated high potential for remote estimation of Ψstem through multispectral WorldView-2 imagery, especially since no distinctions were made between different management systems, irrigation setup and phenology.
3.3. Satellite Level
As an intermediate step in the up-scaling of canopy to satellite reflectance, the canopy reflectance spectra were resampled to match the multispectral bands of the WorldView-2 satellite, using the sensor’s spectral response function (similar to ). These were referred to as the modeled satellite level.
Based on the results of leaf and canopy level measurements (Figures 2 and 3), a set of band combinations was chosen and highlighted. The applied band combinations were based on either the red-edge/NIR region or on the blue to red region, as defined by the sensor’s spectral response function  and the satellite bands (Section 2.2.4).
Firstly, the Red edge Normalized Difference Vegetation Index or ReNDVI (Equation (4)) was chosen, as the red edge was previously shown to be useful in the detection of plant water stress . In Figure 4, the ReNDVI (Equation (4)) on a modeled satellite level and satellite level is depicted against the measured Ψstem, only Ψstem measurements closest to the acquisition were considered.
Results for the ReNDVI, in Figure 4a, showed a similar correlation between the modeled WorldView-2 reflectance and Ψstem (R2 = 0.22; RMSE = 0.38 MPa) if compared to the correlation found between Ψstem and the hyperspectral (small band) ratio indices extracted from the original canopy reflectance spectra (R2 = 0.20; RMSE = 0.39 MPa; Figure 3f). This was particularly interesting since this indicates the sufficient applicability of the broad spectral bands of WorldView-2 to properly monitor Ψstem. The ultimate test was the comparison between per pixel derived ReNDVI values from the actual WorldView-2 imagery and measured Ψstem. The results in Figure 4b clearly showed a similar relationship between Ψstem and ReNDVI (R2 = 0.47; RMSE = 0.36 MPa), undoubtedly showing the potential of WorldView-2 to monitor water stress in pear orchards. With regards to the applicability of the index in multiple orchards, both the (deficit) irrigated and rainfed orchard showed a comparable relation with ReNDVI. However, the correlation of the rainfed orchard declined for both the modeled satellite level (R2 = 0.17; RMSE = 0.30 MPa) and satellite level (R2 = 0.13; RMSE = 0.36 MPa). On the other hand, the correlation of the (deficit) irrigated orchard increased for both the modeled satellite level (R2 = 0.41; RMSE = 0.40 MPa) and satellite level (R2 = 0.67; RMSE = 0.31 MPa). This was the result of the lower Ψstem range in the rainfed orchard.
In addition to ReNDVI, the narrow regions in the VIS region with high R2 values (Section 3.2) were transferred into two indices, namely the Green Band Depth index (GBD; Equation (5)) and the Normalized Difference Green Blue index (Green/Blue; Equation (6)), based on the satellite bands (Section 2.2.4). Similar to ReNDVI, both indices are depicted on a modeled satellite level and satellite level against measured Ψstem in Figures 5 and 6.
The GBD (Equation (5)) showed a strong correlation on the modeled satellite level (R2 = 0.34; RMSE = 0.35 MPa; Figure 5a), with similar distribution for both experimental locations. On the satellite level, the correlation was stronger (R2 = 0.56; RMSE = 0.32 MPa; Figure 5b). However, Figure 5b also showed points very dissimilar to the rest of the dataset. As these effects were not present on the modeled satellite level, they were most likely caused by differences in viewing geometry.
The normalized difference Green/Blue ratio (Equation (6)) also showed a strong correlation on satellite level (R2 = 0.51; RMSE = 0.33 MPa; Figure 6b). However, on the modeled satellite level the differences between experimental locations were distinct and also caused a weaker correlation (R2 = 0.18; RMSE = 0.39 MPa; Figure 6a). These differences were most likely the cause of different management systems (i.e., the V-system and the Spindle bush, see Section 2.1).
Overall, both the GBD and the Green/Blue indices showed strong correlations on either the satellite level or the modeled satellite level but were not robust for both the satellite and modeled satellite level. Therefore, ReNDVI should be preferred for further application out of all the indices applied on a satellite level in this study, as it showed less dependence towards off-nadir viewing angle or orchard architecture (i.e., row orientation, row spacing and orchard management) on either the satellite or the modeled satellite level.
4.1. Potential of High Spatial and Multispectral Satellite Derived Ψstem Estimation
As could be expected, the highest R2 values (R2 > 0.5) were observed in the SWIR on the leaf level (Figure 2). Previous studies have shown the SWIR region to be affected by water absorption and thus highly correlated with leaf water content [17,34,35]. Yet, next to the SWIR region, also the VIS-NIR region showed some significant zones with high R2 values (R2 > 0.2), mostly combinations within the 740–800 nm range. Similar to the SWIR region, several studies previously showed a relation between these wavelengths and leaf water content [36–38].
On the canopy level, similar (yet less pronounced) correlations were observed between SWIR reflectance and Ψstem (Figure 2). Moreover, the VIS-NIR region of the canopy level showed higher R2 values compared to the leaf level. This is most likely the result of the low variability in the VIS region for the leaf measurements (Figure 3b), as a consequence of the sampling design. Only mature healthy leaves were sampled, while the canopy measurements were an integrated measurement of the entire canopy (Section 2.2.3).
For canopy measurements, two regions within the VIS-NIR showed high R2 values and potential for Ψstem estimation. On the one hand, combinations within the blue/green region were found to be strongly correlated with Ψstem (Figure 2). These band combinations were similar to the bands used by the Photochemical Reflectance Index (PRI)  and are related to xanthophyll pigment changes, photosynthetic efficiency, as well providing early warning signs of water deficiency, in fruit orchards [13,14,39]. These narrow band combinations (R531 and R570) were not applicable on the broad satellite wavebands of WorldView-2, as they were included in one band (Green; 510–580 nm).
On the other hand, the combination of the red-edge (700–750 nm) with the NIR region (800–900 nm) also showed a strong correlation with Ψstem (Figure 2). Contrary to the PRI bands, the R2 values remained high for a large range of wavelengths, well suited for application on the broad satellite bands (Full Width Half Maximum between 50 and 100 nm). Previous research showed the relation between the red-edge region and stress detection [13,33], especially the shape and position of the red-edge [40,41]. For healthy leaves, the red-edge inflection point showed a displacement towards the longer wavelengths as a result of high pigment concentrations . However, during stressed periods and as a result of stress-induced chlorosis, the red-edge inflection point would shift to shorter wavelengths . In addition to the correlation with chlorophyll content , the red edge was also found to be highly dependent on phenology, leaf water content and leaf area [41,42], causing lower R2 values for the leaf measurements (Figure 2). As the resulting effect of water deficiency could be manifold, i.e., changes in water content, chlorophyll content, leaf area and leaf angle [43–45], and with the red-edge sensitive to all these influences, the red-edge could provide a good estimation of the overall health and stress level of a canopy. Moreover, it should also have a high correlation with Ψstem, as this is a measurement of the integrated effect of environmental conditions on water availability within the plant. This correlation between water potential measurements and biophysical variables was also shown by , where strong indices for Ψleaf were also strongly correlated with leaf area related indices (i.e., Normalized Difference Vegetation Index; ) and less with water content related indices (i.e., Water Index; ).
Overall, our analysis of leaf and canopy reflectance indicated high potential for the mapping of Ψstem with VIS/NIR derived band combinations, especially when keeping in mind that no distinctions were made between different management systems, irrigation setup, phenology and environmental conditions. We could further demonstrate this potential using WorldView-2 imagery (Figure 4).
The ReNDVI (Equation (4)), derived from the Red-edge and NIR1 band of WorldView-2 (Section 2.2.4.), was shown to be highly dependent on Ψstem and could be used in the remote estimation hereof. As an illustration on how such ReNDVI maps could be used for steering irrigation management, Figure 7 shows the index maps converted to estimates of Ψstem (RMSE = 0.36 MPa) for the (deficit) irrigated orchard.
The image on the left was taken during an extended period of full irrigation, while the image on the right was taken during a deficit water experiment. The maps visualize spatial differences within each orchard block and indicate different zones in the orchard requiring different irrigation management, or the presence of problems related to obstructions in the irrigation system. In general, Ψstem readings below −1.5 MPa should be considered as a cut-off between moderate and severe water stress levels . As expected, due to irrigation, Figure 7a showed no severely stressed trees (red to orange colors), while Figure 7b, acquired during a water stress experiment, showed large differences between different management blocks and water deficiency in some blocks (blue colors). With regular Ψstem maps throughout the growing season, irrigation scheduling with remote sensing input becomes feasible.
4.2. Limitations of High Spatial and Multispectral Satellite Imagery
In this study, no distinction was made between different management systems, irrigation setup, phenology and location, and still we could demonstrate the robustness of the WorldView-2 derived ReNDVI in estimating Ψstem. However, so far we only incorporated imagery with a viewing angle that allowed fully sunlit canopies. The distinction between sunlit and shaded scenes was based on the difference between the row azimuth and the relative azimuth between sensor and sun. Other studies have shown that the inclusion of shaded canopy parts could cause a decrease in correlation [13,14]. In Figure 8, all the acquired images (Table 2) were taken into account, including imagery with different viewing geometry.
Similar to other studies [13,14], the predictive power of the ReNDVI decreased after the inclusion of all the shaded scenes, as is shown in Figure 8 (R2 = 0.18; RMSE = 0.49 MPa). On the other hand, Figure 8a,b also showed a clear influence of viewing geometry on the ReNDVI index value. In addition to the light conditions in the plot, namely sunlit or shaded canopy, the off-nadir viewing angle was also shown to be important. The ReNDVI was only affected by the combination of high off-nadir viewing angles (i.e., off-nadir viewing angles over 20°) and a viewing geometry opposite to the sun with regards to the rows. The relative position of the sensor was important for larger off-nadir viewing angles (>20°), as it determined whether the scene was fully sunlit or fully shaded. However, for smaller off-nadir viewing angles (<20°), the relative position of the sensor with regards to the sun and row orientation was found not to be important, as most of the canopy would still be sunlit. With the shaded scenes with small off-nadir viewing angles (<20°), the R2 increased to 0.56 and RMSE remained at 0.36 MPa (Figure 8c).
One influential parameter that could hamper applicability of the ReNDVI index is the background, i.e., the bare soil, cover crop or shadow. The influence of background on the use of the ReNDVI was not investigated here, as both the (deficit) irrigated and rainfed orchard had grass sown in between the rows to minimize soil erosion. However, some researchers have already indicated that soil background effects could decrease the correlation between vegetation indices and biophysical variables [14,47].
Ongoing research is focusing on developing and applying specific spectral mixture analysis techniques [48–50] to address these issues. As such, we hope to further advance the operational implementation of high resolution multispectral satellite observations, such as WorldView-2, in precision agriculture. The results presented in this work show great promise for the remote steering of irrigation management.
This study aimed at investigating the potential of WorldView-2 satellite imagery for monitoring of stem water potential (Ψstem). To this end, Ψstem was monitored throughout three consecutive growing seasons in a (deficit) irrigated and non-irrigated (rainfed) pear orchard and related to spectral observations of leaves, canopies and WorldView-2 satellite imagery. Through ground measurements of leaf and canopy level, a robust spectral indicator of Ψstem was sought, independent of orchard parameters (i.e., phenology, tree structure and irrigation setup) and environmental conditions (i.e., soil conditions). Through satellite imagery, the sensitivity of the spectral indicator towards sensor parameters (i.e., off-nadir view angle, viewing geometry) was investigated.
For hyperspectral ground measurements, it was shown that next to the Shortwave Infrared Region (SWIR) (combinations of the reflectance at 1,430 nm (R1430) and R2230), also the Visible-Near Infrared Region (VIS-NIR) showed regions with significant R2 values for leaf level measurements. This was more pronounced at canopy level, for which combinations within the blue/green region (R500 to R570) together with the red/red-edge region (R670 to R720) were strongly related to Ψstem. Yet, the combinations of the red-edge (R700 to R750) with the NIR region (R800 to R900) were more suited for application on the broad satellite bands, as they showed a strong correlation with Ψstem for a larger range of wavelengths for both leaf and canopy level.
The Red-edge Normalized Difference Vegetation Index (ReNDVI), derived from the Red-edge (705–745 nm) and NIR1 (Near InfraRed 1; 770–895 nm) band of WorldView-2, was shown to be highly dependent on Ψstem and could be used in the remote estimation hereof, independent of management systems, irrigation setup, phenology and environmental conditions. In addition, results showed that the relation between Ψstem and ReNDVI was independent of the off-nadir viewing angle and almost independent of viewing geometry. For larger off-nadir viewing angles, the relative position of the sensor determined whether the scene was fully sunlit or fully shaded, while for smaller off-nadir viewing angles the scene would be mostly sunlit, independent on the azimuth of the sensor. With the inclusion of these partly shaded but mostly sunlit scenes, the correlation improved (R2 = 0.56; RMSE = 0.36 MPa).
With further research focusing on issues related to viewing geometry and shadows, high spatial water status monitoring with space borne remote sensing is achievable.
This work was supported by the Agency for Innovation by Science and Technology in Flanders (IWT-Vlaanderen). The research was funded through a project in cooperation with the Soil Service of Belgium (BDB) and Proefcentrum Fruitteelt (PC Fruit). The authors would like to thank the Soil Service of Belgium (BDB) for providing the in situ water status data and making this work possible. In addition, the authors would like to acknowledge Jan Vandervelpen and Proefcentrum Fruitteelt (PC Fruit) for opening their orchards.
Conflicts of Interest
The authors declare no conflict of interest.
- Janssens, P.; Deckers, T.; Elsen, F.; Elsen, A.; Schoofs, H.; Verjans, W.; Vandendriessche, H. Sensitivity of root pruned ‘Conference’ pear to water deficit in a temperate climate. Agric. Water Manag 2011, 99, 58–66. [Google Scholar]
- Pinter, P.J., Jr.; Hatfield, J.L.; Schepers, J.S.; Barnes, E.M.; Moran, M.S.; Daughtry, C.S.T.; Upchurch, D.R. Remote sensing for crop management. Photogramm. Eng. Remote Sens. 2003, 69. [Google Scholar] [CrossRef]
- Perry, E.M.; Dezzani, R.J.; Seavert, C.F.; Pierce, F.J. Spatial variation in tree characteristics and yield in a pear orchard. Precis. Agric 2010, 11, 42–60. [Google Scholar]
- Dorigo, W.A.; Zurita-Milla, R.; de Wit, A.J.W.; Brazile, J.; Singh, R.; Schaepman, M.E. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. Int. J. Appl. Earth Obs. Geoinf 2007, 9, 165–193. [Google Scholar]
- Govender, M.; Dye, P.J.; Weiersbye, I.M.; Witkowski, E.T.F.; Ahmed, F. Review of commonly used remote sensing and ground-based technologies to measure plant water stress. Water SA 2009, 35, 741–752. [Google Scholar]
- Sepulcre-Canto, G.; Zarco-Tejada, P.J.; Jimenez-Munoz, J.C.; Sobrino, J.A.; Soriano, M.A.; Fereres, E.; Vega, V.; Pastor, M. Monitoring yield and fruit quality parameters in open-canopy tree crops under water stress. Implications for ASTER. Remote Sens. Environ 2007, 107, 455–470. [Google Scholar]
- Dzikiti, S.; Verreynne, J.S.; Stuckens, J.; Strever, A.; Verstraeten, W.W.; Swennen, R.; Coppin, P. Determining the water status of Satsuma mandarin trees [Citrus Unshiu Marcovitch] using spectral indices and by combining hyperspectral and physiological data. Agric. For. Meteorol 2010, 150, 369–379. [Google Scholar]
- Stuckens, J.; Verstraeten, W.W.; Delalieux, S.; Swennen, R.; Coppin, P. A dorsiventral leaf radiative transfer model: Development, validation and improved model inversion techniques. Remote Sens. Environ 2009, 113, 2560–2573. [Google Scholar]
- Acevedo-Opazo, C.; Tisseyre, B.; Guillaume, S.; Ojeda, H. The potential of high spatial resolution information to define within-vineyard zones related to vine water status. Precis. Agric 2008, 9, 285–302. [Google Scholar]
- Serrano, L.; González-Flor, C.; Gorchs, G. Assessment of grape yield and composition using the reflectance based Water Index in Mediterranean rainfed vineyards. Remote Sens. Environ 2012, 118, 249–258. [Google Scholar]
- Naor, A.; Gal, Y.; Peres, M. The inherent variability of water stress indicators in apple, nectarine and pear orchards, and the validity of a leaf-selection procedure for water potential measurements. Irrig. Sci 2006, 24, 129–135. [Google Scholar]
- McCutchan, H.; Shackel, K.A. Stem-water potential as a sensitive indicator of water stress in prune trees (Prunus domestica L. cv. French). J. Am. Soc. Hortic. Sci 1992, 117, 607–611. [Google Scholar]
- Suárez, L.; Zarco-Tejada, P.J.; Sepulcre-Cantó, G.; Pérez-Priego, O.; Miller, J.R.; Jiménez-Muñoz, J.C.; Sobrino, J. Assessing canopy PRI for water stress detection with diurnal airborne imagery. Remote Sens. Environ 2008, 112, 560–575. [Google Scholar]
- Stagakis, S.; González-Dugo, V.; Cid, P.; Guillén-Climent, M.L.; Zarco-Tejada, P.J. Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band structural and physiological remote sensing indices. ISPRS J. Photogramm. Remote Sens 2012, 71, 47–61. [Google Scholar]
- Marshall, G.J.; Dowdeswell, J.A.; Rees, W.G. The spatial and temporal effect of cloud cover on the acquisition of high quality landsat imagery in the european arctic sector. Remote Sens. Environ 1994, 50, 149–160. [Google Scholar]
- Moran, M.S.; Fitzgerald, G.J.; Rango, A.; Walthall, C.L.; Barnes, E.D.; Bausch, W.C.; Clarke, T.R.; Daughtry, C.S.; Everitt, J.H.; Hatfield, J.L.; et al. Sensor development and radiometric correction for agricultural applications. Photogramm. Eng. Remote Sens 2003, 69, 705–718. [Google Scholar]
- Ceccato, P.; Flasse, S.; Grégoire, J.-M. Designing a spectral index to estimate vegetation water content from remote sensing data: Part 2. Validation and applications. Remote Sens. Environ 2002, 82, 198–207. [Google Scholar]
- Jackson, R.D.; Idso, S.B.; Reginato, R.J.; Pinter, P.J., Jr. Canopy temperature as a crop water stress indicator. Water Resour. Res 1981, 17, 1133–1138. [Google Scholar]
- Zhao, F.; Gu, X.; Verhoef, W.; Wang, Q.; Yu, T.; Liu, Q.; Huang, H.; Qin, W.; Chen, L.; Zhao, H. A spectral directional reflectance model of row crops. Remote Sens. Environ 2010, 114, 265–285. [Google Scholar]
- Haboudane, D.; Miller, J.R.; Pattey, E.; Zarco-Tejada, P.J.; Strachan, I.B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ 2004, 90, 337–352. [Google Scholar]
- Sansavini, S.; Musacchi, S. Canopy architecture, training and pruning in the Modern European pear orchards: An overview. Acta Hort 1994, 367, 152–172. [Google Scholar]
- Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop Evapotranspiration—Guidelines for Computing Crop Water Requirements—FAO Irrigation and Drainage Paper 56; Food and Agriculture Organization of the United Nations: Rome, Italy, 1998. [Google Scholar]
- Mitchell, P.D.; Jerie, P.H.; Chalmers, D.J. Effects of regulated water deficits on pear tree growth, flowering, fruit growth, and yield. J. Am. Soc. Hortic. Sci 1984, 109, 604–606. [Google Scholar]
- Scholander, P.F.; Bradstreet, E.D.; Hemmingsen, E.A.; Hammel, H.T. Sap pressure in vascular plants. Science 1965, 148, 339–346. [Google Scholar]
- Begg, J.E.; Turner, N.C. Water potential gradients in field tobacco. Plant Physiol 1970, 46, 343–346. [Google Scholar]
- Savitsky, A.; Golay, M.J.E. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem 1964, 36, 1627–1639. [Google Scholar]
- Radiometric Use of WorldView-2 Imagery. Available online: http://www.digitalglobe.com/sites/default/files/Radiometric_Use_of_WorldView-2_Imagery%20%281%29.pdf (accessed on 3 December 2013).
- Adler-Golden, S.; Berk, A.; Bernstein, L.S.; Richtsmeier, S.; Acharya, P.K.; Matthew, M.W.; Anderson, G.P.; Allred, C.L.; Jeong, L.S.; Chetwynd, J.H. Flaash, a Modtran4 Atmospheric Correction Package for Hyperspectral Data Retrievals and Simulations. In Summaries of the Seventh JPL Airborne Earth Science Workshop January 12–16, 1998; Jet Propulsion Laboratory, National Aeronautics and Space Administration: Pasadena, CA, USA, 1998. [Google Scholar]
- Grodecki, J.; Dial, G. Block adjustment of high-resolution satellite images described by rational polynomials. Photogramm. Eng. Remote Sens. 2003, 69. [Google Scholar] [CrossRef]
- Araujo, F.; Williams, L.E.; Grimes, D.W.; Matthews, M.A. A comparative study of young ‘Thompson Seedless’ grapevines under drip and furrow irrigation. I. Root and soil water distributions. Sci. Hortic 1995, 60, 235–249. [Google Scholar]
- Fernandez, J.E.; Moreno, F.; Cabrera, F.; Arrue, J.L.; Martın-Aranda, J. Drip irrigation, soil characteristics and the root distribution and root activity of olive trees. Plant Soil 1991, 133, 239–251. [Google Scholar]
- van Leeuwen, W.J.D.; Orr, B.J.; Marsh, S.E.; Herrmann, S.M. Multi-sensor NDVI data continuity: Uncertainties and implications for vegetation monitoring applications. Remote Sens. Environ 2006, 100, 67–81. [Google Scholar]
- Blackburn, G.A. Hyperspectral remote sensing of plant pigments. J. Exp. Bot 2007, 58, 855–67. [Google Scholar]
- Ceccato, P.; Flasse, S.; Tarantola, S.; Jacquemoud, S.; Grégoire, J.-M. Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sens. Environ 2001, 77, 22–33. [Google Scholar]
- Gao, B. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ 1996, 58, 257–266. [Google Scholar]
- Hunt, E.R., Jr.; Rock, B.N. Detection of changes in leaf water content using Near- and Middle-infrared reflectances. Remote Sens. Environ 1989, 30, 43–54. [Google Scholar]
- Danson, F.M.; Steven, M.D.; Malthus, T.J.; Clark, J.A. High-spectral resolution data for determining leaf water content. Int. J. Remote Sens 1992, 13, 461–470. [Google Scholar]
- Mogensen, V.O.; Jensen, C.R.; Mortensen, G.; Thage, J.H.; Koribidis, J.; Ahmed, A. Spectral reflectance index as an indicator of drought of field grown oilseed rape (Brassica napus L.). Eur. J. Agron 1996, 5, 125–135. [Google Scholar]
- Gamon, J.A.; Serrano, L.; Surfus, J.S. The photochemical reflectance index: An optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia 1997, 112, 492–501. [Google Scholar]
- Blackburn, G.A. Relationships between spectral reflectance and pigment concentrations in stacks of deciduous broadleaves. Remote Sens. Environ 1999, 70, 224–237. [Google Scholar]
- Horler, D.N.H.; Dockray, M.; Barber, J. The red edge of plant reflectance. Int. J. Remote Sens 1983, 4, 273–288. [Google Scholar]
- Stagakis, S.; Markos, N.; Sykioti, O.; Kyparissis, A. Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: An application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations. Remote Sens. Environ 2010, 114, 977–994. [Google Scholar]
- Lakso, A.N. The effects of water stress on physiological processes in fruit crops. Acta Hort 1984, 171, 275–290. [Google Scholar]
- Guerfel, M.; Baccouri, O.; Boujnah, D.; Chaïbi, W.; Zarrouk, M. Impacts of water stress on gas exchange, water relations, chlorophyll content and leaf structure in the two main Tunisian olive (Olea europaea L.) cultivars. Sci. Hortic 2009, 119, 257–263. [Google Scholar]
- Haas, R.H.; Schell, J.A. Monitoring the Vernal Advancement and Retrogradation (greenwave Effect) of Natural Vegetation; Remote Sensing Center, Texas A & M University: Denton, TX, USA, 1974. [Google Scholar]
- Penuelas, J.; Filella, I.; Biel, C.; Serrano, L.; Savé, R. The reflectance at the 950–970 nm region as an indicator of plant water status. Int. J. Remote Sens. 1993, 14, 1887–1905. [Google Scholar]
- Zarco-Tejada, P.J.; Miller, J.R.; Morales, A.; Berjón, A.; Agüera, J. Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops. Remote Sens. Environ 2004, 90, 463–476. [Google Scholar]
- Somers, B.; Delalieux, S.; Verstraeten, W.; Coppin, P. A conceptual framework for the simultaneous extraction of sub-pixel spatial extent and spectral characteristics of crops. Photogramm. Eng. Remote Sens 2009, 75, 57–68. [Google Scholar]
- Tits, L.; Somers, B.; Stuckens, J.; Farifteh, J.; Coppin, P. Integration of in situ measured soil status and remotely sensed hyperspectral data to improve plant production system monitoring: Concept, perspectives and limitations. Remote Sens. Environ 2013, 128, 197–211. [Google Scholar]
- Iordache, D.-M.; Tits, L.; Plaza, A.; Bioucas-Dias, J.; Somers, B. A dynamic unmixing framework for site specific monitoring of plant production systems. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2013. submitted. [Google Scholar]
|Table 1. Ψstem, Leaf reflectance and canopy reflectance measurements for both locations during the growing seasons of 2011, 2012 and 2013. Only reflectance measurements coinciding with in situ water status measurements were considered, i.e., within one day of a Ψstem measurement.|
|Location||Year||DOY of Ψstem Measurements||DOY of Leaf Level Reflectance Measurements||DOY of Canopy Level Reflectance Measurements|
|(Deficit) Irrigated Orchard||2011||133, 140, 146, 167, 193, 215 and 238||214||141|
|2012||145, 150, 157, 166, 178, 180, 200, 207, 214, 233 and 242||242||208 and 214|
|2013||159, 166, 170, 183, 187, 194, 205, 215 and 240||159, 166, 170, 183, 187, 194, 215 and 240||195 and 214|
|Rainfed Orchard||2011||132, 141, 151, 179 and 214||214||178|
|2012||146, 151, 171, 185, 206, 217, 223 and 236||217||146 and 207|
|2013||156, 163, 193, 199, 214 and 225||156, 163, 193, 214 and 225||157 and 213|
|Table 2. Metadata of WorldView-2 acquisitions used in this study.|
|Location||Year||DOY||Off-nadir Viewing Angle (°)||Satellite Azimuth (°)||Satellite Elevation (°)|
|(Deficit) Irrigated Orchard||2011||214||10.8||45.9||78|
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