Leaf water content estimation by functional linear regression of field spectroscopy data

Abstract

Vitis vinifera L. Grapevine water status is critical as it affects fruit quality and yield. We assessed the potential of field hyperspectral data in estimating leaf water content (C w) (expressed as equivalent water thickness) in four commercial vineyards of Vitis vinifera L. reflecting four grape varieties (Mencía, Cabernet Sauvignon, Merlot and Tempranillo). Two regression models were evaluated and compared: ordinary least squares regression (OLSR) and functional linear regression (FLR). OLSR was used to fit C w and vegetation indices, whereas FLR considered reflectance in four spectral ranges centred at the 960, 1190, 1465 and 2035 nm wavelengths. The best parameters for the FLR model were determined using cross-validation. Both models were compared using the coefficient of determination (R 2) and percentage root mean squared error (%RMSE). FLR using continuous stretches of the spectrum as input produced more suitable C w models than the vegetation indices, considering both the fit and degree of adjustment and the interpretation of the model. The best model was obtained using FLR in the range centred at 1465 nm (R 2 ¼ 0.70 and % RMSE ¼ 8.485). The results depended on grape variety but also suggested that leaf C w can be predicted on the basis of spectral signature.

Field Spectroscopy: A Non-Destructive Technique for Estimating Water Status in Vineyards

“…The spectral areas of interest (Zones I and II in Figure 6), obtained from raw reflectance data without pre-processing, corresponded to the traditional absorption peaks, located at 1442 nm and 1928 nm, as frequently reported in previous works [8,46,47]. The numerous tests we carried out suggest that the optimum bandwidth to be used around these two absorption peaks spans from immediately before to immediately after the maximums.…”

“…The numerous tests we carried out suggest that the optimum bandwidth to be used around these two absorption peaks spans from immediately before to immediately after the maximums. These zones have traditionally been used for the correlation of many variables related to water in plants [8,25,45,47].…”

The Impact of Pan-Sharpening and Spectral Resolution on Vineyard Segmentation through Machine Learning

“…A potential avenue for future work is to extend this study by incorporating shortwave infrared (SWIR) reflectance from the Worldview-3 satellite. The response of vegetation at these wavelengths is strongly related to their water content, and has been shown in viticultural studies to be highly sensitive to water stress [72], and can be used to infer other biophysical parameters and forecast plant production/yield. The incorporation of SWIR reflectance may also provide benefit in the discrimination of grapevine vegetation from other types of row crops, thereby increasing the accuracy of the automated grapevine detection and image segmentation.…”

Foliar Moisture Content from the Spectral Signature for Wildfire Risk Assessments in Valparaíso-Chile

“…The water content estimation from the spectral signature has been widely studied for a broad variety of vegetation species. Based on the review of the literature, we selected eighteen vegetation indexes that appear frequently in studies related to the use of VIs for the water content estimation (see Table 2 for VIs nomenclatures, references, and a brief description [59][60][61][62][63][64][65][66][67][68][69][70]). For example, in [59] the MSI, NDWI, TM5/TM7, and WI were used to estimate the leaf FMC, and EWT from remotely sensed reflectance.…”

“…However, such eucalyptus species was not Eucalyptus Globulus, and the leaves were not dehydrated. Based on field spectroscopy data, the EWT was estimated using the following indexes WI, SRWI, NDWI, fWBI, SIWSI, and NDII [62]. Also, the same indexes were compared against the use of full-spectrum and continuum removal for leaf-level EWT retrieval in commercial vineyards [63].…”