Estimating Leaf Water Content Research Articles

Towards practical semi-empirical models for the estimation of leaf and canopy water contents from hyperspectral reflectance

The knowledge of crop water status provides valuable information for agricultural water management. Most remote sensing methods for estimating water contents are based on statistical models and spectral features in the shortwave infrared region where water absorption dominates. However, the effects of leaf structure (Ns) and dry matter (expressed as leaf mass per area, LMA) on the spectral features in this region are not fully considered when estimating leaf water content (LWC) and canopy water content (CWC). This issue would lead to the data-specific empirical model, which lacks generality and is not transferable among different conditions. To fill this gap, this study for the first time evaluated the Ns and LMA effects on the spectral features, including the widely used normalized difference (ND)-type vegetation index (VI) and wavelet feature (WF) from wavelet transform. The leaf and canopy radiative transfer model simulations showed that the ND-type VI was sensitive to Ns at both leaf and canopy levels, whereas WF was sensitive to LMA at the canopy level. For example, the relationships between WF and CWC varied clearly with the level of LMA, especially for high values of CWC. Next, the optimal water index (OWI = ND1200,1500) and optimal WF (OWF = WF1600,8) were determined by minimizing their correlations with the Ns and LMA based on the leaf and canopy simulations. Unlike the existing spectral features applicable to only leaf or canopy level alone, OWI and OWF showed more consistent performance on estimating LWC and CWC. OWI- and OWF-based semi-empirical models were calibrated using model simulations and then evaluated using the independent measured datasets. The semi-empirical model based on OWI exhibited moderate estimation accuracy of LWC (R2 = 0.62, RMSE = 24.33 g/m2) and CWC (R2 = 0.69, RMSE = 0.15 kg/m2), outperforming previous ND-type VIs. In comparison, the best estimation accuracies for LWC (R2 = 0.80, RMSE = 17.47 g/m2) and CWC (R2 = 0.85, RMSE = 0.11 kg/m2) were obtained by the OWF-based semi-empirical model when the prior information of LMA was incorporated at the canopy level. These findings have great potential for mapping crop water status over large regions with universal semi-empirical models and hyperspectral satellite imagery.

Comparison of various approaches for estimating leaf water content and stomatal conductance in different plant species using hyperspectral data

Water deficit stress is a frequent phenomenon that inhibits plant growth. This study explores the performance of hyperspectral data for estimating the leaf water content of ten tree species under different water conditions. The three most commonly used leaf water content indicators (relative water content, equivalent water thickness, and fuel moisture content) and stomatal conductance were assessed using narrow-band indices (single band, band ratio, band subtraction, and band difference) and multivariate analyses (partial least squares regression (PLSR), support vector regression, artificial neural network, and random forest) within the 350–2500 nm spectral reflectance range. The results indicated that the best bands and band combinations were mainly concentrated in the short-wavelength infrared region, which is sensitive regarding plant water content. Compared to a single band, dual-band indices exhibited better overall performance among the four kinds of indices. Multivariate analyses are more accurate than narrow-band indices. Among these, PLSR is the most robust and can be considered the optimal technique for predicting the water content of all tree species, except for conifer species. However, accurately predicting stomatal conductance is difficult to predict using these methods. This study shows that the PLSR model can accurately estimate leaf water content in multiple tree species, and hyperspectral technology, such as hyperspectral remote sensing, has potential regarding the estimation of leaf water content.

Estimating Leaf Water Content through Low-Cost LiDAR

In recent years, rapid development has been achieved in technologies and sensors related to autonomous driving and assistive technologies. In this study, low-cost light detection and ranging (LiDAR) was used to estimate leaf water content (LWC) by measuring LiDAR reflectance instead of morphological measurement (e.g., plant size), which is the conventional method. Experimental results suggest that reflection intensity can be corrected using the body temperature of LiDAR, when using reflection intensity observed by LiDAR. Comparisons of corrected LiDAR observation data and changes in reflectance attributed to leaf drying suggest that the reflectance increases with leaf drying in the 905 nm band observed with a hyperspectral camera. The LWC is estimated with an R2 of 0.950, RMSE of 6.78%, and MAPE of 18.6% using LiDAR reflectance. Although the 905 nm wavelength used by LiDAR is not the main water absorption band, the reflectance is closely related to the leaf structure; therefore, it is believed that the reflectance changes with structural changes accompanying drying, which allows for the indirect estimation of LWC. This can help utilize the reflectance of the 905 nm single-wavelength LiDAR, which, to the best of our knowledge has not been used in plant observations for estimating LWC.

Estimating leaf water content at the leaf scale in soybean inoculated with arbuscular mycorrhizal fungi from in situ spectral measurements

Leaf water content (LWC) of crops is a suitable parameter for evaluation of plant water status and arbuscular mycorrhizal effect on the host plant under drought stress. Remote sensing technology provides an effective avenue to estimate LWC in crops. However, few LWC retrieval models have been developed specifically for the arbuscular mycorrhizal inoculated crops. In this study, soybean with inoculation and non-inoculation treatments were planted under the severe drought, moderate drought and normal irrigation levels. The LWC changes under different treatments at the 30th, 45th and 64th day after the inoculation were investigated, and the spectral response characteristics of inoculated and non-inoculated soybean leaves under the three drought stresses were analyzed. Five types of spectral variables/indices including: raw spectral reflectance (R), continuum-removed spectral reflectance (RC), difference vegetation index (DVI), normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) were applied to determine the best estimator of LWC. The results indicate that LWC decreased as the aggravating of drought stress levels. However, LWC in inoculated leaves was higher than that in the counterparts under the same drought stress level, and the values of raw reflectance measured at inoculated leaves were lower than the non-inoculated leaves, especially around 1900 nm and 1410 nm. These water spectral features were more evident in the corresponding continuum-removed spectral reflectance. The newly proposed DVIC(2280, 1900) index, derived from the continuum-removed spectral reflectance at 2280 nm and the raw spectral reflectance at 1900 nm in DVI type of index, was the most robust for soybean LWC assessment, with R2 value of 0.72 (p < 0.01) and root mean square error (RMSE) and mean absolute error (MAE) of 2.12% and 1.75%, respectively. This study provides a means to monitor the mycorrhizal effect on drought-induced crops indirectly and non-destructively. Keywords: leaf water content, remote sensing, arbuscular mycorrhizal fungi, drought, crops DOI: 10.25165/j.ijabe.20191206.4950 Citation: Kong W P, Huang W J, Zhou X F, Mortimer H, Ma L L, Tang L L, et al. Estimating leaf water content at the leaf scale in soybean inoculated with arbuscular mycorrhizal fungi from in situ spectral measurements. Int J Agric & Biol Eng, 2019; 12(6): 149–155.

Effect of dust deposition on spectrum-based estimation of leaf water content in urban plant

Abstract Hyperspectral technology has equipped large-scale efforts to estimate leaf water content. In an urban environmental context, these efforts may be hampered by dust deposition on leaves. However, the influence of dust deposition on the accuracy of estimating water content is still to be elucidated. This study aimed to comprehensively analyze the influence of dust deposition on the spectrum-based estimation of water content in Shanghai, China. We measured the amount of dust deposition, water content, and the spectrum of 707 sample leaves. We then derived the spectrum-based indexes of water content and assessed the influence of dust deposition on the performance of such indexes in estimating leaf water content. The results revealed that dust-polluted leaves retained the correlations between the observed water content indexes, which includes Equivalent Water Thickness (EWT) and Fuel Water Content (FWC), with spectrum-based indexes. However, these correlation coefficients dramatically varied with the changes in dust deposition amount. EWT had weaker while FWC got stronger correlation coefficients with spectral indexes for dusty leaves compared with clean cases. The spectral indexes were correlated with EWT at a consistently stronger extent than with FWC, suggesting an advantage of using EWT over FWC to represent the leaf water content. The correlations between EWT and spectral indexes were stronger with leaf samples from the same tree species compared with the case of using leaf samples from mixed species. These findings can strengthen the understanding of the influence of dust pollution on leaf water content estimation using spectral indexes.

Estimating leaf water content at the leaf scale in soybean inoculated with arbuscular mycorrhizal fungi from in situ spectral measurements

Leaf water content (LWC) of crops is a suitable parameter for evaluation of plant water status and arbuscular mycorrhizal effect on the host plant under drought stress. Remote sensing technology provides an effective avenue to estimate LWC in crops. However, few LWC retrieval models have been developed specifically for the arbuscular mycorrhizal inoculated crops. In this study, soybean with inoculation and non-inoculation treatments were planted under the severe drought, moderate drought and normal irrigation levels. The LWC changes under different treatments at the 30th, 45th and 64th day after the inoculation were investigated, and the spectral response characteristics of inoculated and non-inoculated soybean leaves under the three drought stresses were analyzed. Five types of spectral variables/indices including: raw spectral reflectance (R), continuum-removed spectral reflectance (RC), difference vegetation index (DVI), normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) were applied to determine the best estimator of LWC. The results indicate that LWC decreased as the aggravating of drought stress levels. However, LWC in inoculated leaves was higher than that in the counterparts under the same drought stress level, and the values of raw reflectance measured at inoculated leaves were lower than the non-inoculated leaves, especially around 1900 nm and 1410 nm. These water spectral features were more evident in the corresponding continuum-removed spectral reflectance. The newly proposed DVIC(2280, 1900) index, derived from the continuum-removed spectral reflectance at 2280 nm and the raw spectral reflectance at 1900 nm in DVI type of index, was the most robust for soybean LWC assessment, with R2 value of 0.72 (p < 0.01) and root mean square error (RMSE) and mean absolute error (MAE) of 2.12% and 1.75%, respectively. This study provides a means to monitor the mycorrhizal effect on drought-induced crops indirectly and non-destructively. Keywords: leaf water content, remote sensing, arbuscular mycorrhizal fungi, drought, crops DOI: 10.25165/j.ijabe.20191206.4950 Citation: Kong W P, Huang W J, Zhou X F, Mortimer H, Ma L L, Tang L L, et al. Estimating leaf water content at the leaf scale in soybean inoculated with arbuscular mycorrhizal fungi from in situ spectral measurements. Int J Agric & Biol Eng, 2019; 12(6): 149–155.

Estimating leaf water content at the leaf scale in soybean inoculated with arbuscular mycorrhizal fungi from in situ spectral measurements

Estimating leaf water content at the leaf scale in soybean inoculated with arbuscular mycorrhizal fungi from in situ spectral measurements

Determining optimum wavelengths for leaf water content estimation from reflectance: A distance correlation approach

This paper proposes a method to estimate leaf water content from reflectance in four commercial vineyard varieties by estimating the local maxima of a distance correlation function. First, it applies four different functional regression models to the data and compares the models to test the viability of estimating water content from reflectance. It then applies our methodology to select a small number of wavelengths (optimum wavelengths) from the continuous spectrum, which simplifies the regression problem. Finally, it compares the results to those obtained by means of two different methods: a nonparametric kernel smoothing for variable selection in functional data and a wavelet-based weighted LASSO functional linear regression. Our approach proved to have some advantages over these two testing approaches, mainly in terms of the computing time and the lack of assumption of an underlying model. Finally, the paper concludes that estimating water content from a few wavelengths is almost equivalent to doing so using larger wavelength intervals.

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

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 (Cw) (expressed as equivalent water thickness) in four commercial vineyards of Vitis vinifera L. reflecting four grape varieties (Mencia, 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 Cw 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 (R2) and percentage root mean squared error (%RMSE). FLR using continuous stretches of the spectrum as input produced more suitable Cw 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 (R2 = 0.70 and %RMSE = 8.485). The results depended on grape variety but also suggested that leaf Cw can be predicted on the basis of spectral signature.

Feasibility of estimating leaf water content using spectral indices from WorldView-3’s near-infrared and shortwave infrared bands

ABSTRACTA recently-launched high-resolution commercial satellite, DigitalGlobe’s WorldView-3, has 8 bands in the shortwave infrared (SWIR) wavelength region, which may be capable of estimating canopy water content at 3.7-m spatial resolution. WorldView-3 also has 8 multispectral bands at 1.24-m resolution with two bands in the near-infrared (NIR). The relative spectral response functions for WorldView-3 were provided by DigitalGlobe, Inc., and band reflectances were determined for reflectance spectra of PROSPECT model simulations and leaf data from maize, trees, grasses, and broadleaf herbaceous eudicots. For laboratory measurements, the range of leaf water contents was extended by including drying leaves and leaf stacks of corn, soybean, oaks, and maples. Correlations between leaf water content and spectral indices from model simulations suggested that indices using SWIR band 1 (center wavelength 1210 nm) had low variability with respect to leaf water content, but also low sensitivity. Other indices using SWIR band 5 (2165 nm) had the highest sensitivity, but also had high variability caused by different values of the leaf structure parameter in PROSPECT. Indices using SWIR bands 2, 3 and 4 (1570, 1660, and 1730 nm, respectively) had high correlations and intermediate variability from the leaf structure parameter. Spectral indices calculated from the leaf data had the same overall patterns as the simulations for variation and sensitivity; however, indices using SWIR band 1 had low correlations, and the best correlations were from indices that used SWIR bands 2, 3 and 4. Spectral indices for maize, grasses, and herbaceous crops and weeds had similar responses to leaf water content; tree leaves had higher index values and saturated at lower leaf water contents. The specified width of NIR band 2 (860–1040 nm) overlaps the water absorption feature at 970 nm wavelength; however, the normalized difference of NIR band 1 and 2 was insensitive to water content because NIR band 2’s spectral response was most heavily weighted to wavelengths less than 930 nm. The high spatial resolution of the WorldView-3 SWIR data will help analyze how variation among plant species and functional groups affects spectral responses to differences in canopy water content.

Using field spectrometry and a plant probe accessory to determine leaf water content in commercial vineyards

Vine water status is widely considered to be fundamental to grape yield and quality. Typical Mediterranean vineyards experience seasonal droughts so water deficits need to be controlled. We evaluated the usefulness and effectiveness of a field spectroradiometer used to estimate vine water content at the leaf and canopy levels. The experiment was conducted in four commercial vineyards located in the Bierzo region (northwestern Spain) on four different grape varieties (Mencía, Cabernet Sauvignon, Tempranillo and Merlot). Data on spectral measurements and leaf variables (total specific leaf fresh weight, equivalent water thickness and specific leaf weight) were compiled during the growth phase up to berry set and veraison in 2009 and 2012 and the relationship between leaf variables, vegetation indices and continuum removal variables was studied. The results varied depending on the variety; also, at canopy level they were not suitable for determining water content. Equivalent water thickness and total specific leaf fresh weight for Tempranillo and Mencía were related to the normalized difference infrared and shortwave infrared water stress indices. Using the continuum removal variables, the best correlations for equivalent water thickness were achieved for band area and maximum band depth calculated for the 1200nm, 1450nm and 1950nm intervals. To estimate vine water status, we recommend calculating the band area for 1450nm because of its link to equivalent water thickness (R2=0.681 for Tempranillo). We demonstrate that yield spectroradiometry is a rapid and non-destructive method for estimating leaf water content in commercial vineyard at leaf level.

Estimation of Leaf Water Content in Winter Wheat Using Grey Relational Analysis–Partial Least Squares Modeling with Hyperspectral Data

Water availability limits wheat (Triticum aestivum L.) production in many areas of the world. It may be possible to use crop reflectance to estimate leaf water content (LWC). The objectives of this study were (i) to develop and test a model for assessing water stress in winter wheat, where the regression models, grey relational analysis–partial least squares (GRA‐PLS), the optimal band ratio normalized difference, and three bands algorithms were tested, and (ii) to compare the performance of the proposed models using GRA‐PLS and the three‐band algorithm. Spectral variables and concurrent LWC parameters of samples were acquired in Tongzhou and Shunyi districts, Beijing, China, during 2008 and 2009 winter wheat growth seasons. The GRA‐PLS model was established to estimate LWC. In the combined model, GRA was used to select sensitive spectral variables and PLS was used to perform regression analysis. The widely used three‐band algorithm was applied for LWC estimation to compare model performances. Both the three‐band algorithm and GRA‐PLS resulted in robust LWC estimation (R2 = 0.60 and 0.74, RMSE = 13.15 and 9.82%, respectively). Our results indicated that the GRA‐PLS model has great potential for LWC estimation in winter wheat; however, the three‐band algorithm also has merit, particularly when taking into consideration the simplicity of its application. This method may provide a guideline for improving the estimation of wheat LWC at the regional scale by combining different algorithms. We will further simplify the GRA‐PLS model to apply widely different satellite data in the future.

Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling

This study investigates the applicability of empirical and radiative transfer models to estimate water content at leaf and landscape level. The main goal is to evaluate and compare the accuracy of these two approaches for estimating leaf water content by means of laboratory reflectance/transmittance measurements and for mapping leaf and canopy water content by using airborne Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) data acquired over intensive poplar plantations (Ticino, Italy). At leaf level, we tested the performance of different spectral indices to estimate leaf equivalent water thickness (EWT) and leaf gravimetric water content (GWC) by using inverse ordinary least squares (OLS) regression, and reduced major axis (RMA) regression. The analysis showed that leaf reflectance is related to changes in EWT rather than GWC, with best results obtained by using RMA regression by exploiting the spectral index related to the continuum removed area of the 1200 nm water absorption feature with an explained variance of 61% and prediction error of 6.6%. Moreover, we inverted the PROSPECT leaf radiative transfer model to estimate leaf EWT and GWC and compared the results with those obtained by means of empirical models. The inversion of this model showed that leaf EWT can be successfully estimated with no prior information with mean relative errors of 14% and determination coefficient of 0.65. Inversion of the PROSPECT model showed some difficulties in the simultaneous estimation of leaf EWT and dry matter content, which led to large errors in GWC estimation. At landscape level with MIVIS data, we tested the performance of different spectral indices to estimate canopy water per unit ground area (EWT canopy). We found a relative error of 20% using a continuum removed spectral index around 1200 nm. Furthermore, we used a model simulation to evaluate the possibility of applying empirical models based on appositely developed MIVIS double ratios to estimate mean leaf EWT at landscape level ( EWT ― ). It is shown that combined indices (double ratios) yielded significant results in estimating leaf EWT at landscape level by using MIVIS data (with errors around 2.6%), indicating their potential in reducing the effects of LAI on the recorded signal. The accuracy of the empirical estimation of EWT canopy and EWT ― was finally compared with that obtained from inversion of the PROSPECT + SAILH canopy reflectance model to evaluate the potential of both methods for practical applications. A relative error of 27% was found for EWT canopy and an overestimation of leaf EWT ― with relative errors around 19%. Results arising from this remote sensing application support the robustness of hyperspectral regression indices for estimating water content at both leaf and landscape level, with lower relative errors compared to those obtained from inversion of leaf and 1D canopy radiative transfer models.

Evaluation of Hyperspectral Reflectance Indexes to Detect Grapevine Water Status in Vineyards

Irrigation scheduling is critical as it affects both fruit yield and composition. We examined the potential to use field-measured hyperspectral remote sensing data (reflectance and transmission over the 350–2500 nm wavelength region) to estimate leaf water content, equivalent water thickness (EWT), and leaf water potential (Ψ) in a commercial vineyard of <i>Vitis vinifera</i> cv. Pinot noir. The data allowed us to evaluate a number of reflectance patterns to estimate vine water status through correlations using two spectral approaches: direct measurement of vegetation indexes (VIs) and continuum removal analysis (CRA). Continuum removal analysis was applied to obtain the maximum band depth (MBD) and the band area (BA) of several absorption features sensitive to water content. Correlations were high for EWT at the leaf level using a modification of the Simple Ratio VI (SR2; R² = 0.916) and for CRA with MBD₉₇₀ (R² = 0.917) and BA₁₁₆₀ (R² = 0.897). Correlations with EWT and water potential at the canopy level for SR2 were nonsignificant, which was characteristic for many VIs. For predawn water potential (Ψ_PD) and midday stem water potential (Ψ_stem) at the canopy level, best fits were realized for Modified Triangular VI (MTVI2; R² = 0.360) and Red/Green VI (RGI, R₆₉₅/R₅₅₄; R² = 0.462), respectively. For canopy level water status, the best results were obtained using the difference between the midday stem water potential and the pre-dawn leaf water potential (Ψ_stem - Ψ_PD) with R² = 0.619 for RGI and R² = 0.541 for Structure Intensive Pigment Index (SIPI, R₈₀₀-R₄₄₅/R₈₀₀-R₆₈₀), while for CRA R² = 0.477 for BA₁₆₀₀ and R² = 0.509 for MBD₉₇₀. Results suggest that noninvasive monitoring using hyperspectral data could improve current methods for estimating water status in individual vines. Applications of similar measurements could be produced from airborne hyperspectral imagers to provide spatially resolved estimates of water stress for use in water management of large-scale commercial vineyards.