Canopy Equivalent Water Thickness Research Articles

Improved estimation of canopy water status in cotton using vegetation indices along with textural information from UAV-based multispectral images

Precise and timely estimation of crop canopy water status is of great importance for precision irrigation. The unmanned aerial vehicle (UAV)-based remote sensing technology has become increasingly popular for crop canopy water status estimation. Nevertheless, the capability of vegetation indices (VIs) along with textural information from high-resolution imagery for estimating cotton canopy water status has been rarely explored. The VIs, texture features (TFs), and texture indices (TIs) were obtained from UAV multispectral images of cotton with different irrigation levels and nitrogen rates in the southern Xinjiang of China. The performances of three machine learning models, i.e. support vector machine (SVM), back-propagation neural network (BPNN) and extreme gradient boosting (XGBoost) were evaluated for estimating canopy equivalent water thickness (CEWT) and crop water stress index (CWSI) throughout the cotton growing season. The results showed that combining vegetation indices and textural information significantly improved the estimation accuracy of models compared to vegetation indices or textural information alone. The XGBoost_VIs + TFs model exhibited the best accuracy in estimating CEWT (R2 = 0.75, RMSE = 0.01 cm, RE = 19.46 % at upper half-canopy level, and R2 = 0.65, RMSE = 0.02 cm, RE = 24.59 % at all-canopy level), while the XGBoost_VIs + TFs + TIs model performed best in predicting CWSI among the models (R2 = 0.90, RMSE = 0.05, RE = 5.84 %). Although CEWT estimation was fair to some extent, CWSI estimation was more applicable for diagnosing cotton water stress. The CWSI maps created from the optimal XGBoost_VIs + TFs + TIs model intuitively reflected the cotton canopy water status under various irrigation levels and nitrogen rates, which could help farmers implement timely and precision irrigation in cotton production.

Modeling strategies and influencing factors in retrieving canopy equivalent water thickness of mangrove forest with Sentinel-2 image

Canopy equivalent water thickness (EWT) is an essential indicator of plant water status related to plant growth, temperature maintenance and transpiration. To our knowledge, this study was the first to map the mangrove canopy EWT using Sentinel-2 image at the reserve scale, aiming to explore three modeling strategies in retrieving mangrove canopy EWT, including machine learning models (Random Forest Regression (RFR) and Adaptive Boosting (Adaboost)), radiative transfer models (RTMs, PROSAIL-D and biophysical processor in Sentinel application platform) and hybrid models (PROSAIL-D + RFR and PROSAIL-D + Adaboost). We further investigated the impacts of four ecogeographical factors (species distribution, slope, elevation and distance to dam) on the spatial distribution of canopy EWT using Geodetector method. The results showed that Adaboost (R2 = 0.593, RMSE = 0.771 kg/m2 and RPIQ = 1.820), PROSAIL-D (R2 = 0.451, RMSE = 0.937 kg/m2 and RPIQ = 1.537) and PROSAIL-D + Adaboost (R2 = 0.501, RMSE = 0.890 kg/m2 and RPIQ = 0.887) was the optimal machine learning, RTM and hybrid model in retrieving mangrove canopy EWT, respectively. The red-edge3, NIR and SWIR bands of Sentinel-2 imagery were sensitive to canopy EWT. Moreover, Geodetector analysis demonstrated that species distribution had the most significant impact on canopy EWT, with the interaction between species and distance to dams contributing the most to the spatial difference of canopy EWT. In conclusion, this study suggests that Adaboost model and the hybrid method of PROSAIL-D and Adaboost were promising to map large-scale canopy water conditions in mangrove ecosystems using Sentinel-2 imagery.

MEASURING FOREST CANOPY WATER MASS IN THREE DIMENSIONS USING TERRESTRIAL LASER SCANNING

Abstract. Canopy water mass is an important plant characteristic that can indicate the water status of vegetation. However, the parameter remains under-investigated because measuring it requires defoliating the canopy. This study introduced a non-destructive approach to estimate canopy water mass using terrestrial laser scanning data. Tree 3D models were generated from dual-wavelength TLS data for six forest canopies, then the models were utilized in estimating the canopy LAI, total leaf area, and vertical profiles of canopy leaf area. The estimates were then coupled with canopy equivalent water thickness estimates and vertical profiles of canopy water mass were generated. The results revealed some over- and underestimation in the estimated LAI, but the obtained accuracy was considered sufficient as leaf-on point clouds were used to generate the 3D models. The vertical profiles of canopy water mass showed that the leaf area distribution within the canopy, and the canopy architecture were the main parameters affecting the water mass distribution within the canopy, with mid canopy layers having higher water mass than the other canopy layers. This study showed the potential of TLS to estimate canopy water mass, but controlled experiments that include defoliating canopies are still needed for a direct and accurate validation of the TLS estimates of canopy water mass.

Combining machine learning algorithm and multi-temporal temperature indices to estimate the water status of rice

Real-timely monitoring of the crop water status can improve irrigation scheduling to increase water saving and enhance agricultural sustainability, whereas the canopy temperature measured by thermal imaging is an essential indicator for determining the rice water status. The primary goal of this study was to propose a new temperature index based on the temporal variance of the daily temperature and to develop the rice water status estimation model. Two field experiments involving two rice varieties and multi-irrigation regimes were conducted from 2019 to 2020. A thermal imaging camera was used to measure the canopy temperature from 8:00–16:00 at 2-hour intervals across all growth stages. Three plant water parameters, namely plant water content (PWC), canopy water content (CWC), and canopy equivalent water thickness (CEWT), were collected simultaneously. The results showed that canopy temperature and plant water parameters differed obviously among different irrigation treatments. The relative canopy temperature velocity (RCTV) was developed based on the temporal variance of the daily temperature, and the RCTV8–12 performed well in distinguishing different irrigation treatments and quantifying the rice water status. The coefficient of determination (R2) values of the exponential relationships between the optimal RCTV and plant water parameters reached 0.47 (PWC), 0.39 (CWC) and 0.18 (CEWT). The random forest model, which integrates the multi-temperature indices, achieved a good estimation for PWC (R2 = 0.78), CWC (R2 = 0.77), and CEWT (R2 = 0.64) across all growth stages. In summary, combining the multi-temperature indices derived from the thermal infrared imagery and machine learning algorithm can facilitate the non-destructive estimation of the rice water status and improve the precision irrigation schedule.

Retrieving chlorophyll content and equivalent water thickness of Moso bamboo (Phyllostachys pubescens) forests under Pantana phyllostachysae Chao-induced stress from Sentinel-2A/B images in a multiple LUTs-based PROSAIL framework

Biochemical components of Moso bamboo (Phyllostachys pubescens) are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem. The coupled PROSPECT with SAIL (PROSAIL) radiative transfer model is widely used for vegetation biochemical component content inversion. However, the presence of leaf-eating pests, such as Pantana phyllostachysae Chao (PPC), weakens the performance of the model for estimating biochemical components of Moso bamboo and thus must be considered. Therefore, this study considered pest-induced stress signals associated with Sentinel-2A/B images and field data and established multiple sets of biochemical canopy reflectance look-up tables (LUTs) based on the PROSAIL framework by setting different parameter ranges according to infestation levels. Quantitative inversions of leaf area index (LAI), leaf chlorophyll content (LCC), and leaf equivalent water thickness (LEWT) were derived. The scale conversions from LCC to canopy chlorophyll content (CCC) and LEWT to canopy equivalent water thickness (CEWT) were calculated. The results showed that LAI, CCC, and CEWT were inversely related with PPC-induced stress. When applying multiple LUTs, the p-values were <0.01; the R2 values for LAI, CCC, and CEWT were 0.71, 0.68, and 0.65 with root mean square error (RMSE) (normalized RMSE, NRMSE) values of 0.38 (0.16), 17.56 μg·cm‒2 (0.20), and 0.02 cm (0.51), respectively. Compared to the values obtained for the traditional PROSAIL model, for October, R2 values increased by 0.05 and 0.10 and NRMSE decreased by 0.09 and 0.02 for CCC and CEWT, respectively and RMSE decreased by 0.35 μg·cm‒2 for CCC. The feasibility of the inverse strategy for integrating pest-induced stress factors into the PROSAIL model, while establishing multiple LUTs under different pest-induced damage levels, was successfully demonstrated and can potentially enhance future vegetation parameter inversion and monitoring of bamboo forest health and ecosystems.

Improving water status prediction of winter wheat using multi-source data with machine learning

Water and nitrogen (N) are the most important factors limiting crop productivity. Effective monitoring of the water status of winter wheat under different N treatments is imperative for precision irrigation in improved crop management. Hyperspectral remote sensing is widely used for monitoring the crop water status. However, changes in canopy architecture during ontogeny lead to poor inversion of crop properties and limit the use of spectral indices. This study aimed to improve the water status prediction of winter wheat using multi-source data with machine learning (ML). Two multi-irrigation levels (0, 120, 240, 360 mm) and N rates (75, 225 kg N ha-1) experiments were conducted during the 2019–2021 wheat growing seasons under field conditions using a rainout shelter. Hyperspectral, soil, plant, and climate data were evaluated with two feature selection methods. Selected results were chosen as input variables for prediction models by using three ML algorithms. By constructing the normalized difference spectral index (NDSI), ratio spectral index (RSI), and difference spectral index (DSI), the DSI(2015, 2375), NDSI(2175, 2245), and RSI(720, 1200) showed the strongest correlation with canopy water content (CWC), plant water content (PWC), and canopy equivalent water thickness (CEWT), respectively. The best feature selection method and data were delivered by the Pearson correlation coefficient together with the soil, plant, and climate data. The best performing ML algorithm for CWC and PWC prediction was RF, while SVM was the best ML algorithm for CEWT prediction. The R2 of the optimal models ranged from 0.86 to 0.97. These models with multi-source data significantly improved the prediction accuracy of the water status and can thus assist in precision irrigation management of winter wheat.

Improved estimation of canopy water status in maize using UAV-based digital and hyperspectral images

The estimation of water status of maize is important for evaluating crop growth and conducting precision irrigation. The development of unmanned aerial vehicles (UAVs) equipped with sensor technologies provides high-quality data for estimating maize water status. Only a few studies have been conducted on the estimation of maize equivalent water thickness (EWT) and fuel moisture content (FMC) using UAV hyperspectral images. This study aimed to estimate the leaf and canopy water status of maize inbred lines using UAV digital and hyperspectral data. Leaf area index (LAI) is required to obtain canopy water indicators, canopy equivalent water thickness (EWTc), and canopy fuel moisture content (FMCc). However, obtaining the LAI from remote sensing images requires the support of samples or prior knowledge. The LAI is positively correlated with canopy coverage (CC), which can be extracted accurately from UAV images. Therefore, for EWTc and FMCc, this study aimed to use the CC instead of the LAI to construct and test new canopy water indicators in order to improve the convenience of UAV imaging technology in monitoring maize water status. The results showed that, after the introduction of CC, two indicators, revised canopy equivalent water thickness (r-EWTc) and revised canopy fuel moisture content (r-FMCc), were both sensitive to the difference vegetation index (DVI) derived from UAV hyperspectral images. The r-FMCc was the most effective of the six water indicators used in this study, and the Pearson’s correlation coefficient (r) with DVI was 0.93. The results indicate that the CC extracted directly from UAV digital images is suitable to replace LAI, and helps improve the data availability for estimating canopy water status. The UAV hyperspectrum can accurately estimate the water status in maize inbred lines, which is helpful for further application of UAV data in breeding.

A methodological approach to assess canopy NDVI–based tomato dynamics under irrigation treatments

A tomato field experiment under deficit irrigation treatments was established to assess the feasibility of nonlinear mixed models to predict the change in the canopy Normalized Difference Vegetation Index (NDVI) along the growing season. The irrigation doses were 50, 70, 85, and 100 % of the actual evapotranspiration (ETc). The experiment was performed in the Peruvian Arid Coastal Region (13.10 S, 76.37 W). On-ground destructive plant measurements (7 times) allowed to obtain the leaf area index (LAI) and the canopy equivalent water thickness (EWTcanopy) to validate the experiment. Similarly, volumetric soil moisture was monitored at three hours interval using time-domain reflectometry. High-resolution multispectral imagery was acquired along the growing season (10 times) using an unmanned aerial vehicle. NDVI at plant level was obtained based on oversegmented imagery. The canopy NDVI growth curves were obtained in relation to the irrigation treatments and growing degree days (GDD) using a nonlinear mixed model.Deficit irrigation doses (50, 70 and 85 % ETc) induced lower biomass accumulation and greatly affected LAI and EWTcanopy, attaining lower values (p < 0.05) than the control (100 % ETc). In spite of reaching higher LAI and EWTcanopy values, 70 % ETc was not significantly different from 50 % ETc (p < 0.05) showing the difficulty to accurately discriminate treatments with high deficit irrigation; 85 % ETc produced important differences on EWTcanopy and LAI compared to the other deficit irrigation treatments (p < 0.05) due to a higher leaf area expansion. Deficit irrigation doses led to less vigorous plants resulting in lower NDVI compared to the control. Notwithstanding 50 % ETc obtained lower NDVI values than 70 % ETc, differences were not significant. Volumetric soil moisture was consistent with the irrigation treatments, resulting in a decreased trend with decreasing water application along the growing season. A three-parameter logistic mixed model was found to be well-suited to fit the tomato canopy NDVI growth curves in function of the irrigation treatments (i.e. fixed effects) considering the plant-specific variability (i.e. random effects). At maturity stage, significant differences of the deficit irrigation treatments from the control were obtained (p < 0.05) when cumulative effect of the treatments was well-defined and NDVI reached asymptotic behavior. During the vegetative stage, the inflection point marked the fastest NDVI growth rate for the 100 % ETc treatment whereas the lowest for the 50 % ETc (p < 0.05). The diagnosis plots and the low root mean squared error of the residuals indicated the suitability of using the three-parameter logistic growth curve estimated as a nonlinear mixed model to predict NDVI.

A novel restoration approach for vegetation reflectance spectra at noisy bands using the principal component analysis method

ABSTRACTDue to the signal-to-noise ratio (SNR) of sensors, as well as atmospheric absorption and illumination conditions, etc., hyperspectral data at some bands are of poor quality. Data restoration for noisy bands is important for many remote sensing applications. In this paper, we present a novel data-driven Principal Component Analysis (PCA) approach for restoring leaf reflectance spectra at noisy bands using the spectra at effective bands. The technique decomposes the leaf reflectance spectra into their principal components (PCs), selects the leading PCs that describe the most variance in the data, and restores the data from these components. First, the first 10 PCs were determined from a training dataset simulated by the leaf optical properties model (PROSPECT-5) that contained 99.998% of the total information in the 3636 training samples. Then, the performance of the PCA method for restoration of the reflectance at noisy bands was investigated using the ANGERS leaf optical properties dataset; the results showed the spectral root mean squared error (RMSE) is in the range 6.46 × 10−4 to 6.44 × 10−2, which is about 3 − 34 times more accurate than the stepwise regression method and partial least squares method (PLSR) for the ANGERS dataset. The results also showed that if the noisy bands are far away from the effective bands, the accuracy of the restored leaf reflectance spectra will decrease. Thirdly, the reliability of the restored reflectance spectra for retrieving leaf biochemical contents was assessed using the ANGERS dataset and leaf optical properties dataset established by the Beijing Academy of Agriculture and Forestry Sciences (BAAFS). Three water-sensitive vegetation indices − normalized difference water index (NDWI), normalized difference infrared index (NDII) and Datt water index (DWI), derived from the restored leaf spectra − were employed to retrieve the equivalent water thickness (EWT). The results showed that the leaf water content can be accurately retrieved from the restored leaf reflectance spectra. In addition, the PCA method to restore vegetation spectral reflectance can be applied on canopy level as well. The results showed that the spectral root mean squared error (RMSE) is in the range 8.22 × 10−4 to 1.87 × 10−2. The performance of the restored canopy spectra was investigated according to the results of retrieving canopy equivalent water thickness (CEWT) using the five spectral indices NDWI, NDWI1370, NDWI1890, NDII and DWI. The results indicated that the restored canopy spectra can be used for retrieving CEWT reliably and improve the accuracy of retrieval compared to the results using original canopy reflectance spectra.

Three dimensional mapping of forest canopy equivalent water thickness using dual-wavelength terrestrial laser scanning

Globally, forests are being subjected to numerous threats, including climate change, wildfires, and insect and disease outbreaks, among others. Satellite optical remote sensing data have been widely utilized in early detection of tree and forest stress by estimating water status metrics such as the leaf Equivalent Water Thickness (EWT). This estimate, however, is affected by soil characteristics and understory vegetation and often ignores the effects of the fine-scale heterogeneity of canopy structure and leaf water content. Such effects can be better understood by studying the EWT distribution in three dimensions. In this study, Terrestrial Laser Scanning (TLS) intensity data from the commercially-available Leica P20 and P40 instruments (808 nm and 1550 nm respectively) were combined in a Normalized Difference Index (NDI). NDI was used to map EWT of 12 trees in three dimensions from floor to canopy in a mixed broadleaf forest plot (Wytham Woods, UK). The average error in EWT estimates across three species was less than 8%. The three dimensional point clouds revealed that, in this snapshot, EWT changes vertically, usually increasing towards canopy top. The proposed method has the potential to provide predawn EWT measurements, is independent of solar illumination, and can lead to a better understanding of the factors affecting satellite estimation of EWT.

Hyperspectral Measurement of Seasonal Variation in the Coverage and Impacts of an Invasive Grass in an Experimental Setting

Hyperspectral remote sensing can be a powerful tool for detecting invasive species and their impact across large spatial scales. However, remote sensing studies of invasives rarely occur across multiple seasons, although the properties of invasives often change seasonally. This may limit the detection of invasives using remote sensing through time. We evaluated the ability of hyperspectral measurements to quantify the coverage of a plant invader and its impact on senesced plant coverage and canopy equivalent water thickness (EWT) across seasons. A portable spectroradiometer was used to collect data in a field experiment where uninvaded plant communities were experimentally invaded by cogongrass, a non-native perennial grass, or maintained as an uninvaded reference. Vegetation canopy characteristics, including senesced plant material, the ratio of live to senesced plants, and canopy EWT varied across the seasons and showed different temporal patterns between the invaded and reference plots. Partial least square regression (PLSR) models based on a single season had a limited predictive ability for data from a different season. Models trained with data from multiple seasons successfully predicted invasive plant coverage and vegetation characteristics across multiple seasons and years. Our results suggest that if seasonal variation is accounted for, the hyperspectral measurement of invaders and their effects on uninvaded vegetation may be scaled up to quantify effects at landscape scales using airborne imaging spectrometers.

Evaluation of hyperspectral indices for retrieval of canopy equivalent water thickness and gravimetric water content

ABSTRACTThere are two main parameters describing the amount of water in vegetation: the gravimetric water content (GWC) and the equivalent water thickness (EWT). In this study, we investigated the applicability of hyperspectral water-sensitive indices from canopy spectra for estimating canopy EWT (CEWT) and GWC. First, the spectral reflectance’s response to different levels of canopy water content was analysed and a noticeable increase in the slope of the near-infrared (NIR) shoulder of the canopy spectrum was observed. Next, the correlation between the CEWT and various hyperspectral water-sensitive indices was investigated. It was found that all of the indices could retrieve the CEWT of winter wheat well, with the coefficients of determination (R2) all being higher than 0.80. Finally, the retrieval performance of these indices for canopy GWC was evaluated and no significant correlation was observed between canopy GWC and the water-sensitive indices except for the spectral ratio index in the NIR shoulder region (NSRI). These results showed that the traditional water-sensitive vegetation indices are more sensitive to CEWT than to GWC, especially when the LAI is not highly correlated with the GWC, and that the NSRI is a potential vegetation index for use in the retrieval of GWC.

Remote Estimation of Leaf and Canopy Water Content in Winter Wheat with Different Vertical Distribution of Water-Related Properties

This study analyzed the vertical distribution of gravimetric water content (GWC), relative water content (RWC), and equivalent water thickness (EWT) in winter wheat during heading and early ripening stages, and evaluated the position of leaf number at which Vegetation Indexes (VIs) can best retrieve canopy water-related properties of winter wheat. Results demonstrated that the vertical distribution of these properties followed a near-bell-shaped curve with the highest values at the intermediate leaf position. GWC of the top three or four leaves during the heading stage and the top two or three leaves during the early ripening stage can represent the GWC of the whole canopy, but the RWC and EWT of the whole canopy should be calculated based on the top four leaves. At leaf level, the analysis demonstrated strong relationships between EWT and VIs for the top leaf layer, but for GWCD, GWCF, and RWC, the strongest relationships with VIs were found in the intermediate leaf layers. At canopy level, VIs provided the most accurate estimation of GWCfor the top three or four leaves. Water absorption-based VIs could estimate canopy EWT of winter wheat for the top four leaves, but the suitable bands sensitive to water absorptions should be carefully selected for the studied species.

Measuring cotton water status using water-related vegetation indices at leaf and canopy levels

Drought is one of the major environmental threats in the world. In recent years, the damage from droughts to the environment and economies of some countries has been extensive, and drought monitoring has caused widespread concerns. Remote sensing has a proven ability to provide spatial and temporal measurements of surface properties, and it offers an opportunity for the quantitative assessment of drought indicators such as the vegetation water content at different levels. In this study, sites of cotton field in Shihezi, Xinjiang, Northwest China were sampled. Four classical water content parameters, namely the leaf equivalent water thickness (EWTleaf), the fuel moisture content (FMC), the canopy equivalent water thickness (EWTcanopy) and vegetation water content (VWC) were evaluated against seven widely-used water-related vegetation indices, namely the NDII (normalized difference infrared index), NDWI2130 (normalized difference water index), NDVI (normalized difference vegetation index), MSI (moisture stress index), SRWI (simple ratio water index), NDWI1240 (normalized difference water index) and WI (water index), respectively. The results proved that the relationships between the water-related vegetation indices and EWTleaf were much better than that with FMC, and the relationships between vegetation indices and EWTcanopy were better than that with VWC. Furthermore, comparing the significance of all seven water-related vegetation in- dices, WI and NDII proved to be the best candidates for EWT detecting at leaf and canopy levels, with R 2 of 0.262 and 0.306 for EWTleaf-WI and EWTcanopy-NDII linear models, respectively. Besides, the prediction power of linear regression technique (LR) and artificial neural network (ANN) were compared using calibration and validation dataset, respectively. The results indicated that the performance of ANN as a predictive tool for water status meas- uring was as good as LR. The study should further our understanding of the relationships between water-related vegetation indices and water parameters.

Detection of leaf and canopy EWT by calculating REWT from reflectance spectra

The spectral characteristics of and the interaction between leaves and light were analysed based on the optical absorption coefficients of foliar water and biochemical components. The equations for calculating the radiative-equivalent water thickness (REWT) of leaves and canopy were presented based on the difference in reflectance at 945 and 975 nm. Because of the direct reflection on leaf surface and the leaf internal scattering, the REWT derived from the Beer–Lambert principle was different from the leaf or canopy equivalent water thickness (EWT). Two independent datasets at canopy or leaf scales were designed to calibrate and validate the relationships between EWT and REWT. The results show that (1) the leaf or canopy REWT can be calculated from the reflectance difference between 945 and 975 nm; (2) the leaf REWT was 3.3 times larger than the EWT with a significant determination coefficient (R 2) of 0.80 for our dataset and 0.86 for the Leaf Optical Properties Experiment (LOPEX'93) dataset; (3) the canopy REWT was 1.4 times larger than the EWT with a significant R 2 of 0.56 for the winter wheat canopy spectral dataset in 2002, and 0.61 for the 2004 dataset. Therefore, the leaf or canopy EWT can be detected by calculating REWT from the difference in reflectance at 945 and 975 nm. Furthermore, because the relationship between REWT and EWT reflected the interaction of light with leaves or canopy, the multiple scattering optical pathlength in the near-infrared (NIR) bands can also be calculated by the ratio of REWT to EWT.

Vegetation water content during SMEX04 from ground data and Landsat 5 Thematic Mapper imagery

Vegetation water content is an important parameter for retrieval of soil moisture from microwave data and for other remote sensing applications. Because liquid water absorbs in the shortwave infrared, the normalized difference infrared index (NDII), calculated from Landsat 5 Thematic Mapper band 4 (0.76–0.90 μm wavelength) and band 5 (1.55–1.65 μm wavelength), can be used to determine canopy equivalent water thickness (EWT), which is defined as the water volume per leaf area times the leaf area index (LAI). Alternatively, average canopy EWT can be determined using a landcover classification, because different vegetation types have different average LAI at the peak of the growing season. The primary contribution of this study for the Soil Moisture Experiment 2004 was to sample vegetation for the Arizona and Sonora study areas. Vegetation was sampled to achieve a range of canopy EWT; LAI was measured using a plant canopy analyzer and digital hemispherical (fisheye) photographs. NDII was linearly related to measured canopy EWT with an R 2 of 0.601. Landcover of the Arizona, USA, and Sonora, Mexico, study areas were classified with an overall accuracy of 70% using a rule-based decision tree using three dates of Landsat 5 Thematic Mapper imagery and digital elevation data. There was a large range of NDII per landcover class at the peak of the growing season, indicating that canopy EWT should be estimated directly using NDII or other shortwave-infrared vegetation indices. However, landcover classifications will still be necessary to obtain total vegetation water content from canopy EWT and other data, because considerable liquid water is contained in the non-foliar components of vegetation.

Validation of a hyperspectral curve-fitting model for the estimation of plant water content of agricultural canopies

The estimation of plant water content is essential to the integration of remote sensing into precision agriculture. Hyperspectral models developed to estimate plant water content have had limited application under field conditions and have not been rigorously validated. A physical model using a spectrum matching technique was applied to hyperspectral data to directly calculate the canopy equivalent water thickness (EWT) using a look-up table approach. The objective of this study was to test the validity of this algorithm using plant water content information collected under field conditions and to relate this to the needs of precision agriculture. Image data were acquired over two experimental test sites in Canada, near Clinton, Ontario and Indian Head, Saskatchewan, using the Probe-1 airborne hyperspectral sensor. Plant biomass samples were collected simultaneously from plots spanning fourteen fields of various crop types (wheat, canola, corn, beans, and peas). The model was validated against EWT estimated from biomass samples, as well as more conventional measures of crop water status. The model accurately predicts water content in the range found with all crop types pooled together, with an index of agreement ( D) of 0.92 and a root mean squared error (RMSE) of 26.8% of the average. On an individual crop basis, the model proved to be a poor predictor for wheat crops (RMSE=69.9%). When wheat fields were removed from the overall analysis, the RMSE was 17.9% and the D was 0.87. While the model provided a reasonably accurate prediction of EWT for broadleaf crops like beans, corn, canola, and peas ( D=0.88, 0.69, 0.88, and 0.84, respectively), the error margin in the prediction was too large for to precisely detect within-crop variation for the low variability found in corn and bean crops in this study. EWT is related to plant biomass and leaf area index (LAI), both quantities of interest to precision agriculture.

Airborne remote sensing of canopy water thickness scaled from leaf spectrometer data

The reflectance ratio of the middle-infrared band (MIR) to the near-infrared band (NIR) is linearly related to the log(10) equivalent water thickness (EWT) for single leaves of different morphologies, whereas the MIR/NIR radiance ratio is correlated with the leaf area index (LAI). The hypothesis that the MIR/NIR ratio measures canopy EWT was tested by reanalyzing airborne Thematic Mapper Simulator and field data obtained across a large gradient of LAI in western Oregon, U.S.A. The measured airborne MIR/NIR reflectance ratios for canopies were not significantly different from the predicted ratios using leaf data for canopy EWT, except for two desert woodland sites. The interpretation of the MIR/NIR ratio is scale-dependent, because leaf EWT is determined primarily by variations in LAI.