Estimation of vegetation LAI from hyperspectral reflectance data: Effects of soil type and plant architecture

The retrieval of canopy biophysical variables is known to be affected by confounding factors such as plant type and background reflectance. The effects of soil type and plant architecture on the retrieval of vegetation leaf area index (LAI) from hyperspectral data were assessed in this study. In situ measurements of LAI were related to reflectances in the red and near-infrared and also to five widely used spectral vegetation indices (VIs). The study confirmed that the spectral contrast between leaves and soil background determines the strength of the LAI–reflectance relationship. It was shown that within a given vegetation species, the optimum spectral regions for LAI estimation were similar across the investigated VIs, indicating that the various VIs are basically summarizing the same spectral information for a given vegetation species. Cross-validated results revealed that, narrow-band PVI was less influenced by soil background effects (0.15 ≤ RMSE_cv ≤ 0.56). The results suggest that, when using remote sensing VIs for LAI estimation, not only is the choice of VI of importance but also prior knowledge of plant architecture and soil background. Hence, some kind of landscape stratification is required before using hyperspectral imagery for large-scale mapping of vegetation biophysical variables.     

不同传感器的模拟植被指数对水稻叶面积指数的估测精度和敏感性分析

本研究利用水稻冠层高光谱数据,模拟NOAA-AVHRR,Terra-MODIS和Landsat-TM的可见光波段反射率数据,计算各传感器的多种植被指数(NDVI,RVI,EVI,GNDVI,GRVI和Red-edge RVI),比较植被指数模型对水稻LAI的估测精度,分析不同植被指数对LAI变化的敏感性.相对于红波段植被指数,红边比值植被指数(Red-edge RVI)和绿波段指数GRVI与LAI有更好的线性相关关系,而GNDVI和LAI呈现更好的对数相关关系.MODIS的Red-edge RVI指数不仅模型拟合的精度最高,还有独立数据验证的估测精度也最高,而且它的验证精度较拟合精度下降幅度最小;其次是绿波段构建的GNDVI和GRVI植被指数的估测精度,再次是NDVI和EVI的估测精度,而RVI的估测精度最差.敏感性分析发现,13个植被指数对水稻LAI的估测能力都随着LAI的增加而下降,但归一化类植被指数和比值类植被指数对LAI变化反应的差异明显,归一化类植被指数在LAI较低时(LAI<1.5)对LAI变化的反应开始非常敏感,但迅速下降,而比值类植被指数在LAI较低时,明显小于归一化类植被指数,之后随着LAI的增大(LAI>1.5)比值类植被指数对LAI的变化敏感性,则明显高于归一化类植被指数.Red-edge RVI和绿波段指数GRVI和LAI不仅表现了很好的线性相关关系,而且在LAI大于2.9左右保持较高的敏感性.     

Leaf Area Index Estimation Using Time-Series MODIS Data in Different Types of Vegetation

The aim of this study is to estimate leaf area index (LAI) in different type of plants using vegetation indices (VIs) and neural network algorithms retrieved from MODIS data. Four VI were calculated, and neural networks were built up based on MODIS surface reflectance products. Among the tested VIs, normalized difference vegetation index (NDVI) and chlorophyll index (CI) appeared to be the best candidate indices in estimating LAI across sites with different vegetation types. The models having the highest accuracy were CI for grassland and deciduous broad leaf forest with determination coefficients (R-square above 0.70, and NDVI for crop R-square?=?0.78). Neural network showed better results than VI methods except in grassland sites. The added VI information showed no significant improvement of model accuracy for the neural networks in most sites.     

A novel combined spectral index for estimating the ratio of carotenoid to chlorophyll content to monitor crop physiological and phenological status

Accurate estimation of the ratio of carotenoid (Car) to chlorophyll (Chl) content is crucial to provide valuable insight into diagnoses of plant physiological and phenological status in crop fields. Studies for assessing the ratio of Car to Chl content have been extensively conducted with semi-empirical approaches using spectral indices. However, spectral indices established in previous studies generally relied on site- or species-specific measured data and these indices typically lacked sufficient estimation accuracy for the ratio of Car to Chl content to be used across various species and under different physiological conditions. In this study, we propose a novel combined carotenoid/chlorophyll ratio index (CCRI) in the form of the carotenoid index (CARI) divided by the red-edge chlorophyll index (CI_red-edge): The value of the index is illustrated using synthetic data simulated from the leaf radiative transfer model PROSPECT-5 and with extensive measured datasets at both the leaf and canopy level from the ANGERS dataset and winter wheat and maize field experiments. Results show that CCRI was the index with the highest correlation with the ratio of Car to Chl content in PROSPECT-5 simulations (R² = 0.99, RRMSE = 8.65%) compared to other spectral indices. Calibration and validation results using the ANGERS and winter wheat leaf level data showed that CCRI achieved accurate estimation of the ratio of Car to Chl content (R² = 0.52, RRMSE = 14.10%). CCRI also showed a good performance (R² = 0.54, RRMSE = 17.08%) for estimation of the ratio of Car to Chl content in both calibration and validation with the winter wheat and maize canopy spectra measured in field experiments. Further investigation of the effect of the correlation between leaf Chl and Car content on the performance of CCRI indicated that variation of the correlation affected the retrieval accuracy of CCRI, and CCRI might not be very sensitive to changes of the ratio of Car to Chl content with low values (<0.10).     

Sensitivity of narrowband vegetation indices to boreal forest LAI,reflectance seasonality and species composition

There is growing evidence that imaging spectroscopy could improve the accuracy of satellite-based retrievals of vegetation attributes, such as leaf area index (LAI) and biomass. In this study, we evaluated narrowband vegetation indices (VIs) for estimating overstory effective LAI (LAI_eff) in a southern boreal forest area for the period between the end of snowmelt and maximum LAI using three Hyperion images and concurrent field measurements. We compared the performance of narrowband VIs with two SPOT HRVIR images, which closely corresponded to the imaging dates of the Hyperion data, and with synthetic broadband VIs computed from Hyperion images. According to the results, narrowband VIs based on near infrared (NIR) bands, and NIR and shortwave infrared (SWIR) bands showed the strongest linear relationships with LAI_eff over its typical range of variation and for the studied period of the snow-free season. The relationships were not dependent on dominant tree species (coniferous vs. broadleaved), which is an advantage in heterogeneous boreal forest landscapes. The best VIs, particularly those based on NIR spectral bands close to the 1200 nm liquid water absorption feature, provided a clear improvement over the best broadband VIs.     

基于主成分分析和BP神经网络的法国梧桐叶绿素含量高光谱反演研究

利用ASD便携式野外光谱仪和SPAD-502叶绿素计实测了落叶阔叶树法国梧桐叶片的高光谱反射率与叶片绿度,并对原始光谱反射率及一阶导数光谱与叶片绿度进行了相关分析;综合分析了10个常见光谱植被指数与法国梧桐叶绿素含量的相关性与预测性;最后利用主成分分析对光谱数据进行降维,将得到的主成分得分作为BP人工神经网络模型的输入变量进行了法国梧桐叶绿素含量的估算。结果表明:法国梧桐的叶片反射光谱数据与叶绿素含量的相关性在可见光区域显著,导数光谱数据在绿黄光区和红光区的部分波段与叶绿素含量的相关系数大于对应波段光谱反射率与叶绿素含量的相关关系。在所列举的10个常用植被指数中归一化植被指数与叶绿素含量的关系最密切,相关系数达到了0.7957。主成分分析的BP神经网络模型可以容纳更多的波段信息进行叶绿素含量的估算,预测值与实测值之间的线性回归的确定性系数R2为0.9883,是一种良好的植被叶绿素含量高光谱反演模式。     

Estimating salinity stress in sugarcane fields with spaceborne hyperspectral vegetation indices

The presence of salt in the soil profile negatively affects the growth and development of vegetation. As a result, the spectral reflectance of vegetation canopies varies for different salinity levels. This research was conducted to (1) investigate the capability of satellite-based hyperspectral vegetation indices (VIs) for estimating soil salinity in agricultural fields, (2) evaluate the performance of 21 existing VIs and (3) develop new VIs based on a combination of wavelengths sensitive for multiple stresses and find the best one for estimating soil salinity. For this purpose a Hyperion image of September 2, 2010, and data on soil salinity at 108 locations in sugarcane (Saccharum officina L.) fields were used. Results show that soil salinity could well be estimated by some of these VIs. Indices related to chlorophyll absorption bands or based on a combination of chlorophyll and water absorption bands had the highest correlation with soil salinity. In contrast, indices that are only based on water absorption bands had low to medium correlations, while indices that use only visible bands did not perform well. From the investigated indices the optimized soil-adjusted vegetation index (OSAVI) had the strongest relationship (R² = 0.69) with soil salinity for the training data, but it did not perform well in the validation phase. The validation procedure showed that the new salinity and water stress indices (SWSI) implemented in this study (SWSI-1, SWSI-2, SWSI-3) and the Vogelmann red edge index yielded the best results for estimating soil salinity for independent fields with root mean square errors of 1.14, 1.15, 1.17 and 1.15 dS/m, respectively. Our results show that soil salinity could be estimated by satellite-based hyperspectral VIs, but validation of obtained models for independent data is essential for selecting the best model.     

Estimating the Leaf Area Index,height and biomass of maize using HJ-1 and RADARSAT-2

New optical and microwave integrated vegetation indices (VIs) were designed based on observations from both field experiments and satellite (HJ-1 and RADARSAT-2) data. It was found that these VIs perform better in estimating the structure parameters of maize, such as Leaf Area Index (LAI), height and biomass, than the original ones. This investigation focused on the difference of interaction between the multispectral reflectance and microwave backscattering signatures with the maize growth variables. Because the maize was near the heading stage with large vegetation coverage in the experiment, the reflectance of the near-infrared band of HJ-1 was much less sensitive to the structure variables than that of the visible-light band. Thus, the optical VIs formulated using those bands were saturated to estimate the structure parameters. With respect to the RADARSAT-2 data, there was a relatively strong relationship between the HV cross-polarization and the volume scattering of the maize, which was mostly determined by the crown structure. The modified VIs were designed using both the VIs of HJ-1 and the HV cross-polarization of RADARSAT-2 to overcome the saturation limitation. The validation showed that this integrated method of determining VIs is a good alternative to that using only the optical or microwave observation.     

The potential of spectral mixture analysis to improve the estimation accuracy of tropical forest biomass

A main limitation of pixel-based vegetation indices or reflectance values for estimating above-ground biomass is that they do not consider the mixed spectral components on the earth's surface covered by a pixel. In this research, we decomposed mixed reflectance in each pixel before developing models to achieve higher accuracy in above-ground biomass estimation. Spectral mixture analysis was applied to decompose the mixed spectral components of Landsat-7 ETM+ imagery into fractional images. Afterwards, regression models were developed by integrating training data and fraction images. The results showed that the spectral mixture analysis improved the accuracy of biomass estimation of Dipterocarp forests. When applied to the independent validation data set, the model based on the vegetation fraction reduced 5–16% the root mean square error compared to the models using a single band 4 or 5, multiple bands 4, 5, 7 and all non-thermal bands of Landsat ETM+.     

Spatially Explicit Estimation of Leaf Area Index Using EO-1 Hyperion and Landsat ETM+ Data: Implications of Spectral Bandwidth and Shortwave Infrared Data on Prediction Accuracy in a Tropical Montane Environment

This study evaluated the utility of narrowband (EO-1 Hyperion) and broadband (Landsat ETM+) remote sensing data for the estimation of leaf area index (LAI) in a tropical environment in Sulawesi, Indonesia. LAI was inferred from canopy gap fraction measurements taken in natural tropical forest and cocoa plantations. Single and multiple spectral bands and spectral indices were used as predictor variables in reduced major axis (RMA) and ordinary least squares (OLS) regression models. The predictive power of most regression models was notably higher when employing narrowband data instead of broadband data. Highly significant relationships between LAI and spectral reflectance were observed near the red-edge region and in most shortwave infrared (SWIR) bands. In contrast to most near-infrared (NIR) narrow bands, the correlation between SWIR reflectance and LAI was not confounded when including both vegetation types and did not suffer from saturation. The results demonstrate that leaf area index of a challenging tropical environment can be estimated with satisfactory accuracy from hyperspectral remote sensing data.     

Angle effects of vegetation indices and the influence on prediction of SPAD values in soybean and maize

To study the anisotropy of vegetation indices (VIs) and explore its influence on the retrieval accuracy of canopy soil-plant analyzer development (SPAD) value, the bidirectional reflectance distribution function (BRDF) models of soybean and maize are calculated from the multi-angle hyperspectral images acquired by UAV, respectively. According to the reflectance extracted from the BRDF model, the dependences of 16 commonly-used VIs on observation angles are analyzed, and the SPAD values of maize and soybean canopy are predicted by using the 16 VI values at different observation angles and their combinations as input parameters. The results show that the 16 VIs have different sensitivity to angle in the principal plane: green ratio vegetation index (GRVI), ratio vegetation index (RVI), red edge chlorophyll index (CIRE), and modified chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index (MCARI/OSAVI) are very sensitive to angles, among which MCARI/OSAVI of maize fluctuated the most (138.83 %); in contrast, the green optimal soil adjusted vegetation index (GOSAVI), normalized difference vegetation index (NDVI), and green normalized difference vegetation index (GNDVI) hardly change with the observation angles. In terms of SPAD prediction, the accuracy of different VI is different, the mean absolute error (MAE) showed that MCARI1 provided the highest accuracy of retrieval for soybean (MAE=1.617), while for maize it was MCARI/OSAVI (MAE=2.422). However, when using the same VI, there was no significant difference in the accuracy of the predicted results, whether the VI from different angles was used or the combination of multi-angles was used. The present results provide guiding significance and practical value for the retrieval of SPAD value in vegetation canopies and in-depth applications of multi-angular remote sensing.     

Study of fractional vegetation cover using high spectral resolution data

A field experiment was conducted to study the effect of vegetation cover on soil spectra and relationship of spectral indices with vegetation cover. Multi-date spectral measurements were carried out on twelve wheat fields. Five sets of measurements were taken during the growth period of wheat crop. Field reflectance data were collected in the range 350 to 1800 nm using ASD spectroradiometer. Analysis of data was done to select narrow spectral bands for estimation of ground cover. The ratio of reflectance from vegetation covered soil and reflectance from bare soil indicated that spectral reflectance at 670 and 710 nm are the most sensitive bands. Two bands in visible (670 and 560 nm), three bands in near infrared (710, 870 and 1100 nm) and three bands in middle infrared (1480, 1700 and 1800 nm) were found highly correlated with fractional cover. Vegetation indices developed using narrow band spectral data have been found to be better than those developed using broad- band data for estimation of ground cover.     

Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice

The common spectra wavebands and vegetation indices (VI) were identified for indicating leaf nitrogen accumulation (LNA), and the quantitative relationships of LNA to canopy reflectance spectra were determined in both wheat (Triticum aestivum L.) and rice (Oryza sativa L.). The 810 and 870 nm are two common spectral wavebands indicating LNA in both wheat and rice. Among all ratio vegetation indices (RVI), difference vegetation indices (DVI) and normalized difference vegetation indices (NDVI) of 16 wavebands from the MSR16 radiometer, RVI (870, 660) and RVI (810, 660) were most highly correlated to LNA in both wheat and rice. In addition, the relations between VIs and LNA gave better results than relations between single wavebands and LNA in both wheat and rice. Thus LNA in both wheat and rice could be indicated with common VIs, but separate regression equations are better for LNA monitoring.     

Understanding the optical responses of leaf nitrogen in Mediterranean Holm oak (Quercus ilex) using field spectroscopy

The direct estimation of nitrogen (N) in fresh vegetation is challenging due to its weak influence on leaf reflectance and the overlaps with absorption features of other compounds. Different empirical models relate in this work leaf nitrogen concentration ([N]_Leaf) on Holm oak to leaf reflectance as well as derived spectral indices such as normalized difference indices (NDIs), the three bands indices (TBIs) and indices previously used to predict leaf N and chlorophyll. The models were calibrated and assessed their accuracy, robustness and the strength of relationship when other biochemicals were considered. Red edge was the spectral region most strongly correlated with [N]_Leaf, whereas most of the published spectral indexes did not provide accurate estimations. NDIs and TBIs based models could achieve robust and acceptable accuracies (TBI_{1310,1720,730}: R² = 0.76, [0.64,0.86]; RMSE (%) = 9.36, [7.04,12.83]). These models sometimes included indices with bands close to absorption features of N bonds or nitrogenous compounds, but also of other biochemicals. Models were independently and inter-annually validated using the bootstrap method, which allowed discarding those models non-robust across different years. Partial correlation analysis revealed that spectral estimators did not strongly respond to [N]_Leaf but to other leaf variables such as chlorophyll and water, even if bands close to absorption features of N bonds or compounds were present in the models.     

Estimating soil salinity in Pingluo County of China using QuickBird data and soil reflectance spectra

Soil salinization is a worldwide environmental problem with severe economic and social consequences. In this paper, estimating the soil salinity of Pingluo County, China by a partial least squares regression (PLSR) predictive model was carried out using QuickBird data and soil reflectance spectra. At first, a relationship between the sensitive bands of soil salinity acquired from measured reflectance spectra and the spectral coverage of seven commonly used optical sensors was analyzed. Secondly, the potentiality of QuickBird data in estimating soil salinity by analyzing the correlations between the measured reflectance spectra and reflectance spectra derived from QuickBird data and analyzing the contributions of each band of QuickBird data to soil salinity estimation Finally, a PLSR predictive model of soil salinity was developed using reflectance spectra from QuickBird data and eight spectral indices derived from QuickBird data. The results indicated that the sensitive bands covered several bands of each optical sensor and these sensors can be used for soil salinity estimation. The result of estimation model showed that an accurate prediction of soil salinity can be made based on the PLSR method (R² = 0.992, RMSE = 0.195). The PLSR model's performance was better than that of the stepwise multiple regression (SMR) method. The results also indicated that using spectral indices such as intensity within spectral bands (Int1, Int2), soil salinity indices (SI1, SI2, SI3), the brightness index (BI), the normalized difference vegetation index (NDVI) and the ratio vegetation index (RVI) as independent model variables can help to increase the accuracy of soil salinity mapping. The NDVI and RVI can help to reduce the influences of vegetation cover and soil moisture on prediction accuracy. The method developed in this paper can be applied in other arid and semi-arid areas, such as western China.     

Sentinel-2数据的冬小麦地上干生物量估算及评价

作物生物量快速精确的监测对于农业资源的合理利用与农田的精准管理具有重要意义。近年来,遥感技术因其独特的优势已被广泛用于作物生物量的估算中。本文主要针对不同宽波段植被指数在冬小麦生物量(文中的生物量均是指地上干生物量)估算方面的表现进行探索。首先利用欧洲空间局最新的Sentinel-2A卫星数据提取出17种常见的植被指数,之后分别构建其与相应时期内采集的冬小麦地上生物量间的最优估算模型,通过分析两者间的相关性与敏感性,获取适宜进行生物量估算的指数。最后,绘制了研究区的生物量空间分布图。结果表明,所选的植被指数均与生物量显著相关。其中,红边叶绿素指数(CI_(re))与生物量的估算精度最高(决定性系数R~2为0.83;均方根误差RMSE为180.29 g·m~(–2))。虽然相关性较高,但部分指数,如归一化差值植被指数(NDVI)等在生物量较高时会出现饱和现象,从而导致生物量的低估。而加入红边波段的指数不仅能够延缓指数的饱和趋势,而且能够提高反演精度。此外,通过敏感性分析发现,归一化差值指数和比值指数分别在作物生长的早期和中后期对生物量的变化保持较高的敏感性。由于红边比值指数(SR_(re))和MERIS叶绿素敏感指数(MTCI)在冬小麦全生长季内一直对生物量的变化保持高灵敏性,二者是生物量估算中最为稳定的指数。     

Herbaceous Biomass Estimation from SPOT 5 Imagery in Semiarid Rangelands of Idaho

Eight vegetation indices (VI) commonly used for above-ground biomass (AGB) estimation were derived from Satellite Pour l'Observation de la Terre 5 (SPOT 5) imagery and used to predict herbaceous AGB at a semiarid rangeland study site in southeastern Idaho. The relationship between herbaceous AGB and vegetation water content was also evaluated and as a result, a suite of water-sensitive vegetation indices (WSVI) were developed. Correlation coefficients between herbaceous AGB, VIs, and WSVIs were calculated, demonstrating that WSVIs were correlated (r ² ≥ 0.51) with vegetation water content and performed better than standard VIs in herbaceous AGB estimates within the semiarid rangelands of Idaho.     

Quantification winter wheat LAI with HJ-1CCD image features over multiple growing seasons

Remote sensing images are widely used to map leaf area index (LAI) continuously over landscape. The objective of this study is to explore the ideal image features from Chinese HJ-1 A/B CCD images for estimating winter wheat LAI in Beijing. Image features were extracted from such images over four seasons of winter wheat growth, including five vegetation indices (VIs), principal components (PC), tasseled cap transformations (TCT) and texture parameters. The LAI was significantly correlated with the near-infrared reflectance band, five VIs [normalized difference vegetation index, enhanced vegetation index (EVI), modified nonlinear vegetation index (MNLI), optimization of soil-adjusted vegetation index, and ratio vegetation index], the first principal component (PC1) and the second TCT component (TCT2). However, these image features cannot significantly improve the estimation accuracy of winter wheat LAI in conjunction with eight texture measures. To determine the few ideal features with the best estimation accuracy, partial least squares regression (PLSR) and variable importance in projection (VIP) were applied to predict LAI values. Four remote sensing features (TCT2, PC1, MNLI and EVI) were chosen based on VIP values. The result of leave-one-out cross-validation demonstrated that the PLSR model based on these four features produced better result than the ten features’ model, throughout the whole growing season. The results of this study suggest that selecting a few ideal image features is sufficient for LAI estimation.     

Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance

The objective of this research is to select the most sensitive wavelengths for the discrimination of the imperceptible spectral variations of paddy rice under different cultivation conditions. The paddy rice was cultivated under four different nitrogen cultivation levels and three water irrigation levels. There are 2151 hyperspectral wavelengths available, both in hyperspectral reflectance and energy space transformed spectral data. Based on these two data sets, the principal component analysis (PCA) and band-band correlation methods were used to select significant wavelengths with no reference to leaf biochemical properties, while the partial least squares (PLS) method assessed the contribution of each narrow band to leaf biochemical content associated with each loading weight across the nitrogen and water stresses. Moreover, several significant narrow bands and other broad bands were selected to establish eight kinds of wavelength (broad-band) combinations, focusing on comparing the performance of the narrow-band combinations instead of broad-band combinations for rice supervising applications. Finally, to investigate the capability of the selected wavelengths to diagnose the stress conditions across the different cultivation levels, four selected narrow bands (552, 675, 705 and 776 nm) were calculated and compared between nitrogen-stressed and non-stressed rice leaves using linear discriminant analysis (LDA). Also, wavelengths of 1158, 1378 and 1965 nm were identified as the most useful bands to diagnose the stress condition across three irrigation levels. Results indicated that good discrimination was achieved. Overall, the narrow bands based on hyperspectral reflectance data appear to have great potential for discriminating rice of differing cultivation conditions and for detecting stress in rice vegetation; these selected wavelengths also have great potential use for the designing of future sensors.     

Predicting plant water content in Eucalyptus grandis forest stands in KwaZulu-Natal,South Africa using field spectra resampled to the Sumbandila Satellite Sensor

The measurement of plant water content is essential to assess stress and disturbance in forest plantations. Traditional techniques to assess plant water content are costly, time consuming and spatially restrictive. Remote sensing techniques offer the alternative of a non-destructive and instantaneous method of assessing plant water content over large spatial scales where ground measurements would be impossible on a regular basis. In the context of South Africa, due to the cost and availability of imagery, studies focusing on the estimation of plant water content using remote sensing data have been limited. With the scheduled launch of the South African satellite SumbandilaSat evident in 2009, it is imperative to test the utility of this satellite in estimating plant water content. This study resamples field spectral data measured from a field spectrometer to the band settings of the SumbandilaSat in order to test its potential in estimating plant water content in a Eucalyptus plantation. The resampled SumbandilaSat wavebands were input into a neural network due to its ability to model non-linearity in a dataset and its inherent ability to perform better than conventional linear models. The integrated approach involving neural networks and the resampled field spectral data successfully predicted plant water content with a correlation coefficient of 0.74 and a root mean square error (RMSE) of 1.41% on an independent test dataset outperforming the traditional multiple regression method of estimation. The best-trained neural network algorithm that was chosen for assessing the relationship between plant water content and the SumbandilaSat bands was based on a few points only and more research is required to test the robustness and effectiveness of this sensor in estimating plant water content across different species and seasons. This is critical for monitoring plantation health in South Africa using a cheaply available local sensor containing key vegetation wavelengths.