Simple Ratio Vegetation Index Research Articles

The Use of Sentinel-2 Vegetation Indices Imagery in Detecting the Effect of Plant Distance to the Productivity of Corn Crops

There are several variations in planting distance used for corn plants, including 70×40 cm, 70×20 cm, and irregular. This affects the productivity of corn plants because of competition between corn plants. Satellite imagery, such as Sentinel-2 imagery, can be used to observe this effect. This research aimed to determine the effectiveness of Sentinel-2 satellite imagery in detecting the effect of plant distance on corn plant productivity. This research used Modified Red-edge Simple Ratio (mRE-SR) Vegetation Index imagery from Sentinel-2 imagery. The three plant distances used were 70×40 cm, 70×20 cm, and irregular. The images used corresponded to the corn planting times at research location, i.e. 10 and 20 December 2022, 14 January 2023, 28 February 2023 and 15 March 2023. Linear regression analysis was carried out to test the correlation between corn plant productivity of each plant distance and the mRE-SR Vegetation Index image. The results of this research indicated that the effect of plant distance on corn plant productivity can be studied using mRE-SR Sentinel-2 imagery.

The reliability of Unmanned Aerial Vehicles (UAVs) equipped with multispectral cameras for estimating chlorophyll content, plant height, canopy area, and fruit total number of Lemons (Citrus limon)

Monitoring lemon production requires appropriate and efficient technology. The use of UAVs can addressed these challenges. The purpose of this study was to determine the best vegetation indices (VIs) for estimating chlorophyll content, plant height (PH), canopy area (CA), and fruit total numberas (FTN). CCM 200 was used as a tool to measure the chlorophyll content index (CCI), the number of fruits was measured by hand-counter, and other variables were recorded in meters. The UAV used was a Phantom 4 with a multispectral camera capable of capturing five different bands. The VIs was obtained via analysis of digital numbers generated by the multispectral camera. Then, the VIs was correlated with the CCI, PH, CA and FTN. VIs tested included the following: the normalized difference vegetation index (NDVI), the normalized difference vegetation index-green (NDVIg), the normalized different index (NDI), green minus red (GMR), simple ratio (SR), the Visible Atmospherically Resistant Index (VARI), normalized difference red edge (NDRE), simple ratio red-edge (SR<sub>RE</sub>), the simple ratio vegetation index (SR<sub>VI</sub>), and the Canopy Chlorophyll Content Index (CCCI). The best model for predicting CCI was obtained using the NDVIg (R<sup>2</sup>=0.8480; RMSE=6.1665 and RRMSE=0.0908). Meanwhile, SR turned out to be the best model for predicting PH (R<sup>2</sup>=0.8266; RMSE=15.6432 and RRMSE=0.0883), CA (R<sup>2</sup>=0.6886; RMSE= 0.8826 and RRMSE=0.1907), and FTN (R<sup>2</sup>=0.6850; RMSE=24.5574 and RRMSE=0.3503). The implication of these results for future activities includes establishing early monitoring and evaluation systems for lemon yield and production. This model was developed and tested in this specific location and under these environmental conditions.

Evaluating the saturation effect of vegetation indices in forests using 3D radiative transfer simulations and satellite observations

Vegetation indices (VIs) have been used extensively for qualitative and quantitative remote sensing monitoring of vegetation vigor and growth dynamics. However, the saturation phenomenon of VIs (i.e., insignificant change at moderate to high vegetation densities) poses a known limitation to their ability to characterize surface vegetation over the dense canopy. Although the mechanisms underlying saturation are relatively straightforward and several VIs have been proposed to mitigate the saturation effect, the assessment of the saturation effect of VIs remains insufficient. Notably, no unified metric has been proposed to quantify the VI saturation phenomenon, limiting VI selection in practical applications. In this study, we proposed two indicators to describe the saturation phenomenon and utilized a well-validated three-dimensional (3D) canopy radiative transfer (RT) model large-scale remote sensing data and image simulation framework (LESS) to simulate the bidirectional reflectance factor (BRF) of six forests scenes and assessed the variations in VIs in relation to leaf area index (LAI) values over different backgrounds, sun-sensor geometries, and spatial distribution types. The saturation characteristics of 36 VIs were evaluated in combination with simulation results and satellite observations from multiple sensors. The ranking of VI saturation from simulated and satellite results revealed a good agreement. Our results indicated that the simple ratio vegetation index (SR) performed best with the highest saturation point and can well characterize the surface vegetation condition until LAI reaches 4. Besides, we found that the saturation effect of VIs was influenced by soil brightness, sun-sensor geometry, and canopy structure. SR, modified simple ratio (MSR) and normalized green red difference index (NGRDI) were the most susceptible to these disturbing factors, although they had higher resistance to saturation. Modified triangular vegetation index 1 (MTVI1), modified non-linear vegetation index (MNLI), triangular greenness index (TGI), and triangular vegetation index (TriVI) performed well overall, combining the ability to resist saturation and disturbance factors. Appropriate application of VIs can help better understand vegetation responses to climate change and accurately assess ecosystem status. Our results contribute to the understanding of the VI saturation effect and provide a combined model and satellite data experimental workflow in appropriate VI selection to accurately characterize vegetation.

Exploring the potential of transmittance vegetation indices for leaf functional traits retrieval

ABSTRACT Leaf functional traits are key indicators of plant functions useful for inferring complex plant processes, including their responses to environmental changes. Vegetation indices (VIs) composed of a few reflectance wavelengths hold the advantages of being relatively simple and effective and have been widely used within remote sensing to estimate leaf traits. However, the difference between the reflectance from the upper and lower part of the leaf suggests that leaf reflectance mainly provides one-sided information, constraining its ability to estimate leaf functional traits. Leaf transmittance, on the other hand, gives information about the whole leaf and has more potential to be sensitive to changes in leaf biochemistry. As transmittance-based VI is rare, this study aims to propose new transmittance-based VIs for accurate estimations of leaf traits. Three forms, i.e. the normalized difference VI, the simple ratio VI, and the difference VI were employed, and wavelength selection for transmittance-based and reflectance-based VIs were conducted, respectively. The applicability of these VIs for estimating four leaf functional traits (leaf chlorophyll (Cab), leaf carotenoids (Car), equivalent water thickness (EWT), and leaf mass per area (LMA)) were evaluated. Cross-validation using three datasets of field observations and sensitivity analysis showed that the VIs constructed using transmittance were relatively less affected by interferences from other leaf parameters, improving the estimation accuracy of Car, EWT, and LMA compared to their optimal reflectance counterparts (RMSE reduced by 2% to 15%, and MAE reduced by 7% to 20% for the pooled dataset). Our study revealed that the normalized difference VI based on transmittance showed considerable sensitivity to Car, EWT, and LMA, whereas the difference VI based on reflectance was effective in indicating Cab. The proposed transmittance-based VIs will aid remote monitoring of leaf traits and thereby plant adaptations and acclimation to changes in environmental conditions.

高分一号宽幅多光谱影像辐射定标偏差及其植被指数影响

PDF HTML阅读 XML下载 导出引用 引用提醒 高分一号宽幅多光谱影像辐射定标偏差及其植被指数影响 DOI: 10.5846/stxb202205241462 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划课题项目2019YFB2102004）；福建省自然科学基金项目（2021J011190）；厦门理工学院高层次人才项目（4010520004）；江西省水利科技项目（202224ZDKT11） Radiometric calibration bias of the Gaofen-1 WFV multispectral imagery and its influence on vegetation index Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:自2013年4月在轨运行以来，高分一号（GF-1）宽幅（WFV）多光谱相机已实现持续对地观测，为包括生态学在内的相关研究领域与行业应用提供了丰富的数据源。当前场地定标频次偏低以及定标参数更新、发布滞后的现状，一定程度上影响了GF-1 WFV多光谱数据的定量应用。然而，现有文献仅在数据预处理流程中谈及对GF-1 WFV影像的辐射定标处理，很少讨论定标参数选取不当甚至误用产生的可能影响。基于已公布的辐射定标参数（2014-2021年）和4景GF-1 WFV Level1A级影像产品数据，重点围绕辐射定标偏差及其对多光谱波段星上反射率和常用植被指数的影响等展开模型分析和讨论。结果显示：即使两个相邻年份间，在多数情况下误用辐射定标参数会导致不可忽视的波段星上反射率相对偏差；进而给不同类型植被指数的实际应用带来不同程度的挑战。因辐射定标偏差的影响，常用的两波段归一化型植被指数在监测稀疏植被覆盖区时会存在明显的误差；而对高植被覆盖区的监测时采用两波段简单比值型植被指数将面临更大的挑战。针对存档GF-1 WFV Level1A数据的应用需求，提出利用时间距离加权的线性内插法来修正基于公开定标参数的辐射定标结果，并通过案例分析表明了该处理方法的有效性。最后，希望研究结果能引起普通用户对卫星遥感影像辐射定标的关注和重视。 Abstract:Since its on-orbit operation in April 2013, the wide field of view (WFV) sensors onboard Gaofen-1 (GF-1), where "Gaofen" means high resolution in Chinese, have continuously collected multispectral imagery of the Earth's surface. The GF-1 WFV sensors have provided abundant data sources for many fields including ecology. The fact that insufficient site calibration (i.e. once a year) as well as the updates lag of calibration parameters might limit the quantitative application of the GF-1 WFV imagery to a certain extent. However, current literature has only mentioned radiometric calibration of the GF-1 WFV images as a pre-processing procedure, whereas has rarely discussed the possible impacts caused by improper selection or misuse of calibration parameters. Based on the radiometric calibration parameters published for the GF-1 WFV imagery (from 2014 to 2021) and four scenes of the Level1A data products, the issue on radiometric calibration bias was investigated. Furthermore, impacts of the radiometric calibration bias on the top of atmosphere (TOA) reflectance and on several vegetation indices commonly used were discussed accordingly. It showed that, generally, even the calibration difference between two neighboring years were considered, in most cases, improper selection of the calibration parameters could result in a significantly relative bias in the TOA reflectance. The bias in TOA reflectance further challenged different types of vegetation indices with varying degrees of patterns in practice. In particular, mainly due to the radiometric calibration biases, the obvious errors were more likely in the normalized difference vegetation index based on two bands, for monitoring sparse vegetation coverage area. Actually, the normalized difference vegetation index has been commonly used in assessing vegetation status as well as surface dynamics. At the same time, for monitoring high vegetation coverage area, the simple ratio vegetation index with two bands was possibly confronted with greater challenges. Consequently, to make full use of the GF-1 WFV Level1A products, it is critical to solve the radiometric calibration problems. In this study, a time weighted linear interpolation method was proposed. In the interpolation process for a specific GF-1 WFV imagery, both radiometric parameters obtained closely before and after the imagery acquisition were integrated. A case study suggested the effectiveness of the proposed processing to improve the radiometric calibration of the GF-1 WFV images in Level1A products, as compared against the results obtained merely based on public calibration parameters. Finally, general users should pay much attention to the radiometric calibration of satellite remotely sensed data (e.g., the GF-1 WFV multispectral imagery) for their quantitative applications, as discussed in this investigation. 参考文献 相似文献 引证文献

Spectral reflection and crop parameters: can the disentanglement of primary and secondary traits lead to more robust and extensible prediction models?

Recently the application of spectral reflection data for the prediction of crop parameters for applications in precision agriculture, such as green area index (GAI), total aboveground dry matter (DM), and total aboveground nitrogen content (N content) increases. However, the usability of vegetation indices (VI) for the prediction of crop parameters is strongly limited by the fact that most VI calibrations are only valid for specific crops and growth periods. The results of the presented study based on the differentiation of primary (main driver of the reflectance signal) and secondary (not directly related to reflectance signal) crop parameters. For GAI prediction, a universal (without crop-specific parametrization) simple ratio vegetation index (SR) provided good calibration (R2 adj. = 0.90, MAE = 0.32, rMAE = 22%) and evaluation results (MAE = 0.33, rMAE = 18%). The disentanglement of primary and secondary traits allowed the development of a functional two-step model for the estimation of the N content during vegetative growth (MAE = 19.2 g N m−1, rMAE = 44%). This model was based on fundamental, crop-specific relationships between the crop parameters GAI and N content. Additionally, an advanced functional approach was tested enabling the whole-season prediction of DM and confirming a reliable GAI estimation throughout the whole growing season (R2 = 0.89–0.93).

Prediction of Strawberry Dry Biomass from UAV Multispectral Imagery Using Multiple Machine Learning Methods

Biomass is a key biophysical parameter for precision agriculture and plant breeding. Fast, accurate and non-destructive monitoring of biomass enables various applications related to crop growth. In this paper, strawberry dry biomass weight was modeled using 4 canopy geometric parameters (area, average height, volume, standard deviation of height) and 25 spectral variables (5 band original reflectance values and 20 vegetation indices (VIs)) extracted from the Unmanned Aerial Vehicle (UAV) multispectral imagery. Six regression techniques—multiple linear regression (MLR), random forest (RF), support vector machine (SVM), multivariate adaptive regression splines (MARS), eXtreme Gradient Boosting (XGBoost) and artificial neural network (ANN)—were employed and evaluated for biomass prediction. The ANN had the highest accuracy in a five-fold cross-validation, with R2 of 0.89~0.93, RMSE of 7.16~8.98 g and MAE of 5.06~6.29 g. As for the other five models, the addition of VIs increased the R2 from 0.77~0.80 to 0.83~0.86, and reduced the RMSE from 8.89~9.58 to 7.35~8.09 g and the MAE from 6.30~6.70 to 5.25~5.47 g, respectively. Red-edge-related VIs, including the normalized difference red-edge index (NDRE), simple ratio vegetation index red-edge (SRRedEdge), modified simple ratio red-edge (MSRRedEdge) and chlorophyll index red and red-edge (CIred&RE), were the most influential VIs for biomass modeling. In conclusion, the combination of canopy geometric parameters and VIs obtained from the UAV imagery was effective for strawberry dry biomass estimation using machine learning models.

Comparison of UAV RGB Imagery and Hyperspectral Remote-Sensing Data for Monitoring Winter Wheat Growth

Although crop-growth monitoring is important for agricultural managers, it has always been a difficult research topic. However, unmanned aerial vehicles (UAVs) equipped with RGB and hyperspectral cameras can now acquire high-resolution remote-sensing images, which facilitates and accelerates such monitoring. To explore the effect of monitoring a single crop-growth indicator and multiple indicators, this study combines six growth indicators (plant nitrogen content, above-ground biomass, plant water content, chlorophyll, leaf area index, and plant height) into the new comprehensive growth index (CGI). We investigate the performance of RGB imagery and hyperspectral data for monitoring crop growth based on multi-time estimation of the CGI. The CGI is estimated from the vegetation indices based on UAV hyperspectral data treated by linear, nonlinear, and multiple linear regression (MLR), partial least squares (PLSR), and random forest (RF). The results are as follows: (1) The RGB-imagery indices red reflectance (r), the excess-red index (EXR), the vegetation atmospherically resistant index (VARI), and the modified green-red vegetation index (MGRVI), as well as the spectral indices consisting of the linear combination index (LCI), the modified simple ratio index (MSR), the simple ratio vegetation index (SR), and the normalized difference vegetation index (NDVI), are more strongly correlated with the CGI than a single growth-monitoring indicator. (2) The CGI estimation model is constructed by comparing a single RGB-imagery index and a spectral index, and the optimal RGB-imagery index corresponding to each of the four growth stages in order is r, r, r, EXR; the optimal spectral index is LCI for all four growth stages. (3) The MLR, PLSR, and RF methods are used to estimate the CGI. The MLR method produces the best estimates. (4) Finally, the CGI is more accurately estimated using the UAV hyperspectral indices than using the RGB-image indices.

Remote Prediction of Oilseed Rape Yield via Gaofen-1 Images and a Crop Model

The fast and accurate prediction of crop yield at the regional scale is of great significance to food policies or trade. In this study, a new model is developed to predict the yield of oilseed rape from high-resolution remote sensing images. In order to derive this model, the ground experiment and remote sensing data analysis are carried out successively. In the ground experiment, the leaf area index (LAI) of four growing stages are measured, and a regression model is established to predict yield from ground LAI. In the remote sensing analysis, a new model is built to predict ground LAI from Gaofen-1 images where the simple ratio vegetation index at the bolting stage and the VARIgreen vegetation index at the flowering stage are used. The WOFOSTWOrld FOod STudy (WOFOST) crop model is used to generate time-series ground LAI from discontinuous ground LAI, which is calibrated coarsely with the MODerate resolution imaging spectroradiometer LAI product and finely with the ground-measured data. By combining the two conclusive formulas, an estimation model is built from Gaofen-1 images to the yield of oilseed rape. The effectiveness of the proposed model is verified in Wuxue City, Hubei Province from 2014 to 2019, with the pyramid bottleneck residual network to extract oilseed rape planting areas, the proposed model to estimate yields, and the China statistical yearbooks for comparison. The validation shows that the prediction error of the proposed algorithm is less than 5.5%, which highlights the feasibility of our method for accurate prediction of the oilseed rape yield in a large area.

Estimation of aboveground mangrove carbon stocks from WorldView-2 imagery based on generic and species-specific allometric equations

The aboveground mangrove carbon stock can be estimated and mapped from remote-sensing data using allometric equations. However, the distinct effects of applying the generic or species-specific allometric equations remain unclear in the mangrove environment. The aims of this study are to (1) describe the relationship between field biomass and image pixels using generic and species-specific allometric equations, (2) estimate and map the mangrove carbon stock at the generic and species-specific levels, and (3) compare the accuracy and distribution of the resulting mangrove carbon stock maps. We tested the performance of these allometric equations using a resampled WorldView-2 image (6 m pixel size). The study site was in Clungup Mangrove Conservation (CMC), Malang, East Java and included natural mangrove forests and well-planted mangroves. We used the simple ratio (SR) vegetation index to estimate and map the aboveground carbon stock (AGC) of the mangroves based on the WorldView-2 image. Overall, the results indicate that the use of a generic allometric equation estimates mangrove AGC more accurately than the species-specific equations. However, we found a low coefficient of determination (R2) for both models: 0.2175 for the generic model and 0.1801 for the species-specific one. This may be due to the mangrove objects’ high variation in internal spectral reflectance at the tested image pixel size. The total estimated mangrove AGC obtained was 3,834.15 tons with a standard error of 0.12 for the generic model and 4,927.90 tons with a standard error of 0.17 for the species-specific model. Our findings substantiate the importance of the selection of allometric equations for mangrove AGC studies based on remote-sensing images.

Radiative Transfer Image Simulation Using L-System Modeled Strawberry Canopies

The image-based modeling and simulation of plant growth have numerous and diverse applications. In this study, we used image-based and manual field measurements to develop and validate a methodology to simulate strawberry (Fragaria × ananassa Duch.) plant canopies throughout the Florida strawberry growing season. The simulated plants were used to create a synthetic image using radiative transfer modeling. Observed canopy properties were incorporated into an L-system simulator, and a series of strawberry canopies corresponding to specific weekly observation dates were created. The simulated canopies were compared visually with actual plant images and quantitatively with in-situ leaf area throughout the strawberry season. A simple regression model with L-system-derived and in-situ total leaf areas had an Adj R2 value of 0.78. The L-system simulated canopies were used to derive information needed for image simulation, such as leaf area and leaf angle distribution. Spectral and plant canopy information were used to create synthetic high spatial resolution multispectral images using the Discrete Anisotropic Radiative Transfer (DART) software. Vegetation spectral indices were extracted from the simulated image and used to develop multiple regression models of in-situ biophysical parameters (leaf area and dry biomass), achieving Adj R2 values of 0.63 and 0.50, respectively. The Normalized Difference Vegetation Index (NDVI) and the Red Edge Simple Ratio (SRre) vegetation indices, which utilize the red, red edge, and near infrared bands of the spectrum, were identified as statistically significant variables (p < 0.10). This study showed that both geometric (canopy seize metrics) and spectral variables were successful in modeling in-situ biomass and leaf area. Combining the geometric and spectral variables, however, only slightly improved the prediction model. These results show the feasibility of simulating strawberry canopies and images with inherent geometrical, topological, and spectral properties of real strawberry plants. The simulated canopies and images can be used in applications beyond creating realistic computer graphics for quantitative applications requiring the depiction of vegetation biological processes, such as stress modeling and remote sensing mission planning.

Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery

The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops.

Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes

Accurate measurement of canopy chlorophyll content (CCC) is essential for the understanding of terrestrial ecosystem dynamics through monitoring and evaluating properties such as carbon and water flux, productivity, light use efficiency as well as nutritional and environmental stresses. Information on the amount and distribution of CCC helps to assess and report biodiversity indicators related to ecosystem processes and functional aspects. Therefore, measuring CCC continuously and globally from earth observation data is critical to monitor the status of the biosphere. However, generic and robust methods for regional and global mapping of CCC are not well defined. This study aimed at examining the spatiotemporal consistency and scalability of selected methods for CCC mapping across biomes. Four methods (i.e., radiative transfer models (RTMs) inversion using a look-up table (LUT), the biophysical processor approach integrated into the Sentinel application platform (SNAP toolbox), simple ratio vegetation index (SRVI), and partial least square regression (PLSR)) were evaluated. Similarities and differences among CCC products generated by applying the four methods on actual Sentinel-2 data in four biomes (temperate forest, tropical forest, wetland, and Arctic tundra) were examined by computing statistical measures and spatiotemporal consistency pairwise comparisons. Pairwise comparison of CCC predictions by the selected methods demonstrated strong agreement. The highest correlation (R2 = 0.93, RMSE = 0.4371 g/m2) was obtained between CCC predictions of PROSAIL inversion by LUT and SNAP toolbox approach in a wetland when a single Sentinel-2 image was used. However, when time-series data were used, it was PROSAIL inversion against SRVI (R2 = 0.88, RMSE = 0.19) that showed greatest similarity to the single date predictions (R2 = 0.83, RMSE = 0.17 g/m2) in this biome. Generally, the CCC products obtained using the SNAP toolbox approach resulted in a systematic over/under-estimation of CCC. RTMs inversion by LUT (INFORM and PROSAIL) resulted in a non-biased, spatiotemporally consistent prediction of CCC with a range closer to expectations. Therefore, the RTM inversion using LUT approaches particularly, INFORM for ‘forest’ and PROSAIL for ‘short vegetation’ ecosystems, are recommended for CCC mapping from Sentinel-2 data for worldwide mapping of CCC. Additional validation of the two RTMs with field data of CCC across biomes is required in the future.

Comparing methods for mapping canopy chlorophyll content in a mixed mountain forest using Sentinel-2 data

The Sentinel-2 Multi-Spectral Imager (MSI) has three spectral bands centered at 705, 740, and 783 nm wavelengths that exploit the red-edge information useful for quantifying plant biochemical traits. This sensor configuration is expected to improve the prediction accuracy of vegetation chlorophyll content. In this work, we assessed the performance of several statistical and physical-based methods in retrieving canopy chlorophyll content (CCC) from Sentinel-2 in a heterogeneous mixed mountain forest. Amongst the algorithms presented in the literature, 13 different vegetation indices (VIs), a non-parametric statistical approach, and two radiative transfer models (RTM) were used to assess the CCC prediction accuracy. A field campaign was conducted in July 2017 to collect in situ measurements of CCC in Bavarian forest national park, and the cloud-free Sentinel-2 image was acquired on 13 July 2017. The leave-one-out cross-validation technique was used to compare the VIs and the non-parametric approach. Whereas physical-based methods were calibrated using simulated data and validated using the in situ reference dataset. The statistical-based approaches, such as the modified simple ratio (mSR) vegetation index and the partial least square regression (PLSR) outperformed all other techniques. As such the modified simple ratio (mSR3) (665, 865) gave the lowest cross-validated RMSE of 0.21 g/m2 (R2 = 0.75). The PLSR resulted in the highest R2 of 0.78, and slightly higher RMSE =0.22 g/m2 than mSR3. The physical-based approach-INFORM inversion using look-up table resulted in an RMSE =0.31 g/m2, and R2 = 0.67. Although mapping CCC using these methods revealed similar spatial distribution patterns, over and underestimation of low and high CCC values were observed mainly in the statistical approaches. Further validation using in situ data from different terrestrial ecosystems is imperative for both the statistical and physical-based approaches' effectiveness to quantify CCC before selecting the best operational algorithm to map CCC from Sentinel-2 for long-term terrestrial ecosystems monitoring across the globe.

Validating the Predictive Power of Statistical Models in Retrieving Leaf Dry Matter Content of a Coastal Wetland from a Sentinel-2 Image

Leaf dry matter content (LDMC), the ratio of leaf dry mass to its fresh mass, is a key plant trait, which is an indicator for many critical aspects of plant growth and survival. Accurate and fast detection of the spatiotemporal dynamics of LDMC would help understanding plants’ carbon assimilation and relative growth rate, and may then be used as an input for vegetation process models to monitor ecosystems. Satellite remote sensing is an effective tool for predicting such plant traits non-destructively. However, studies on the applicability of remote sensing for LDMC retrieval are scarce. Only a few studies have looked into the practicality of using remotely sensed data for the prediction of LDMC in a forest ecosystem. In this study, we assessed the performance of partial least squares regression (PLSR) plus 11 widely used vegetation indices (VIs), calculated based on different combinations of Sentinel-2 bands, in predicting LDMC in a coastal wetland. The accuracy of the selected methods was validated using LDMC, destructively measured in 50 randomly distributed sample plots at the study site in Schiermonnikoog, the Netherlands. The PLSR applied to canopy reflectance of Sentinel-2 bands resulted in accurate prediction of LDMC (coefficient of determination (R2) = 0.71, RMSE = 0.033). PLSR applied to the studied VIs provided an R2 of 0.70 and RMSE of 0.033. Four vegetation indices (enhanced vegetation index(EVI), specific leaf area vegetation index (SLAVI), simple ratio vegetation index (SRVI), and visible atmospherically resistant index (VARI)) computed using band 3 (green) and band 11 of the Sentinel-2 performed equally well and achieved a good measure of accuracy (R2 = 0.67, RMSE = 0.034). Our findings demonstrate the feasibility of using Sentinel-2 surface reflectance data to map LDMC in a coastal wetland.

Using Hyperspectral and Multispectral Indices to Detect Water Stress for an Urban Turfgrass System

Spectral reflectance measurements collected from hyperspectral and multispectral radiometers have the potential to be a management tool for detecting water and nutrient stress in turfgrass. Hyperspectral radiometers collect hundreds of narrowband reflectance data compared to multispectral radiometers that collect three to ten broadband reflectance data for a cheaper cost. Spectral reflectance data have been used to create vegetation indices such as the normalized difference vegetation index (NDVI) and the simple ratio vegetation index (RVI) to assess crop growth, density, and fertility. Other indices such as the water band index (WBI) (narrowband index) and green-to-red ratio index (GRI) (both broadband and narrowband index) have been proposed to predict soil moisture status in turfgrass systems. The objective of this study was to compare the value of multispectral and hyperspectral radiometers to assess soil volumetric water content (VWC) and tall fescue (Festuca arundinacea Schreb.) responses. The multispectral radiometer VI had the strongest relationships to turfgrass quality, biomass, and tissue N accumulation during the trial period (April 2017–August 2018). Soil VWC had the strongest relationship to WBI (r = 0.60), followed by GRI and NDVI (both r = 0.54) for the 0% evapotranspiration (ET). Nonlinear regression showed strong relationships at high water stress periods in each year for WBI (r = 0.69–0.79), GRI (r = 0.64–0.75), and NDVI (r = 0.58–0.79). Broadband index data collected using a mobile multispectral sensor is a cheaper alternative to hyperspectral radiometry and can provide better spatial coverage.

Effects of Growth Stage Development on Paddy Rice Leaf Area Index Prediction Models

A in situ hyperspectral dataset containing multiple growth stages over multiple growing seasons was used to build paddy rice leaf area index (LAI) estimation models with a special focus on the effects of paddy rice growth stage development. The univariate regression method applied to the vegetation index (VI), the traditional multivariate calibration method of partial least squares regression (PLSR), and modern machine learning methods such as support vector regression (SVR), random forests (RF), and artificial neural networks (ANN) based on the original and first-derivative hyperspectral data were evaluated in this study for paddy rice LAI estimation. All the models were built on the whole growing season and on each separate vegetative, reproductive and ripening growth stage of paddy rice separately. To ensure a fair comparison, the models of the whole growing season were also validated on data for each separate growth stage of the standalone validation dataset. Moreover, the optimal band pairs for calculating narrowband difference vegetative index (DVI), normalized difference vegetation index (NDVI) and simple ratio vegetation index (SR) were determined for the whole growing season and for each separate growth stage separately. The results showed that for both the whole growing season and for each single growth stage, the red-edge and near-infrared band pairs are optimal for formulating the narrowband DVI, NDVI and SR. Among the four multivariate calibration methods, SVR and RF yielded more accurate results than the other two methods. The SVR and RF models built on first-derivative spectra provided more accurate results than the corresponding models on the original spectra for both whole growing season models and separate growth stage models. Comparing the prediction accuracy based on the whole growing season revealed that the RF and SVR models showed an advantage over the VI models. However, comparing the prediction accuracy based on each growth stage separately showed that the VI models provided more accurate results for the vegetative growth stages. The SVR and RF models provided more accurate results for the ripening growth stage. However, the whole growing season RF model on first-derivative spectra could provide reasonable accuracy for each single growth stage.

Estimating oak forest parameters in the western mountains of Iran using satellite-based vegetation indices

Remote sensing is an important tool for studying and modeling forest stands. Vegetation indices obtained from Landsat-8 remotely sensed data were used to estimate the forest parameters in the western mountains of Iran. Thirty-four sample points were selected on the map of Bayangan County, Kermanshah province, Iran. At each point, a cluster of five rectangular plots of 8100 m2 and 200 m apart was established. Some clusters were primarily sampled to determine the variance of the forest parameters. The coefficient of variation was used as a criterion for sample allocation. Stand density, canopy cover and basal area per hectare were calculated for each plot. Vegetation indices were extracted from the Landsat-8 images for each plot. Simple linear and nonlinear regressions were conducted to develop the models. The models were validated using an independent data set. Stability of model parameters was statistically evaluated. The results showed that Normalized difference vegetation index (NDVI) and Transformed normalized difference vegetation index (TNDVI) followed by Simple ratio vegetation index (SRVI) were the best predictors, explaining up to 91% of the variations with high precision. For NDVI, Soil adjusted vegetation index 2 (SAVI2) and SRVI, the cubic model was more appropriate than the linear model for predicting the forest parameters. For this model, the R-square value increased while NRMSE decreased significantly. For Infrared percentage vegetation index (IPVI), the quadratic model was better, but, for other vegetation indices, nonlinear models were not superior to linear ones. Finally, it can be concluded that Landsat-8 imagery is suitable for predicting forest parameters in the oak forests of western Iran. Of course, large plots must be selected, and pre-classification is necessary to gain accurate and precise estimations.

Combined use of in situ hyperspectral vegetation indices for estimating pasture biomass at peak productive period for harvest decision

Since the typical destructive methods for measuring aboveground biomass (AGB) have many limitations, a variety of non-destructive techniques have been developed. In this paper, the potential of ground-based hyperspectral remote-sensed data for non-destructive assessment of semi-arid pasture AGB at the peak productive period was investigated. The reflectance spectrometric and AGB data were sampled at the end of the growing season (almost peak biomass) over two locations at pastures in the southern Horqin sandy land, eastern Inner Mongolia, China. All combinations (two-band and three-band) of narrow bands in the forms of simple ratio vegetation index (SRVI), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI) and enhanced vegetation index (EVI) were used in a linear regression analysis against AGB. The predictive performance of the stepwise multiple linear regression (SMLR) using 4 best VIs as input variables was compared with the performance of multivariate partial least squares regression (PLSR) using all reflectance bands as input variables to estimate AGB. It was observed that the relationship between AGB and single band spectral reflectance was low, while the estimation performance of the best VIs based on all available wavebands was improved considerably. In addition, the best VIs based on all available wavebands had considerably better fitting performance than those based on traditionally used wavebands for estimating AGB. In comparison to PLSR using the full individual reflectance as input variables, SMLR using the best VIs as input variables performed much better, with the maximum decrease in RMSECV of 37% and the relative mean absolute errors always below 12.5%. The study demonstrated the high potential to estimate pasture AGB, which is a proxy for pasture forage yield, at the peak productive period using a hyperspectral technique.

Estimating LAI and mapping canopy storage capacity for hydrological applications in wattle infested ecosystems using Sentinel-2 MSI derived red edge bands

This study assessed the strength of Sentinel-2 multispectral instrument (MSI) derived Red Edge (RE) bands in estimating Leaf Area Index (LAI) and mapping canopy storage capacity (CSC) for hydrological applications in wattle infested ecosystems. To accomplish this objective, this study compared the estimation strength of models derived, using standard bands (all bands excluding the RE band) with those including RE bands, as well as different vegetation indices. Sparse Partial Least Squares (SPLSR) and Partial Least Squares Regression (PLSR) ensembles were used in this study. Results showed that the RE spectrum covered by the Sentinel-2 MSI satellite reduced the estimation error by a magnitude of 0.125 based on simple ratio (RE SR) vegetation indices from 0.157 m2· m−2 based on standard bands, and by 0.078 m2· m−2 based on red edge normalised difference vegetation (NDVI-RE). The optimal models for estimating LAI to map CSC were obtained based on the RE bands centered at 705 nm (Band 5), 740 nm (Band 6), 783 nm (Band 7) as well as 865 nm (Band 8a). A root mean square error of prediction (RMSEP) of 0.507 m2· m−2 a relative root mean square error of prediction (RRMSEP) of 11.3% and R2 of 0.91 for LAI and a RMSEP of 0.246 m2/m2 (RRMSEP = 7.9%) and R2 of 0.91 for CSC were obtained. Overall, the findings of this study underscore the relevance of the new copernicus satellite product in rapid monitoring of ecosystems that are invaded by alien invasive species.