Characterizing Leaf Nutrients of Wetland Plants and Agricultural Crops with Nonparametric Approach Using Sentinel-2 Imagery Data

In arid environments of the world, particularly in sub-Saharan Africa and Asia, floodplain wetlands are a valuable agricultural resource. However, the water reticulation role by wetlands and crop production can negatively impact wetland plants. Knowledge on the foliar biochemical elements of wetland plants enhances understanding of the impacts of agricultural practices in wetlands. This study thus used Sentinel-2 multispectral data to predict seasonal variations in the concentrations of nine foliar biochemical elements in plant leaves of key floodplain wetland vegetation types and crops in the uMfolozi floodplain system (UFS). Nutrient concentrations in different floodplain plant species were estimated using Sentinel-2 multispectral data derived vegetation indices in concert with the random forest regression. The results showed a mean R2 of 0.87 and 0.86 for the dry winter and wet summer seasons, respectively. However, copper, sulphur, and magnesium were poorly correlated (R2 ≤ 0.5) with vegetation indices during the summer season. The average % relative root mean square errors (RMSE’s) for seasonal nutrient estimation accuracies for crops and wetland vegetation were 15.2 % and 26.8%, respectively. There was a significant difference in nutrient concentrations between the two plant types, (R2 = 0.94 (crops), R2 = 0.84 (vegetation). The red-edge position 1 (REP1) and the normalised difference vegetation index (NDVI) were the best nutrient predictors. These results demonstrate the usefulness of Sentinel-2 imagery and random forests regression in predicting seasonal, nutrient concentrations as well as the accumulation of chemicals in wetland vegetation and crops.

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Retrieval of Maize Leaf Area Index Using Hyperspectral and Multispectral Data

Remote Sensing ◽

10.3390/rs10121942 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1942 ◽

Cited By ~ 5

Author(s):

Sosdito Mananze ◽

Isabel Pôças ◽

Mario Cunha

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Vegetation Indices ◽

Cost Functions ◽

Support Vector ◽

Area Index ◽

Maize Leaf ◽

Multispectral Data ◽

Simple Ratio ◽

Sentinel 2

Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra were used to investigate the applicability of hyperspectral and multispectral data in retrieving the maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising two and three band indices, and nine machine learning regression algorithms (MLRA) were tested for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion approach. The three band vegetation indices were selected, specifically the modified difference index (mDId: 725; 715; 565) for the hyperspectral dataset and the modified simple ratio (mSRc: 740; 705; 865) for the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665; 865; 783) for the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of field spectra (multispectral and hyperspectral) while the support vector machine was selected for the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the study area.

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Accuracy Enhancement for Land Cover Classification Using LiDAR and Multitemporal Sentinel 2 Images in a Forested Watershed

Proceedings ◽

10.3390/proceedings2201280 ◽

2018 ◽

Vol 2 (20) ◽

pp. 1280 ◽

Cited By ~ 3

Author(s):

Laura Fragoso-Campón ◽

Elia Quirós ◽

Julián Mora ◽

José Antonio Gutiérrez ◽

Pablo Durán-Barroso

Keyword(s):

Land Cover ◽

Vegetation Cover ◽

Spectral Response ◽

Vegetation Indices ◽

Land Cover Classification ◽

Lidar Data ◽

Accuracy Enhancement ◽

Multispectral Data ◽

Sentinel 2

Mapping land cover with high accuracy has become a reality with the application of current remote sensing techniques. Due to the specific spectral response of the vegetation, soil and vegetation indices are adequate tools to help in the discrimination of land uses. Additionally, the accuracy of satellite imagery classification can be improved using multitemporal series combined with LiDAR data. This datafusion takes advantage of the information provided by LiDAR for the vegetation cover density, and the capability of multispectral data to detect the type of vegetation. The main goal of this study is to analyze the accuracy enhancement in land cover classification of two forested watersheds when using datafusion of annual time series of Sentinel-2 images complemented with low density LiDAR. The obtained results show that overall accuracy is better if LiDAR data is included in the classification. This improvement can be a significant issue in land cover classification of forest watershed due to relationship and influence that vegetation cover has on runoff estimation.

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Nutrient content of wetland plants in constructed wetlands receiving municipal effluent in tropical Australia

Water Science & Technology ◽

10.2166/wst.1997.0182 ◽

1997 ◽

Vol 35 (5) ◽

pp. 135-142 ◽

Cited By ~ 10

Author(s):

Margaret Greenway

Keyword(s):

Constructed Wetlands ◽

Municipal Wastewater ◽

Free Water ◽

Nutrient Content ◽

Wetland Plants ◽

Nutrient Concentrations ◽

Tissue Nutrient Content ◽

Significant Difference ◽

Plant Components ◽

Emergent Species

Several pilot wetlands have been constructed in Queensland to treat municipal wastewater. The wetlands are in tropical, subtropical and arid geographical locations. Most wetlands are free water surface and contain a variety of macrophyte types and species. A total of 49 native and 11 exotic species of wetland plants have been identified. This paper examines tissue nutrient content in different species and plant components from 7 wetlands. Most species translocated to the constructed wetlands flourished indicating their ability to tolerate nutrient enriched waters, and tended to have higher tissue nutrient concentrations than their controls in natural wetlands. Submerged and free floating species exhibited higher nutrient concentrations than floating leaved and emergent species. Maximum dry weight nutrient concentrations (mg.g−1) were recorded in duckweed 18 mgP.g−1; 58 mgN.g−1; Ceratophyllum 14 mgP.g−1, 35 mgN.g−1; Monochoria cyanea (a native relative of the water hyacinth) 13 mgP.g−1, 30 mgN.g−1; waterlilies: Nymphoides indica 16 mgP.g−1, 40 mgN.g−1; aquatic vines Ipomoea diamantinensis 10 mgP.g−1, 53 mgN.g−1, I. aquatica 9.5 mgP.g−1, 53 mgN.g−1; Ludwigia peploides 10 mgP.g−1, 52 mgN.g−1; and the water ferns Ceratopteris thalictroides 10 mgP.g−1, 31 mgN.g−1,Marsilea 10 mgP.g−1, 43 mgN.g−1. Emergent species with the highest nutrients (P or N) were Eleocharis sphacelata 9.4 mgP.g−1, 31.7 mgN.g−1, Baumea articulata 8.7 mgP.g−1, 24 mgN.g−1,Typha domingensis 7.2 mgP.g−1, 51.8 mgN.g−1 and Cyperus involucratus 7 mgP.g−1, 44.6 mgN.g−1. Pooled data showed no significant difference between tissue nutrient content in plant components, though nitrogen was highest in the leaves and phosphorus highest in the roots of most species. There was some evidence of spatial variation in tissue nutrient content between different wetlands but it has not been possible to correlate this with nutrient loadings or removal efficiencies.

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Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform

OENO One ◽

10.20870/oeno-one.2020.54.1.2557 ◽

2020 ◽

Vol 54 (2) ◽

pp. 189-197 ◽

Cited By ~ 2

Author(s):

Marco Sozzi ◽

Ahmed Kayad ◽

Francesco Marinello ◽

James Taylor ◽

Bruno Tisseyre

Keyword(s):

Unmanned Aerial Vehicle ◽

Vegetation Index ◽

Vegetation Indices ◽

Significance Test ◽

Support Vector ◽

Field Scale ◽

Mixed Pixel ◽

Normalised Difference Vegetation Index ◽

Aerial Vehicle ◽

Sentinel 2

Aim: The recent availability of Sentinel-2 satellites has led to an increasing interest in their use in viticulture. The aim of this short communication is to determine performance and limitation of a Sentinel-2 vegetation index in precision viticulture applications, in terms of correlation and variability assessment, compared to the same vegetation index derived from an unmanned aerial vehicle (UAV). Normalised difference vegetation index (NDVI) was used as reference vegetation index.Methods and Results: UAV and Sentinel-2 vegetation indices were acquired for 30 vineyard blocks located in the south of France without inter-row grass. From the UAV imagery, the vegetation index was calculated using both a mixed pixels approach (both vine and inter-row) and from pure vine-only pixels. In addition, the vine projected area data were extracted using a support vector machine algorithm for vineyard segmentation. The vegetation index was obtained from Sentinel-2 imagery obtained at approximately the same time as the UAV imagery. The Sentinel-2 images used a mixed pixel approach as pixel size is greater than the row width. The correlation between these three layers and the Sentinel-2 derived vegetation indices were calculated, considering spatial autocorrelation correction for the significance test. The Gini coefficient was used to estimate variability detected by each sensor at the within-field scale. The effects of block border and dimension on correlations were estimated.Conclusions: The comparison between Sentinel-2 and UAV vegetation index showed an increase in correlation when border pixels were removed. Block dimensions did not affect the significance of correlation unless blocks were < 0.5 ha. Below this threshold, the correlation was non-significant in most cases. Sentinel-2 acquired data were strongly correlated with UAV-acquired data at both the field (R2 = 0.87) and sub-field scale (R2 = 0.84). In terms of variability detected, Sentinel-2 proved to be able to detect the same amount of variability as the UAV mixed pixel vegetation index.Significance and impact of the study: This study showed at which field conditions the Sentinel-2 vegetation index can be used instead of UAV-acquired images when high spatial resolution (vine-specific) management is not needed and the vineyard is characterised by no inter-row grass. This type of information may help growers to choose the most appropriate information sources to detect variability according to their vineyard characteristics.

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Biomass quantification of Pinus taeda L. from remote optical sensor data

Advances in Forestry Science ◽

10.34062/afs.v6i2.7086 ◽

2019 ◽

Vol 6 (2) ◽

Author(s):

Carla Talita Pertille ◽

Marcos Felipe Nicoletti ◽

Larissa Regina Topanotti ◽

Thiago Floriani Stepka

Keyword(s):

Pinus Taeda ◽

Vegetation Indices ◽

Sensor Data ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Individual Tree ◽

Significant Difference ◽

Pinus Taeda L ◽

The Individual ◽

Sentinel 2

This research aimed to estimate the biomass of the trunk area of a Pinus taeda L. stand from vegetation indices from Landsat-8/OLI and Sentinel-2/MSI optical remote sensors. In order to obtain the biomass, a forest inventory was carried out with the installation of 33 circular plots of 400 m², in which all the individuals had the diameter at breast height (cm) and the total height (m) measured. Then, 30 trees were scaled by the Smalian method. The individual tree volume was estimated by the Meyer regression volumetric equation. The biomass was obtained through the product of the individual tree volume by the wood basic density. Subsequently, aerial biomass was obtained per plot. The processed orbital images were gathered from the Landsat-8/OLI and Sentinel-2/MSI sensors. We derived 19 vegetation indices for both images, which were correlated with the biomass per plot. The indexes with the best correlation with the biomass were considered as regression variables to develop models by the Stepwise technique (Backward and Forward). The correlation was significant among the variables and the best model was derived from the Landsat-8 data, which estimated the biomass per plot with an error of 8.75% and an adjusted coefficient of determination of 0.8173. Nevertheless, the statistical analysis revealed that there was no significant difference between the biomass estimated by the inventory and by the remotely located data.

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Comparison of Proximal and Remote Sensing for the Diagnosis of Crop Status in Site-Specific Crop Management

Sensors ◽

10.3390/s22010019 ◽

2021 ◽

Vol 22 (1) ◽

pp. 19

Author(s):

Jiří Mezera ◽

Vojtěch Lukas ◽

Igor Horniaček ◽

Vladimír Smutný ◽

Jakub Elbl

Keyword(s):

Remote Sensing ◽

Winter Wheat ◽

Vegetation Indices ◽

Wheat Yield ◽

Nitrogen Fertilizers ◽

Wheat Crop ◽

Significant Difference ◽

Winter Wheat Crop ◽

The Individual ◽

Sentinel 2

The presented paper deals with the issue of selecting a suitable system for monitoring the winter wheat crop in order to determine its condition as a basis for variable applications of nitrogen fertilizers. In a four-year (2017–2020) field experiment, 1400 ha of winter wheat crop were monitored using the ISARIA on-the-go system and remote sensing using Sentinel-2 multispectral satellite images. The results of spectral measurements of ISARIA vegetation indices (IRMI, IBI) were statistically compared with the values of selected vegetation indices obtained from Sentinel-2 (EVI, GNDVI, NDMI, NDRE, NDVI and NRERI) in order to determine potential hips. Positive correlations were found between the vegetation indices determined by the ISARIA system and indices obtained by multispectral images from Sentinel-2 satellites. The correlations were medium to strong (r = 0.51–0.89). Therefore, it can be stated that both technologies were able to capture a similar trend in the development of vegetation. Furthermore, the influence of climatic conditions on the vegetation indices was analyzed in individual years of the experiment. The values of vegetation indices show significant differences between the individual years. The results of vegetation indices obtained by the analysis of spectral images from Sentinel-2 satellites varied the most. The values of winter wheat yield varied between the individual years. Yield was the highest in 2017 (7.83 t/ha), while the lowest was recorded in 2020 (6.96 t/ha). There was no statistically significant difference between 2018 (7.27 t/ha) and 2019 (7.44 t/ha).

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The Utility of Sentinel-2 MSI Data to Estimate Wetland Vegetation Leaf Area Index in Natural and Rehabilitated Wetlands

Geographies ◽

10.3390/geographies1030011 ◽

2021 ◽

Vol 1 (3) ◽

pp. 178-191

Author(s):

Nonjabulo Neliswa Tshabalala ◽

Onisimo Mutanga ◽

Mbulisi Sibanda

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Vegetation Indices ◽

Wetland Vegetation ◽

Electromagnetic Spectrum ◽

Vegetation Productivity ◽

Area Index ◽

Wetland Ecosystems ◽

Red Edge ◽

Sentinel 2

Wetland ecosystems are being modified and threatened due to anthropogenic activities and climate change, hence the urgent need for wetland restoration. Wetland rehabilitation is important in the reversal of these dire conditions, and this can be pursued through restoring damaged wetland ecosystems and recovering wetland vegetation. Wetland biophysical properties such as leaf area index (LAI) are important indicators of vegetation productivity and stress. Therefore, the study sought to test the potential of Sentinel-2 multispectral instrument (MSI) derived standard bands, traditional vegetation indices and red-edge derived vegetation indices in estimating wetland vegetation LAI across natural and rehabilitated wetlands. Traditional field surveys were carried out for LAI measurement of wetland vegetation using the LAI-2200 Plant Canopy Analyser. Partial Least Squares Regression (PLSR) algorithms were used to compare the estimation strength of models derived from all Sentinel-2 MSI bands, conventional vegetation indices and red-edge derived vegetation indices. Leave-one-out cross-validation (LOOCV) was completed on a selected measured dataset to evaluate the performance and accuracy of the estimation models. The optimal models for estimating wetland vegetation LAI were produced based on red-edge bands centred between the 705–783 nm as well as the 865 nm (Band 8a) of the electromagnetic spectrum. The results showed that vegetation indices derived from red-edge bands performed better at estimating LAI for both wetlands with a root mean square error of prediction (RMSE) of 0.32 m2/m2 and R2 of 0.61 for the natural wetland, and RMSE of 0.51 m2/m2 and R2 of 0.75 for the rehabilitated wetland. The optimal model for predicting LAI across natural and rehabilitated wetlands was attained based on red-edge bands centred at 705 nm (Band 5), 740 nm (Band 6), 783 nm (Band 7) as well as 865 nm (Band 8a) yielding a RMSE of 0.51 m2/m2 and R2 of 0.54. Overall, the results underscore the importance of remotely sensed derived data and vegetation indices in the optimal characterisation of wetland vegetation productivity which can be utilized in the monitoring and management of wetland ecosystems.

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Soil salinity assessment using vegetation indices derived from Sentinel-2 multispectral data. application to Lezíria Grande, Portugal

Agricultural Water Management ◽

10.1016/j.agwat.2020.106387 ◽

2020 ◽

Vol 241 ◽

pp. 106387 ◽

Cited By ~ 1

Author(s):

Tiago B. Ramos ◽

Nádia Castanheira ◽

Ana R. Oliveira ◽

Ana Marta Paz ◽

Hanaa Darouich ◽

...

Keyword(s):

Soil Salinity ◽

Vegetation Indices ◽

Multispectral Data ◽

Data Application ◽

Sentinel 2

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Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana

Horticulturae ◽

10.3390/horticulturae8010011 ◽

2021 ◽

Vol 8 (1) ◽

pp. 11

Author(s):

Benjamin Adjah Torgbor ◽

Muhammad Moshiur Rahman ◽

Andrew Robson ◽

James Brinkhoff ◽

Azeem Khan

Keyword(s):

Remote Sensing ◽

Time Series ◽

Value Chain ◽

Global Economy ◽

Mangifera Indica ◽

Vegetation Indices ◽

African Countries ◽

Phenological Stages ◽

Significant Difference ◽

Sentinel 2

In 2020, mango (Mangifera indica) exports contributed over 40 million tons, worth around US$20 billion, to the global economy. Only 10% of this contribution was made from African countries including Ghana, largely due to lower investment in the sector and general paucity of research into the mango value chain, especially production, quality and volume. Considering the global economic importance of mango coupled with the gap in the use of the remote sensing technology in the sector, this study tested the hypothesis that phenological stages of mango can be retrieved from Sentinel-2 (S2) derived time series vegetation indices (VIs) data. The study was conducted on four mango farms in the Yilo Krobo Municipal Area of Ghana. Seasonal (temporal) growth curves using four VIs (NDVI, GNDVI, EVI and SAVI) for the period from 2017 to 2020 were derived for each of the selected orchards and then aligned with five known phenology stages: Flowering/Fruitset (F/FS), Fruit Development (FRD), Maturity/Harvesting (M/H), Flushing (FLU) and Dormancy (D). The significance of the variation “within” and “between” farms obtained from the VI metrics of the S2 data were tested using single-factor and two-factor analysis of variance (ANOVA). Furthermore, to identify which specific variable pairs (phenology stages) were significantly different, a Tukey honest significant difference (HSD) post-hoc test was conducted, following the results of the ANOVA. Whilst it was possible to differentiate the phenological stages using all the four VIs, EVI was found to be the best related with p < 0.05 for most of the studied farms. A distinct annual trend was identified with a peak in June/July and troughs in December/January. The derivation of remote sensing based ‘time series’ growth profiles for commercial mango orchards supports the ‘benchmarking’ of annual and seasonal orchard performance and therefore offers a near ‘real time’ technology for identifying significant variations resulting from pest and disease incursions and the potential impacts of seasonal weather variations.

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Comparison of Capability of SAR and Optical Data in Mapping Forest above Ground Biomass Based on Machine Learning

Environmental Sciences Proceedings ◽

10.3390/iecg2020-07916 ◽

2020 ◽

Vol 5 (1) ◽

pp. 13

Author(s):

Negar Tavasoli ◽

Hossein Arefi

Keyword(s):

Machine Learning ◽

Carbon Stock ◽

Vegetation Indices ◽

Forest Biomass ◽

Principal Component ◽

Optical Data ◽

Above Ground Biomass ◽

Ground Biomass ◽

Texture Characteristics ◽

Sentinel 2

Assessment of forest above ground biomass (AGB) is critical for managing forest and understanding the role of forest as source of carbon fluxes. Recently, satellite remote sensing products offer the chance to map forest biomass and carbon stock. The present study focuses on comparing the potential use of combination of ALOSPALSAR and Sentinel-1 SAR data, with Sentinel-2 optical data to estimate above ground biomass and carbon stock using Genetic-Random forest machine learning (GA-RF) algorithm. Polarimetric decompositions, texture characteristics and backscatter coefficients of ALOSPALSAR and Sentinel-1, and vegetation indices, tasseled cap, texture parameters and principal component analysis (PCA) of Sentinel-2 based on measured AGB samples were used to estimate biomass. The overall coefficient (R2) of AGB modelling using combination of ALOSPALSAR and Sentinel-1 data, and Sentinel-2 data were respectively 0.70 and 0.62. The result showed that Combining ALOSPALSAR and Sentinel-1 data to predict AGB by using GA-RF model performed better than Sentinel-2 data.

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