Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices

Biomass is a principal variable in crop monitoring and management and in assessing carbon cycling. Remote sensing combined with field measurements can be used to estimate biomass over large areas. This study assessed leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre production in tropical and subtropical regions. Furthermore, the residue from fibre production can be used to produce bioenergy through anaerobic digestion. First, biomass was estimated for 58 field plots using an allometric approach. Then, Sentinel-2 multispectral satellite imagery was used to model biomass in an 8851-ha plantation in semi-arid south-eastern Kenya. Generalised Additive Models were employed to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (explained deviance = 76%, RMSE = 5.15 Mg ha−1) was achieved with ratio and normalised difference VIs based on the green (R560), red-edge (R740 and R783), and near-infrared (R865) spectral bands. Heterogeneity of ground vegetation and resulting background effects seemed to limit model performance. The best performing VI (R740/R783) was used to predict plantation biomass that ranged from 0 to 46.7 Mg ha−1 (mean biomass 10.6 Mg ha−1). The modelling showed that multispectral data are suitable for assessing sisal leaf biomass at the plantation level and in individual blocks. Although these results demonstrate the value of Sentinel-2 red-edge bands at 20-m resolution, the difference from the best model based on green and near-infrared bands at 10-m resolution was rather small.

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A New Vegetation Index to Detect Periodically Submerged Mangrove Forest Using Single-Tide Sentinel-2 Imagery

Remote Sensing ◽

10.3390/rs11172043 ◽

2019 ◽

Vol 11 (17) ◽

pp. 2043 ◽

Cited By ~ 26

Author(s):

Jia ◽

Wang ◽

Mao ◽

Zhang

Keyword(s):

Mangrove Forest ◽

Vegetation Index ◽

Near Infrared ◽

Aquatic Vegetation ◽

Vegetation Indices ◽

Short Wave ◽

Mangrove Forests ◽

Red Edge ◽

Jensen Shannon Divergence ◽

Sentinel 2

Mangrove forests are tropical trees and shrubs that grow in sheltered intertidal zones. Accurate mapping of mangrove forests is a great challenge for remote sensing because mangroves are periodically submerged by tidal floods. Traditionally, multi-tides images were needed to remove the influence of water; however, such images are often unavailable due to rainy climates and uncertain local tidal conditions. Therefore, extracting mangrove forests from a single-tide imagery is of great importance. In this study, reflectance of red-edge bands in Sentinel-2 imagery were utilized to establish a new vegetation index that is sensitive to submerged mangrove forests. Specifically, red and short-wave near infrared bands were used to build a linear baseline; the average reflectance value of four red-edge bands above the baseline is defined as the Mangrove Forest Index (MFI). To evaluate MFI, capabilities of detecting mangrove forests were quantitatively assessed between MFI and four widely used vegetation indices (VIs). Additionally, the practical roles of MFI were validated by applying it to three mangrove forest sites globally. Results showed that: (1) theoretically, Jensen–Shannon divergence demonstrated that a submerged mangrove forest and water pixels have the largest distance in MFI compared to other VIs. In addition, the boxplot showed that all submerged mangrove forests could be separated from the water background in the MFI image. Furthermore, in the MFI image, to separate mangrove forests and water, the threshold is a constant that is equal to zero. (2) Practically, after applying the MFI to three global sites, 99–102% of submerged mangrove forests were successfully extracted by MFI. Although there are still some uncertainties and limitations, the MFI offers great benefits in accurately mapping mangrove forests as well as other coastal and aquatic vegetation worldwide.

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Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2

Land ◽

10.3390/land10050505 ◽

2021 ◽

Vol 10 (5) ◽

pp. 505

Author(s):

Gregoriy Kaplan ◽

Offer Rozenstein

Keyword(s):

Water Vapor ◽

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Near Infrared ◽

Linear Regression Models ◽

Area Index ◽

Estimation Performance ◽

Red Edge ◽

Sentinel 2

Satellite remote sensing is a useful tool for estimating crop variables, particularly Leaf Area Index (LAI), which plays a pivotal role in monitoring crop development. The goal of this study was to identify the optimal Sentinel-2 bands for LAI estimation and to derive Vegetation Indices (VI) that are well correlated with LAI. Linear regression models between time series of Sentinel-2 imagery and field-measured LAI showed that Sentinel-2 Band-8A—Narrow Near InfraRed (NIR) is more accurate for LAI estimation than the traditionally used Band-8 (NIR). Band-5 (Red edge-1) showed the lowest performance out of all red edge bands in tomato and cotton. A novel finding was that Band 9 (Water vapor) showed a very high correlation with LAI. Bands 1, 2, 3, 4, 5, 11, and 12 were saturated at LAI ≈ 3 in cotton and tomato. Bands 6, 7, 8, 8A, and 9 were not saturated at high LAI values in cotton and tomato. The tomato, cotton, and wheat LAI estimation performance of ReNDVI (R2 = 0.79, 0.98, 0.83, respectively) and two new VIs (WEVI (Water vapor red Edge Vegetation Index) (R2 = 0.81, 0.96, 0.71, respectively) and WNEVI (Water vapor narrow NIR red Edge Vegetation index) (R2 = 0.79, 0.98, 0.79, respectively)) were higher than the LAI estimation performance of the commonly used NDVI (R2 = 0.66, 0.83, 0.05, respectively) and other common VIs tested in this study. Consequently, reNDVI, WEVI, and WNEVI can facilitate more accurate agricultural monitoring than traditional VIs.

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Red-Edge Band Vegetation Indices for Leaf Area Index Estimation From Sentinel-2/MSI Imagery

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2019.2940826 ◽

2020 ◽

Vol 58 (2) ◽

pp. 826-840 ◽

Cited By ~ 5

Author(s):

Yuanheng Sun ◽

Qiming Qin ◽

Huazhong Ren ◽

Tianyuan Zhang ◽

Shanshan Chen

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Vegetation Indices ◽

Area Index ◽

Red Edge ◽

Index Estimation ◽

Edge Band ◽

Sentinel 2

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VIS-NIR, Red-Edge and NIR-Shoulder Based Normalized Vegetation Indices Response to Co-Varying Leaf and Canopy Structural Traits in Heterogeneous Grasslands

Remote Sensing ◽

10.3390/rs12142254 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2254 ◽

Cited By ~ 1

Author(s):

Hafiz Ali Imran ◽

Damiano Gianelle ◽

Duccio Rocchini ◽

Michele Dalponte ◽

M. Pilar Martín ◽

...

Keyword(s):

Near Infrared ◽

Spatial Scales ◽

Vegetation Indices ◽

Radiative Transfer Model ◽

Transfer Model ◽

Sharp Change ◽

Full Potential ◽

Area Index ◽

Red Edge ◽

Temporal And Spatial

Red-edge (RE) spectral vegetation indices (SVIs)—combining bands on the sharp change region between near infrared (NIR) and visible (VIS) bands—alongside with SVIs solely based on NIR-shoulder bands (wavelengths 750–900 nm) have been shown to perform well in estimating leaf area index (LAI) from proximal and remote sensors. In this work, we used RE and NIR-shoulder SVIs to assess the full potential of bands provided by Sentinel-2 (S-2) and Sentinel-3 (S-3) sensors at both temporal and spatial scales for grassland LAI estimations. Ground temporal and spatial observations of hyperspectral reflectance and LAI were carried out at two grassland sites (Monte Bondone, Italy, and Neustift, Austria). A strong correlation (R2 > 0.8) was observed between grassland LAI and both RE and NIR-shoulder SVIs on a temporal basis, but not on a spatial basis. Using the PROSAIL Radiative Transfer Model (RTM), we demonstrated that grassland structural heterogeneity strongly affects the ability to retrieve LAI, with high uncertainties due to structural and biochemical PTs co-variation. The RENDVI783.740 SVI was the least affected by traits co-variation, and more studies are needed to confirm its potential for heterogeneous grasslands LAI monitoring using S-2, S-3, or Gaofen-5 (GF-5) and PRISMA bands.

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Monitoring and predicting crop growth and analysing agricultural ecosystems by remote sensing

Agricultural and Food Science ◽

10.23986/afsci.72741 ◽

1996 ◽

Vol 5 (3) ◽

pp. 367-376 ◽

Cited By ~ 1

Author(s):

Tsuyoshi Akiyama ◽

Y. Inoue ◽

M. Shibayama ◽

Y. Awaya ◽

N. Tanaka

Keyword(s):

Remote Sensing ◽

Near Infrared ◽

Vegetation Indices ◽

Nutrient Deficiency ◽

The Arctic ◽

Ground Vegetation ◽

Spectral Vegetation Indices ◽

Project Monitoring ◽

Crop Types ◽

Crop Discrimination

LANDSAT/TM data, which are characterized by high spectral/spatial resolutions, are able to contribute to practical agricultural management. In the first part of the paper, the authors review some recent applications of satellite remote sensing in agriculture. Techniques for crop discrimination and mapping have made such rapid progress that we can classify crop types with more than 80% accuracy. The estimation of crop biomass using satellite data, including leaf area, dry and fresh weights, and the prediction of grain yield, has been attempted using various spectral vegetation indices. Plant stresses caused by nutrient deficiency and water deficit have also been analysed successfully. Such information may be useful for farm management. In the latter half of the paper, we introduce the Arctic Science Project, which was carried out under the Science and Technology Agency of Japan collaborating with Finnish scientists. In this project, monitoring of the boreal forest was carried out using LANDSAT data. Changes in the phenology of subarctic ground vegetation, based on spectral properties, were measured by a boom-mounted, four-band spectroradiometer. The turning point dates of the seasonal near-infrared (NIR) and red (R) reflectance factors might indicate the end of growth and the beginning of autumnal tints, respectively.

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Sharpening the Sentinel-2 10 and 20 m Bands to Planetscope-0 3 m Resolution

Remote Sensing ◽

10.3390/rs12152406 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2406 ◽

Cited By ~ 1

Author(s):

Zhongbin Li ◽

Hankui K. Zhang ◽

David P. Roy ◽

Lin Yan ◽

Haiyan Huang

Keyword(s):

Land Surface ◽

Large Scale ◽

Near Infrared ◽

Detailed Examination ◽

Modulation Transfer ◽

Computationally Efficient ◽

Red Edge ◽

Shortwave Infrared ◽

Sentinel 2 ◽

Surface Changes

Combination of near daily 3 m red, green, blue, and near infrared (NIR) Planetscope reflectance with lower temporal resolution 10 m and 20 m red, green, blue, NIR, red-edge, and shortwave infrared (SWIR) Sentinel-2 reflectance provides potential for improved global monitoring. Sharpening the Sentinel-2 reflectance with the Planetscope reflectance may enable near-daily 3 m monitoring in the visible, red-edge, NIR, and SWIR. However, there are two major issues, namely the different and spectrally nonoverlapping bands between the two sensors and surface changes that may occur in the period between the different sensor acquisitions. They are examined in this study that considers Sentinel-2 and Planetscope imagery acquired one day apart over three sites where land surface changes due to biomass burning occurred. Two well-established sharpening methods, high pass modulation (HPM) and Model 3 (M3), were used as they are multiresolution analysis methods that preserve the spectral properties of the low spatial resolution Sentinel-2 imagery (that are better radiometrically calibrated than Planetscope) and are relatively computationally efficient so that they can be applied at large scale. The Sentinel-2 point spread function (PSF) needed for the sharpening was derived analytically from published modulation transfer function (MTF) values. Synthetic Planetscope red-edge and SWIR bands were derived by linear regression of the Planetscope visible and NIR bands with the Sentinel-2 red-edge and SWIR bands. The HPM and M3 sharpening results were evaluated visually and quantitatively using the Q2n metric that quantifies spectral and spatial distortion. The HPM and M3 sharpening methods provided visually coherent and spatially detailed visible and NIR wavelength sharpened results with low distortion (Q2n values > 0.91). The sharpened red-edge and SWIR results were also coherent but had greater distortion (Q2n values > 0.76). Detailed examination at locations where surface changes between the Sentinel-2 and the Planetscope acquisitions occurred revealed that the HPM method, unlike the M3 method, could reliably sharpen the bands affected by the change. This is because HPM sharpening uses a per-pixel reflectance ratio in the spatial detail modulation which is relatively stable to reflectance changes. The paper concludes with a discussion of the implications of this research and the recommendation that the HPM sharpening be used considering its better performance when there are surface changes.

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Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery

Remote Sensing ◽

10.3390/rs12071176 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1176 ◽

Cited By ~ 1

Author(s):

Yukun Lin ◽

Zhe Zhu ◽

Wenxuan Guo ◽

Yazhou Sun ◽

Xiaoyuan Yang ◽

...

Keyword(s):

Machine Learning ◽

Linear Regression ◽

Water Potential ◽

Large Scale ◽

Temporal Frequency ◽

Vegetation Indices ◽

Stem Water Potential ◽

Spectral Bands ◽

Red Edge ◽

Sentinel 2

Monitoring cotton status during the growing season is critical in increasing production efficiency. The water status in cotton is a key factor for yield and cotton quality. Stem water potential (SWP) is a precise indicator for assessing cotton water status. Satellite remote sensing is an effective approach for monitoring cotton growth at a large scale. The aim of this study is to estimate cotton water stress at a high temporal frequency and at a large scale. In this study, we measured midday SWP samples according to the acquisition dates of Sentinel-2 images and used them to build linear-regression-based and machine-learning-based models to estimate cotton water stress during the growing season (June to August, 2018). For the linear-regression-based method, we estimated SWP based on different Sentinel-2 spectral bands and vegetation indices, where the normalized difference index 45 (NDI45) achieved the best performance (R2 = 0.6269; RMSE = 3.6802 (-1*swp (bars))). For the machine-learning-based method, we used random forest regression to estimate SWP and received even better results (R2 = 0.6709; RMSE = 3.3742 (-1*swp (bars))). To find the best selection of input variables for the machine-learning-based approach, we tried three different data input datasets, including (1) 9 original spectral bands (e.g., blue, green, red, red edge, near infrared (NIR), and shortwave infrared (SWIR)), (2) 21 vegetation indices, and (3) a combination of original Sentinel-2 spectral bands and vegetation indices. The highest accuracy was achieved when only the original spectral bands were used. We also found the SWIR and red edge band were the most important spectral bands, and the vegetation indices based on red edge and NIR bands were particularly helpful. Finally, we applied the best approach for the linear-regression-based and the machine-learning-based methods to generate cotton water potential maps at a large scale and high temporal frequency. Results suggests that the methods developed here has the potential for continuous monitoring of SWP at large scales and the machine-learning-based method is preferred.

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Crop Type Classification Using Vegetation Indices of RapidEye Imagery

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-195-2014 ◽

2014 ◽

Vol XL-7 ◽

pp. 195-198 ◽

Cited By ~ 23

Author(s):

M. Ustuner ◽

F. B. Sanli ◽

S. Abdikan ◽

M. T. Esetlili ◽

Y. Kurucu

Keyword(s):

Remote Sensing ◽

Classification Accuracy ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Support Vector ◽

Red Edge ◽

Crop Type ◽

Type Classification

Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46 % was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.

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Estimating biophysical properties of coffee (Coffea canephora) plants with above-canopy field measurements, using CropSpec®

International Agrophysics ◽

10.1515/intag-2017-0009 ◽

2018 ◽

Vol 32 (2) ◽

pp. 183-191 ◽

Cited By ~ 1

Author(s):

Bayu T. Widjaja Putra ◽

Peeyush Soni ◽

Eiji Morimoto ◽

Pujiyanto Pujiyanto

Keyword(s):

Nitrogen Content ◽

Near Infrared ◽

Field Experiments ◽

Vegetation Indices ◽

Coffea Canephora ◽

Biophysical Properties ◽

Robusta Coffee ◽

Red Edge ◽

Coffee Plants ◽

Non Destructive

Abstract Remote sensing technologies have been applied to many crops, but tree crops like Robusta coffee (Coffea canephora) under shade conditions require additional attention while making above-canopy measurements. The objective of this study was to determine how well chlorophyll and nitrogen status of Robusta coffee plants can be estimated with the laser-based (CropSpec®) active sensor. This study also identified appropriate vegetation indices for estimating Nitrogen content by above-canopy measurement, using near-infra red and red-edge bands. Varying light intensity and different background of the plants were considered in developing the indices. Field experiments were conducted involving different non-destructive tools (CropSpec® and SPAD-502 chlorophyll meter). Subsequently, Kjeldahl laboratory analyses were performed to determine the actual Nitrogen content of the plants with different ages and field conditions used in the non-destructive previous stage. Measurements were undertaken for assessing the biophysical properties of tree plant. The usefulness of near-infrared and red-edge bands from these sensors in measuring critical nitrogen levels of coffee plants by above-canopy measurement are investigated in this study.

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Monitoring Mediterranean grass phenology from digital terrestrial camera and Sentinel-2 vegetation indices in an oak-grass savanna ecosystem

10.5194/egusphere-egu2020-17923 ◽

2020 ◽

Author(s):

Maria P. González-Dugo ◽

Pedro J. Gómez-Giraldez ◽

María J. Pérez-Palazón ◽

María J. Polo

Keyword(s):

Vegetation Indices ◽

Broad Band ◽

Primary Source ◽

Hydrological Regime ◽

Agroforestry System ◽

Red Edge ◽

Annual Grasslands ◽

The Difference ◽

Phenological Dates ◽

Sentinel 2

<p>Annual grasslands are an essential component of Mediterranean oak savannas, the most extensive agroforestry system in Europe, as the primary source of fodder for livestock and wildlife. Monitoring its phenology is key to adequately assess the impacts of global warming on different time scales and identify pre-critical states in the framework of early warning decision making systems. The natural variability of the climatic-hydrological regime in these areas and the usually complex spatial patterns of the vegetation, with sparse distribution and multiple layers, encourage the exploitation of available data from remote sensing sources. This work presents an assessment of vegetation indexes (VI) from Sentinel-2 validated against field data from terrestrial photography in an oak-grass system in southern Spain as a multi-approach method to monitor phenology in grass pastures. The analysis also has provided an insight into the links of the phenology dynamics with hydrological variables under these conditions.</p><p>From December 2017 to May 2019 a quantitative value of grassland greenness was computed using the Green Chromatic Coordinate (GCC) index. The phenological parameters of the start of the season (SOS), the peak of the season (POS) and end of the season (EOS) were extracted using the 50% amplitude method and confirmed using field photography. These values were compared with those provided by eight VI's derived from Sentinel-2 (NDVI, GNDVI, SAVI, EVI, EVI2, MTCI, IRECI and S2REP) and the difference in days between the key phenological dates were estimated. The results showed that for annual grasslands NDVI was the index providing estimations closest to those of ground GCC, with differences below 10 days for all phenological dates and the best correlation with GCC values (r = 0.83, p <0.001). None of the VIs using bands in the red-edge region have improved the NDVI results. Two of them, MTCI and S2REP, followed a different trend that the rest of explored indices, presenting a high temporal variability. The high diversity of species, typical of Mediterranean grasslands, might explain the high variability observed in these values. However, the third index using red-edge bands, IRECI, presented a high correlation with GCC. In this case, the index was designed to focus on the chlorophyll content of the canopy instead of the leaf scale addressed by S2REP. The influence of the vegetation ground coverage and foliage density is then higher and more similar to the broad-band indices. GNDVI also provided good general results. Soil moisture (SM) time-series were also used to estimate phenology and have presented a good agreement with GCC in SOS and EOS estimations, with SM reaching threshold values a few days before greenness ones, as measured by GCC. However, SM was not a good indicator of the POS, presenting significant biases with respect to GCC estimations.</p>

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