Examples of Differences in Red Edge Reflectance and Normalized Difference Vegetative Index Caused by Stresses in Peanut

Perairan laut Lampung sebagai bagian kecil dari ekosistem terumbu karang Indonesia terindikasi memiliki tren penurunan kualitas karena aktivitas pelayaran dan pariwisata yang ekstensif khususnya di Pulau Pahawang. Kontrol kondisi terumbu karang pada wilayah ini menjadi kegiatan vital dalam rangkaian konservasi sumber daya laut. Sayangnya, pemetaan kesehatan terumbu karang memerlukan survei detail yang memakan banyak waktu, biaya, dan tenaga. Citra sebagai produk data penginderaan jauh hadir sebagai solusi monitoring terumbu karang secara cepat, murah, dan dalam jangkauan wilayah yang relatif luas. Tujuan dari penelitian ini adalah untuk memetakan kesehatan terumbu karang melalui citra WorldView-2 (WV-2) serta menguji akurasi peta yang dihasilkan. Metode yang digunakan untuk memetakan kesehatan terumbu karang adalah transformasi nilai pixel pada band-band WV-2 menjadi nilai original objek dengan urutan: 1) koreksi atmosfer (Top of Atmospheric Reflectance), 2) koreksi kilap air (sun glint), dan 3) koreksi kolom air (metode lyzenga) menghasilkan 15 band DII (depth invariant bottom index). Kelima belas band DII tersebut diubah menjadi nilai kesehatan terumbu karang dengan cara regresi antara nilai pixel pada band DII dengan nilai rasio kesehatan terumbu karang aktual yang diperoleh dari proses kalkulasi acak titik foto transek di lapangan. Tiga tipe regresi (linier, eksponen, dan polinomial) dilakukan untuk melihat persamaan terbaik yang bisa digunakan untuk mentransformasi nilai pixel ke nilai kesehatan terumbu karang. Persamaan terbaik kemudian diimplementasikan menjadi model kesehatan terumbu karang untuk kemudian diuji akurasi menggunakan metode Standard Error Estimation. Hasil terbaik diperoleh pada regresi antara rasio kesehatan terumbu karang dengan band DII Coastal Red-Edge dengan koefisien determinasi (R2) sebesar 0,6553 dan akurasi pemetaan sebesar 70,191%. Nilai tersebut menunjukan bahwa citra WV-2 memiliki potensi untuk menjadi instrumen monitoring ekosistem marine yang layak.Kata Kunci: Depth Invariant Bottom Index, Kesehatan Terumbu Karang, Lyzenga, Regresi, WorldView-2

Download Full-text

Effect of water condition on hyperspectral and red-edge characteristics of tobacco leaf

CHINESE JOURNAL OF ECO-AGRICULTURE ◽

10.3724/sp.j.1011.2011.01330 ◽

2012 ◽

Vol 19 (6) ◽

pp. 1330-1335

Author(s):

Fang-Fang JIA ◽

Xin-Ming MA ◽

Chun-Ming LI ◽

Shi-Zhao LIN ◽

Yan-Qiang LI

Keyword(s):

Tobacco Leaf ◽

Water Condition ◽

Effect Of Water ◽

Red Edge

Download Full-text

Relational analysis of spectra and red-edge characteristics of plant leaf and leaf biochemical constituent

CHINESE JOURNAL OF ECO-AGRICULTURE ◽

10.3724/sp.j.1011.2010.00804 ◽

2010 ◽

Vol 18 (4) ◽

pp. 804-809 ◽

Cited By ~ 3

Author(s):

Shou-Zhen LIANG ◽

Ping SHI ◽

Wan-Dong MA ◽

Qian-Guo XING ◽

Liang-Ju YU

Keyword(s):

Plant Leaf ◽

Relational Analysis ◽

Red Edge ◽

Biochemical Constituent

Download Full-text

Gaia-assisted discovery of a detached low-ionisation BAL quasar with very large ejection velocities

Astronomy and Astrophysics ◽

10.1051/0004-6361/201936957 ◽

2020 ◽

Vol 634 ◽

pp. A111 ◽

Cited By ~ 2

Author(s):

J. P. U. Fynbo ◽

P. Møller ◽

K. E. Heintz ◽

J. N. Burchett ◽

L. Christensen ◽

...

Keyword(s):

Emission Line ◽

Absorption Line ◽

Emission Lines ◽

Broad Absorption ◽

Broad Absorption Line ◽

Red Edge ◽

First Case ◽

Bl Lac ◽

Spatially Extended ◽

Extended Emission

We report on the discovery of a peculiar broad absorption line (BAL) quasar identified in our Gaia-assisted survey of red quasars. The systemic redshift of this quasar was difficult to establish because of the absence of conspicuous emission lines. Based on deep and broad BAL troughs of at least Si IV, C IV, and Al III, a redshift of z = 2.41 was established under the assumption that the systemic redshift can be inferred from the red edge of the BAL troughs. However, we observe a weak and spatially extended emission line at 4450 Å that is most likely due to Lyman-α emission, which implies a systemic redshift of z = 2.66 if correctly identified. There is also evidence for the onset of Lyman-α forest absorption bluewards of 4450 Å and evidence for Hα emission in the K band consistent with a systemic redshift of z = 2.66. If this redshift is correct, the quasar is an extreme example of a detached low-ionisation BAL quasar. The BALs must originate from material moving with very large velocities ranging from 22 000 km s−1 to 40 000 km s−1. To our knowledge, this is the first case of a systemic-redshift measurement based on extended Lyman-α emission for a BAL quasar. This method could also be useful in cases of sufficiently distant BL Lac quasars without systemic-redshift information.

Download Full-text

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

Remote Sensing ◽

10.3390/rs13020233 ◽

2021 ◽

Vol 13 (2) ◽

pp. 233

Author(s):

Ilja Vuorinne ◽

Janne Heiskanen ◽

Petri K. E. Pellikka

Keyword(s):

Near Infrared ◽

Vegetation Indices ◽

Fibre Production ◽

Additive Models ◽

Leaf Biomass ◽

Ground Vegetation ◽

Perennial Crop ◽

Red Edge ◽

Agave Sisalana ◽

Sentinel 2

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.

Download Full-text

Estimating Aboveground Biomass Using Sentinel-2 MSI Data and Ensemble Algorithms for Grassland in the Shengjin Lake Wetland, China

Remote Sensing ◽

10.3390/rs13081595 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1595

Author(s):

Chunhua Li ◽

Lizhi Zhou ◽

Wenbin Xu

Keyword(s):

Aboveground Biomass ◽

Carbon Accounting ◽

Dynamic Monitoring ◽

Gradient Boosting ◽

Biomass Estimation ◽

Floodplain Wetlands ◽

Red Edge ◽

Extreme Gradient Boosting ◽

Ensemble Algorithms ◽

Sentinel 2

Wetland vegetation aboveground biomass (AGB) directly indicates wetland ecosystem health and is critical for water purification, carbon cycle, and biodiversity conservation. Accurate AGB estimation is essential for the monitoring and supervision of ecosystems, especially in seasonal floodplain wetlands. This paper explored the capability of spectral and texture features from the Sentinel-2 Multispectral Instrument (MSI) for modeling grassland AGB using random forest (RF) and extreme gradient boosting (XGBoost) algorithms in Shengjin Lake wetland (a Ramsar site). We use five-fold cross-validation to verify the model effectiveness. The results indicated that the RF and XGBoost models had a robust and efficient performance (with root mean square error (RMSE) of 126.571 g·m−2 and R2 of 0.844 for RF, RMSE of 112.425 g·m−2 and R2 of 0.869 for XGBoost), and the XGBoost models, by contrast, performed better. Both traditional and red-edge vegetation indices (VIs) obtained satisfactory results of AGB estimation (RMSE = 127.936 g·m−2, RMSE = 125.879 g·m−2 in XGBoost models, respectively), with the red-edge VIs contributed more to the AGB models. Moreover, we selected eight gray-level co-occurrence matrix (GLCM) textures calculated by four processing window sizes using the mean value of four offsets, and further analyzed the results of three analysis sets. Textures derived from traditional and red-edge bands using a 7 × 7 window size performed better in biomass estimation. This finding suggested that textures derived from the traditional bands were as important as the red-edge bands. The introduction of textures moderately improved the accuracy of modeling AGB, whereas the use of textures alo ne was not satisfactory. This research demonstrated that using the Sentinel-2 MSI and the two ensemble algorithms is an effective method for long-term dynamic monitoring and assessment of grass AGB in seasonal floodplain wetlands, which can support sustainable management and carbon accounting of wetland ecosystems.

Download Full-text

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

Remote Sensing ◽

10.3390/rs13142755 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2755

Author(s):

Peng Fang ◽

Nana Yan ◽

Panpan Wei ◽

Yifan Zhao ◽

Xiwang Zhang

Keyword(s):

Aboveground Biomass ◽

Production Management ◽

Vegetation Index ◽

Net Primary Productivity ◽

Vegetation Indices ◽

Biophysical Parameters ◽

Edge Information ◽

Casa Model ◽

Red Edge ◽

Biomass Mapping

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.

Download Full-text

Status of Phenological Research Using Sentinel-2 Data: A Review

Remote Sensing ◽

10.3390/rs12172760 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2760

Author(s):

Gourav Misra ◽

Fiona Cawkwell ◽

Astrid Wingler

Keyword(s):

Vegetation Index ◽

Temporal Frequency ◽

Short Wave ◽

Current State ◽

Red Edge ◽

Natural Grasslands ◽

Wide Range ◽

Normalised Difference Vegetation Index ◽

Satellite Sensors ◽

Sentinel 2

Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

Download Full-text

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.

Download Full-text