scholarly journals Deriving seasonal dynamics in ecosystem properties of semi-arid savanna grasslands from in situ-based hyperspectral reflectance

2015 ◽  
Vol 12 (15) ◽  
pp. 4621-4635 ◽  
Author(s):  
T. Tagesson ◽  
R. Fensholt ◽  
S. Huber ◽  
S. Horion ◽  
I. Guiro ◽  
...  
Keyword(s):  
Seasonal Dynamics ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Arid Ecosystems ◽  
Hyperspectral Reflectance ◽  
Data Set ◽  
Ecosystem Properties ◽  
Shortwave Infrared ◽  
Semi Arid

Abstract. This paper investigates how hyperspectral reflectance (between 350 and 1800 nm) can be used to infer ecosystem properties for a semi-arid savanna grassland in West Africa using a unique in situ-based multi-angular data set of hemispherical conical reflectance factor (HCRF) measurements. Relationships between seasonal dynamics in hyperspectral HCRF and ecosystem properties (biomass, gross primary productivity (GPP), light use efficiency (LUE), and fraction of photosynthetically active radiation absorbed by vegetation (FAPAR)) were analysed. HCRF data (ρ) were used to study the relationship between normalised difference spectral indices (NDSIs) and the measured ecosystem properties. Finally, the effects of variable sun sensor viewing geometry on different NDSI wavelength combinations were analysed. The wavelengths with the strongest correlation to seasonal dynamics in ecosystem properties were shortwave infrared (biomass), the peak absorption band for chlorophyll a and b (at 682 nm) (GPP), the oxygen A band at 761 nm used for estimating chlorophyll fluorescence (GPP and LUE), and blue wavelengths (ρ412) (FAPAR). The NDSI with the strongest correlation to (i) biomass combined red-edge HCRF (ρ705) with green HCRF (ρ587), (ii) GPP combined wavelengths at the peak of green reflection (ρ518, ρ556), (iii) LUE combined red (ρ688) with blue HCRF (ρ436), and (iv) FAPAR combined blue (ρ399) and near-infrared (ρ1295) wavelengths. NDSIs combining near infrared and shortwave infrared were strongly affected by solar zenith angles and sensor viewing geometry, as were many combinations of visible wavelengths. This study provides analyses based upon novel multi-angular hyperspectral data for validation of Earth-observation-based properties of semi-arid ecosystems, as well as insights for designing spectral characteristics of future sensors for ecosystem monitoring.

scholarly journals Deriving seasonal dynamics in ecosystem properties of semi-arid savannas using in situ based hyperspectral reflectance

2015 ◽  
Vol 12 (4) ◽  
pp. 3315-3347
Author(s):  
T. Tagesson ◽  
R. Fensholt ◽  
S. Huber ◽  
S. Horion ◽  
I. Guiro ◽  
...  
Keyword(s):  
Seasonal Dynamics ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Arid Ecosystems ◽  
Hyperspectral Reflectance ◽  
Ecosystem Properties ◽  
Shortwave Infrared ◽  
Reflectance Data ◽  
Semi Arid

Abstract. This paper investigates how seasonal hyperspectral reflectance data (between 350 and 1800 nm) can be used to infer ecosystem properties for a semi-arid savanna ecosystem in West Africa using a unique in situ based dataset. Relationships between seasonal dynamics in hyperspectral reflectance, and ecosystem properties (biomass, gross primary productivity (GPP), light use efficiency (LUE), and fraction of photosynthetically active radiation absorbed by vegetation (FAPAR)) were analysed. Reflectance data (ρ) were used to study the relationship between normalised difference spectral indices (NDSI) and the measured ecosystem properties. Finally, also the effects of variable sun sensor viewing geometry on different NDSI wavelength combinations were analysed. The wavelengths with the strongest correlation to seasonal dynamics in ecosystem properties were shortwave infrared (biomass), the peak absorption band for chlorophyll a and b (at 682 nm) (GPP), the oxygen A-band at 761 nm used for estimating chlorophyll fluorescence (GPP, and LUE), and blue wavelengths (FAPAR). The NDSI with the strongest correlation to: (i) biomass combined red edge reflectance (ρ705) with green reflectance (ρ587), (ii) GPP combined wavelengths at the peak of green reflection (ρ518, ρ556), (iii) the LUE combined red (ρ688) with blue reflectance (ρ436), and (iv) FAPAR combined blue (ρ399) and near infrared (ρ1295) wavelengths. NDSI combining near infrared and shortwave infrared were strongly affected by solar zenith angles and sensor viewing geometry, as were many combinations of visible wavelengths. This study provides analyses based upon novel multi-angular hyperspectral data for validation of Earth Observation based properties of semi-arid ecosystems, as well as insights for designing spectral characteristics of future sensors for ecosystem monitoring.

scholarly journals Hazardous Noxious Substance Detection Based on Ground Experiment and Hyperspectral Remote Sensing

2021 ◽  
Vol 13 (2) ◽  
pp. 318
Author(s):  
Jae-Jin Park ◽  
Kyung-Ae Park ◽  
Pierre-Yves Foucher ◽  
Philippe Deliot ◽  
Stephane Le Floch ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spectral Characteristics ◽  
Chemical Properties ◽  
Principal Component ◽  
Component Analysis ◽  
Noise Removal ◽  
Vertex Component Analysis ◽  
Shortwave Infrared ◽  
Spectral Mixture ◽  
Physical And Chemical

With an increase in the overseas maritime transport of hazardous and noxious substances (HNSs), HNS-related spill accidents are on the rise. Thus, there is a need to completely understand the physical and chemical properties of HNSs. This can be achieved through establishing a library of spectral characteristics with respect to wavelengths from visible and near-infrared (VNIR) bands to shortwave infrared (SWIR) wavelengths. In this study, a ground HNS measurement experiment was conducted for artificially spilled HNS by using two hyperspectral cameras at VNIR and SWIR wavelengths. Representative HNSs such as styrene and toluene were spilled into an outdoor pool and their spectral characteristics were obtained. The relative ratio of HNS to seawater decreased and increased at 550 nm and showed different constant ratios at the SWIR wavelength. Noise removal and dimensional compression procedures were conducted by applying principal component analysis on HNS hyperspectral images. Pure HNS and seawater endmember spectra were extracted using four spectral mixture techniques—N-FINDR, pixel purity index (PPI), independent component analysis (ICA), and vertex component analysis (VCA). The accuracy of detection values of styrene and toluene through the comparison of the abundance fraction were 99.42% and 99.56%, respectively. The results of this study are useful for spectrum-based HNS detection in marine HNS accidents.

Determination of Producible Hydrocarbon Type and Oil Quality in Wells Drilled With Synthetic Oil-Based Muds

1999 ◽  
Vol 2 (02) ◽  
pp. 125-133 ◽  
Author(s):  
M.N. Hashem ◽  
E.C. Thomas ◽  
R.I. McNeil ◽  
Oliver Mullins
Keyword(s):  
Spectroscopic Data ◽  
Near Infrared ◽  
Data Set ◽  
Synthetic Oil ◽  
Open Hole ◽  
Invaded Zone ◽  
Mud Filtrate

Summary Determination of the type and quality of hydrocarbon fluid that can be produced from a formation prior to construction of production facilities is of equal economic importance to predicting the fluid rate and flowing pressure. We have become adept at making such estimates for formations drilled with water-based muds, using open-hole formation evaluation procedures. However, these standard open-hole methods are somewhat handicapped in wells drilled with synthetic oil-based mud because of the chemical and physical similarity between the synthetic oil-based filtrate and any producible oil that may be present. Also complicating the prediction is that in situ hydrocarbons will be miscibly displaced away from the wellbore by the invading oil-based mud filtrate, leaving little or no trace of the original hydrocarbon in the invaded zone. Thus, normal methods that sample fluids in the invaded zone will be of little use in predicting the in situ type and quality of hydrocarbons deeper in the formation. Only when we can pump significant volume of filtrate from the invaded zone to reconnect and sample the virgin fluids are we successful. However, since the in situ oil and filtrate are miscible, diffusion mixes the materials and blurs the interface; as mud filtrate is pumped from the formation into the borehole, the degree of contamination is greater than one might expect, and it is difficult to know when to stop pumping and start sampling. What level of filtrate contamination in the in situ fluid is tolerable? We propose a procedure for enhancing the value of the data derived from a particular open-hole wireline formation tester by quantitatively evaluating in real time the quality of the fluid being collected. The approach focuses on expanding the display of the spectroscopic data as a function of time on a more sensitive scale than has been used previously. This enhanced sensitivity allows one to confidently decide when in the pumping cycle to begin the sampling procedure. The study also utilizes laboratory determined PVT information on collected samples to form a data set that we use to correlate to the wireline derived spectroscopic data. The accuracy of these correlations has been verified with subsequent predictions and corroborated with laboratory measurements. Lastly, we provide a guideline for predicting the pump-out time needed to obtain a fluid sample of a pre-determined level of contamination when sampling conditions fall within our range of empirical data. Conclusions This empirical study validates that PVT quality hydrocarbon samples can be obtained from boreholes drilled with synthetic oil-based mud utilizing wireline formation testers deployed with downhole pump-out and optical analyzer modules. The data set for this study has the following boundary conditions: samples were obtained in the Gulf of Mexico area; the rock formations are unconsolidated to slightly consolidated, clean to slightly shaly sandstones; the in situ hydrocarbons and the synthetic oil-based mud filtrate have measurable differences in their visible and/or near infrared spectra. Specifically, this study demonstrates that during the pump-out phase of operations we can use the optical analyzer response to predict the API gravity and gas/oil ratio of the reservoir hydrocarbons prior to securing a downhole sample. Additionally, we can predict the pump out time required to obtain a reservoir sample with less than 10% mud filtrate contamination if we know or can estimate reservoir fluid viscosity and formation permeability. Extension of this method to other formations and locales should be possible using similar empirical correlation methodology. Introduction The high cost of offshore production facilities construction and deployment require accurate prediction of hydrocarbon PVT properties prior to fabrication. In the offshore Gulf of Mexico, one method to obtain a PVT quality hydrocarbon sample is to use a cased hole drill stem test. However, this procedure is usually quite costly due to the need for sand control. Shell has been an advocate of eliminating this costly step by utilizing openhole wireline test tools to obtain the PVT quality sample of the reservoir hydrocarbon. The success of this approach depends upon the availability of a wireline tool with a downhole pump that permits removal of the mud filtrate contamination prior to sampling the reservoir fluids, and a downhole fluid analyzer that can distinguish reservoir fluid from filtrate. One such tool is the Modular Formation Dynamics Tester (MDT).1 The optical fluid analyzer module of the MDT functions by subjecting the fluids being pumped to absorption spectroscopy in the visible and near-infrared (NIR) ranges. Interpretation of these spectra is the subject of this paper. Tool descriptions and basic theory of operations were presented in an earlier text.2 The concept of using visible and/or NIR spectroscopy to characterize the fluids being sampled while pumping is straightforward when there are measurable differences in the spectra of the mud filtrate and the reservoir hydrocarbons. As shown in Fig. 1, there are well known areas3,4 of the NIR spectrum (800-2000 nm) that are diagnostic of water and oil. The optical fluid analyzer module (OFA) of the MDT has channels tuned at 10 locations as indicated in Fig. 1, and thus the response in channels 6, 8, and 9 can be used to discern water from hydrocarbon. Another section of the OFA is designed to detect gas by measuring reflected polarized light from the pumped fluids, but we do not discuss its operation further except to say that it is a reliable gas indicator.

scholarly journals Conditional Generative Adversarial for in-Situ Layerwise AM

2021 ◽  
Author(s):  
Goberta, Christian ◽  
Arrietab Edel ◽  
McWilliams Brandon
Keyword(s):  
Near Infrared ◽  
Turbine Blades ◽  
Optical Tomography ◽  
In Situ Monitoring ◽  
Generative Adversarial Networks ◽  
Data Set ◽  
Tomography System ◽  
In Situ Data ◽  
Real Time Information

Conditional generative adversarial networks (CGANs) learn a mapping from conditional input to observed image and perform tasks in image generation, manipulation and translation. In-situ monitoring uses sensors to obtain real-time information of additive manufacturing (AM) processes that relate to process stability and part quality. Understanding the correlations between process inputs and in-situ process signatures through machine learning can enable experimental-driven predictions of future process inputs. In this research, in-situ data obtained during a metallic powder bed fusion AM process is mapped with a CGAN. A single build of two turbine blades is monitored using EOSTATE Exposure OT, a near-infrared optical tomography system of the EOS M290 system. Layerwise images generated from the in-situ monitoring system were paired with a conditional image that labeled the specimen cross-section, laser-scan stripe overlap and z-distance to part surfaces. A CGAN was trained using the turbine blade data set and employed to generate new in-situ layerwise images for unseen conditional inputs.

scholarly journals Estimating growth and photosynthetic properties of wheat grown in simulated saline field conditions using hyperspectral reflectance sensing and multivariate analysis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Salah El-Hendawy ◽  
Nasser Al-Suhaibani ◽  
Majed Alotaibi ◽  
Wael Hassan ◽  
Salah Elsayed ◽  
...  
Keyword(s):  
Near Infrared ◽  
Agricultural Sector ◽  
Hyperspectral Data ◽  
Field Conditions ◽  
Least Squares Regression ◽  
Hyperspectral Reflectance ◽  
Full Spectrum ◽  
Salinity Levels ◽  
Shortwave Infrared ◽  
Related Trait

Abstract The timely estimation of growth and photosynthetic-related traits in an easy and nondestructive manner using hyperspectral data will become imperative for addressing the challenges of environmental stresses inherent to the agricultural sector in arid conditions. However, the handling and analysis of these data by exploiting the full spectrum remains the determining factor for refining the estimation of crop variables. The main objective of this study was to estimate growth and traits underpinning photosynthetic efficiency of two wheat cultivars grown under simulated saline field conditions and exposed to three salinity levels using hyperspectral reflectance information from 350–2500 nm obtained at two years. Partial least squares regression (PLSR) based on the full spectrum was applied to develop predictive models for estimating the measured parameters in different conditions (salinity levels, cultivars, and years). Variable importance in projection (VIP) of PLSR in combination with multiple linear regression (MLR) was implemented to identify important waveband regions and influential wavelengths related to the measured parameters. The results showed that the PLSR models exhibited moderate to high coefficients of determination (R2) in both the calibration and validation datasets (0.30–0.95), but that this range of R2 values depended on parameters and conditions. The PLSR models based on the full spectrum accurately and robustly predicted three of four parameters across all conditions. Based on the combination of PLSR-VIP and MLR analysis, the wavelengths selected within the visible (VIS), red-edge, and middle near-infrared (NIR) wavebands were the most sensitive to all parameters in all conditions, whereas those selected within the shortwave infrared (SWIR) waveband were effective for some parameters in particular conditions. Overall, these results indicated that the PLSR analysis and band selection techniques can offer a rapid and nondestructive alternative approach to accurately estimate growth- and photosynthetic-related trait responses to salinity stress.

scholarly journals Improving Estimation of Gross Primary Production in Dryland Ecosystems by a Model-Data Fusion Approach

2019 ◽  
Vol 11 (3) ◽  
pp. 225 ◽  
Author(s):  
Haibo Wang ◽  
Xin Li ◽  
Mingguo Ma ◽  
Liying Geng
Keyword(s):  
Carbon Cycle ◽  
Arid Region ◽  
Arid Regions ◽  
Meteorological Data ◽  
Gross Primary Production ◽  
Arid Ecosystems ◽  
Desert Ecosystems ◽  
Dryland Ecosystems ◽  
Semi Arid

Accurate and continuous monitoring of the production of arid ecosystems is of great importance for global and regional carbon cycle estimation. However, the magnitude of carbon sequestration in arid regions and its contribution to the global carbon cycle is poorly understood due to the worldwide paucity of measurements of carbon exchange in arid ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) product provides worldwide high-frequency monitoring of terrestrial GPP. While there have been a large number of studies to validate the MODIS GPP product with ground-based measurements over a range of biome types. Few studies have comprehensively validated the performance of MODIS estimates in arid and semi-arid ecosystems, especially for the newly released Collection 6 GPP products, whose resolution have been improved from 1000 m to 500 m. Thus, this study examined the performance of MODIS-derived GPP by compared with eddy covariance (EC)-observed GPP at different timescales for the main ecosystems in arid and semi-arid regions of China. Meanwhile, we also improved the estimation of MODIS GPP by using in situ meteorological forcing data and optimization of biome-specific parameters with the Bayesian approach. Our results revealed that the current MOD17A2H GPP algorithm could, on the whole, capture the broad trends of GPP at eight-day time scales for the most investigated sites. However, GPP was underestimated in some ecosystems in the arid region, especially for the irrigated cropland and forest ecosystems (with R2 = 0.80, RMSE = 2.66 gC/m2/day and R2 = 0.53, RMSE = 2.12 gC/m2/day, respectively). At the eight-day time scale, the slope of the original MOD17A2H GPP relative to the EC-based GPP was only 0.49, which showed significant underestimation compared with tower-based GPP. However, after using in situ meteorological data to optimize the biome-based parameters of MODIS GPP algorithm, the model could explain 91% of the EC-observed GPP of the sites. Our study revealed that the current MODIS GPP model works well after improving the maximum light-use efficiency (εmax or LUEmax), as well as the temperature and water-constrained parameters of the main ecosystems in the arid region. Nevertheless, there are still large uncertainties surrounding GPP modelling in dryland ecosystems, especially for desert ecosystems. Further improvements in GPP simulation in dryland ecosystems are needed in future studies, for example, improvements of remote sensing products and the GPP estimation algorithm, implementation of data-driven methods, or physiology models.

scholarly journals Hyperspectral Monitoring of Non-Native Tropical Grasses over Phenological Seasons

2021 ◽  
Vol 13 (4) ◽  
pp. 738
Author(s):  
Kirrilly Pfitzner ◽  
Renee Bartolo ◽  
Tim Whiteside ◽  
David Loewensteiner ◽  
Andrew Esparon
Keyword(s):  
Near Infrared ◽  
Spectral Response ◽  
Grass Species ◽  
Wet Season ◽  
Native Grass ◽  
Data Collections ◽  
Spectral Patterns ◽  
Shortwave Infrared ◽  
Over Time

The miniaturisation of hyperspectral sensors for use on drones has provided an opportunity to obtain hyper temporal data that may be used to identify and monitor non-native grass species. However, a good understanding of variation in spectra for species over time is required to target such data collections. Five taxological and morphologically similar non-native grass species were hyper spectrally characterised from multitemporal spectra (17 samples over 14 months) over phenological seasons to determine their temporal spectral response. The grasses were sampled from maintained plots of homogenous non-native grass cover. A robust in situ standardised sampling method using a non-imaging field spectrometer measuring reflectance across the 350–2500 nm wavelength range was used to obtain reliable spectral replicates both within and between plots. The visible-near infrared (VNIR) to shortwave infrared (SWIR) and continuum removed spectra were utilised. The spectra were then resampled to the VNIR only range to simulate the spectral response from more affordable VNIR only hyperspectral scanners suitable to be mounted on drones. We found that species were separable compared to similar but different species. The spectral patterns were similar over time, but the spectral shape and absorption features differed between species, indicating these subtle characteristics could be used to distinguish between species. It was the late dry season and the end of the wet season that provided maximum separability of the non-native grass species sampled. Overall the VNIR-SWIR results highlighted more dissimilarity for unlike species when compared to the VNIR results alone. The SWIR is useful for discriminating species, particularly around water absorption.

scholarly journals Spektroskopi Reflektansi Sampel Tanah dan Batuan yang Mengandung Mineral Pembawa Unsur Tanah Jarang dan Radioaktif

EKSPLORIUM ◽  
2019 ◽  
Vol 40 (2) ◽  
pp. 89
Author(s):  
Arie Naftali Hawu Hede ◽  
Muhammad Anugrah Firdaus ◽  
Yogi La Ode Prianata ◽  
Mohamad Nur Heriawan ◽  
Syafrizal Syafrizal ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Rare Earth ◽  
Optical Sensors ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Infrared Range ◽  
Basic Principles ◽  
Spectral Signatures ◽  
Shortwave Infrared ◽  
Remote Sensing Methods

ABSTRAKSpektroskopi reflektansi merupakan salah satu metode nondestruktif untuk identifikasi mineral dan sebagai dasar dalam analisis pengindraan jauh (indraja) sensor optik. Penelitian ini bertujuan melakukan kajian penerapan spektroskopi reflektansi pada panjang gelombang 350–2.500 nm untuk sampel tanah dan batuan pembawa unsur tanah jarang (rare earth element-REE) dan radioaktif. Sampel diambil dari beberapa lokasi di Bangka Selatan dan Mamuju yang sebelumnya telah diidentifikasi memiliki potensi REE dan unsur radioaktif. Kurva reflektansi hasil analisis sampel dari Bangka Selatan menunjukan adanya kenampakan absorpsi yang menjadi karakteristik untuk kehadiran REE, dalam bentuk mineral monasit, zirkon, dan xenotime khususnya pada sampel yang berasal dari material tailing dan konsentrat bijih timah. Panjang gelombang yang menjadi kunci khususnya berada pada rentang visible-near infrared (VNIR; 400–1.300 nm). Sedangkan untuk sampel yang berasal dari Mamuju, yang merupakan daerah prospeksi mineral radioaktif, karakteristik spektral memperlihatkan beberapa panjang gelombang kunci terutama pada rentang shortwave infrared (1.300–2.500 nm). Hasil interpretasi menunjukkan mineral mayor berupa mineral lempung, sulfat, spesies NH4, dan mineral yang mengandung Al-OH lainnya, sedangkan untuk beberapa sampel pada panjang gelombang VNIR diidentifikasi mengandung mineral besi oksida/hidroksida. Hasil penelitian ini diharapkan dapat berguna untuk pemetaan eksplorasi REE dan radioaktif dengan menggunakan metode indraja.ABSTRACTReflectance spectroscopy is one of the nondestructive methods of mineral identification and is one of the basic principles in the remote sensing analysis using optical sensors. This research aimed at applying reflectance spectroscopy at 350–2,500 nm wavelength range for samples containing rare earth elements (REE) and radioactive minerals. Samples were taken from several locations in South Bangka and Mamuju that had previously been identified as potential location of REE and radioactive-bearing minerals. Reflectance data shows that there are absorption characteristics for REE-bearing minerals; monazite, zircon, and xenotime minerals especially from tailings and tin ore concentrate for the samples from South Bangka. The key wavelengths are specifically in the visible-near infrared range (VNIR; 400–1300 nm). For the samples from Mamuju, which is known as radioactive mineral prospecting areas, spectral characteristics provide information that there are spectral signatures in the shortwave infrared range (1,300–2,500 nm). The results of major mineral interpretations include clay minerals, sulfates, NH4 species, and other minerals containing Al-OH. However, some samples at the VNIR wavelength identified as iron oxide/hydroxide minerals. It is hoped that these results can be useful for REE and radioactive exploration mapping using remote sensing methods.

scholarly journals C-band radar data and in situ measurements for the monitoring of wheat crops in a semi-arid area (center of Morocco)

2021 ◽  
Author(s):  
Nadia Ouaadi ◽  
Jamal Ezzahar ◽  
Saïd Khabba ◽  
Salah Er-Raki ◽  
Adnane Chakir ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Scientific Community ◽  
Surface Soil ◽  
Radar Data ◽  
Surface Soil Moisture ◽  
Data Set ◽  
Semi Arid

Abstract. A better understanding of the hydrological functioning of irrigated crops using remote sensing observations is of prime importance in the semi-arid areas where the water resources are limited. Radar observations, available at high resolution and revisit time since the launch of Sentinel-1 in 2014, have shown great potential for the monitoring of the water content of the upper soil and of the canopy. In this paper, a complete set of data for radar signal analysis is shared to the scientific community for the first time to our knowledge. The data set is composed of Sentinel-1 products and in situ measurements of soil and vegetation variables collected during three agricultural seasons over drip-irrigated winter wheat in the Haouz plain in Morocco. The in situ data gathers soil measurements (time series of half-hourly surface soil moisture, surface roughness and agricultural practices) and vegetation measurements collected every week/two weeks including above-ground fresh and dry biomasses, vegetation water content based on destructive measurements, cover fraction, leaf area index and plant height. Radar data are the backscattering coefficient and the interferometric coherence derived from Sentinel-1 GRDH (Ground Range Detected High resolution) and SLC (Single Look Complex) products, respectively. The normalized difference vegetation index derived from Sentinel-2 data based on Level-2A (surface reflectance and cloud mask) atmospheric effects-corrected products is also provided. This database, which is the first of its kind made available in open access, is described here comprehensively in order to help the scientific community to evaluate and to develop new or existing remote sensing algorithms for monitoring wheat canopy under semi-arid conditions. The data set is particularly relevant for the development of radar applications including surface soil moisture and vegetation parameters retrieval using either physically based or empirical approaches such as machine and deep learning algorithms. The database is archived in the DataSuds repository and is freely-accessible via the DOI: https://doi.org/10.23708/8D6WQC (Ouaadi et al., 2020a).

scholarly journals Understanding the representativeness of FLUXNET for upscaling carbon flux from eddy covariance measurements

2016 ◽  
Author(s):  
Jitendra Kumar ◽  
Forrest M. Hoffman ◽  
William W. Hargrove ◽  
Nathan Collier
Keyword(s):  
Carbon Cycle ◽  
Eddy Covariance ◽  
Temporal Dynamics ◽  
Data Availability ◽  
Full Potential ◽  
Data Set ◽  
Ecosystem Properties ◽  
Eddy Covariance Measurements ◽  
Spatio Temporal

Abstract. Eddy covariance data from regional flux networks are direct in situ measurement of carbon, water, and energy fluxes and are of vital importance for understanding the spatio-temporal dynamics of the the global carbon cycle. FLUXNET links regional networks of eddy covariance sites across the globe to quantify the spatial and temporal variability of fluxes at regional to global scales and to detect emergent ecosystem properties. This study presents an assessment of the representativeness of FLUXNET based on the recently released FLUXNET2015 data set. We present a detailed high resolution analysis of the evolving representativeness of FLUXNET through time. Results provide quantitative insights into the extent that various biomes are sampled by the network of networks, the role of the spatial distribution of the sites on the network scale representativeness at any given time, and how that representativeness has changed through time due to changing operational status and data availability at sites in the network. To realize the full potential of FLUXNET observations for understanding emergent ecosystem properties at regional and global scales, we present an approach for upscaling eddy covariance measurements. Informed by the representativeness of observations at the flux sites in the network, the upscaled data reflects the spatio-temporal dynamics of the carbon cycle captured by the in situ measurements. This study presents a method for optimal use of the rich point measurements from FLUXNET to derive an understanding of upscaled carbon fluxes, which can be routinely updated as new data become available, and direct network expansion by identifying regions poorly sampled by the current network. Data from this study are available at http://dx.doi.org/10.15486/NGT/1279968

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