Hyperspectral remote sensing monitoring of cyanobacteria blooms in a large South American reservoir: high- and medium-spatial resolution satellite algorithm simulation

2020 ◽  
Vol 71 (5) ◽  
pp. 593 ◽  
Author(s):  
A. Drozd ◽  
P. de Tezanos Pinto ◽  
V. Fernández ◽  
M. Bazzalo ◽  
F. Bordet ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Near Infrared ◽  
Monitoring Program ◽  
Hyperspectral Remote Sensing ◽  
Coefficient Of Determination ◽  
South American ◽  
Cell Abundance ◽  
Hyperspectral Signatures ◽  
Sentinel 2

We used hyperspectral remote sensing with the aim of establishing a monitoring program for cyanobacteria in a South American reservoir. We sampled at a wide temporal (2012–16; 10 seasons) and spatial (30km) gradient, and retrieved 111 field hyperspectral signatures, chlorophyll-a, cyanobacteria densities and total suspended solids. The hyperspectral signatures for cyanobacteria-dominated situations (n=75) were used to select the most suitable spectral bands in seven high- and medium-spatial resolution satellites (Sentinel 2, Landsat 5, 7 and 8, SPOT-4/5 and -6/7, WorldView 2), and for the development of chlorophyll and cyanobacteria cell abundance algorithms (λ550 – λ650+λ800) ÷ (λ550+λ650+λ800). The best-performing chlorophyll algorithm was Sentinel 2 ((λ560 – λ660+λ703) ÷ (λ560+λ660+λ703); R2=0.80), followed by WorldView 2 ((λ550 – λ660+λ720) ÷ (λ550+λ660+λ720); R2=0.78), Landsat and the SPOT series ((λ550 – λ650+λ800) ÷ (λ550+λ650+λ800); R2=0.67–0.74). When these models were run for cyanobacteria abundance, the coefficient of determination remained similar, but the root mean square error increased. This could affect the estimate of cyanobacteria cell abundance by ~20%, yet it still enable assessment of the alert level categories for risk assessment. The results of this study highlight the importance of the red and near-infrared region for identifying cyanobacteria in hypereutrophic waters, demonstrating coherence with field cyanobacteria abundance and enabling assessment of bloom distribution in this ecosystem.

scholarly journals Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery

2021 ◽  
Vol 13 (5) ◽  
pp. 872 ◽  
Author(s):  
Sergii Skakun ◽  
Natacha I. Kalecinski ◽  
Meredith G. L. Brown ◽  
David M. Johnson ◽  
Eric F. Vermote ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Near Infrared ◽  
Temporal Frequency ◽  
Coefficient Of Determination ◽  
Yield Variability ◽  
Landsat 8 ◽  
Yield Data ◽  
Resolution Data ◽  
Sentinel 2

Crop yield monitoring is an important component in agricultural assessment. Multi-spectral remote sensing instruments onboard space-borne platforms such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for efficiently generating timely and synoptic information on the yield status of crops across regional levels. However, the coarse spatial resolution data inherent to these sensors provides little utility at the management level. Recent satellite imagery collection advances toward finer spatial resolution (down to 1 m) alongside increased observational cadence (near daily) implies information on crops obtainable at field and within-field scales to support farming needs is now possible. To test this premise, we focus on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic, Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa. Precision yield data as referenced to combine harvesters’ GPS systems were used for validation. We show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield variability for corn and soybean. Our simulation results show that moving to coarser resolution data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively. We show that the most important spectral bands explaining yield variability were green (0.560 μm), red-edge (0.726 μm), and near-infrared (NIR − 0.865 μm). Furthermore, the high temporal frequency of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection for yield map generation. Overall, we observed mixed performance of satellite-derived models with the coefficient of determination (R2) varying from 0.21 to 0.88 (averaging 0.56) for the 30 m HLS and from 0.09 to 0.77 (averaging 0.30) for 3 m Planet. R2 was lower for fields with higher yields, suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions are likely needed alongside the optical imagery to fully explain the yields.

Spectral characteristics of leafy spurge (Euphorbia esula) leaves and flower bracts

Weed Science ◽  
2004 ◽  
Vol 52 (4) ◽  
pp. 492-497 ◽  
Author(s):  
E. Raymond Hunt ◽  
James E. McMurtrey ◽  
Amy E. Parker Williams ◽  
Lawrence A. Corp
Keyword(s):  
Remote Sensing ◽  
High Performance ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Leafy Spurge ◽  
Hyperspectral Remote Sensing ◽  
Total Carotenoids ◽  
Physiological Basis ◽  
Infrared Wavelengths ◽  
Pigment Concentrations

Leafy spurge can be detected during flowering with either aerial photography or hyperspectral remote sensing because of the distinctive yellow-green color of the flower bracts. The spectral characteristics of flower bracts and leaves were compared with pigment concentrations to determine the physiological basis of the remote sensing signature. Compared with leaves of leafy spurge, flower bracts had lower reflectance at blue wavelengths (400 to 500 nm), greater reflectance at green, yellow, and orange wavelengths (525 to 650 nm), and approximately equal reflectances at 680 nm (red) and at near-infrared wavelengths (725 to 850 nm). Pigments from leaves and flower bracts were extracted in dimethyl sulfoxide, and the pigment concentrations were determined spectrophotometrically. Carotenoid pigments were identified using high-performance liquid chromatography. Flower bracts had 84% less chlorophylla, 82% less chlorophyllb, and 44% less total carotenoids than leaves, thus absorptance by the flower bracts should be less and the reflectance should be greater at blue and red wavelengths. The carotenoid to chlorophyll ratio of the flower bracts was approximately 1:1, explaining the hue of the flower bracts but not the value of reflectance. The primary carotenoids were lutein, β-carotene, and β-cryptoxanthin in a 3.7:1.5:1 ratio for flower bracts and in a 4.8:1.3:1 ratio for leaves, respectively. There was 10.2 μg g−1fresh weight of colorless phytofluene present in the flower bracts and none in the leaves. The fluorescence spectrum indicated high blue, red, and far-red emission for leaves compared with flower bracts. Fluorescent emissions from leaves may contribute to the higher apparent leaf reflectance in the blue and red wavelength regions. The spectral characteristics of leafy spurge are important for constructing a well-documented spectral library that could be used with hyperspectral remote sensing.

scholarly journals Deep learning para la clasificación de usos de suelo agrícola con Sentinel-2

2020 ◽  
pp. 35
Author(s):  
M. Campos-Taberner ◽  
F.J. García-Haro ◽  
B. Martínez ◽  
M.A. Gilabert
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Near Infrared ◽  
Spatial Information ◽  
Learning Algorithm ◽  
Support Vector ◽  
Sensing Applications ◽  
Learning Techniques ◽  
Remote Sensing Applications ◽  
Sentinel 2

<p class="p1">The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. These results open the door to new studies in the field of the explainable deep learning in remote sensing applications.</p>

scholarly journals Evaluation of TsHARP Utility for Thermal Sharpening of Sentinel-3 Satellite Images Using Sentinel-2 Visual Imagery

2019 ◽  
Vol 11 (19) ◽  
pp. 2304 ◽  
Author(s):  
Hanna Huryna ◽  
Yafit Cohen ◽  
Arnon Karnieli ◽  
Natalya Panov ◽  
William P. Kustas ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Land Surface ◽  
Near Infrared ◽  
Landsat 8 ◽  
Large Area ◽  
Primary Methods ◽  
Thermal Images ◽  
Spatially Distributed ◽  
The Difference ◽  
Sentinel 2

A spatially distributed land surface temperature is important for many studies. The recent launch of the Sentinel satellite programs paves the way for an abundance of opportunities for both large area and long-term investigations. However, the spatial resolution of Sentinel-3 thermal images is not suitable for monitoring small fragmented fields. Thermal sharpening is one of the primary methods used to obtain thermal images at finer spatial resolution at a daily revisit time. In the current study, the utility of the TsHARP method to sharpen the low resolution of Sentinel-3 thermal data was examined using Sentinel-2 visible-near infrared imagery. Compared to Landsat 8 fine thermal images, the sharpening resulted in mean absolute errors of ~1 °C, with errors increasing as the difference between the native and the target resolutions increases. Part of the error is attributed to the discrepancy between the thermal images acquired by the two platforms. Further research is due to test additional sites and conditions, and potentially additional sharpening methods, applied to the Sentinel platforms.

scholarly journals MAPPING OF FOLIAR CONTENT USING RADIATIVE TRANSFER MODELING AND VIS-NIR HYPERSPECTRAL CLOSE-RANGE REMOTE-SENSING

2015 ◽  
Vol XL-3/W3 ◽  
pp. 467-472 ◽  
Author(s):  
S. Jay ◽  
R. Bendoula ◽  
X. Hadoux ◽  
N. Gorretta
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Spatial Resolution ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Radiative Transfer Model ◽  
Integrating Sphere ◽  
Leaf Angle ◽  
Transfer Model

Most methods for retrieving foliar content from hyperspectral data are well adapted either to remote-sensing scale, for which each spectral measurement has a spatial resolution ranging from a few dozen centimeters to a few hundred meters, or to leaf scale, for which an integrating sphere is required to collect the spectral data. In this study, we present a method for estimating leaf optical properties from hyperspectral images having a spatial resolution of a few millimeters or centimeters. In presence of a single light source assumed to be directional, it is shown that leaf hyperspectral measurements can be related to the directional hemispherical reflectance simulated by the PROSPECT radiative transfer model using two other parameters. The first one is a multiplicative term that is related to local leaf angle and illumination zenith angle. The second parameter is an additive specular-related term that models BRDF effects. <br><br> Our model was tested on visible and near infrared hyperspectral images of leaves of various species, that were acquired under laboratory conditions. Introducing these two additional parameters into the inversion scheme leads to improved estimation results of PROSPECT parameters when compared to original PROSPECT. In particular, the RMSE for local chlorophyll content estimation was reduced by 21% (resp. 32%) when tested on leaves placed in horizontal (resp. sloping) position. Furthermore, inverting this model provides interesting information on local leaf angle, which is a crucial parameter in classical remote-sensing.

scholarly journals Hyperspectral Remote Sensing for Determining Water and Nitrogen Stress in Maize during Rabi Season

2020 ◽  
pp. 1-9
Author(s):  
H. R. Naveen ◽  
B. Balaji Naik ◽  
G. Sreenivas ◽  
Ajay Kumar ◽  
J. Adinarayana ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Hyperspectral Remote Sensing ◽  
Nitrogen Stress ◽  
Fixed Amount ◽  
Nitrogen Levels ◽  
Stress Environment

Aims/Objectives: Is to examine the use of spectral reflectance characteristics and explore the effectiveness of spectral indices under water and nitrogen stress environment. Study Design: Split-plot. Place and Duration of Study: Agro Climate Research Center, A.R.I., P.J.T.S. Agricultural University, Rajendranagar, Hyderabad, India in 2018-19. Methodology: Fixed amount of 5 cm depth of water was applied to each plot when the ratio of irrigation water and cumulative pan evaporation (IW/CPE) arrives at pre-determined levels of 0.6, 0.8 & 1.2 as main-plot and 3 nitrogen levels viz. 100, 200 & 300 kg N ha-1 as a subplot to create water and nitrogen stress environment. Spectral reflectance from each treatment was measured using Spectroradiometer and analyzed using statistical software package SPSS 17, SAS and trial version of UNSCRABLER. Results: At tasseling and dough stages, the reflectance pattern of maize was found to be higher in visible light spectrum of 400 to700 nm whereas lower in near-infrared region (700 to 900) in both underwater (IW/CPE ratio of 0.6) and nitrogen stress (100 kg N ha-1) environment as compared to moderate and no stress irrigation (IW/CPE ratio of 0.8 & 1.2) and nitrogen (200 and 300 kg N ha-1) treatments. The discriminant analysis of NDVI, GNDVI, WBI and SR indicated that 72.2% and 66.7% of the original grouped cases and 55.6% and 38.9% of the cross-validated grouped cases under irrigation and nitrogen levels, respectively were correctly classified. Conclusion: Hyperspectral remote sensing can be used as a tool to detect and quantify the water and nitrogen stress in maize non-destructively. Spectral vegetation indices viz. Normalized Difference Vegetation Index (NDVI) and Green Normalized Difference Vegetation Index (GNDVI) were found effective to distinguish water and nitrogen stress severity in maize.

scholarly journals Remote Sensing Phenology of Antarctic Green and Red Snow Algae Using WorldView Satellites

2021 ◽  
Vol 12 ◽  
Author(s):  
Andrew Gray ◽  
Monika Krolikowski ◽  
Peter Fretwell ◽  
Peter Convey ◽  
Lloyd S. Peck ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cell Density ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Algal Blooms ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Snow Algae ◽  
Antarctic Snow ◽  
Sentinel 2

Snow algae are an important group of terrestrial photosynthetic organisms in Antarctica, where they mostly grow in low lying coastal snow fields. Reliable observations of Antarctic snow algae are difficult owing to the transient nature of their blooms and the logistics involved to travel and work there. Previous studies have used Sentinel 2 satellite imagery to detect and monitor snow algal blooms remotely, but were limited by the coarse spatial resolution and difficulties detecting red blooms. Here, for the first time, we use high-resolution WorldView multispectral satellite imagery to study Antarctic snow algal blooms in detail, tracking the growth of red and green blooms throughout the summer. Our remote sensing approach was developed alongside two Antarctic field seasons, where field spectroscopy was used to build a detection model capable of estimating cell density. Global Positioning System (GPS) tagging of blooms and in situ life cycle analysis was used to validate and verify our model output. WorldView imagery was then used successfully to identify red and green snow algae on Anchorage Island (Ryder Bay, 67°S), estimating peak coverage to be 9.48 × 104 and 6.26 × 104 m2, respectively. Combined, this was greater than terrestrial vegetation area coverage for the island, measured using a normalized difference vegetation index. Green snow algae had greater cell density and average layer thickness than red blooms (6.0 × 104 vs. 4.3 × 104 cells ml−1) and so for Anchorage Island we estimated that green algae dry biomass was over three times that of red algae (567 vs. 180 kg, respectively). Because the high spatial resolution of the WorldView imagery and its ability to detect red blooms, calculated snow algal area was 17.5 times greater than estimated with Sentinel 2 imagery. This highlights a scaling problem of using coarse resolution imagery and suggests snow algal contribution to net primary productivity on Antarctica may be far greater than previously recognized.

scholarly journals Wheat Crop Acreage Estimation Based on Remote Sensing and GIS in Jabalpur (Madhya Pradesh, India)

2021 ◽  
pp. 88-94
Author(s):  
Umakant Rawat ◽  
Ankit Yadav ◽  
P.S. Pawar ◽  
Aniket Rajput ◽  
Devendra Vasht ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Unsupervised Classification ◽  
Good Choice ◽  
Madhya Pradesh ◽  
Wheat Crop ◽  
Kappa Statistics ◽  
Crop Acreage ◽  
Spectral Bands ◽  
Sentinel 2

Mapping and classification crop by using satellite images is a challenging task that can minimize the complexities of field visits. The recently launched Sentinel-2 satellite has thirteen spectral bands, short revisit time and determination at three different resolutions (10 m, 20 m and 60 m), besides that, the free availability of the images makes it a good choice for vegetation mapping. This study aims to classify crop, using single date Sentinel-2 imagery within the Jabalpur, state of Madhya Pradesh, India. The classification was performed by using Unsupervised Classification. In this study, four spectral bands, i.e., Near Infrared, Red, Green, and Blue of Sentinel-2 were stacked for the classification. The results show that the area of wheat crop corresponds to 83.07%; Gram/ Pulses, 14.64%; and other crop, 2.28%. The overall accuracy and overall Kappa Statistics of the classification using Sentinel-2 imagery are 85.71% and 0.819%, respectively. Therefore, this study has found that Sentinel-2 presented great potential in the mapping of the agriculture areas of Jabalpur by remote sensing.

scholarly journals Probabilistic Mangrove Species Mapping with Multiple-Source Remote-Sensing Datasets Using Label Distribution Learning in Xuan Thuy National Park, Vietnam

2020 ◽  
Vol 12 (22) ◽  
pp. 3834 ◽  
Author(s):  
Junshi Xia ◽  
Naoto Yokoya ◽  
Tien Dat Pham
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Tree Species ◽  
National Park ◽  
Mangrove Species ◽  
Mangrove Tree ◽  
Label Distribution Learning ◽  
Label Distribution ◽  
Mangrove Communities ◽  
Sentinel 2

Mangrove forests play an important role in maintaining water quality, mitigating climate change impacts, and providing a wide range of ecosystem services. Effective identification of mangrove species using remote-sensing images remains a challenge. The combinations of multi-source remote-sensing datasets (with different spectral/spatial resolution) are beneficial to the improvement of mangrove tree species discrimination. In this paper, various combinations of remote-sensing datasets including Sentinel-1 dual-polarimetric synthetic aperture radar (SAR), Sentinel-2 multispectral, and Gaofen-3 full-polarimetric SAR data were used to classify the mangrove communities in Xuan Thuy National Park, Vietnam. The mixture of mangrove communities consisting of small and shrub mangrove patches is generally difficult to separate using low/medium spatial resolution. To alleviate this problem, we propose to use label distribution learning (LDL) to provide the probabilistic mapping of tree species, including Sonneratia caseolaris (SC), Kandelia obovata (KO), Aegiceras corniculatum (AC), Rhizophora stylosa (RS), and Avicennia marina (AM). The experimental results show that the best classification performance was achieved by an integration of Sentinel-2 and Gaofen-3 datasets, demonstrating that full-polarimetric Gaofen-3 data is superior to the dual-polarimetric Sentinel-1 data for mapping mangrove tree species in the tropics.

THE APPLICATION OF REMOTE SENSING IN PRECISION AGRICULTURE

1970 ◽  
pp. 10-16
Author(s):  
S. Yu. Blokhina
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Spatial Resolution ◽  
Precision Agriculture ◽  
Near Infrared ◽  
Crop Yields ◽  
Temporal Frequency ◽  
Organic Matter Content ◽  
Sensing Applications ◽  
Remote Sensing Applications

The paper provides an overview of foreign literature on the remote sensing applications in precision agriculture. Remote sensing applications in precision agriculture began with sensors for soil organic matter content, and have quickly advanced to include hand held sensors to tractor or aerial or satellite mounted sensors. Wavelengths of electromagnetic radiation initially focused on a few key visible or near infrared bands, and nowadays electromagnetic wavelengths in use range from the ultraviolet to microwave portions of the spectrum. Spectral bandwidth has decreased dramatically with the advent of hyperspectral remote sensing, allowing improved analysis of crop stress, crop biophysical or biochemical characteristics and specific compounds. A variety of spectral indices have been widely implemented within various precision agriculture applications, rather than a focus on only normalized difference vegetation indices. Spatial resolution and temporal frequency of remote sensing imagery has increased significantly, allowing evaluation of soil and crop properties at fine spatial resolution at the expense of increased data storage and processing requirements. At present there is considerable interest in collecting remote sensing for operational management of soil and crop yields, as well as control over the spread of pests and weeds practically in real time.

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