scholarly journals Comparison and Ground Truthing of Different Remote and Proximal Sensing Platforms to Characterize Variability in a Hedgerow-Trained Vineyard

2021 ◽  
Vol 13 (11) ◽  
pp. 2056
Author(s):  
Cecilia Squeri ◽  
Stefano Poni ◽  
Salvatore Filippo Di Gennaro ◽  
Alessandro Matese ◽  
Matteo Gatti
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growth Yield ◽  
Ground Truth ◽  
Large Degree ◽  
Proximal Sensing ◽  
Ground Truth Data ◽  
Ground Truthing ◽  
Sentinel 2

Appropriate characterization of intra-parcel variability is a key element for the effective application of precision farming techniques. Nowadays there are many platforms available to end users differing for pixel spatial resolution and the type of acquisition (remote or proximal). A challenging aspect pertaining to remote sensing image acquisition in the vineyard ecosystem is that, in a large majority of cases, vegetation is discontinuous and single rows alternate with strips of either bare or grassed soil. In this paper, four different satellite platforms (Sentinel-2, Spot-6, Pleiades, and WorldView-3) having different spatial resolution and MECS-VINE® proximity sensor were compared in terms of accuracy at describing spatial variability. Vineyard mapping was coupled with detailed ground truthing of growth, yield, and grape composition variables. The analysis was conducted based on vigor indices (Normalized Difference Vegetation Index or Canopy Index) and using the Moran Index (MI) to assess the degree of spatial auto-correlation for the different variables. The results obtained showed a large degree of intra-plot variability in the main agronomic parameters (pruning weight CV: 33.86%, yield: 32.09%). The univariate Moran index showed a log-linear function relating MI coefficients to the resolution levels. Comparison between vigor indices and agronomic data showed that the highest bivariate MI was reached by Pleiades followed by MECS-VINE® which also did not exhibit the negative effect of the border pixel owing to the proximal scanning acquisition. Despite WorldView-3′s high resolution (1.24 m pixel) allowing very detailed data imaging, the comparison with ground-truth data was not encouraging, probably due to the presence of pure ground pixels, while Sentinel-2 was affected by the oversized pixel at 10 m.

scholarly journals Applying the Tropical Peatland Combustion Algorithm to Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery

2020 ◽  
Vol 12 (23) ◽  
pp. 3958
Author(s):  
Parwati Sofan ◽  
David Bruce ◽  
Eriita Jones ◽  
M. Rokhis Khomarudin ◽  
Orbita Roswintiarti
Keyword(s):  
Spatial Resolution ◽  
Fire Detection ◽  
Ground Truth ◽  
Thermal Infrared ◽  
Landsat 8 ◽  
Ground Truth Data ◽  
Spectral Instrument ◽  
Tropical Peatland ◽  
Tropical Environments ◽  
Sentinel 2

This study establishes a new technique for peatland fire detection in tropical environments using Landsat-8 and Sentinel-2. The Tropical Peatland Combustion Algorithm (ToPeCAl) without longwave thermal infrared (TIR) (henceforth known as ToPeCAl-2) was tested on Landsat-8 Operational Land Imager (OLI) data and then applied to Sentinel-2 Multi Spectral Instrument (MSI) data. The research is aimed at establishing peatland fire information at higher spatial resolution and more frequent observation than from Landsat-8 data over Indonesia’s peatlands. ToPeCAl-2 applied to Sentinel-2 was assessed by comparing fires detected from the original ToPeCAl applied to Landsat-8 OLI/Thermal Infrared Sensor (TIRS) verified through comparison with ground truth data. An adjustment of ToPeCAl-2 was applied to minimise false positive errors by implementing pre-process masking for water and permanent bright objects and filtering ToPeCAl-2’s resultant detected fires by implementing contextual testing and cloud masking. Both ToPeCAl-2 with contextual test and ToPeCAl with cloud mask applied to Sentinel-2 provided high detection of unambiguous fire pixels (>95%) at 20 m spatial resolution. Smouldering pixels were less likely to be detected by ToPeCAl-2. The detected smouldering pixels from ToPeCAl-2 applied to Sentinel-2 with contextual testing and with cloud masking were only 35% and 56% correct, respectively; this needs further investigation and validation. These results demonstrate that even in the absence of TIR data, an adjusted ToPeCAl algorithm (ToPeCAl-2) can be applied to detect peatland fires at 20 m resolution with high accuracy especially for flaming. Overall, the implementation of ToPeCAl applied to cost-free and available Landsat-8 and Sentinel-2 data enables regular peatland fire monitoring in tropical environments at higher spatial resolution than other satellite-derived fire products.

Estimating Regional Soil Moisture with Synergistic Use of AMSR2 and MODIS Images

2021 ◽  
Vol 87 (9) ◽  
pp. 649-660
Author(s):  
Majid Rahimzadegan ◽  
Arash Davari ◽  
Ali Sayadi
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Regional Scale ◽  
Ground Truth ◽  
Absolute Error ◽  
Simple Method ◽  
Ground Truth Data ◽  
Pi Index

Soil moisture content (SMC), product of Advanced Microwave Scanning Radiometer 2 (AMSR2), is not at an adequate level of accuracy on a regional scale. The aim of this study is to introduce a simple method to estimate SMC while synergistically using AMSR2 and Moderate Resolution Imaging Spectroradiometer (MODIS) measurements with a higher accuracy on a regional scale. Two MODIS products, including daily reflectance (MYD021) and nighttime land surface temperature (LST) products were used. In 2015, 1442 in situ SMC measurements from six stations in Iran were used as ground-truth data. Twenty models were evaluated using combinations of polarization index (PI), index of soil wetness (ISW), normalized difference vegetation index (NDVI), and LST. The model revealed the best results using a quadratic combination of PI and ISW, a linear form of LST, and a constant value. The overall correlation coefficient, root-mean-square error, and mean absolute error were 0.59, 4.62%, and 3.01%, respectively.

scholarly journals Spatio-Temporal Variation of Vegetation in Godavari Eastern Delta

2019 ◽  
pp. 1-7
Author(s):  
G. Kishore Kumar ◽  
M. Raghu Babu ◽  
A. Mani ◽  
M. Matin Luther ◽  
V. Srinivasa Rao
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Land Use Changes ◽  
Satellite Image ◽  
Ground Truth ◽  
Likelihood Method ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Wide Range

Spatial variability in land use changes creates a need for a wide range of applications, including landslide, erosion, land planning, global warming etc. This study presents the analysis of satellite image based on Normalized Difference Vegetation Index (NDVI) in Godavari eastern delta. Four spectral indices were investigated in this study. These indices were NIR (red and near infrared) based NDVI, green and NIR based GVI (Green Vegetation Index), red and NIR based soil adjusted vegetation index (SAVI), and red and NIR based perpendicular vegetation index (PVI). These four indices were investigated for 2011-12 kharif, rabi and 2016-17 kharif, rabi of Godavari eastern delta. Different threshold values of NDVI are used for generating the false colour composite of the classified objects. For this purpose, supervised classification is applied to Landsat images acquired in 2011-12 and 2016-17. Image classification of six reflective bands of two Landsat images is carried out by using maximum likelihood method with the aid of ground truth data obtained from satellite images of 2011-12 and 2016-17. There was 11% and 30% increase in vegetation during kharif and rabi seasons from 2011-12 to 2016-17. The vegetation analysis can be used to provide humanitarian aid, damage assessment in case of unfortunate natural disasters and furthermore to device new protection strategies.

scholarly journals FUSION OF SENTINEL-2 AND PLANETSCOPE IMAGERY FOR VEGETATION DETECTION AND MONITORING

2018 ◽  
Vol XLII-1 ◽  
pp. 155-160 ◽  
Author(s):  
M. Gašparović ◽  
D. Medak ◽  
I. Pilaš ◽  
L. Jurjević ◽  
I. Balenović
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Characteristics ◽  
Land Cover Classification ◽  
Vegetation Monitoring ◽  
Sentinel 2

<p><strong>Abstract.</strong> Different spatial resolutions satellite imagery with global almost daily revisit time provide valuable information about the earth surface in a short time. Based on the remote sensing methods satellite imagery can have different applications like environmental development, urban monitoring, etc. For accurate vegetation detection and monitoring, especially in urban areas, spectral characteristics, as well as the spatial resolution of satellite imagery is important. In this research, 10-m and 20-m Sentinel-2 and 3.7-m PlanetScope satellite imagery were used. Although in nowadays research Sentinel-2 satellite imagery is often used for land-cover classification or vegetation detection and monitoring, we decided to test a fusion of Sentinel-2 imagery with PlanetScope because of its higher spatial resolution. The main goal of this research is a new method for Sentinel-2 and PlanetScope imagery fusion. The fusion method validation was provided based on the land-cover classification accuracy. Three land-cover classifications were made based on the Sentinel-2, PlanetScope and fused imagery. As expected, results show better accuracy for PS and fused imagery than the Sentinel-2 imagery. PlanetScope and fused imagery have almost the same accuracy. For the vegetation monitoring testing, the Normalized Difference Vegetation Index (NDVI) from Sentinel-2 and fused imagery was calculated and mutually compared. In this research, all methods and tests, image fusion and satellite imagery classification were made in the free and open source programs. The method developed and presented in this paper can easily be applied to other sciences, such as urbanism, forestry, agronomy, ecology and geology.</p>

scholarly journals Super-Resolution of Sentinel-2 Images Using Convolutional Neural Networks and Real Ground Truth Data

2020 ◽  
Vol 12 (18) ◽  
pp. 2941
Author(s):  
Mikel Galar ◽  
Rubén Sesma ◽  
Christian Ayala ◽  
Lourdes Albizua ◽  
Carlos Aranda
Keyword(s):  
Spatial Resolution ◽  
Near Infrared ◽  
Super Resolution ◽  
Ground Truth ◽  
Specific Model ◽  
Observation Data ◽  
Ground Truth Data ◽  
Additional Information ◽  
Main Challenge ◽  
Sentinel 2

Earth observation data is becoming more accessible and affordable thanks to the Copernicus programme and its Sentinel missions. Every location worldwide can be freely monitored approximately every 5 days using the multi-spectral images provided by Sentinel-2. The spatial resolution of these images for RGBN (RGB + Near-infrared) bands is 10 m, which is more than enough for many tasks but falls short for many others. For this reason, if their spatial resolution could be enhanced without additional costs, any posterior analyses based on these images would be benefited. Previous works have mainly focused on increasing the resolution of lower resolution bands of Sentinel-2 (20 m and 60 m) to 10 m resolution. In these cases, super-resolution is supported by bands captured at finer resolutions (RGBN at 10 m). On the contrary, this paper focuses on the problem of increasing the spatial resolution of 10 m bands to either 5 m or 2.5 m resolutions, without having additional information available. This problem is known as single-image super-resolution. For standard images, deep learning techniques have become the de facto standard to learn the mapping from lower to higher resolution images due to their learning capacity. However, super-resolution models learned for standard images do not work well with satellite images and hence, a specific model for this problem needs to be learned. The main challenge that this paper aims to solve is how to train a super-resolution model for Sentinel-2 images when no ground truth exists (Sentinel-2 images at 5 m or 2.5 m). Our proposal consists of using a reference satellite with a high similarity in terms of spectral bands with respect to Sentinel-2, but with higher spatial resolution, to create image pairs at both the source and target resolutions. This way, we can train a state-of-the-art Convolutional Neural Network to recover details not present in the original RGBN bands. An exhaustive experimental study is carried out to validate our proposal, including a comparison with the most extended strategy for super-resolving Sentinel-2, which consists in learning a model to super-resolve from an under-sampled version at either 40 m or 20 m to the original 10 m resolution and then, applying this model to super-resolve from 10 m to 5 m or 2.5 m. Finally, we will also show that the spectral radiometry of the native bands is maintained when super-resolving images, in such a way that they can be used for any subsequent processing as if they were images acquired by Sentinel-2.

scholarly journals LEARNING SUPER-RESOLUTION FOR SENTINEL-2 IMAGES WITH REAL GROUND TRUTH DATA FROM A REFERENCE SATELLITE

2020 ◽  
Vol V-1-2020 ◽  
pp. 9-16
Author(s):  
M. Galar ◽  
R. Sesma ◽  
C. Ayala ◽  
L. Albizua ◽  
C. Aranda
Keyword(s):  
Deep Learning ◽  
Spatial Resolution ◽  
Near Infrared ◽  
State Of The Art ◽  
Super Resolution ◽  
Ground Truth ◽  
Ground Truth Data ◽  
Additional Information ◽  
Image Super Resolution ◽  
Sentinel 2

Abstract. Copernicus program via its Sentinel missions is making earth observation more accessible and affordable for everybody. Sentinel-2 images provide multi-spectral information every 5 days for each location. However, the maximum spatial resolution of its bands is 10m for RGB and near-infrared bands. Increasing the spatial resolution of Sentinel-2 images without additional costs, would make any posterior analysis more accurate. Most approaches on super-resolution for Sentinel-2 have focused on obtaining 10m resolution images for those at lower resolutions (20m and 60m), taking advantage of the information provided by bands of finer resolutions (10m). Otherwise, our focus is on increasing the resolution of the 10m bands, that is, super-resolving 10m bands to 2.5m resolution, where no additional information is available. This problem is known as single-image super-resolution and deep learning-based approaches have become the state-of-the-art for this problem on standard images. Obviously, models learned for standard images do not translate well to satellite images. Hence, the problem is how to train a deep learning model for super-resolving Sentinel-2 images when no ground truth exist (Sentinel-2 images at 2.5m). We propose a methodology for learning Convolutional Neural Networks for Sentinel-2 image super-resolution making use of images from other sensors having a high similarity with Sentinel-2 in terms of spectral bands, but greater spatial resolution. Our proposal is tested with a state-of-the-art neural network showing that it can be useful for learning to increase the spatial resolution of RGB and near-infrared bands of Sentinel-2.

scholarly journals Monitoring seasonal progress of rice stubble burning in major rice growing districts of Haryana, India, using multidate AWiFS data

2014 ◽  
Vol XL-8 ◽  
pp. 1003-1009 ◽  
Author(s):  
M. Yadav ◽  
R. Prawasi ◽  
S. Jangra ◽  
P. Rana ◽  
K. Kumari ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Ground Truth ◽  
Ground Truth Data ◽  
Spectral Bands ◽  
Rice Area ◽  
Analysis Technique ◽  
Agricultural Areas ◽  
Year 2013 ◽  
Stubble Burning

The present paper describes the methodology and results of assessment of seasonal progress of rice stubble burning for 10 major rice growing districts of Haryana state in India. These 10 districts contribute about 84 per cent of total rice area of the state. As the rice fields are immediately required to be vacated for the sowing of next crop the farmers opt for mechanized harvesting and easy way out of burning the stubbles in the field. Such burning result in release of polluting gases and aerosols. Besides, the heating of the soil kills the useful micro-flora of the soil causing soil degradation. Multi-date AWiFS data from Resourcesat 1 and 2 satellites acquired between October 16, 2013 to November 26, 2013 were used for estimating paddy stubble burning areas at different intervals for the year 2013 crop growing season. In season collected ground truth data using hand held GPS along with field photographs were used to identify paddy stubble burning areas and other land features. Complete enumeration approach and Iterative Self-organizing Data Analysis Technique (ISODATA) unsupervised classifier was used for digital analysis. Normalized Difference Vegetation Index (NDVI) of each date was also used with other spectral bands of temporal images. To improve the classification accuracy the non-agricultural areas were masked out. The area was estimated by computing pixels under the classified image mask. Progress of paddy stubble burning was estimated at different intervals for the year 2013 using available cloud free multi-date IRS-P6 AWiFS data to identify the crucial period when stubbles burning takes place in major area so that preventive measures can be taken to curb the menace.

scholarly journals Calibrating Sentinel-2 Imagery with Multispectral UAV Derived Information to Quantify Damages in Mediterranean Rice Crops Caused by Western Swamphen (Porphyrio porphyrio)

Drones ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 45 ◽  
Author(s):  
Magda Pla ◽  
Gerard Bota ◽  
Andrea Duane ◽  
Jaume Balagué ◽  
Antoni Curcó ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Biological Diversity ◽  
Ground Truth ◽  
Western Mediterranean ◽  
Ebro Delta ◽  
Crop Damage ◽  
Pixel Resolution ◽  
Porphyrio Porphyrio ◽  
Sentinel 2

Making agricultural production compatible with the conservation of biological diversity is a priority in areas in which human–wildlife conflicts arise. The threatened Western Swamphen (Porphyrio porphyrio) feeds on rice, inducing crop damage and leading to decreases in rice production. Due to the Swamphen protection status, economic compensation policies have been put in place to compensate farmers for these damages, thus requiring an accurate, quantitative, and cost-effective evaluation of rice crop losses over large territories. We used information captured from a UAV (Unmanned Aerial Vehicle) equipped with a multispectral Parrot SEQUOIA camera as ground-truth information to calibrate Sentinel-2 imagery to quantify damages in the region of Ebro Delta, western Mediterranean. UAV vegetation index NDVI (Normalized Difference Vegetation Index) allowed estimation of damages in rice crops at 10 cm pixel resolution by discriminating no-green vegetation pixels. Once co-registered with Sentinel grid, we predicted the UAV damage proportion at a 10 m resolution as a function of Sentinel-2 NDVI, and then we extrapolated the fitted model to the whole Sentinel-2 Ebro Delta image. Finally, the damage predicted with Sentinel-2 data was quantified at the agricultural plot level and validated with field information compiled on the ground by Rangers Service. We found that Sentinel2-NDVI data explained up to 57% of damage reported with UAV. The final validation with Rangers Service data pointed out some limitations in our procedure that leads the way to improving future development. Sentinel2 imagery calibrated with UAV information proved to be a viable and cost-efficient alternative to quantify damages in rice crops at large scales.

scholarly journals ASSESSING CROP MONITORING POTENTIAL OF SENTINEL-2 IN A SPATIO-TEMPORAL SCALE

2018 ◽  
Vol XLII-5 ◽  
pp. 227-231 ◽  
Author(s):  
P. Ghosh ◽  
D. Mandal ◽  
A. Bhattacharya ◽  
M. K. Nanda ◽  
S. Bera
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Crop Monitoring ◽  
Red Edge ◽  
Plant Area Index ◽  
Spatio Temporal ◽  
Sentinel 2

<p><strong>Abstract.</strong> Spatio-temporal variability of crop growth descriptors is of prime importance for crop risk assessment and yield gap analysis. The incorporation of three bands (viz., B5, B6, B7) in ‘red-edge’ position (i.e., 705<span class="thinspace"></span>nm, 740<span class="thinspace"></span>nm, 783<span class="thinspace"></span>nm) in Sentinel-2 with 10&amp;ndash;20<span class="thinspace"></span>m spatial resolution images with five days revisit period have unfolded opportunity for meticulous crop monitoring. In the present study, the potential of Sentinel-2 have been appraised for monitoring phenological stages of potato over Bardhaman district in the state of West Bengal, India. Due to the competency of Vegetation indices (VI) to evaluate the status of crop growth; we have used the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Index45 (NDI45) for crop monitoring. Time series analysis of the VIs exhibited increasing trend as the crop started approaching maturity and attained a maximum value during the tuber development stage and started decreasing as the crop advances to senescence. Inter-field variability of VIs highlighted the need of crop monitoring at high spatial resolution. Among the three vegetation indices, the GNDVI (<i>r</i><span class="thinspace"></span>=<span class="thinspace"></span>0.636), NDVI (<i>r</i><span class="thinspace"></span>=<span class="thinspace"></span>0.620) had the highest correlation with biomass and Plant Area Index (PAI), respectively. NDI45 had comparatively a lower correlation (<i>r</i><span class="thinspace"></span>=<span class="thinspace"></span>0.572 and 0.585 for PAI and biomass, respectively) with both parameters as compared to other two indices. It is interesting to note that the use of Sentinel-2 Green band (B3) instead of the Red band (B4) in GNDVI resulted in 2.5% increase of correlation with biomass. However, the improvement in correlations between NDI45 with crop biophysical parameters is not apparent in this particular study with the inclusion of the Vegetation Red Edge band (B5) in VI. Nevertheless, the strong correlation of VIs with biomass and PAI asserted proficiency of Sentinel-2 for crop monitoring and potential for crop biophysical parameter retrieval with optimum accuracy.</p>

scholarly journals Potential of Normalized Difference Vegetation Index for Mapping of Soft Clay Area in Paddy Fields of Kedah, Malaysia

2020 ◽  
Vol 52 (1) ◽  
pp. 22
Author(s):  
Muhammad Rendana ◽  
Wan Mohd Razi Idris ◽  
Sahibin Abdul Rahim ◽  
Zulfahmi Ali Rahman ◽  
Tukimat Lihan
Keyword(s):  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soft Clay ◽  
Ground Truth ◽  
Paddy Fields ◽  
In Situ Observation ◽  
Landsat 8 ◽  
Ground Truth Data ◽  
Gis Technique

Mapping of soft clay area in paddy fields uses remote sensing and GIS technique is the fastest way to obtain an accurate location of soft clay in a large scale area. It can be an alternative way to change conventional method like in-situ observation that is expensive and labor intensive. Therefore, this study aimed to investigate the normalized difference vegetation index (NDVI) to map soft clay area in paddy fields Kedah, Malaysia. To analyze soft clay area comprehensively, the study was carried out in three different periods; before paddy planting, after paddy planting and harvest. Ground-truth data of soft clay area was collected from study area during fieldwork activity and compared with NDVI values that produced from Landsat 8 image. Result of study showed NDVI map in period of before paddy planting could be a good indicator for mapping soft clay area because it gave a higher accuracy value than the other periods, with overall accuracy (85%) and kappa coefficient (0,84). Total area of soft clay from the highest value was showed in period of before paddy planting (1.856,97 ha), followed by after paddy planting (656,73 ha) and harvest (401,85 ha) periods, respectively.

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