Novel Vegetation Indices for Cotton Boll Opening Status Estimation Using Sentinel-2 Data

The application of chemical harvest aids to defoliate leaves and ripen bolls plays a significant role in the once-over machine harvest of cotton (Gossypium hirsutum L.) fields. The boll opening rate (BOR) is a key indicator for the determination of harvest aid spraying times. However, the most commonly used method to determine BOR is manual investigation, which is subjective and cannot have a holistic judgment of the entire area. Remote sensing can be employed to overcome these limitations, due to a wide field of vision, acceptably spatial and temporal resolution, and rich spectral information beyond the perception of the human eye. The reflectance of open cotton bolls is relatively high in the visible and near-infrared bands. High reflectance of open bolls has a great influence on the reflectance of the mixed pixels on remote sensing imagery. Therefore, it is an effective method to detect boll opening status by constructing vegetation indices with the sensitive spectral bands of imagery. In this study, we proposed two new vegetation indices based on Sentinel-2 remote sensing data, namely, the boll area ratio index (BARI) and the boll opening rate index (BORI), in order to estimate the boll opening status on a regional scale. The proposed indices were strongly correlated with the boll area ratio (BAR) and BOR. In particular, BARI exhibited the most accurate and robust performance with BAR in the prediction (R2 = 0.754, RMSE = 2.56%) and validation (R2 = 0.706, RMSE = 5.00%) among all the indices, including published indices we chose. Furthermore, when comparing to all other indices, BORI demonstrated the best and satisfactory estimation with BOR in the prediction (R2 = 0.675, RMSE = 7.96%) and validation (R2 = 0.616, RMSE = 2.79%). Meanwhile, an exponential growth relationship between BOR and BAR was identified, and the underlying mechanisms behind this phenomenon were discussed. Overall, through our study, we provided convenient and accurate vegetation indices for the investigation of boll opening status in a cotton-producing area by accessible and free Sentinel-2 imagery.

Download Full-text

Estimating natural grassland biomass by vegetation indices using Sentinel 2 remote sensing data

International Journal of Remote Sensing ◽

10.1080/01431161.2019.1697004 ◽

2019 ◽

Vol 41 (8) ◽

pp. 2861-2876 ◽

Cited By ~ 6

Author(s):

Marildo Guerini Filho ◽

Tatiana Mora Kuplich ◽

Fernando L. F. De Quadros

Keyword(s):

Remote Sensing ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Sensing Data ◽

Natural Grassland ◽

Sentinel 2

Download Full-text

Monitoring Tree-Shrub, and Meadow Vegetation in the Volga-Akhtuba Floodplain Using Remote Sensing Methods (Based on “Krasnyy Buksir” Range)

10.15688/nsr.jvolsu.2019.3.7 ◽

2020 ◽

pp. 53-60

Author(s):

Irina Ozerina

Keyword(s):

Remote Sensing ◽

Great Influence ◽

Mechanical Damage ◽

Remote Sensing Data ◽

The State ◽

Protection Measures ◽

Entire Area ◽

Pasture Digression ◽

Canopy Area ◽

Remote Sensing Methods

The article deals with the assessment of the state of tree-shrub and meadow vegetation by remote sensing methods. The author reveals the reasons of degradation and change of floristic richness of vegetation. These include: trampling of grass cover and forest floor (1), mechanical damage of trees (2), increasing the area of fire pits (3), and littering and contamination of the territory (4). In addition, mowing and grazing have a great influence. The development of the path network leads to changes in water-physical properties of the soil. Changes in the species composition of meadow vegetation indicate an increase in the processes of xerophytization and pasture digression. The article considers various methods of decoding forest plantations. Among them, the method of aerospace research is the most effective. This method makes it possible to assess the state of tree-shrub and meadow vegetation in dynamics and to carry out continuous monitoring. For a greater accuracy of the assessment, Earth remote sensing data are confirmed by on-site studies. Projective coverage has been used as a sign of degradation for the assessment. The level of preservation of plantings has been determined by the area of the canopy of the stand, that is the ratio of the canopy area to the entire area of the plantings. Under the area of the canopy a set of pixels that match the tone assigned to the canopy is understood. Using the methodology given in the article, the author has carried out the vegetation analysis at “Krasnyy Buksir” range. The area of land belonging to different environmental levels has been calculated for this purpose. The author draws the conclusion that the studied site is subject to a significant anthropogenic load and environmental protection measures are strongly necessary in this area.

Download Full-text

SENTINEL-1 AND SENTINEL-2 DATA FUSION FOR WETLANDS MAPPING: BALIKDAMI, TURKEY

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

10.5194/isprs-archives-xlii-3-729-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 729-734 ◽

Cited By ~ 4

Author(s):

G. Kaplan ◽

U. Avdan

Keyword(s):

Remote Sensing ◽

Classification Accuracy ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Economic Benefits ◽

Future Research ◽

Wetland Classification ◽

Optical Remote Sensing ◽

Data Set ◽

Sentinel 2

Wetlands provide a number of environmental and socio-economic benefits such as their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Remote sensing technology has proven to be a useful and frequent application in monitoring and mapping wetlands. Combining optical and microwave satellite data can help with mapping and monitoring the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing radar and optical remote sensing data can increase the wetland classification accuracy. In this paper, data from the fine spatial resolution optical satellite, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, were fused for mapping wetlands. Both Sentinel-1 and Sentinel-2 images were pre-processed. After the pre-processing, vegetation indices were calculated using the Sentinel-2 bands and the results were included in the fusion data set. For the classification of the fused data, three different classification approaches were used and compared. The results showed significant improvement in the wetland classification using both multispectral and microwave data. Also, the presence of the red edge bands and the vegetation indices used in the data set showed significant improvement in the discrimination between wetlands and other vegetated areas. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, showing an overall classification accuracy of approximately 90&thinsp;% in the object-based classification method. For future research, we recommend multi-temporal image use, terrain data collection, as well as a comparison of the used method with the traditional image fusion techniques.

Download Full-text

Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

Remote Sensing ◽

10.3390/rs13030536 ◽

2021 ◽

Vol 13 (3) ◽

pp. 536

Author(s):

Eve Laroche-Pinel ◽

Mohanad Albughdadi ◽

Sylvie Duthoit ◽

Véronique Chéret ◽

Jacques Rousseau ◽

...

Keyword(s):

Near Infrared ◽

Water Status ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Short Wave ◽

Spatial And Temporal Scales ◽

Spectral Bands ◽

Main Challenge ◽

Spectral Scale ◽

Grape Varieties

The main challenge encountered by Mediterranean winegrowers is water management. Indeed, with climate change, drought events are becoming more intense each year, dragging the yield down. Moreover, the quality of the vineyards is affected and the level of alcohol increases. Remote sensing data are a potential solution to measure water status in vineyards. However, important questions are still open such as which spectral, spatial, and temporal scales are adapted to achieve the latter. This study aims at using hyperspectral measurements to investigate the spectral scale adapted to measure their water status. The final objective is to find out whether it would be possible to monitor the vine water status with the spectral bands available in multispectral satellites such as Sentinel-2. Four Mediterranean vine plots with three grape varieties and different water status management systems are considered for the analysis. Results show the main significant domains related to vine water status (Short Wave Infrared, Near Infrared, and Red-Edge) and the best vegetation indices that combine these domains. These results give some promising perspectives to monitor vine water status.

Download Full-text

Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data

Remote Sensing ◽

10.3390/rs13122313 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2313

Author(s):

Elena Prudnikova ◽

Igor Savin

Keyword(s):

Remote Sensing ◽

Organic Matter ◽

Soil Organic Matter ◽

Spectral Reflectance ◽

Remote Sensing Data ◽

Soil Surface ◽

Optical Remote Sensing ◽

Test Field ◽

Sensing Data ◽

Sentinel 2

Optical remote sensing only provides information about the very thin surface layer of soil. Rainfall splash alters soil surface properties and its spectral reflectance. We analyzed the impact of rainfall on the success of soil organic matter (SOM) content (% by mass) detection and mapping based on optical remote sensing data. The subject of the study was the arable soils of a test field located in the Tula region (Russia), their spectral reflectance, and Sentinel-2 data. Our research demonstrated that rainfall negatively affects the accuracy of SOM predictions based on Sentinel-2 data. Depending on the average precipitation per day, the R2cv of models varied from 0.67 to 0.72, RMSEcv from 0.64 to 1.1% and RPIQ from 1.4 to 2.3. The incorporation of information on the soil surface state in the model resulted in an increase in accuracy of SOM content detection based on Sentinel-2 data: the R2cv of the models increased up to 0.78 to 0.84, the RMSEcv decreased to 0.61 to 0.71%, and the RPIQ increased to 2.1 to 2.4. Further studies are necessary to identify how the SOM content and composition of the soil surface change under the influence of rainfall for other soils, and to determine the relationships between rainfall-induced SOM changes and soil surface spectral reflectance.

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

Snow dunes and glazed surfaces in Antarctica: new field and remote-sensing data

Annals of Glaciology ◽

10.3189/172756402781817851 ◽

2002 ◽

Vol 34 ◽

pp. 81-88 ◽

Cited By ~ 57

Author(s):

Massimo Frezzotti ◽

Stefano Gandolfi ◽

Floriana La Marca ◽

Stefano Urbini

Keyword(s):

Remote Sensing ◽

Near Infrared ◽

Accumulation Rate ◽

Spectral Response ◽

Remote Sensing Data ◽

Snow Accumulation ◽

Remotely Sensed Data ◽

Surface Mass ◽

Glazed Surface ◽

Transverse Dunes

AbstractAs part of the International Trans-Antarctic Scientific Expedition project, the Italian Antarctic Programme undertook two traverses from the Terra Nova station to Talos Dome and to Dome C. Along the traverses, the party carried out several tasks (drilling, glaciological and geophysical exploration). The difference in spectral response between glazed surfaces and snow makes it simple to identify these areas on visible/near-infrared satellite images. Integration of field observation and remotely sensed data allows the description of different mega-morphologic features: wide glazed surfaces, sastrugi glazed surface fields, transverse dunes and megadunes. Topography global positioning system, ground penetrating radar and detailed snow-surface surveys have been carried out, providing new information about the formation and evolution of mega-morphologic features. The extensive presence, (up to 30%) of glazed surface caused by a long hiatus in accumulation, with an accumulation rate of nil or slightly negative, has a significant impact on the surface mass balance of a wide area of the interior part of East Antarctica. The aeolian processes creating these features have important implications for the selection of optimum sites for ice coring, because orographic variations of even a few metres per kilometre have a significant impact on the snow-accumulation process. Remote-sensing surveys of aeolian macro-morphology provide a proven, high-quality method for detailed mapping of the interior of the ice sheet’s prevalent wind direction and could provide a relative indication of wind intensity.

Download Full-text

Early stress detection in grapevine – can remotely-sensed sun-induced chlorophyll fluorescence do the job?

10.5194/egusphere-egu21-5171 ◽

2021 ◽

Author(s):

Georg Wohlfahrt ◽

Albin Hammerle ◽

Barbara Rainer ◽

Florian Haas

Keyword(s):

Chlorophyll Fluorescence ◽

Vegetation Index ◽

Near Infrared ◽

Census Data ◽

Meteorological Data ◽

Vegetation Indices ◽

Wavelength Region ◽

Remote Sensing Data ◽

Warning Signs ◽

Mitigation Measures

Ongoing changes in climate (both in the means and the extremes) are increasingly challenging grapevine production in the province of South Tyrol (Italy). Here we ask the question whether sun-induced chlorophyll fluorescence (SIF) observed remotely from space can detect early warning signs of stress in grapevine and thus help guide mitigation measures.Chlorophyll fluorescence refers to light absorbed by chlorophyll molecules that is re-emitted in the red to far-red wavelength region. Previous research at leaf and canopy scale indicated that SIF correlates with the plant photosynthetic uptake of carbon dioxide as it competes for the same energy pool.To address this question, we use time series of two down-scaled SIF products (GOME-2 and OCO-2, 2007/14-2018) as well as the original OCO-2 data (2014-2019). As a benchmark, we use several vegetation indices related to canopy greenness, as well as a novel near-infrared radiation-based vegetation index (2000-2019). Meteorological data fields are used to explore possible weather-related causes for observed deviations in remote sensing data. Regional DOC grapevine census data (2000-2019) are used as a reference for the analyses.

Download Full-text

Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

Remote Sensing ◽

10.3390/rs13214483 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4483

Author(s):

W. Gareth Rees ◽

Jack Tomaney ◽

Olga Tutubalina ◽

Vasily Zharko ◽

Sergey Bartalev

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Boreal Forest ◽

Remote Sensing Data ◽

Land Cover Classification ◽

Fundamental Parameter ◽

Sensing Data ◽

Growing Stock ◽

Stock Volume ◽

Sentinel 2

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data.

Download Full-text

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

Revista de Teledetección ◽

10.4995/raet.2020.13337 ◽

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

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.

Download Full-text