Sen2Grass: A Cloud-Based Solution to Generate Field-Specific Grassland Information Derived from Sentinel-2 Imagery

Grasslands are important for their ecological values and for agricultural activities such as livestock production worldwide. Efficient grassland management is vital to these values and activities, and remote sensing technologies are increasingly being used to characterize the spatiotemporal variation of grasslands to support those management practices. For this study, Sentinel-2 satellite imagery was used as an input to develop an open-source and automated monitoring system (Sen2Grass) to gain field-specific grassland information on the national and regional level for any given time range as of January 2016. This system was implemented in a cloud-computing platform (StellaSpark Nexus) designed to process large geospatial data streams from a variety of sources and was tested for a number of parcels from the Haus Riswick experimental farm in Germany. Despite outliers due to fluctuating weather conditions, vegetation index time series suggested four distinct growing cycles per growing season. Established relationships between vegetation indices and grassland yield showed poor to moderate positive trends, implying that vegetation indices could be a potential predictor for grassland biomass and chlorophyll content. However, the inclusion of larger and additional datasets such as Sentinel-1 imagery could be beneficial to developing more robust prediction models and for automatic detection of mowing events for grasslands.

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Monitoring Rainfed Alfalfa Growth in Semiarid Agrosystems Using Sentinel-2 Imagery

Remote Sensing ◽

10.3390/rs13224719 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4719

Author(s):

Andrés Echeverría ◽

Alejandro Urmeneta ◽

María González-Audícana ◽

Esther M González

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Weather Conditions ◽

Early Summer ◽

Pasture Management ◽

Semiarid Areas ◽

Potential Success ◽

Highly Correlated ◽

Sentinel 2

The aim of this study was to assess the utility of Sentinel-2 images in the monitoring of the fractional vegetation cover (FVC) of rainfed alfalfa in semiarid areas such as that of Bardenas Reales in Spain. FVC was sampled in situ using 1 m2 surfaces at 172 points inside 18 alfalfa fields from late spring to early summer in 2017 and 2018. Different vegetation indices derived from a series of Sentinel-2 images were calculated and were then correlated with the FVC measurements at the pixel and parcel levels using different types of equations. The results indicate that the normalized difference vegetation index (NDVI) and FVC were highly correlated at the parcel level (R2 = 0.712), whereas the correlation at the pixel level remained moderate across each of the years studied. Based on the findings, another 29 alfalfa plots (28 rainfed; 1 irrigated) were remotely monitored operationally for 3 years (2017–2019), revealing that location and weather conditions were strong determinants of alfalfa growth in Bardenas Reales. The results of this study indicate that Sentinel-2 imagery is a suitable tool for monitoring rainfed alfalfa pastures in semiarid areas, thus increasing the potential success of pasture management.

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ESTIMATION OF MANGROVE FOREST ABOVEGROUND BIOMASS USING MULTISPECTRAL BANDS, VEGETATION INDICES AND BIOPHYSICAL VARIABLES DERIVED FROM OPTICAL SATELLITE IMAGERIES: RAPIDEYE, PLANETSCOPE AND SENTINEL-2

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-3-29-2018 ◽

2018 ◽

Vol IV-3 ◽

pp. 29-36 ◽

Cited By ~ 9

Author(s):

Alvin Balidoy Baloloy ◽

Ariel Conferido Blanco ◽

Christian Gumbao Candido ◽

Reginal Jay Labadisos Argamosa ◽

John Bart Lovern Caboboy Dumalag ◽

...

Keyword(s):

Linear Regression ◽

Mangrove Forest ◽

Field Data ◽

Vegetation Index ◽

Prediction Models ◽

Vegetation Indices ◽

Satellite Systems ◽

Red Edge ◽

Simple Ratio ◽

Sentinel 2

Aboveground biomass estimation (AGB) is essential in determining the environmental and economic values of mangrove forests. Biomass prediction models can be developed through integration of remote sensing, field data and statistical models. This study aims to assess and compare the biomass predictor potential of multispectral bands, vegetation indices and biophysical variables that can be derived from three optical satellite systems: the Sentinel-2 with 10&thinsp;m, 20&thinsp;m and 60&thinsp;m resolution; RapidEye with 5m resolution and PlanetScope with 3m ground resolution. Field data for biomass were collected from a Rhizophoraceae-dominated mangrove forest in Masinloc, Zambales, Philippines where 30 test plots (1.2&thinsp;ha) and 5 validation plots (0.2&thinsp;ha) were established. Prior to the generation of indices, images from the three satellite systems were pre-processed using atmospheric correction tools in SNAP (Sentinel-2), ENVI (RapidEye) and python (PlanetScope). The major predictor bands tested are Blue, Green and Red, which are present in the three systems; and Red-edge band from Sentinel-2 and Rapideye. The tested vegetation index predictors are Normalized Differenced Vegetation Index (NDVI), Soil-adjusted Vegetation Index (SAVI), Green-NDVI (GNDVI), Simple Ratio (SR), and Red-edge Simple Ratio (SRre). The study generated prediction models through conventional linear regression and multivariate regression. Higher coefficient of determination (r2) values were obtained using multispectral band predictors for Sentinel-2 (r2&thinsp;=&thinsp;0.89) and Planetscope (r2&thinsp;=&thinsp;0.80); and vegetation indices for RapidEye (r2&thinsp;=&thinsp;0.92). Multivariate Adaptive Regression Spline (MARS) models performed better than the linear regression models with r2 ranging from 0.62 to 0.92. Based on the r2 and root-mean-square errors (RMSE’s), the best biomass prediction model per satellite were chosen and maps were generated. The accuracy of predicted biomass maps were high for both Sentinel-2 (r2&thinsp;=&thinsp;0.92) and RapidEye data (r2&thinsp;=&thinsp;0.91).

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Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽

10.3390/agronomy11081486 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1486

Author(s):

Chris Cavalaris ◽

Sofia Megoudi ◽

Maria Maxouri ◽

Konstantinos Anatolitis ◽

Marios Sifakis ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Model Development ◽

Wheat Yield ◽

Growth Period ◽

Yield Data ◽

Water Index ◽

Normalized Difference Water Index ◽

Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

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Analysis of Wave Energy Conversion with Dynamic Systems Theory

E3S Web of Conferences ◽

10.1051/e3sconf/201910302003 ◽

2019 ◽

Vol 103 ◽

pp. 02003

Author(s):

Sorin Ciortan ◽

Eugen Rusu

Keyword(s):

Dynamic System ◽

Energy Conversion ◽

Wave Energy ◽

Prediction Models ◽

Weather Conditions ◽

Point Of View ◽

The Black Sea ◽

Time Range ◽

Wave Energy Conversion ◽

Dynamic System Theory

In wave energy conversion one of the most important steps is building scenarios about long term efficiency, taking into account that several factors are involved. Based on the assumption that actually the weather conditions show important modifications year by year, analyses of wave power evolution during the exploitation time range must rely on both prediction models and on several options for the conversion device. From this point of view, the wave energy conversion process can be considered a dynamic system. The dynamic system theory based methodology approach systems behaviour through relationships between systems components. Comparing to usual scientific approaches, which try to decompose the analyzed system, this methodology offers a view of entire system behaviour The paper presents a method for building scenarios of wave energy conversion, in the nearshore of the Black Sea, based on a model which includes also forecasts of the weather influence.

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Time-Series of Vegetation Indices (VNIR/SWIR) Derived from Sentinel-2 (A/B) to Assess Turgor Pressure in Kiwifruit

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9110641 ◽

2020 ◽

Vol 9 (11) ◽

pp. 641

Author(s):

Alberto Jopia ◽

Francisco Zambrano ◽

Waldo Pérez-Martínez ◽

Paulina Vidal-Páez ◽

Julio Molina ◽

...

Keyword(s):

Time Series ◽

Crop Production ◽

Management Practices ◽

Water Status ◽

Vegetation Indices ◽

Turgor Pressure ◽

Security Risk ◽

Vegetative Development ◽

Water Sensor ◽

Sentinel 2

For more than ten years, Central Chile has faced drought conditions, which impact crop production and quality, increasing food security risk. Under this scenario, implementing management practices that allow increasing water use efficiency is urgent. The study was carried out on kiwifruit trees, located in the O’Higgins region, Chile for season 2018–2019 and 2019–2020. We evaluate the time-series of nine vegetation indices in the VNIR and SWIR regions derived from Sentinel-2 (A/B) satellites to establish how much variability in the canopy water status there was. Over the study’s site, eleven sensors were installed in five trees, which continuously measured the leaf’s turgor pressure (Yara Water-Sensor). A strong Spearman’s (ρ) correlation between turgor pressure and vegetation indices was obtained, having −0.88 with EVI and −0.81 with GVMI for season 2018–2019, and lower correlation for season 2019–2020, reaching −0.65 with Rededge1 and −0.66 with EVI. However, the NIR range’s indices were influenced by the vegetative development of the crop rather than its water status. The red-edge showed better performance as the vegetative growth did not affect it. It is necessary to expand the study to consider higher variability in kiwifruit’s water conditions and incorporate the sensitivity of different wavelengths.

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Land Cover and Forest Type Classification by Values of Vegetation Indices and Forest Structure of Tropical Lowland Forests in Central Vietnam

International Journal of Forestry Research ◽

10.1155/2020/8896310 ◽

2020 ◽

Vol 2020 ◽

pp. 1-18

Author(s):

Hung Nguyen Trong ◽

The Dung Nguyen ◽

Martin Kappas

Keyword(s):

Land Cover ◽

Vegetation Index ◽

Forest Type ◽

Vegetation Indices ◽

Landsat 8 ◽

Tropical Lowland ◽

Central Vietnam ◽

Lowland Forests ◽

Type Classification ◽

Sentinel 2

This paper aims to (i) optimize the application of multiple bands of satellite images for land cover classification by using random forest algorithms and (ii) assess correlations and regression of vegetation indices of a better-performed land cover classification image with vertical and horizontal structures of tropical lowland forests in Central Vietnam. In this study, we used Sentinel-2 and Landsat-8 to classify seven land cover classes of which three forest types were substratified as undisturbed, low disturbed, and disturbed forests where forest inventory of 90 plots, as ground-truth, was randomly sampled to measure forest tree parameters. A total of 3226 training points were sampled on seven land cover types. The performance of Landsat-8 showed out-of-bag error of 31.6%, overall accuracy of 68%, kappa of 67.5%, while Sentinel-2 showed out-of-bag error of 14.3% and overall accuracy of 85.7% and kappa of 83%. Ten vegetation indices of the better-performed image were extracted to find out (i) the correlation and regression of horizontal and vertical structures of trees and (ii) assess the variation values between ground-truthing plots and training sample plots in three forest types. The result of the t test on vegetation indices showed that six out of ten vegetation indices were significant at p<0.05. Seven vegetation indices had a correlation with the horizontal structure, but four vegetation indices, namely, Enhanced Vegetation Index, Perpendicular Vegetation Index, Difference Vegetation Index, and Transformed Normalized Difference Vegetation Index, had better correlations r = 0.66, 0.65, 0.65, 0.63 and regression results were of R2 = 0.44, 0.43, 0.43, and 0.40, respectively. The correlations of tree height were r = 0.46, 0.43, 0.43, and 0.49 and its regressions were of R2 = 0.21, 0.19, 0.18, and 0.24, respectively. The results show the possibility of using random forest algorithm with Sentinel-2 in forest type classification in line with vegetation indices application.

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Spatial and Temporal Monitoring of Pasture Ecological Quality: Sentinel-2-Based Estimation of Crude Protein and Neutral Detergent Fiber Contents

Remote Sensing ◽

10.3390/rs11070799 ◽

2019 ◽

Vol 11 (7) ◽

pp. 799 ◽

Cited By ~ 4

Author(s):

Rachel Lugassi ◽

Eli Zaady ◽

Naftaly Goldshleger ◽

Maxim Shoshany ◽

Alexandra Chudnovsky

Keyword(s):

Crude Protein ◽

Vegetation Index ◽

Vegetation Indices ◽

Field Measurements ◽

Neutral Detergent Fiber ◽

Ecological Quality ◽

Spatial And Temporal Patterns ◽

Validation Data ◽

Calibration And Validation ◽

Sentinel 2

Frequent, region-wide monitoring of changes in pasture quality due to human disturbances or climatic conditions is impossible by field measurements or traditional ecological surveying methods. Remote sensing imagery offers distinctive advantages for monitoring spatial and temporal patterns. The chemical parameters that are widely used as indicators of ecological quality are crude protein (CP) content and neutral detergent fiber (NDF) content. In this study, we investigated the relationship between CP, NDF, and reflectance in the visible–near-infrared–shortwave infrared (VIS–NIR–SWIR) spectral range, using field, laboratory measurements, and satellite imagery (Sentinel-2). Statistical models were developed using different calibration and validation data sample sets: (1) a mix of laboratory and field measurements (e.g., fresh and dry vegetation) and (2) random selection. In addition, we used three vegetation indices (Normalized Difference Vegetative Index (NDVI), Soil-adjusted Vegetation Index (SAVI) and Wide Dynamic Range Vegetation Index (WDRVI)) as proxies to CP and NDF estimation. The best models found for predicting CP and NDF contents were based on reflectance measurements (R2 = 0.71, RMSEP = 2.1% for CP; and R2 = 0.78, RMSEP = 5.5% for NDF). These models contained fresh and dry vegetation samples in calibration and validation data sets. Random sample selection in a model generated similar accuracy estimations. Our results also indicate that vegetation indices provide poor accuracy. Eight Sentinel-2 images (December 2015–April 2017) were examined in order to better understand the variability of vegetation quality over spatial and temporal scales. The spatial and temporal patterns of CP and NDF contents exhibit strong seasonal dependence, influenced by climatological (precipitation) and topographical (northern vs. southern hillslopes) conditions. The total CP/NDF content increases/decrease (respectively) from December to March, when the concentrations reach their maximum/minimum values, followed by a decline/incline that begins in April, reaching minimum values in July.

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Comparative Analysis of Landsat-8, Sentinel-2, and GF-1 Data for Retrieving Soil Moisture over Wheat Farmlands

Remote Sensing ◽

10.3390/rs12172708 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2708 ◽

Cited By ~ 1

Author(s):

Qi Wang ◽

Jiancheng Li ◽

Taoyong Jin ◽

Xin Chang ◽

Yongchao Zhu ◽

...

Keyword(s):

Soil Moisture ◽

Optical Sensors ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Cloud Model ◽

Vegetation Indices ◽

Optical Data ◽

Landsat 8 ◽

Different Sources ◽

Sentinel 2

Soil moisture is an important variable in ecological, hydrological, and meteorological studies. An effective method for improving the accuracy of soil moisture retrieval is the mutual supplementation of multi-source data. The sensor configuration and band settings of different optical sensors lead to differences in band reflectivity in the inter-data, further resulting in the differences between vegetation indices. The combination of synthetic aperture radar (SAR) data with multi-source optical data has been widely used for soil moisture retrieval. However, the influence of vegetation indices derived from different sources of optical data on retrieval accuracy has not been comparatively analyzed thus far. Therefore, the suitability of vegetation parameters derived from different sources of optical data for accurate soil moisture retrieval requires further investigation. In this study, vegetation indices derived from GF-1, Landsat-8, and Sentinel-2 were compared. Based on Sentinel-1 SAR and three optical data, combined with the water cloud model (WCM) and the advanced integral equation model (AIEM), the accuracy of soil moisture retrieval was investigated. The results indicate that, Sentinel-2 data were more sensitive to vegetation characteristics and had a stronger capability for vegetation signal detection. The ranking of normalized difference vegetation index (NDVI) values from the three sensors was as follows: the largest was in Sentinel-2, followed by Landsat-8, and the value of GF-1 was the smallest. The normalized difference water index (NDWI) value of Landsat-8 was larger than that of Sentinel-2. With reference to the relative components in the WCM model, the contribution of vegetation scattering exceeded that of soil scattering within a vegetation index range of approximately 0.55–0.6 in NDVI-based models and all ranges in NDWI1-based models. The threshold value of NDWI2 for calculating vegetation water content (VWC) was approximately an NDVI value of 0.4–0.55. In the soil moisture retrieval, Sentinel-2 data achieved higher accuracy than data from the other sources and thus was more suitable for the study for combination with SAR in soil moisture retrieval. Furthermore, compared with NDVI, higher accuracy of soil moisture could be retrieved by using NDWI1 (R2 = 0.623, RMSE = 4.73%). This study provides a reference for the selection of optical data for combination with SAR in soil moisture retrieval.

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Determination of Crop Health Monitoring in MPKV – Rahuri, Using Remote Sensing Approach

Current Journal of Applied Science and Technology ◽

10.9734/cjast/2021/v40i431291 ◽

2021 ◽

pp. 11-16

Author(s):

Ankita P. Kamble ◽

A. A. Atre ◽

Payal A. Mahadule ◽

C. B. Pande ◽

N. S. Kute ◽

...

Keyword(s):

Remote Sensing ◽

Health Monitoring ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Plant Stress ◽

Vegetation Indices ◽

Remotely Sensed Data ◽

Pests And Diseases ◽

Crop Health ◽

Sentinel 2

Pests and diseases cause major harm during crop development. Also plant stress affects crop quality and quantity. Recent developments in high resolution remotely sensed data has seen a great potential in mapping cropland areas infected by pests and diseases, as well as potential vulnerable areas over expansive areas. Crop health monitoring in this study was carried out using remote sensing techniques. The present study was carried out in MPKV, Rahuri, Ahmednagar District, Maharashtra. Vegetation indices like Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were used to classify the crops into healthy and dead or unhealthy one. Sentinel-2 image data from October 2019 to January 2020 processed in Arc GIS 10.1 were used for this study. Vegetation is a key component of the ecosystem and plays an important role in stabilizing the global environment. The result showed that the average vegetation cover was decreased in the month of November and healthy vegetation was found more in month of October as compared to December and January. This shows that NDVI and SAVI indices for Sentinel-2 images can be used for crop health monitoring.

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Assessing the Use of Sentinel-2 Time Series for Monitoring Cork Oak Decline in Portugal

10.20944/preprints201909.0316.v1 ◽

2019 ◽

Author(s):

Ana Navarro ◽

João Catalão ◽

João Calvão

Keyword(s):

Time Series ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Temporal Dynamics ◽

Vegetation Indices ◽

Quercus Suber ◽

Cork Oak ◽

Tree Level ◽

Oak Decline ◽

Sentinel 2

In Portugal, cork oak (Quercus suber L.) stands cover 737 Mha, being the most predominant species of the montado agroforestry system, contributing for the economic, social and environmental development of the country. Cork oak decline is a known problem since the late years of the 19th century that has recently worsen. The causes of oak decline seem to be a result of slow and cumulative processes, although the role of each environmental factor is not yet established. The availability of Sentinel-2 high spatial and temporal resolution dense time series enables gradual processes monitoring. These processes can be monitored using spectral vegetation indices (VI) once their temporal dynamics are expected to be related with green biomass and photosynthetic efficiency. The Normalized Difference Vegetation Index (NDVI) is sensitive to structural canopy changes, however it tends to saturate at moderate-to-dense canopies. Modified VI have been proposed to incorporate the reflectance in the red-edge spectral region, which is highly sensitive to chlorophyll content while largely unaffected by structural properties. In this research, in-situ data on the location and vitality status of cork oak trees are used to assess the correlation between chlorophyll indices (CI) and NDVI time series trends and cork oak vitality at the tree level. Preliminary results seem to be promising since differences between healthy and unhealthy (diseased/dead) trees were observed.

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