Reviewing the Potential of Sentinel-2 in Assessing the Drought

Varghese, Dani; Radulović, Mirjana; Stojković, S.M.; Crnojević, Vladimir

doi:10.3390/rs13173355

Cited by 32 publications

(17 citation statements)

References 207 publications

(274 reference statements)

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“…In this study, there are many three-band SRIs that combine the VIS, red-edge, and NIR wavelengths, and were effective for estimating the plant water status indicators and GY under different growth conditions. Interestingly, many studies have reported that there are several satellite-derived indices, especially those based on VIS/red-edge, NIR/VIS, and NIR/red-edge, that can achieve accurate estimation of different field measured agronomic traits, such as AGB [44,88], GY [89,90], and plant water status indicators [91,92]. Additionally, Herrmann et al [93] reported that the satellite-derived indices have been shown to provide almost comparable results as hyperspectral data.…”

Section: Application Of Hyperspectral Data For Simulated Satellite Datamentioning

confidence: 99%

Estimating the Leaf Water Status and Grain Yield of Wheat under Different Irrigation Regimes Using Optimized Two- and Three-Band Hyperspectral Indices and Multivariate Regression Models

Elsayed

El-Hendawy

Dewir

et al. 2021

Water

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Spectral reflectance indices (SRIs) often show inconsistency in estimating plant traits across different growth conditions; thus, it is still necessary to develop further optimized SRIs to guarantee the performance of SRIs as a simple and rapid approach to accurately estimate plant traits. The primary goal of this study was to develop optimized two- and three-band vegetation- and water-SRIs and to apply different multivariate regression models based on these SRIs for accurately estimating the relative water content (RWC), gravimetric water content (GWCF), and grain yield (GY) of two wheat cultivars evaluated under three irrigation regimes (100%, 75%, and 50% of crop evapotranspiration (ETc)) for two seasons. Results showed that the three plant traits and all SRIs showed significant differences (p < 0.05) between the three irrigation treatments for each wheat cultivar. The three-band water-SRIs (NWIs-3b) showed the best performance in estimating the three plant traits for both cultivars (R2 > 0.80), and RWC and GWCF under 75% ETc (R2 ≥ 0.65). Four out of six three-band vegetation-SRIs (NDVIs-3b) performed better than any other SRIs for estimating GY under 100% ETc and 50% ETC, and RWC under 100% ETc (R2 ≥ 0.60). All types of SRIs demonstrated excellent performance in estimating the three plant traits (R2 ≥ 0.70) when the data of all growth conditions were combined and analyzed together. The NWIs-3b coupled with Random Forest models predicted the three plant traits with satisfactory accuracy for the calibration (R2 ≥ 0.96) and validation (R2 ≥ 0.93) datasets. The overall results of this study elucidate that extracting an optimized NWIs-3b from the full spectrum data and combined with an appropriate regression technique could be a practical approach for managing deficit irrigation regimes of crops through accurately, timely, and non-destructively monitoring the water status and final potential yield.

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Section: Application Of Hyperspectral Data For Simulated Satellite Datamentioning

confidence: 99%

Estimating the Leaf Water Status and Grain Yield of Wheat under Different Irrigation Regimes Using Optimized Two- and Three-Band Hyperspectral Indices and Multivariate Regression Models

Elsayed

El-Hendawy

Dewir

et al. 2021

Water

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“…The selected indices were the MNDWI index with a threshold of −0.15 for Landsat-5 images, the MNDWI index and a threshold of −0.25 for Lansat-8 images, and the NDWI index and a threshold of −0.20 for Sentinel-2 images. These indices are among the most used for detecting water surfaces with both sensors, as they have a high ability to separate water zones from other coverages [6,14,18]. The NDWI index often identifies built-up areas as water.…”

Section: Discussionmentioning

confidence: 99%

“…The role of flooding extent and duration is crucial for biodiversity (wildlife habitat) and other wetland ecosystem services (e.g., carbon storage, water quality, storing, floodwater, and maintaining the water levels in the dry season) [6]. In ecology, historical trends and observation of long-term change is central to understanding [7].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Temporal Reconstruction of the Flooded Area in “Tablas de Daimiel” Inland Wetland 2000–2021

Pena-Regueiro

Estornell

Aguilar-Maldonado

et al. 2023

Sensors

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Tablas de Daimiel National Park (TDNP) is a unique inland wetland located in the Mancha plain (Spain). It is recognized at the international level, and it is protected by different figures, such as Biosphere Reserve. However, this ecosystem is endangered due to aquifer overexploitation, and it is at risk of losing its protection figures. The objective of our study is to analyze the evolution of the flooded area between the year 2000 and 2021 by Landsat (5, 7 and 8) and Sentinel-2 images, and to assess the TDNP state through an anomaly analysis of the total water body surface. Several water indices were tested, but the NDWI index for Sentinel-2 (threshold −0.20), the MNDWI for Landsat-5 (threshold −0.15), and the MNDWI for Landsat-8 (threshold −0.25) showed the highest accuracy to calculate the flooded surface inside the protected area’s limits. During the period 2015–2021, we compared the performance of Landsat-8 and Sentinel-2 and an R2 value of 0.87 was obtained for this analysis, indicating a high correspondence between both sensors. Our results indicate a high variability of the flooded areas during the analyzed period with significant peaks, the most notorious in the second quarter of 2010. Minimum flooded areas were observed with negative precipitation index anomalies since fourth quarter of 2004 to fourth quarter of 2009. This period corresponds to a severe drought that affected this region and caused important deterioration. No significant correlation was observed between water surface anomalies and precipitation anomalies, and the significant correlation with flow and piezometric anomalies was moderate. This can be explained because of the complexity of water uses in this wetland, which includes illegal wells and the geological heterogeneity.

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“…One of the most widely used satellites in PA and PV is the Sentinel-2 this satellite is capable of sampling 13 spectral bands up to a resolution of 10m. The main advantage of Sentinel-2 over other satellites is that the data are open-source [89]. The use of data derived from satellite platforms is very wide, examples include studies to identify vineyards growing in large regions or areas [90], monitor vineyard variability, predict yield variability [91].…”

Section: Satellitementioning

confidence: 99%

Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review

Ferro

Catania

2023

Horticulturae

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The potential of precision viticulture has been highlighted since the first studies performed in the context of viticulture, but especially in the last decade there have been excellent results have been achieved in terms of innovation and simple application. The deployment of new sensors for vineyard monitoring is set to increase in the coming years, enabling large amounts of information to be obtained. However, the large number of sensors developed and the great amount of data that can be collected are not always easy to manage, as it requires cross-sectoral expertise. The preliminary section of the review presents the scenario of precision viticulture, highlighting its potential and possible applications. This review illustrates the types of sensors and their operating principles. Remote platforms such as satellites, unmanned aerial vehicles (UAV) and proximal platforms are also presented. Some supervised and unsupervised algorithms used for object-based image segmentation and classification (OBIA) are then discussed, as well as a description of some vegetation indices (VI) used in viticulture. Photogrammetric algorithms for 3D canopy modelling using dense point clouds are illustrated. Finally, some machine learning and deep learning algorithms are illustrated for processing and interpreting big data to understand the vineyard agronomic and physiological status. This review shows that to perform accurate vineyard surveys and evaluations, it is important to select the appropriate sensor or platform, so the algorithms used in post-processing depend on the type of data collected. Several aspects discussed are fundamental to the understanding and implementation of vineyard variability monitoring techniques. However, it is evident that in the future, artificial intelligence and new equipment will become increasingly relevant for the detection and management of spatial variability through an autonomous approach.

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Reviewing the Potential of Sentinel-2 in Assessing the Drought

Cited by 32 publications

References 207 publications

Estimating the Leaf Water Status and Grain Yield of Wheat under Different Irrigation Regimes Using Optimized Two- and Three-Band Hyperspectral Indices and Multivariate Regression Models

Estimating the Leaf Water Status and Grain Yield of Wheat under Different Irrigation Regimes Using Optimized Two- and Three-Band Hyperspectral Indices and Multivariate Regression Models

Remote Sensing Temporal Reconstruction of the Flooded Area in “Tablas de Daimiel” Inland Wetland 2000–2021

Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review

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