Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform

Aim: The recent availability of Sentinel-2 satellites has led to an increasing interest in their use in viticulture. The aim of this short communication is to determine performance and limitation of a Sentinel-2 vegetation index in precision viticulture applications, in terms of correlation and variability assessment, compared to the same vegetation index derived from an unmanned aerial vehicle (UAV). Normalised difference vegetation index (NDVI) was used as reference vegetation index.Methods and Results: UAV and Sentinel-2 vegetation indices were acquired for 30 vineyard blocks located in the south of France without inter-row grass. From the UAV imagery, the vegetation index was calculated using both a mixed pixels approach (both vine and inter-row) and from pure vine-only pixels. In addition, the vine projected area data were extracted using a support vector machine algorithm for vineyard segmentation. The vegetation index was obtained from Sentinel-2 imagery obtained at approximately the same time as the UAV imagery. The Sentinel-2 images used a mixed pixel approach as pixel size is greater than the row width. The correlation between these three layers and the Sentinel-2 derived vegetation indices were calculated, considering spatial autocorrelation correction for the significance test. The Gini coefficient was used to estimate variability detected by each sensor at the within-field scale. The effects of block border and dimension on correlations were estimated.Conclusions: The comparison between Sentinel-2 and UAV vegetation index showed an increase in correlation when border pixels were removed. Block dimensions did not affect the significance of correlation unless blocks were < 0.5 ha. Below this threshold, the correlation was non-significant in most cases. Sentinel-2 acquired data were strongly correlated with UAV-acquired data at both the field (R2 = 0.87) and sub-field scale (R2 = 0.84). In terms of variability detected, Sentinel-2 proved to be able to detect the same amount of variability as the UAV mixed pixel vegetation index.Significance and impact of the study: This study showed at which field conditions the Sentinel-2 vegetation index can be used instead of UAV-acquired images when high spatial resolution (vine-specific) management is not needed and the vineyard is characterised by no inter-row grass. This type of information may help growers to choose the most appropriate information sources to detect variability according to their vineyard characteristics

Effect of missing vines on total leaf area determined by NDVI calculated from sentinel satellite data : progressive vine removal experiments

CITATION: Velez, S. et al 2020. Effect of missing vines on total leaf area determined by NDVI calculated from sentinel satellite data : progressive vine removal experiments. Applied Sciences, 10(10):3612, doi:10.3390/app10103612.The original publication is available at https://www.mdpi.comRemote Sensing (RS) allows the estimation of some important vineyard parameters. There are several platforms for obtaining RS information. In this context, Sentinel satellites are a valuable tool for RS since they provide free and regular images of the earth’s surface. However, several problems regarding the low-resolution of the imagery arise when using this technology, such as handling mixed pixels that include vegetation, soil and shadows. Under this condition, the Normalized Difference Vegetation Index (NDVI) value in a particular pixel is an indicator of the amount of vegetation (canopy area) rather than the NDVI from the canopy (as a vigour expression), but its reliability varies depending on several factors, such as the presence of mixed pixels or the effect of missing vines (a vineyard, once established, generally loses grapevines each year due to diseases, abiotic stress, etc.). In this study, a vine removal simulation (greenhouse experiment) and an actual vine removal (field experiment) were carried out. In the field experiment, the position of the Sentinel-2 pixels was marked using high-precision GPS. Controlled removal of vines from a block of cv. Cabernet Sauvignon was done in four steps. The removal of the vines was done during the summer of 2019, matching with the start of the maximum vegetative growth. The Total Leaf Area (TLA) of each pixel was calculated using destructive field measurements. The operations were planned to have two satellite images available between each removal step. As a result, a strong linear relationship (R2 = 0.986 and R2 = 0.72) was obtained between the TLA and NDVI reductions, which quantitatively indicates the effect of the missing vines on the NDVI values.https://www.mdpi.com/2076-3417/10/10/3612Publisher's versio

Medium-resolution multispectral data from sentinel-2 to assess the damage and the recovery time of late frost on Vineyards

In a climate-change context, the advancement of phenological stages may endanger viticultural areas in the event of a late frost. This study evaluated the potential of satellite-based remote sensing to assess the damage and the recovery time after a late frost event in 2017 in northern Italian vineyards. Several vegetation indices (VIs) normalized on a two-year dataset (2018-2019) were compared over a frost-affected area (F) and a control area (NF) using unpaired two-sample t-test. Furthermore, the must quality data (total acidity, sugar content and pH) of F and NF were analyzed. The VIs most sensitive in the detection of frost damage were Chlorophyll Absorption Ratio Index (CARI), Enhanced Vegetation Index (EVI), and Modified Triangular Vegetation Index 1 (MTVI1) (-5.26%,-16.59%, and-5.77% compared to NF, respectively). The spectral bands Near-Infrared (NIR) and Red Edge 7 were able to identify the frost damage (-16.55 and-16.67% compared to NF, respectively). Moreover, CARI, EVI, MTVI1, NIR, Red Edge 7, the Normalized Difference Vegetation Index (NDVI) and the Modified Simple Ratio (MSR) provided precise information on the full recovery time (+17.7%, +22.42%, +29.67%, +5.89%, +5.91%, +16.48%, and +8.73% compared to NF, respectively) approximately 40 days after the frost event. The must analysis showed that total acidity was higher (+5.98%), and pH was lower (-2.47%) in F compared to NF. These results suggest that medium-resolution multispectral data from Sentinel-2 constellation may represent a cost-effective tool for frost damage assessment and recovery management

Assessment of map based variable rate strategies for copper reduction in hedge vineyards

Reduction of Plant Protection Products (PPP) has become a priority in agriculture, led by European directives and regulations due to the negative impacts and social awareness that pesticides have raised in the population. In viticulture, the use of copper to control Downy Mildew (Plasmopara viticola) is posing severe problems of soil, water and environment contamination, and European Commission is regulating its use through the EU regulation 2018/1981. A targeted spraying management can be done benefiting from the novel development of variable rate application (VRA) technology for orchard sprayers, resulting in savings of PPP. In this research, the quality of the application in terms of copper deposit in leaves of two VRA strategies where compared versus the conventional strategy performed by the farmer (REF).This research was developed with funding from the Spanish Ministry of Agriculture Fisheries and Food within the project GOPHYTOVID [contract 2018002001192], and partial funding from the FI-AGAUR grant from the Generalitat de Catalunya (2017 FI_B 00893).Postprint (published version

드론을 활용한 위성 지표반사도 산출물 공간 패턴 분석

학위논문(석사) -- 서울대학교대학원 : 농업생명과학대학 생태조경·지역시스템공학부(생태조경학), 2021.8. 조대솔.High-resolution satellites are assigned to monitor land surface in detail. The reliable surface reflectance (SR) is the fundamental in terrestrial ecosystem modeling so the temporal and spatial validation is essential. Usually based on multiple ground control points (GCPs), field spectroscopy guarantees the temporal continuity. Due to limited sampling, however, it hardly illustrates the spatial pattern. As a map, the pixelwise spatial variability of SR products is not well-documented. In this study, we introduced drone-based hyperspectral image (HSI) as a reference and compared the map with Sentinel 2 and Landsat 8 SR products on a heterogeneous rice paddy landscape. First, HSI was validated by field spectroscopy and swath overlapping, which assured qualitative radiometric accuracy within the viewing geometry. Second, HSI was matched to the satellite SRs. It involves spectral and spatial aggregation, co-registration and nadir bidirectional reflectance distribution function (BRDF)-adjusted reflectance (NBAR) conversion. Then, we 1) quantified the spatial variability of the satellite SRs and the vegetation indices (VIs) including NDVI and NIRv by APU matrix, 2) qualified them pixelwise by theoretical error budget and 3) examined the improvement by BRDF normalization. Sentinel 2 SR exhibits overall good agreement with drone HSI while the two NIRs are biased up to 10%. Despite the bias in NIR, the NDVI shows a good match on vegetated areas and the NIRv only displays the discrepancy on built-in areas. Landsat 8 SR was biased over the VIS bands (-9 ~ -7.6%). BRDF normalization just contributed to a minor improvement. Our results demonstrate the potential of drone HSI to replace in-situ observation and evaluate SR or atmospheric correction algorithms over the flat terrain. Future researches should replicate the results over the complex terrain and canopy structure (i.e. forest).원격탐사에서 지표 반사도(SR)는 지표정보를 비파괴적이고 즉각적인 방법으로 전달해주는 매개체 역할을 한다. 신뢰할 수 있는 SR은 육상 생태계 모델링의 기본이고, 이에 따라 SR의 시공간적 검증이 요구된다. 일반적으로 SR은 여러 지상 기준점(GCP)을 기반으로 하는 현장 분광법을 통해서 시간적 연속성이 보장된다. 그러나 현장 분광법은 제한적인 샘플링으로 공간 패턴을 거의 보여주지 않아, 위성 SR의 픽셀 별 공간 변동성은 잘 분석되지 않았다. 본 연구에서는 드론 기반의 초분광 영상(HSI)을 참고자료로 도입하여, 이를 이질적인 논 경관에서 Sentinel 2 및 Landsat 8 SR과 비교하였다. 우선, 드론 HSI는 현장 분광법 및 경로 중첩을 통해서 관측각도 범위 내에서 정성적인 방사 측정을 보장한다고 검증되었다. 이후, 드론 HSI는 위성 SR의 분광반응특성, 공간해상도 및 좌표계를 기준으로 맞춰졌고, 관측 기하를 통일하기 위해서 드론 HIS와 위성 SR은 각각 양방향반사율분포함수 (BRDF) 정규화 반사도 (NBAR)로 변환되었다. 마지막으로, 1) APU 행렬으로 위성 SR과 NDVI, NIRv를 포함하는 식생지수(VI)의 공간변동성을 정량화 했고, 2) 대기보정의 이론적 오차를 기준으로 SR과 VI를 픽셀별로 평가했고, 3) BRDF 정규화를 통한 개선 사항을 검토했다. Sentinel 2 SR은 드론 HSI와 전반적으로 좋은 일치를 보이나, 두 NIR 채널은 최대 10% 편향되었다. NIR의 편향은 식생지수에서 토지 피복에 따라 다른 영향을 미쳤다. NDVI는 식생에서는 낮은 편향을 보여줬고, NIRv는 도시시설물 영역에서만 높은 편향을 보였다. Landsat 8 SR은 VIS 채널에 대해 편향되었다 (-9 ~ -7.6%). BRDF 정규화는 위성 SR의 품질을 개선했지만, 그 영향은 부수적이었다. 본 연구에서는 평탄한 지형에서 드론 HSI가 현장 관측을 대체할 수 있고, 따라서 위성 SR이나 대기보정 알고리즘을 평가하는데 활용될 수 있다는 것을 보였다. 향후 연구에서는 산림으로 대상지를 확대하여, 지형과 캐노피 구조가 드론 HSI 및 위성 SR에 미치는 영향을 분석할 필요가 있다.Chapter 1. Introduction 1 1.1 Background 1 Chapter 2. Method 3 2.1 Study Site 3 2.2 Drone campaign 4 2.3 Data processing 4 2.3.1 Sensor calibration 5 2.3.2 Bidirectional reflectance factor (BRF) calculation 7 2.3.3 BRDF correction 7 2.3.4 Orthorectification 8 2.3.5 Spatial Aggregation 9 2.3.6 Co-registration 10 2.4 Satellite dataset 10 2.4.2 Landsat 8 12 Chapter 3. Result and Discussion 12 3.1 Drone BRF map quality assessment 12 3.1.1 Radiometric accuracy 12 3.1.2 BRDF effect 15 3.2 Spatial variability in satellite surface reflectance product 16 3.2.1 Sentinel 2B (10m) 17 3.2.2 Sentinel 2B (20m) 22 3.2.3 Landsat 8 26 Chapter 4. Conclusion 28 Supplemental Materials 30 Bibliography 34 Abstract in Korean 43석

Investigating the metabolic potential of Merlot in a characterised vineyard

Viticulture and Oenolog

Sentinel-2 Validation for Spatial Variability Assessment in Overhead Trellis System Viticulture Versus UAV and Agronomic Data

Several remote sensing technologies have been tested in precision viticulture to characterize vineyard spatial variability, from traditional aircraft and satellite platforms to recent unmanned aerial vehicles (UAVs). Imagery processing is still a challenge due to the traditional row-based architecture, where the inter-row soil provides a high to full presence of mixed pixels. In this case, UAV images combined with filtering techniques represent the solution to analyze pure canopy pixels and were used to benchmark the effectiveness of Sentinel-2 (S2) performance in overhead training systems. At harvest time, UAV filtered and unfiltered images and ground sampling data were used to validate the correlation between the S2 normalized difference vegetation indices (NDVIs) with vegetative and productive parameters in two vineyards (V1 and V2). Regarding the UAV vs. S2 NDVI comparison, in both vineyards, satellite data showed a high correlation both with UAV unfiltered and filtered images (V1 R2 = 0.80 and V2 R2 = 0.60 mean values). Ground data and remote sensing platform NDVIs correlation were strong for yield and biomass in both vineyards (R2 from 0.60 to 0.95). These results demonstrate the effectiveness of spatial resolution provided by S2 on overhead trellis system viticulture, promoting precision viticulture also within areas that are currently managed without the support of innovative technologies

Using multispectral imagery and monitored key parameters to optimise the efficient management of vineyards ("Vitis vinifera" L.)

288 p.[ES] Según la ESA (Agencia Espacial Europea), la teledetección es una forma de recoger y analizar datos para obtener información sobre un objeto, sin que el instrumento utilizado para ello esté en contacto directo con el mismo. Esta herramienta ha demostrado su utilidad en un amplio abanico de campos, incluida la agricultura, ámbito en el que se ha generalizado el uso de imágenes multiespectrales, y podría convertirse en una importante herramienta no sólo para gestionar el cultivo, sino también en la lucha contra el cambio climático. Esta información puede utilizarse sola o combinada con otros datos para obtener mejores resultados, aportando información útil sobre el estado del viñedo. Cuatro elementos son esenciales en la teledetección: una plataforma, un objeto a medir, un sensor y la forma de utilizar y almacenar la información obtenida. En la actualidad, existen varias plataformas para obtener información: satélites, drones, aviones, vehículos terrestres, etc. De tal manera que, dependiendo de la plataforma y del sensor, se obtendrán datos con diferentes características de resolución espacial, temporal, espectral y radiométrica y, por tanto, el coste será diferente en función de la tecnología utilizada. Las aplicaciones de la teledetección en la agricultura son una innovación reconocida y con un potencial cada vez mayor. Esta herramienta se puede emplear para diversos usos de forma muy diversa. Así, en agricultura, la información disponible suele ser tratada empleando índices de vegetación. De igual modo, se puede emplear una sola imagen en un momento determinado del ciclo fenológico (en viticultura suele ser el envero, que está relacionado con el máximo de vegetación) o también es posible emplear todas las imágenes disponibles y trabajar con series temporales. En viticultura, los estudios de investigación muestran que las técnicas de teledetección permiten evaluar la variabilidad del viñedo (Vitis vinifera L.) y controlar la calidad y producción de uva, además, esta herramienta se ha empleado exitosamente para estimar diversos parámetros críticos del viñedo, como el índice de área foliar (LAI). En la presente tesis doctoral, se emplearon las imágenes obtenidas de los satélites Sentinel-2 para comprobar si tenían relación con los parámetros agronómicos y enológicos de varias parcelas situadas en la Denominación de Origen Rueda, Valladolid. Para ello se analizó una serie temporal de imágenes, confirmando que el estado fenológico de envero es un buen momento para el empleo de las imágenes. Se tomaron datos de campo en cada parcela y se mostró que las imágenes de satélite eran capaces de clasificar las parcelas en función de su desarrollo vegetativo, encontrando diferencias significativas en diversos parámetros agronómicos y de calidad de la uva. Adicionalmente, se realizó un ensayo similar en pistacho para comprobar su aplicabilidad, observando diferencias significativas en el rendimiento. Finalmente, se emplearon imágenes Landsat-8 en diversas parcelas de Galicia de las que se disponía de datos de campo relacionados con las poblaciones de levaduras para comprobar si la vegetación, identificada empleando en NDVI de las imágenes, estaba relacionada con la riqueza de especies de levaduras, encontrando diferencias significativas con respecto a las parcelas y el NDVI. Por otra parte, se desarrolló un ensayo experimental en el que se arrancó un viñedo, marcando los píxeles del satélite sobre la superficie del viñedo y coordinando las labores con las pasadas de los satélites Sentinel-2, para comprobar el efecto de la reducción de vegetación sobre la información espectral captada por los satélites (a través del NDVI) en un cultivo como el viñedo, sometido a la problemática de los píxeles mixtos. Se midió minuciosamente en laboratorio la vegetación arrancada para comprobar la superficie exacta de vegetación extraída de la parcela, encontrando que para un viñedo en espaldera como el del estudio, cada 20% de reducción en la cantidad de vegetación supuso una reducción en el NDVI de alrededor del 6%. Adicionalmente, antes de los arranques, se tomaron ortofotografías con UAV y cámaras multiespectrales para desarrollar un método novedoso para estimar el área foliar del viñedo (LAI) empleando las sombras de las plantas proyectadas sobre el suelo del viñedo. Con este fin, se planeó la hora del vuelo con exactitud, para maximizar las sombras, posibilitando a los pilotos no sólo el empleo de un nuevo método de bajo coste con una precisión similar a métodos más costosos, sino también otorgando una mayor flexibilidad a la hora de realizar los trabajos, ya que con este nuevo método los pilotos no necesitan volar el dron al mediodía solar. Finalmente, se realizaron dos estudios de campo exhaustivos en dos viñedos: uno en la DO Rueda y otro en la DO Ribera del Duero, en España. Se creó una malla de muestreo para tratar de captar la variabilidad espacial de los viñedos y se emplearon las imágenes de los satélites Sentinel-2 de todo un año para construir una serie temporal y aplicar un análisis funcional basado en componentes principales (f-PCA). Los resultados muestran que con dos componentes principales se explica la mayor parte de la variabilidad del viñedo y que, a partir de la tercera componente, la relación con los parámetros de campo no está clara. Por otra parte, se encontró que el empleo del f-PCA permitió alcanzar resultados mejores que simplemente una imagen de envero y cada componente principal fue capaz de explicar la variabilidad ocasionada por distintas variables del viñedo. En la presente tesis doctoral: i) se cuantifica la relación entre la información espectral obtenida de las imágenes y los parámetros del viñedo, ii) se implementan herramientas para establecer unidades de manejo diferenciado en viñedo, incluyendo aquellas derivadas de imágenes Sentinel-2, iii) se verifica que las diferencias se trasladan a los vinos elaborados de esas unidades diferenciadas, iv) las herramientas empleadas permiten monitorizar de manera dinámica los viñedos, v) son herramientas basadas en teledetección, accesibles para los productores y de bajo coste y vi) aportan conocimiento práctico, que puede ser empleado por el sector. Además, se refuerzan los resultados a nivel global dado que los experimentos incluyeron diversos cultivares de vid, en diferentes localidades y situaciones de cultivo. La idea más relevante de la presente tesis doctoral es que el gran reto de esta "era digital en la viticultura" es disponer de profesionales con la suficiente formación para aprovechar las enormes oportunidades que brinda este tipo de tecnología y ofrecer soluciones prácticas a los agricultores y viticultores.[EN] According to ESA (European Space Agency), remote sensing is a way of collecting and analysing data to obtain information about an object, without the instrument used to collect the data being in direct contact with said object. This tool has proven useful in a wide range of fields, including agriculture, where the use of multispectral imagery has become widespread and could become an important tool to manage vineyards and fight against climate change. Furthermore, these images can be used alone or combined with other data for better results, providing helpful information on the state of crops. Four elements are essential in remote sensing: a platform, a target object, a sensor, and a way to use and store the information obtained. Nowadays, there are several platforms for obtaining information, such as satellites, drones, aircraft, and ground vehicles. Thus, data will be obtained with different spatial, temporal, spectral and radiometric resolution characteristics depending on the platform and sensor. Consequently, the cost will be different depending on the technology used. Remote sensing applications in agriculture are a recognised innovation with increasing potential. This tool can be used for various applications in a wide range of fields. In agriculture, the available information can be processed using vegetation indices. Similarly, it is possible to use a single image at a specific moment of the phenological cycle (usually veraison, which is related to the maximum amount of vegetation), or it is also possible to use all available images and work with time series. In viticulture, research studies show that remote sensing techniques allow the assessment of vineyard (Vitis vinifera L.) variability and the control of grape quality and quantity. Remote sensing has been successfully used to estimate several vineyard parameters, such as leaf area index (LAI). In this PhD thesis, Sentinel-2 satellite imagery was used to check if they were related to the agronomic and oenological parameters of several vineyards located in the Appellation of Origin Rueda, Valladolid. For this purpose, a time series of images was analysed, confirming that the phenological stage of veraison is a good moment for the use of the images. Field data was taken in each vineyard, and it was found that the satellite images were able to classify the vineyards according to their vegetative development, finding significant differences in several agronomic and quality parameters. In addition, a similar experiment was carried out on pistachio to check the applicability of the method, observing significant differences in yield. Finally, Landsat-8 images were used on several vineyards in Galicia. Field data related to yeast populations was compared using NDVI as an indicator of the amount of vineyard vegetation. As a result, significant differences were found concerning the plots and NDVI. On the other hand, to study the effect of mixed pixels in vineyards, an experimental trial was carried out in a vineyard where vines were progressively removed. Thus, satellite pixels were marked on the surface, and the removals were synchronized with the Sentinel-2 satellites imagery. The effect of the reduction of vegetation on the spectral information captured by the satellites was analysed (using NDVI). Then, the removed vegetation was carefully measured in the laboratory to check the exact leaf area, finding that for a trellised vineyard, every 20% reduction in the amount of vegetation meant a reduction of around 6% in NDVI. Additionally, before each vine removal, orthophotographs were taken with UAV and multispectral cameras to develop a novel method for estimating the leaf area of the vineyard (LAI) using the shadows of the plants projected on the ground. The flight time was carefully planned to maximise shadows, enabling pilots not only to use a new low-cost method with similar accuracy to other more expensive methods but also by providing flexibility when carrying out the work, as with this new method, pilots do not need to fly the drone in the solar midday. Finally, two comprehensive field studies were conducted in separate vineyards: one in the DO Rueda and the other in the DO Ribera del Duero in Spain. A sampling grid was created to try to capture the spatial variability of the vineyards, and Sentinel-2 imagery taken over the course of one year was employed to construct a time series and apply a functional principal component analysis (f-PCA). The results show that the two principal components explain most of the variability in the vineyard, and that from the third component onwards, the relationship between the components and the field parameters is not clear. On the other hand, it was found that f-PCA allowed better results than solely a veraison image, and each principal component explained the variability caused by different variables in the vineyard. In this doctoral thesis: i) the relationship between the spectral information obtained from the images and the vineyard parameters is quantified, ii) tools are implemented to establish differentiated vineyard management units, including those derived from Sentinel-2 images, iii) it is verified that the differences are transferred to the wines produced from these differentiated units, iv) the tools allow dynamic monitoring of the vineyards, v) they are remote sensing-based tools accessible to producers and low cost, and vi) they provide knowledge and present a useful product for the sector. The great challenge of this "digital era in viticulture" is to have professionals with sufficient training to take advantage of the immense opportunities of this technology and to offer practical solutions to farmers and winegrowers