Evaluation of the Uncertainty in Satellite-Based Crop State Variable Retrievals Due to Site and Growth Stage Specific Factors and Their Potential in Coupling with Crop Growth Models

Coupling crop growth models and remote sensing provides the potential to improve our understanding of the genotype x environment x management (G X E X M) variability of crop growth on a global scale. Unfortunately, the uncertainty in the relationship between the satellite measurements and the crop state variables across different sites and growth stages makes it diffcult to perform the coupling. In this study, we evaluate the effects of this uncertainty with MODIS data at the Mead, Nebraska Ameriflux sites (US-Ne1, US-Ne2, and US-Ne3) and accurate, collocated Hybrid-Maize (HM) simulations of leaf area index (LAI) and canopy light use effciency (LUECanopy). The simulations are used to both explore the sensitivity of the satellite-estimated genotype X management (G X M) parameters to the satellite retrieval regression coeffcients and to quantify the amount of uncertainty attributable to site and growth stage specific factors. Additional ground-truth datasets of LAI and LUECanopy are used to validate the analysis. The results show that uncertainty in the LAI/satellite measurement regression coeffcients lead to large uncertainty in the G X Mparameters retrievable from satellites. In addition to traditional leave-one-site-out regression analysis, the regression coeffcient uncertainty is assessed by evaluating the retrieval performance of the temporal change in LAI and LUECanopy. The weekly change in LAI is shown to be retrievable with a correlation coeffcient absolute value (|r|) of 0.70 and root-mean square error (RMSE) value of 0.4, which is significantly better than the performance expected if the uncertainty was caused by random error rather than secondary effects caused by site and growth stage specific factors (an expected |r| value of 0.36 and RMSE value of 1.46 assuming random error). As a result, this study highlights the importance of accounting for site and growth stage specific factors in remote sensing retrievals for future work developing methods coupling remote sensing with crop growth models

Effects of Growth Stage Development on Paddy Rice Leaf Area Index Prediction Models

A in situ hyperspectral dataset containing multiple growth stages over multiple growing seasons was used to build paddy rice leaf area index (LAI) estimation models with a special focus on the effects of paddy rice growth stage development. The univariate regression method applied to the vegetation index (VI), the traditional multivariate calibration method of partial least squares regression (PLSR), and modern machine learning methods such as support vector regression (SVR), random forests (RF), and artificial neural networks (ANN) based on the original and first-derivative hyperspectral data were evaluated in this study for paddy rice LAI estimation. All the models were built on the whole growing season and on each separate vegetative, reproductive and ripening growth stage of paddy rice separately. To ensure a fair comparison, the models of the whole growing season were also validated on data for each separate growth stage of the standalone validation dataset. Moreover, the optimal band pairs for calculating narrowband difference vegetative index (DVI), normalized difference vegetation index (NDVI) and simple ratio vegetation index (SR) were determined for the whole growing season and for each separate growth stage separately. The results showed that for both the whole growing season and for each single growth stage, the red-edge and near-infrared band pairs are optimal for formulating the narrowband DVI, NDVI and SR. Among the four multivariate calibration methods, SVR and RF yielded more accurate results than the other two methods. The SVR and RF models built on first-derivative spectra provided more accurate results than the corresponding models on the original spectra for both whole growing season models and separate growth stage models. Comparing the prediction accuracy based on the whole growing season revealed that the RF and SVR models showed an advantage over the VI models. However, comparing the prediction accuracy based on each growth stage separately showed that the VI models provided more accurate results for the vegetative growth stages. The SVR and RF models provided more accurate results for the ripening growth stage. However, the whole growing season RF model on first-derivative spectra could provide reasonable accuracy for each single growth stage

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

Improving Retrievals of Crop Vegetation Parameters from Remote Sensing Data

Agricultural systems are difficult to model because crop growth is driven by the strongly nonlinear interaction of Genotype x Environment x Management (G x E x M) factors. Due to the nonlinearity in the interaction of these factors, the amount of data necessary to develop and utilize models to accurately predict the performance of agricultural systems at an operational scale is large. Satellite remote sensing provides the potential to vastly increase the amount of data available for modelling agricultural systems as a result of its high revisit time and spatial coverage. Unfortunately, there have been significant difficulties in deploying remote sensing for many agricultural modelling applications because of the uncertainty involved in the retrievals. In this dissertation, we show that collecting farmer-provided agro-managment information has the potential to reduce the uncertainty in the retrieval products obtained from remote sensing observations. Specifically, both field-scale and regional-scale analysis are used to show that secondary factor variability is a very significant cause of uncertainty in both crop growth modelling and agricultural remote sensing that needs to be addressed through increased data collection. In order to address this need for increased data availability, a method is developed that allows geolocated crop growth model simulations to be used to train satellite-based crop state variable retrievals, which is then validated at regional scale. The method developed provides a general robust methodology to create a large-scale platform that would allow farmers to share data with government agencies and universities to improve crop state variable retrievals and crop growth modelling and provide farmers, government, industry, and researchers with insights and predictive capability into crop growth at both field and regional scales