Estimation of key biophysical parameters related to crop stress through new remote sensors and multi-crop in situ data

El monitoreo de los cultivos a lo largo de toda la etapa de crecimiento es esencial para detectar anomalías y optimizar los costes y recursos del sector agrícola. Las variables biofísicas de la vegetación, principalmente el contenido en agua, el índice de área foliar o la clorofila, están consideradas indicadores importantes de la salud, crecimiento y productividad de la vegetación. Además, estos parámetros biofísicos no solo son importantes por proporcionar información por sí mismos del estado fisiológico de la vegetación, también porque son parámetros clave de entrada en importantes modelos agronómicos. La medida directa de estos parámetros biofísicos sobre el terreno requiere elevados recursos económicos y de tiempo, por lo que es necesaria una metodología de estimación alternativa. La detección remota a través de satélites y sensores aerotransportados se ha convertido en una técnica comúnmente utilizada para el monitoreo de los cultivos debido a su capacidad de adquirir información a diferentes escalas, tanto espaciales como temporales. Para una óptima monitorización de la agricultura a través de teledetección, la resolución espacial debe ser al menos de 20 m y, preferiblemente, de 10 m, con el fin de hacer posible la gestión de áreas específicas. En cuanto a la resolución temporal, es necesaria una resolución menor a una semana para detectar cambios en las condiciones de los cultivos y proporcionar una respuesta eficaz. Actualmente está cobrando especial importancia en los estudios agronómicos la serie de satélites Sentinel-2 del programa Copernicus, de la Agencia Espacial Europea (ESA). Sentinel-2 es una constelación de satélites, de los cuales actualmente están en órbita los satélites Sentinel-2A y Sentinel-2B. Juntos proporcionan un periodo de revisita de 5 días de la superficie de la Tierra con un tamaño de píxeles de 10, 20 y 60 m. Sentinel-2A y Sentinel-2B llevan a bordo un sensor idéntico, denominado Multi-Spectral Imager (MSI), que abarca la región del espectro comprendida entre 443 nm y 2190 nm mediante 13 bandas localizadas en las regiones espectrales del visible – VIS (440 – 690 nm), del infrarrojo cercano - NIR (750 – 1300 nm) y del infrarrojo de onda corta - SWIR (1300 – 2500 nm). Por lo tanto, la misión Sentinel-2 mejora la resolución temporal, espacial y espectral de los datos de teledetección, en comparación con otras misiones operativas multiespectrales anteriores, como Landsat, ofreciendo grandes oportunidades para la monitorización agrícola. Además, actualmente la ESA ha incorporado, en las imágenes de Sentinel-2 corregidas atmosféricamente (Nivel 2A), productos de parámetros biofísicos, como el LAI y el CCC, proporcionados a través del programa SNAP (Sentinel Application Platform). Estos parámetros son productos automáticos obtenidos a través de una red neuronal artificial (ANN), calibrada con bases de datos simuladas generadas a través de modelos de transferencia radiativa (RTMs). Por otro lado, también están cobrando especial importancia los sensores hiperespectrales, que pueden estar transportados por satélites como las futuras misiones EnMAP - Environmental Mapping and Analysis Program y PRISMA - PRecursore IperSpettrale della Missione Applicativa, o aerotransportados a través de aviones de manera puntual sobre la zona de estudio correspondiente, como AVIRIS - Airborne Visible/Infrared Imaging Spectrometer y HyMap. Este tipo de sensores permiten identificar y discriminar con gran precisión la superficie terrestre, gracias a su alta resolución espectral, permitiendo la detección de anomalías con alta precisión. Hay que destacar especialmente la futura misión hiperespectral FLEX, de la cual la ESA está realizando el desarrollo científico actualmente. Esta misión presenta como principal objetivo la observación de la fluorescencia de las plantas desde el espacio. La medida de la fluorescencia proporciona información directa del proceso de fotosíntesis, constituyendo una novedosa herramienta para la rápida detección del estrés en la vegetación, antes de que el daño sea irreversible. En este contexto, todas las metodologías desarrolladas para estimar parámetros biofísicos indicadores del estado fisiológico de la vegetación son, por tanto, fundamentales para entender el funcionamiento de dicha fluorescencia. De manera general, existen tres tipos de metodologías para estimar los parámetros biofísicos a partir de datos obtenidos por teledetección: (1) métodos empíricos, que consisten en relacionar el parámetro biofísico de interés con la información espectral a través de relaciones simples (p.ej. índices de vegetación – VIs), (2) métodos estadísticos, los cuales definen funciones de regresión más complejas a través de la información espectral (p.ej. ANN), y (3) métodos físicos, que se refieren a la inversión de RTMs. Existen numerosas aportaciones científicas de estimación de parámetros biofísicos a través de sensores remotos. Pero la gran mayoría de estas aportaciones están enfocadas a un solo tipo o a un número reducido de tipos de cultivo. El reto surge cuando se quieren utilizar estas técnicas de teledetección en un contexto general, es decir, aplicables a numerosos tipos de cultivos. Esto es realmente importante ya que las zonas agrícolas suelen presentar heterogeneidad de cultivos. Esta tesis intenta conseguir técnicas con ese carácter general de los tres indicadores de la salud de la vegetación analizados en el contexto de esta tesis: contenido en agua (CWC), índice de área foliar (LAI) y contenido en clorofila (CCC). Las metodologías finalmente propuestas deben presentar base física y producir buenos estadísticos para un rango amplio de cultivos.Evidence suggests that human-induced greenhouse gases emissions have altered our climate at a relatively rapid rate, rising the global temperatures and inducing drastic changes in precipitation patterns to water-limited environments and agricultural areas, restricting crop yield, production rates and food availability. Biophysical parameters, such as leaf water content (LWC), leaf area index (LAI) or leaf chlorophyll content (LCC), are considered important indicators of health, growth and productivity of crops. As they define the status of the vegetation, they provide important inputs to models quantifying the exchange of energy and matter between the land surface and the atmosphere. Also, knowledge of their spatial and temporal distribution is highly useful for regional or global-scale applications related to crop monitoring, weather prediction and climate change studies. The direct field measurements of biophysical parameters require continuous updates and can be extremely time-consuming and expensive, therefore, an alternative estimation methodology is necessary. Remote sensing from satellite and airborne sensors has become a commonly used technique for monitoring agricultural areas due to its ability to acquire synoptic information at different times and spatial scales. For an optimal agricultural monitoring by remote sensing, the spatial resolution should be at least 20 m and, preferably, 10 m, and a temporal resolution of less than a week, in order to follow-up acute changes in the crop condition and provide a timely response in management practices. In this context, the Sentinel-2 (S2) missions from the European Space Agency (ESA) Copernicus program respond to such operational requirements. S2 is a constellation of satellites, with currently the Sentinel-2A (S2A) and Sentinel-2B (S2B) satellites in orbit. Together, they provide a 5-day nominal revisit, at the Equator, of the Earth’s land surfaces with a 10, 20 and 60 m of pixel size. S2A and S2B carry on-board a virtually identical sensor, the Multi-Spectral Imager (MSI), covering a spectral range from 443 to 2190 nm through 13 bands located in the visible (VIS, 440 – 690 nm), the near-infrared (NIR, 750 – 1300 nm) and the shortwave-infrared (SWIR, 1300 – 2500 nm) spectral regions. With the narrow band configurations specifically located for vegetation monitoring, the S2 missions improve the temporal, spatial and spectral resolution of remote sensing data, compared to other multi-spectral missions, such as Landsat, and offers great opportunities for agricultural monitoring. The mission’s main objective is providing quality information for agricultural and forestry practices and, hence, helping management and food security applications. In addition, ESA has incorporated a user-friendly Biophysical Processor toolbox within the SNAP (Sentinel Application Platform) program, for the straightforward delivering of biophysical parameter products, such as LAI and canopy chlorophyll content (CCC). These parameters are automatic products, associated with a quality indicator, produced through an artificial neural network (ANN) which has been trained with simulated spectra generated from well-known radiative transfer models (RTMs), i.e., physically-based models that describe the absorption and scattering of light throughout the leaf, canopy and atmosphere. Only eight bands are used (B3, B4, B5, B6, B7, B8a, B11 and B12) for the biophysical parameter products estimation. This way, the values of biophysical parameters can be obtained in any study area with available S2 images, being very useful in operational agronomic studies. On the other hand, hyperspectral sensors are becoming more and more relevant, which will be available by satellites such as the future EnMAP (Environmental Mapping and Analysis Program) mission or the recently launched PRISMA (PRecursore IperSpettrale della Missione Applicativa) satellite, or through airplanes punctually over the corresponding study area, such as AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) and HyMap (Hyperspectral Mapper) sensors. This type of sensors allows to identify and to discriminate with great precision the surface, thanks to its high spectral resolution, allowing the detection of anomalies with precision. The future FLEX (Fluorescence Explorer) mission should also be specially highlighted, of which ESA is currently carrying out scientific development. The main objective of FLEX mission is to observe the vegetation functioning from space, based on the emitted fluorescence signal. The fluorescence measurement provides direct information of the photosynthesis process, constituting a novel tool for the rapid detection of vegetation stress, before damage is irreversible. In this context, all the methodologies developed to estimate biophysical parameters that are physiological state indicators are, therefore, fundamental to understand the behaviour of terrestrial vegetation at the global scale. In general, there are three approaches for estimating biophysical parameters from remotely sensed data, i.e., (1) empirical retrieval methods, which consist of relating the biophysical parameter of interest against spectral data by means of simple relations (e.g., vegetation indices—VIs), (2) statistical methods, which define complex regression functions according to information from remote sensing data (e.g., artificial neural network—ANN) and (3) physically-based retrieval methods, which typically refers to the inversion of RTMs. There are numerous scientific contributions related to the biophysical parameters estimation through remote sensing, but most of these studies focus on a single or a small number of crop types. The challenge arises when these remote sensing techniques are applied in a general context, i.e. for a high diversity in crop types. This is important as agricultural areas are often composed of multi-crop types but also because the retrieval algorithms should be robust on a global scale. This Thesis attempts to achieve techniques to assess the general character of three important vegetation health indicators for crop monitoring: canopy water content (CWC), LAI and CCC. The methodologies finally proposed aim to present a physical basis for applied crop monitoring and produce accurate results for a wide range of crop types

Mapping Productivity and Essential Biophysical Parameters of Cultivated Tropical Grasslands from Sentinel-2 Imagery

Nitrogen (N) is the main nutrient element that maintains productivity in forages; it is inextricably linked to dry matter increase and plant support capacity. In recent years, high spectral and spatial resolution remote sensors, e.g., the European Space Agency (ESA)'s Sentinel satellite missions, have become freely available for agricultural science, and have proven to be powerful monitoring tools. The use of vegetation indices has been essential for crop monitoring and biomass estimation models. The objective of this work is to test and demonstrate the applicability of different vegetation indices to estimate the biomass productivity, the foliar nitrogen content (FNC), the plant height and the leaf area index (LAI) of several tropical grasslands species submitted to different nitrogen (N) rates in an experimental area of São Paulo, Brazil. Field reflectance data of Panicum maximum and Urochloa brizantha species' cultivars were taken and convoluted to the Sentinel-2 satellite bands. Subsequently, different vegetation indices (Normalized Difference Vegetation Index (NDI), Three Band Index (TBI), Difference light Height (DLH), Three Band Dall'Olmo (DO), and Normalized Area Over reflectance Curve (NAOC)) were tested for the experimental grassland areas, and composed of Urochloa decumbens and Urochloa brizantha grass species, which were sampled and destructively analyzed. Our results show the use of different relevant Sentinel-2 bands in the visible (VIS)-near infrared (NIR) regions for the estimation of the different biophysical parameters. The FNC obtained the best correlation for the TBI index combining blue, green and red bands with a determination coefficient (R2) of 0.38 and Root Mean Square Error (RMSE) of 3.4 g kg−1. The estimation of grassland productivity based on red-edge and NIR bands showed a R2 = 0.54 and a RMSE = 1800 kg ha−1. For the LAI, the best index was the NAOC (R2 = 0.57 and RMSE = 1.4 m2 m−2). High values of FNC, productivity and LAI based on different sets of Sentinel-2 bands were consistently obtained for areas under N fertilization

Retrieval of canopy water content of different crop types with two new hyperspectral indices: Water Absorption Area Index and Depth Water Index

Crop canopy water content (CWC) is an essential indicator of the crop's physiological state. While a diverse range of vegetation indices have earlier been developed for the remote estimation of CWC, most of them are defined for specific crop types and areas, making them less universally applicable. We propose two new water content indices applicable to a wide variety of crop types, allowing to derive CWC maps at a large spatial scale. These indices were developed based on PROSAIL simulations and then optimized with an experimental dataset (SPARC03; Barrax, Spain). This dataset consists of water content and other biophysical variables for five common crop types (lucerne, corn, potato, sugar beet and onion) and corresponding top-of-canopy (TOC) reflectance spectra acquired by the hyperspectral HyMap airborne sensor. First, commonly used water content index formulations were analysed and validated for the variety of crops, overall resulting in a R2 lower than 0.6. In an attempt to move towards more generically applicable indices, the two new CWC indices exploit the principal water absorption features in the near-infrared by using multiple bands sensitive to water content. We propose the Water Absorption Area Index (WAAI) as the difference between the area under the null water content of TOC reflectance (reference line) simulated with PROSAIL and the area under measured TOC reflectance between 911 and 1271 nm. We also propose the Depth Water Index (DWI), a simplified four-band index based on the spectral depths produced by the water absorption at 970 and 1200 nm and two reference bands. Both the WAAI and DWI outperform established indices in predicting CWC when applied to heterogeneous croplands, with a R2 of 0.8 and 0.7, respectively, using an exponential fit. However, these indices did not perform well for species with a low fractional vegetation cover (<30%). HyMap CWC maps calculated with both indices are shown for the Barrax region. The results confirmed the potential of using generically applicable indices for calculating CWC over a great variety of crops