Retrieval of evapotranspiration from sentinel-2: Comparison of vegetation indices, semi-empirical models and SNAP biophysical processor approach

Remote sensing evapotranspiration estimation over agricultural areas is increasingly used for irrigation management during the crop growing cycle. Different methodologies based on remote sensing have emerged for the leaf area index (LAI) and the canopy chlorophyll content (CCC) estimation, essential biophysical parameters for crop evapotranspiration monitoring. Using Sentinel-2 (S2) spectral information, this studyperformeda comparative analysis of empirical (vegetation indices), semi-empirical (CLAIR model with fixed and calibrated extinction coefficient) and artificial neural network S2 products derived from the Sentinel Application Platform Software (SNAP) biophysical processor (ANN S2 products) approaches for the estimation of LAI and CCC. Four independent in situ collected datasets of LAI and CCC, obtained with standard instruments (LAI-2000, SPAD) and a smartphone application (PocketLAI), were used. The ANN S2 products present good statistics for LAI (R2 > 0.70, root mean square error (RMSE) 0.75, RMSE < 0.68 g/m2) retrievals. The normalized Sentinel-2 LAI index (SeLI) is the index that presents good statistics in each dataset (R2 > 0.71, RMSE < 0.78) and for the CCC, the ratio red-edge chlorophyll index (CIred-edge) (R2 > 0.67, RMSE < 0.62 g/m2). Both indices use bands located in the red-edge zone, highlighting the importance of this region. The LAI CLAIR model with a fixed extinction coefficient value produces a R2 > 0.63 and a RMSE < 1.47 and calibrating this coefficient for each study area only improves the statistics in two areas (RMSE 0.70). Finally, this study analyzed the influence of the LAI parameter estimated with the different methodologies in the calculation of crop potential evapotranspiration (ETc) with the adapted Penman–Monteith (FAO-56 PM), using a multi-temporal dataset. The results were compared with ETc estimated as the product of the reference evapotranspiration (ETo) and on the crop coefficient (Kc) derived fromFAO table values. In the absence of independent reference ET data, the estimated ETc with the LAI in situ values were considered as the proxy of the ground-truth. ETc estimated with the ANN S2 LAI product is the closest to the ETc values calculated with the LAI in situ (R2 > 0.90, RMSE < 0.41 mm/d). Our findings indicate the good validation of ANN S2 LAI and CCC products and their further suitability for the implementation in evapotranspiration retrieval of agricultural areas

Non-Parametric Statistical Approaches for Leaf Area Index Estimation from Sentinel-2 Data: A Multi-Crop Assessment

The leaf area index (LAI) is a key biophysical variable for agroecosystem monitoring, as well as a relevant state variable in crop modelling. For this reason, temporal and spatial determination of LAI are required to improve the understanding of several land surface processes related to vegetation dynamics and crop growth. Despite the large number of retrieved LAI products and the efforts to develop new and updated algorithms for LAI estimation, the available products are not yet capable of capturing site-specific variability, as requested in many agricultural applications. The objective of this study was to evaluate the potential of non-parametric approaches for multi-temporal LAI retrieval by Sentinel-2 multispectral data, in comparison with a VI-based parametric approach. For this purpose, we built a large database combining a multispectral satellite data set and ground LAI measurements collected over two growing seasons (2018 and 2019), including three crops (i.e., winter wheat, maize, and alfalfa) characterized by different growing cycles and canopy structures, and considering different agronomic conditions (i.e., at three farms in three different sites). The accuracy of parametric and non-parametric methods for LAI estimation was assessed by cross-validation (CV) at both the pixel and field levels over mixed-crop (MC) and crop-specific (CS) data sets. Overall, the non-parametric approach showed a higher accuracy of prediction at pixel level than parametric methods, and it was also observed that Gaussian Process Regression (GPR) did not provide any significant difference (p-value > 0.05) between the predicted values of LAI in the MC and CS data sets, regardless of the crop. Indeed, GPR at the field level showed a cross-validated coefficient of determination (R2CV) higher than 0.80 for all three crops

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

Monitoring rainfed alfalfa growth in semiarid agrosystems using Sentinel-2 imagery

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 (R 2 = 0.712), where as 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.Andres Echeverria was supported by a predoctoral fellowship from the Government of Navarra. This work was supported by the knowledge transfer contract 2018020023 UPNA-Bardenas Reales Committee with partial collaboration of the project PID2019-107386RB-I00/AEI/10.13039/ 501100011033 (MINECO/FEDER-UE)

Analysis of biophysical variables in an onion crop (Allium cepa L.) with nitrogen fertilization by sentinel-2 observations

The production of onions bulbs (Allium cepa L.) requires a high amount of nitrogen. Ac cording to the demand of sustainable agriculture, the information-development and communication technologies allow for improving the efficiency of nitrogen fertilization. In the south of the province of Buenos Aires, Argentina, between 8000 and 10,000 hectares per year−1 are cultivated in the districts of Villarino and Patagones. This work aimed to analyze the relationship of biophysical variables: leaf area index (LAI), canopy chlorophyll content (CCC), and canopy cover factor (fCOVER), with the nitrogen fertilization of an intermediate cycle onion crop and its effects on yield. A field trial study with different doses of granulated urea and granulated urea was carried out, where biophysical char acteristics were evaluated in the field and in Sentinel-2 satellite observations. Field data correlated well with satellite data, with an R2 of 0.91, 0.96, and 0.85 for LAI, fCOVER, and CCC, respectively. The application of nitrogen in all its doses produced significantly higher yields than the control. The LAI and CCC variables had a positive correlation with yield in the months of November and December. A significant difference was observed between U250 (62 Mg ha−1) and the other treatments. The U500 dose led to a yield increase of 27% compared to U250, while the difference between U750 and U500 was 6%.Fil: Casella, Alejandra. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Orden, Luciano. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina. Universidad Miguel Hernández. Centro de Investigación e Innovación Agroalimentaria y Agroambiental. GIAAMA Reseach Group; EspañaFil: Pezzola, Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Bellaccomo, Maria Carolina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Winschel, Cristina Ines. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Caballero, Gabriel. Technological University of Uruguay. Departamento de Montevideo, UruguayFil: Delegido, Jesús. Universidad de Valencia. Image Processing Laboratory (IPL); EspañaFil: Navas Gracia, Luis Manuel. Universidad de Valladolid. Departamento de Ingenieria Agrícola y Forestal; EspañaFil: Verrelst, Jochem. Universidad de Valencia. Image Processing Laboratory (IPL); Españ

Improving the remote estimation of soil organic carbon in complex ecosystems with Sentinel‑2 and GIS using Gaussian processes regression

Background and aims The quantitative retrieval of soil organic carbon (SOC) storage, particularly for soils with a large potential for carbon sequestration, is of global interest due to its link with the carbon cycle and the mitigation of climate change. However, complex ecosystems with good soil qualities for SOC storage are poorly studied. Methods The interrelation between SOC and various vegetation remote sensing drivers is understood to demonstrate the link between the carbon stored in the vegetation layer and SOC of the top soil layers. Based on the mapping of SOC in two horizons (0-30 cm and 30-60 cm) we predict SOC with high accuracy in the complex and mountainous heterogeneous páramo system in Ecuador. A large SOC database (in weight % and in Mg/ha) of 493 and 494 SOC sampling data points from 0-30 cm and 30-60 cm soil profiles, respectively, were used to calibrate GPR models using Sentinel-2 and GIS predictors (i.e., Temperature, Elevation, Soil Taxonomy, Geological Unit, Slope Length and Steepness (LS Factor), Orientation and Precipitation). Results In the 0-30 cm soil profile, the models achieved a R2 of 0.85 (SOC%) and a R2 of 0.79 (SOC Mg/ha). In the 30-60 cm soil profile, models achieved a R2 of 0.86 (SOC%), and a R2 of 0.79 (SOC Mg/ha). Conclusions The used Sentinel-2 variables (FVC, CWC, LCC/Cab, band 5 (705 nm) and SeLI index) were able to improve the estimation accuracy between 3-21% compared to previous results of the same study area. CWC emerged as the most relevant biophysical variable for SOC prediction

CubeSat constellations provide enhanced crop phenology and digital agricultural insights using daily leaf area index retrievals

Satellite remote sensing has great potential to deliver on the promise of a data-driven agricultural revolution, with emerging space-based platforms providing spatiotemporal insights into precisionlevel attributes such as crop water use, vegetation health and condition and crop response to management practices. Using a harmonized collection of high-resolution Planet CubeSat, Sentinel-2, Landsat-8 and additional coarser resolution imagery from MODIS and VIIRS, we exploit a multisatellite data fusion and machine learning approach to deliver a radiometrically calibrated and gap-filled time-series of daily leaf area index (LAI) at an unprecedented spatial resolution of 3 m. The insights available from such high-resolution CubeSat-based LAI data are demonstrated through tracking the growth cycle of a maize crop and identifying observable within-field spatial and temporal variations across key phenological stages. Daily LAI retrievals peaked at the tasseling stage, demonstrating their value for fertilizer and irrigation scheduling. An evaluation of satellite-based retrievals against field-measured LAI data collected from both rain-fed and irrigated fields shows high correlation and captures the spatiotemporal development of intra- and inter-field variations. Novel agricultural insights related to individual vegetative and reproductive growth stages were obtained, showcasing the capacity for new high-resolution CubeSat platforms to deliver actionable intelligence for precision agricultural and related applications

Validation of sentinel-2 leaf area index (LAI) product derived from SNAP toolbox and its comparison with global LAI products in an African semi-arid agricultural landscape

This study validated SNAP-derived LAI from Sentinel-2 and its consistency with existing global LAI products. The validation and intercomparison experiments were performed on two processing levels, i.e., Top-of-Atmosphere and Bottom-of-Atmosphere reflectances and two spatial resolutions, i.e., 10 m, and 20 m. These were chosen to determine their effect on retrieved LAI accuracy and consistency. The results showed moderate R2, i.e., ~0.6 to ~0.7 between SNAPderived LAI and in-situ LAI, but with high errors, i.e., RMSE, BIAS, and MAE &gt;2 m2 m–2 with marked differences between processing levels and insignificant differences between spatial resolutions. In contrast, inter-comparison of SNAP-derived LAI with MODIS and Proba-V LAI products revealed moderate to high consistencies, i. e., R2 of ~0.55 and ~0.8 respectively, and RMSE of ~0.5 m2 m–2 and ~0.6 m2 m–2, respectively. The results in this study have implications for future use of SNAP-derived LAI from Sentinel-2 in agricultural landscapes, suggesting its global applicability that is essential for large-scale agricultural monitoring. However, enormous errors in characterizing field-level LAI variability indicate that SNAP-derived LAI is not suitable for precision farming. In fact, from the study, the need for further improvement of LAI retrieval arises, especially to support farm-level agricultural management decisions

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

Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI

Space-based cropland phenology monitoring substantially assists agricultural managing practices and plays an important role in crop yield predictions. Multitemporal satellite observations allow analyzing vegetation seasonal dynamics over large areas by using vegetation indices or by deriving biophysical variables. The Nile Delta represents about half of all agricultural lands of Egypt. In this region, intensifying farming systems are predominant and multi-cropping rotations schemes are increasing, requiring a high temporal and spatial resolution monitoring for capturing successive crop growth cycles. This study presents a workflow for cropland phenology characterization and mapping based on time series of green Leaf Area Index (LAI) generated from NASA’s Harmonized Landsat 8 (L8) and Sentinel-2 (S2) surface reflectance dataset from 2016 to 2019. LAI time series were processed for each satellite dataset, which were used separately and combined to identify seasonal dynamics for a selection of crop types (wheat, clover, maize and rice). For the combination of L8 with S2 LAI products, we proposed two time series smoothing and fitting methods: (1) the Savitzky–Golay (SG) filter and (2) the Gaussian Processes Regression (GPR) fitting function. Single-sensor and L8-S2 combined LAI time series were used for the calculation of key crop Land Surface Phenology (LSP) metrics (start of season, end of season, length of season), whereby the detection of cropland growing seasons was based on two established threshold methods, i.e., a seasonal or a relative amplitude value. Overall, the developed phenology extraction scheme enabled identifying up to two successive crop cycles within a year, with a superior performance observed for the seasonal than for the relative threshold method, in terms of consistency and cropland season detection capability. Differences between the time series collections were analyzed by comparing the phenology metrics per crop type and year. Results suggest that L8-S2 combined LAI data streams with GPR led to a more precise detection of the start and end of growing seasons for most crop types, reaching an overall detection of 74% over the total planted crops versus 69% with S2 and 63% with L8 alone. Finally, the phenology mapping allowed us to evaluate the spatial and temporal evolution of the croplands over the agroecosystem in the Nile Delta.E.A. was supported by the predoctoral scholarship, grant number ACIF/2019/187, funded by the Generalitat Valenciana and co-funded by the European Social Fund. J.V. and S.B. were supported by the European Research Council (ERC) under the ERC-2017-STG SENTIFLEX project, grant number 755617. J.V. was additionally supported by a Ramón y Cajal Contract (Spanish Ministry of Science, Innovation and Universities). S.B. was additionally supported by the Generalitat Valenciana SEJIGENT program (SEJIGENT/2021/001) and European Union—NextGenerationEU (ZAMBRANO 21-04)