Eatidal Amin

Parc Científic de la Universitat de València

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Jochem Verrelst

Parc Científic de la Universitat de València

Santiago Belda

University of Alicante

Luca Pipia

Institut Cartogràfic i Geològic de Catalunya

J. Moreno

Parc Científic de la Universitat de València

Juan Pablo Rivera

Universidad Autónoma de Nayarit

Charlotte De Grave

Parc Científic de la Universitat de València

Katja Berger

Parc Científic de la Universitat de València

Miguel Morata

Max Planck Institute for Biogeochemistry

Pablo Reyes

Menlo School

Pablo Morcillo-Pallarés

Universitat Politècnica de València

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Universitat de València

Parc Científic de la Universitat de València

Ludwig-Maximilians-Universität München

Helmholtz Centre for Environmental Research

Universitat Politècnica de València

Agroscope

ETH Zurich

European Space Agency

Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences

University of Milan

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Mapping vegetation variables in Google Earth Engine using Gaussian Process Regression models.   

Matías Salinero Delgado Luca Pipia Eatidal Amin Santiago Belda Jochem Verrelst

The aim of ESA's forthcoming FLuorescence EXplorer (FLEX) is to achieve a global monitoring of the vegetation's chlorophyll fluorescence by means of an imaging spectrometer, FLORIS. For the retrieval of the fluorescence signal measured from space, other vegetation variables need to be retrieved simultaneously, such as (1) Leaf Area Index (LAI), (2) Leaf Chlorophyll content (Cab), and (3) Fractional Vegetation cover (FCover), among others. The undergoing SENTIFLEX ERC project has already demonstrated the feasibility to operationally infer these variables by hybrid retrieval approaches, which combine the generalization capabilities offered by radiative transfer models (RTMs) and computational efficiency of machine learning methods. Reflectance spectra corresponding to a large variety of canopy realizations served as input to train a Gaussian Process Regression (GPR) algorithm for each targeted variable. Following this approach, sets of GPR retrieval models have been trained for Sentinel-2 and -3 reflectance images.In that direction, we started to explore the potential of Google Earth Engine (GEE) to facilitate regional to global mapping. &#160;GEE is a platform with multi-petabyte satellite imagery catalog and geospatial datasets with planetary-scale analysis capabilities, which is freely available for scientific purposes. Among the different EO archives, it is possible to access the whole collection of Sentinel-2 ground reflectance data. In this work, we present the results of an efficient implementation of the GPR-based vegetation models developed for Sentinel-2 in the framework of SENSAGRI H2020 project in GEE. By taking advantage of GEE cloud-computing power, we are able to avoid the typical bottleneck of downloading and process large amounts of data locally and generate results of GPR-based retrieval models developed for Sentinel-2 in a fast and efficient way, covering large areas in matter of seconds. As a first step in that direction we present here an open web-based GEE application able to generate LAI Green and LAI Brown maps from Sentinel-2- imagery at 20m in a tile-wise manner all over the world, and time series of selected pixels during user-defined time interval.To illustrate this functionalities and have better understanding of the phenology, we targeted a region in Castilla y Le&#243;n (Spain) from where we will present results for 2018 classified per crop type. This land cover classification was generated by the ITACYL (Instituto Tecnol&#243;gico Agrario de Castilla y Le&#243;n) during SENSAGRI.Future development will tackle the possibility to extend our analysis capability to additional variables, such as FCover and Cab, maintaining the computational efficiency as the main driver to ensure that the GEE application continues to be an agile and easy tool for spatiotemporal Earth observation studies.

Earth observation

Photochemical Reflectance Index

10.5194/egusphere-egu21-12359

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Gaussian Process Regression hyperparameter optimization for image time series gap-filling of Earth observation data and crop monitoring

Santiago Belda Matías Salinero Eatidal Amin Luca Pipia Pablo Morcillo-Pallarés

In general, modeling phenological evolution represents a challenging task mainly because of time series gaps and noisy data, coming from different viewing and illumination geometries, cloud cover, seasonal snow and the interval needed to revisit and acquire data for the exact same location. For that reason, the use of reliable gap-filling fitting functions and smoothing filters is frequently required for retrievals at the highest feasible accuracy. Of specific interest to filling gaps in time series is the emergence of machine learning regression algorithms (MLRAs) which can serve as fitting functions. Among the multiple MLRA approaches currently available, the kernel-based methods developed in a Bayesian framework deserve special attention because of both being adaptive and providing associated uncertainty estimates, such as Gaussian Process Regression (GPR).Recent studies demonstrated the effectiveness of GPR for gap-filling of biophysical parameter time series because the hyperparameters can be optimally set for each time series (one for each pixel in the area) with a single optimization procedure. The entire procedure of learning a GPR model only relies on appropriate selection of the type of kernel and the hyperparameters involved in the estimation of input data covariance. Despite its clear strategic advantage, the most important shortcomings of this technique are the (1) high computational cost and (2) memory requirements of their training, which grows cubically and quadratically with the number of model&#8217;s samples, respectively. This can become problematic in view of processing a large amount of data, such as in Sentinel-2 (S2) time series tiles. Hence, optimization strategies need to be developed on how to speed up the GPR processing while maintaining the superior performance in terms of accuracy.To mitigate its computational burden and to address such shortcoming and repetitive procedure, we evaluated whether the GPR hyperparameters can be preoptimized over a reduced set of representative pixels and kept fixed over a more extended crop area. We used S2 LAI time series over an agricultural region in Castile and Leon (North-West Spain) and testing different functions for Covariance estimation such as exponential Kernel, Squared exponential kernel and matern kernel with parameter 3/2 or 5/2. The performance of image reconstructions was compared against the standard per-pixel GPR time series training process. Results showed that accuracies were on the same order (12% RMSE degradation) whereas processing time accelerated up to 90 times. Crop phenology indicators were also calculated and compared, revealing similar temporal patterns with differences in start and end of growing season of no more than five days. To the benefit of crop monitoring applications, all the gap-filling and phenology indicators retrieval techniques have been implemented into the freely downloadable GUI toolbox DATimeS (Decomposition and Analysis of Time Series Software - https://artmotoolbox.com/).

Hyperparameter

Bayesian Optimization

Smoothing

Hyperparameter Optimization

Kernel (algebra)

10.5194/egusphere-egu21-14322

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APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO EVALUATE WELD DEFECTS OF NUCLEAR COMPONENTS

Eatidal Amin

Artificial neural networks (ANNs) are computational representations based on the biological neural architecture of the brain. ANNs have been successfully applied to a wide range of engineering and scientific applications, such as signal, image processing and data analysis. Although Radiographic testing is widely used for welding defects, it is unsuccessful in identifying some welding defects because of the nature of image formation and quality. Neoteric algorithms have been used for the purpose of weld defects identifications in radiographic images to replace the expert knowledge. The application of artificial neural networks in noise detection of radiographic films is used. Radial Basis (RB) and learning vector quantization (LVQ) were applied. The method shows good performance in weld defects recognition and classification problems.

Learning vector quantization

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Prototyping Sentinel-2 green LAI and brown LAI products for cropland monitoring

Remote Sensing of Environment (2020)

Eatidal Amin Jochem Verrelst Juan Pablo Rivera Luca Pipia Antonio Ruíz-Verdú

10.1016/j.rse.2020.112168

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Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes

Remote Sensing of Environment (2019)

Luca Pipia Jordi Muñoz-Marı́Eatidal Amin Santiago Belda Gustau Camps‐Valls

10.1016/j.rse.2019.111452

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

Remote Sensing (2022)

Eatidal Amin Santiago Belda Luca Pipia Zoltan Szantoi Ahmed A. El Baroudy

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.

Nile delta

10.3390/rs14081812

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Citations (20)

The Sensagri Sentinel-2 Lai Green and Brown Product: from Algorithm Development Towards Operational Mapping

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (2018)

Eatidal Amin Jochem Verrelst Juan Pablo Rivera Nieves Pasqualotto Jesús Delegido

While the mapping of LAI green (LAI _G ) is well established, current operational products are not calibrated for LAI brown (LAI _B ), i.e. LAI estimation over senescent vegetation. With Sentinel-2 (S2) new opportunities are opened to estimate LAI brown. An explicit distinction between LAI _G and LAI _B can be achieved thanks to the S2 bands in the red edge (B5: 705 nm and B6: 740 nm) and in the shortwave infrared (B11: 1610 nm). By using LAI ground measurements data from multiple campaigns together with available S2 data, independent LAI _G and LAI _B models were optimized using Gaussian processes regression (LAI ^G : R ² = 0.89, NRMSE= 7.1%; LAI _B : R ² = 0.75, NRMSE= 13.7%). These models can then be combined into LAI _GB composite maps. The uncertainty estimates were used to map only those LAI estimated values that fall within a 50% uncertainty threshold. As only the vegetated areas fall within that threshold there is no need to apply additional masks. For multiple European core test sites, LAI _GB composite maps were generated from S2 images, enabling to quantify when crops start senescing across the European regions.

10.1109/igarss.2018.8518938

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Untangling the Causal Links between Satellite Vegetation Products and Environmental Drivers on a Global Scale by the Granger Causality Method

Remote Sensing (2023)

Dávid Kovács Eatidal Amin Katja Berger Pablo Reyes-Muñoz Jochem Verrelst

The Granger Causality (GC) statistical test explores the causal relationships between different time series variables. By employing the GC method, the underlying causal links between environmental drivers and global vegetation properties can be untangled, which opens possibilities to forecast the increasing strain on ecosystems by droughts, global warming, and climate change. This study aimed to quantify the spatial distribution of four distinct satellite vegetation products’ (VPs) sensitivities to four environmental land variables (ELVs) at the global scale given the GC method. The GC analysis assessed the spatially explicit response of the VPs: (i) the fraction of absorbed photosynthetically active radiation (FAPAR), (ii) the leaf area index (LAI), (iii) solar-induced fluorescence (SIF), and, finally, (iv) the normalized difference vegetation index (NDVI) to the ELVs. These ELVs can be categorized as water availability assessing root zone soil moisture (SM) and accumulated precipitation (P), as well as, energy availability considering the effect of air temperature (T) and solar shortwave (R) radiation. The results indicate SM and P are key drivers, particularly causing changes in the LAI. SM alone accounts for 43%, while P accounts for 41%, of the explicitly caused areas over arid biomes. SM further significantly influences the LAI at northern latitudes, covering 44% of cold and 50% of polar biome areas. These areas exhibit a predominant response to R, which is a possible trigger for snowmelt, showing more than 40% caused by both cold and polar biomes for all VPs. Finally, T’s causality is evenly distributed amongst all biomes with fractional covers between ∼10 and 20%. By using the GC method, the analysis presents a novel way to monitor the planet’s ecosystem, based on solely two years as input data, with four VPs acquired by the synergy of Sentinel-3 (S3) and 5P (S5P) satellite data streams. The findings indicated unique, biome-specific responses of vegetation to distinct environmental drivers.

Biome

Shortwave radiation

Photosynthetically active radiation

10.3390/rs15204956

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In-season forecasting of within-field grain yield from Sentinel-2 time series data

International Journal of Applied Earth Observation and Geoinformation (2023)

Eatidal Amin Luca Pipia Santiago Belda Gregor Perich Lukas Valentin Graf

Precise knowledge of cropland productivity is relevant for farmers to enable optimizing managing practices; particularly with the perspective of anticipating crop yield ahead of harvest. The current availability of high spatiotemporal resolution Sentinel-2 satellite data offers a unique opportunity to monitor croplands over time. In this context, the recently introduced kernel NDVI (kNDVI) statistically optimizes the conventional NDVI formulation by applying a nonlinear function to the involved bands, and so maximizes the spectral information extraction. This study proposes a workflow for within-field yield forecasting from Sentinel-2 kNDVI time series analysis focusing on winter cereal croplands in Switzerland over three years, comparing with NDVI as baseline. For a temporally continuous modelling of crop yields, Gaussian Process Regression (GPR) was applied to reconstruct cloud-free time series of the complete crop growing seasons. Following, distinct machine learning regression models (GPR, Kernel Ridge Regression and Random Forest) were developed to forecast yield at any point in time throughout the cropland growing season. The integration of Growing Degree Days (GDD) information as temporal spacing reference of the time series considerably improved the accuracy and consistency of in-season yield forecasting. Training and testing within the same year demonstrated that yield can be accurately forecast approximately 2–2.5 months ahead of harvest, at crops' anthesis (flowering) phase, with an RMSE up to 0.71 t/ha and a relative RMSE of 7.60%. Although the forecasting accuracy of the models decreased when predicting yield for the unseen years, still satisfactory results were obtained: RMSE = 0.97 t/ha, relative RMSE = 11.47%.

Growing degree-day

Growing season

10.1016/j.jag.2023.103636

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Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine

Remote Sensing (2021)

Luca Pipia Eatidal Amin Santiago Belda Matías Salinero-Delgado Jochem Verrelst

For the last decade, Gaussian process regression (GPR) proved to be a competitive machine learning regression algorithm for Earth observation applications, with attractive unique properties such as band relevance ranking and uncertainty estimates. More recently, GPR also proved to be a proficient time series processor to fill up gaps in optical imagery, typically due to cloud cover. This makes GPR perfectly suited for large-scale spatiotemporal processing of satellite imageries into cloud-free products of biophysical variables. With the advent of the Google Earth Engine (GEE) cloud platform, new opportunities emerged to process local-to-planetary scale satellite data using advanced machine learning techniques and convert them into gap-filled vegetation properties products. However, GPR is not yet part of the GEE ecosystem. To circumvent this limitation, this work proposes a general adaptation of GPR formulation to parallel processing framework and its integration into GEE. To demonstrate the functioning and utility of the developed workflow, a GPR model predicting green leaf area index (LAI

10.3390/rs13030403

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Citations (59)