A sensor invariant atmospheric correction method for satellite images

Land surface reflectance is the fundamental variable for the most of earth observation (EO) missions, and corrections of the atmospheric disturbs from the cloud, gaseous, aerosol help to get accurate spectral description of earth surface. Unlike the previous empirical ways of atmospheric correction, we propose a data fusion method for atmospheric correction of satellite images, with an initial attempt to include the uncertainty information from different data source. It takes advantage of the high temporal resolution of MODIS observations to get BRDF description of the earth surface as the prior information of the earth surface property, uses the ECMWF CAMS Near-real-time as the prior information of the atmospheric sates, to get optimal estimations of the atmospheric parameters. It guarantees the correction is consistent cross different satellites image tiles and even cross different sensors. The validations against the AERONET sites are also show high correlation at around 0.9, with a RMSE of about 0.02

Combining multitemporal optical and SAR data for LAI imputation with BiLSTM network

The Leaf Area Index (LAI) is vital for predicting winter wheat yield. Acquisition of crop conditions via Sentinel-2 remote sensing images can be hindered by persistent clouds, affecting yield predictions. Synthetic Aperture Radar (SAR) provides all-weather imagery, and the ratio between its cross- and co-polarized channels (C-band) shows a high correlation with time series LAI over winter wheat regions. This study evaluates the use of time series Sentinel-1 VH/VV for LAI imputation, aiming to increase spatial-temporal density. We utilize a bidirectional LSTM (BiLSTM) network to impute time series LAI and use half mean squared error for each time step as the loss function. We trained models on data from southern Germany and the North China Plain using only LAI data generated by Sentinel-1 VH/VV and Sentinel-2. Experimental results show BiLSTM outperforms traditional regression methods, capturing nonlinear dynamics between multiple time series. It proves robust in various growing conditions and is effective even with limited Sentinel-2 images. BiLSTM's performance surpasses that of LSTM, particularly over the senescence period. Therefore, BiLSTM can be used to impute LAI with time-series Sentinel-1 VH/VV and Sentinel-2 data, and this method could be applied to other time-series imputation issues

Simulating arbitrary hyperspectral bandsets from multispectral observations via a generic Earth Observation-Land Data Assimilation System (EO-LDAS)

This paper presents results of using multi-sensor and multi-angular constraints in the generic Earth Observation-Land Data Assimilation System (EO-LDAS) for reproducing arbitrary bandsets of hyperspectral reflectance at the top-of-canopy (TOC) level by merging observations from multispectral sensors with different spectral characteristics. This is demonstrated by combining Multi-angle Imaging Spectroradiometer (MISR) and Landsat Enhanced Thematic Mapper Plus (ETM+) data to simulate the Compact High Resolution Imaging Spectrometer CHRIS/PROBA hyperspectral signal over an agricultural test site, in Barrax, Spain. However, the method can be more generally applied to any combination of spectral data, providing a tool for merging EO data to any arbitrary hyperspectral bandset. Comparisons are presented using both synthetic and observed MISR and Landsat data, and retrieving surface biophysical properties. We find that when using simulated MISR and Landsat data, the CHRIS/PROBA hyperspectral signal is reproduced with RMSE 0.0001– 0.04. LAI is retrieved with r2 from 0.97 to 0.99 and RMSE of from 0.21 to 0.38. The results based on observed MISR and Landsat data have lower performances, with RMSE for the reproduced CHRIS/PROBA hyperspectral signal varying from 0.007 to 0.2. LAI is retrievedwith r2 from 0.7 to 0.9 and RMSE from 0.7 to 1.4. We found that for the data considered here the main spectral variations in the visible and near infrared regions can be described by a limited number of parameters (3–4) that can be estimated from multispectral information. Results show that the method can be used to simulate arbitrary bandsets, which will be of importance to any application which requires combining new and existing streams of new EO data in the optical domain, particularly intercalibration of EO satellites in order to get continuous time series of surface reflectance, across programmes and sensors of different designs

Uncertainty characterization & validation within ESA Fire-CCI

Uncertainty characterisation and validation are critical phases to generate any Essential Climate Variable (ECV), and therefore both have been included as key deliverables of the ESA CCI programme [1]. All products generated by the CCI are required to have an associated per pixel uncertainty characterisation. This paper describes both the uncertainty characterisation framework and the related uncertainty validation exercise of the Fire-CCI projectinfo:eu-repo/semantics/publishedVersio

Uncertainty for Burnt Area Products

Burnt area (BA) products are usually provided as a binary mask, indicating whether within a particular time interval, a pixel has or has not burnt. However, this is an inference derived from assessing e.g. the change in reflectance due to the fire. These calculations are prone to uncertainty from a number of sources: thermal noise in the sensor, residual atmospheric correction shortcomings or insufficient temporal sampling, etc. In this contribution, we aim to provide a framework for uncertainty characterisation of BA products. The uncertainty framework is Bayesian in nature, and provides a way to propagate uncertainty from the observations, across scales, but also allows one to propagate uncertainty in algorithm parameterisation. We illustrate the framework with a simple example based on logistic regression. Finally, we discuss how the uncertainty at the pixel level can be aggregated to the climate modeller grid (CMG), providing a consistent way to treat uncertainty from the observations and algorithm parameters to the final product

Land Surface Processes Analysis Using Sentinel-3 OLCI and Modis Data

This communication describes the optical processing chain to use Sentinel-3 OLCI and MODIS data as part of the ESA funded Synergy project of the Scientific Exploitation of Sentinel Missions (SEOM) component of the EO Envelope programme. One of the goals of the project is to use Data Assimilation techniques to produce land surface products combining the data from Sentinels-2 and 3. Some of the derived products are the OLCI atmospherically corrected data that can be used to generate a spectral BRDF product from OLCI and MODIS, broadband albedo and different vegetation parameters. The project also implements a series of efficiency improvements to the algorithms to speed up the processing. The demonstrator product uses one year of OLCI and MODIS data (2017)

A Survey on Gaussian Processes for Earth-Observation Data Analysis: A Comprehensive Investigation

Gaussian processes (GPs) have experienced tremendous success in biogeophysical parameter retrieval in the last few years. GPs constitute a solid Bayesian framework to consistently formulate many function approximation problems. This article reviews the main theoretical GP developments in the field, considering new algorithms that respect signal and noise characteristics, extract knowledge via automatic relevance kernels to yield feature rankings automatically, and allow applicability of associated uncertainty intervals to transport GP models in space and time that can be used to uncover causal relations between variables and can encode physically meaningful prior knowledge via radiative transfer model (RTM) emulation. The important issue of computational efficiency will also be addressed. These developments are illustrated in the field of geosciences and remote sensing at local and global scales through a set of illustrative examples. In particular, important problems for land, ocean, and atmosphere monitoring are considered, from accurately estimating oceanic chlorophyll content and pigments to retrieving vegetation properties from multi- and hyperspectral sensors as well as estimating atmospheric parameters (e.g., temperature, moisture, and ozone) from infrared sounders

An Earth Observation Land Data Assimilation System (EO-LDAS)

Current methods for estimating vegetation parameters are generally sub-optimal in the way they exploit information and do not generally consider uncertainties. We look forward to a future where operational data assimilation schemes improve estimates by tracking land surface processes and exploiting multiple types of observations. Data assimilation schemes seek to combine observations and models in a statistically optimal way taking into account uncertainty in both, but have not yet been much exploited in this area. The EO-LDAS scheme and prototype, developed under ESA funding, is designed to exploit the anticipated wealth of data that will be available under GMES missions, such as the Sentinel family of satellites, to provide improved mapping of land surface biophysical parameters. This paper describes the EO-LDAS implementation, and explores some of its core functionality. EO-LDAS is a weak constraint variational data assimilation system. The prototype provides a mechanism for constraint based on a prior estimate of the state vector, a linear dynamic model, and Earth Observation data (top-of-canopy reflectance here). The observation operator is a non-linear optical radiative transfer model for a vegetation canopy with a soil lower boundary, operating over the range 400 to 2500 nm. Adjoint codes for all model and operator components are provided in the prototype by automatic differentiation of the computer codes. In this paper, EO-LDAS is applied to the problem of daily estimation of six of the parameters controlling the radiative transfer operator over the course of a year (> 2000 state vector elements). Zero and first order process model constraints are implemented and explored as the dynamic model. The assimilation estimates all state vector elements simultaneously. This is performed in the context of a typical Sentinel-2 MSI operating scenario, using synthetic MSI observations simulated with the observation operator, with uncertainties typical of those achieved by optical sensors supposed for the data. The experiments consider a baseline state vector estimation case where dynamic constraints are applied, and assess the impact of dynamic constraints on the a posteriori uncertainties. The results demonstrate that reductions in uncertainty by a factor of up to two might be obtained by applying the sorts of dynamic constraints used here. The hyperparameter (dynamic model uncertainty) required to control the assimilation are estimated by a cross-validation exercise. The result of the assimilation is seen to be robust to missing observations with quite large data gaps

Uncertainty Characterisation & Validation within ESA Fire-CCI

Uncertainty characterisation and validation are critical phases to generate any Essential Climate Variable (ECV), and therefore both have been included as key deliverables of the ESA CCI programme [1]. All products generated by the CCI are required to have an associated per pixel uncertainty characterisation. This paper describes both the uncertainty characterisation framework and the related uncertainty validation exercise of the Fire-CCI project