Luis Guanter

Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence.

Significance Global food and biofuel production and their vulnerability in a changing climate are of paramount societal importance. However, current global model predictions of crop photosynthesis are highly uncertain. Here we demonstrate that new space-based observations of chlorophyll fluorescence, an emission intrinsically linked to plant biochemistry, enable an accurate, global, and time-resolved measurement of crop photosynthesis, which is not possible from any other remote vegetation measurement. Our results show that chlorophyll fluorescence data can be used as a unique benchmark to improve our global models, thus providing more reliable projections of agricultural productivity and climate impact on crop yields. The enormous increase of the observational capabilities for fluorescence in the very near future strengthens the relevance of this study. Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants. Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50–75% higher than results from state-of-the-art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle.

Land Surface Emissivity Retrieval From Different VNIR and TIR Sensors

This paper discusses the application and adaptation of two existing operational algorithms for land surface emissivity (epsiv) retrieval from different operational satellite/airborne sensors with bands in the visible and near-infrared (VNIR) and thermal IR (TIR) regions: (1) the temperature and emissivity separation algorithm, which retrieves epsiv only from TIR data and (2) the normalized-difference vegetation index thresholds method, in which epsiv is retrieved from VNIR data.

Forest productivity and water stress in Amazonia: observations from GOSAT chlorophyll fluorescence

It is unclear to what extent seasonal water stress impacts on plant productivity over Amazonia. Using new Greenhouse gases Observing SATellite (GOSAT) satellite measurements of sun-induced chlorophyll fluorescence, we show that midday fluorescence varies with water availability, both of which decrease in the dry season over Amazonian regions with substantial dry season length, suggesting a parallel decrease in gross primary production (GPP). Using additional SeaWinds Scatterometer onboard QuikSCAT satellite measurements of canopy water content, we found a concomitant decrease in daily storage of canopy water content within branches and leaves during the dry season, supporting our conclusion. A large part (r2 = 0.75) of the variance in observed monthly midday fluorescence from GOSAT is explained by water stress over moderately stressed evergreen forests over Amazonia, which is reproduced by model simulations that include a full physiological representation of photosynthesis and fluorescence. The strong relationship between GOSAT and model fluorescence (r2 = 0.79) was obtained using a fixed leaf area index, indicating that GPP changes are more related to environmental conditions than chlorophyll contents. When the dry season extended to drought in 2010 over Amazonia, midday basin-wide GPP was reduced by 15 per cent compared with 2009.

The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

Imaging spectroscopy, also known as hyperspectral remote sensing, is based on the characterization of Earth surface materials and processes through spectrally-resolved measurements of the light interacting with matter. The potential of imaging spectroscopy for Earth remote sensing has been demonstrated since the 1980s. However, most of the developments and applications in imaging spectroscopy have largely relied on airborne spectrometers, as the amount and quality of space-based imaging spectroscopy data remain relatively low to date. The upcoming Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill this gap. An overview of the main characteristics and current status of the mission is provided in this contribution. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible and near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km-wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to four days at the Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide.

Spectral calibration of hyperspectral imagery using atmospheric absorption features

One of the initial steps in the preprocessing of remote sensing data is the atmospheric correction of the at-sensor radiance images, i.e., radiances recorded at the sensor aperture. Apart from the accuracy in the estimation of the concentrations of the main atmospheric species, the retrieved surface reflectance is also influenced by the spectral calibration of the sensor, especially in those wavelengths mostly affected by gaseous absorptions. In particular, errors in the surface reflectance appear when a systematic shift in the nominal channel positions occurs. A method to assess the spectral calibration of hyperspectral imaging spectrometers from the acquired imagery is presented in this paper. The fundamental basis of the method is the calculation of the value of the spectral shift that minimizes the error in the estimates of surface reflectance. This is performed by an optimization procedure that minimizes the deviation between a surface reflectance spectrum and a smoothed one resulting from the application of a low-pass filter. A sensitivity analysis was performed using synthetic data generated with the MODTRAN4 radiative transfer code for several values of the spectral shift and the water vapor column content. The error detected in the retrieval is less than +/- 0.2 nm for spectral shifts smaller than 2 nm, and less than +/- 1.0 nm for extreme spectral shifts of 5 nm. A low sensitivity to uncertainties in the estimation of water vapor content was found, which reinforces the robustness of the algorithm. The method was successfully applied to data acquired by different hyperspectral sensors.

Cloud-Screening Algorithm for ENVISAT/MERIS Multispectral Images

This paper presents a methodology for cloud screening of multispectral images acquired with the Medium Resolution Imaging Spectrometer (MERIS) instrument on-board the Environmental Satellite (ENVISAT). The method yields both a discrete cloud mask and a cloud-abundance product from MERIS level-1b data on a per-pixel basis. The cloud-screening method relies on the extraction of meaningful physical features (e.g., brightness and whiteness), which are combined with atmospheric-absorption features at specific MERIS-band locations (oxygen and water-vapor absorptions) to increase the cloud-detection accuracy. All these features are inputs to an unsupervised classification algorithm; the cloud-probability output is then combined with a spectral unmixing procedure to provide a cloud-abundance product instead of binary flags. The method is conceived to be robust and applicable to a broad range of actual situations with high variability of cloud types, presence of ground covers with bright and white spectra, and changing illumination conditions or observation geometry. The presented method has been shown to outperform the MERIS level-2 cloud flag in critical cloud-screening situations, such as over ice/snow covers and around cloud borders. The proposed modular methodology constitutes a general framework that can be applied to multispectral images acquired by spaceborne sensors working in the visible and near-infrared spectral range with proper spectral information to characterize atmospheric-oxygen and water-vapor absorptions.

A method for the surface reflectance retrieval from PROBA/CHRIS data over land: application to ESA SPARC campaigns

The Compact High Resolution Imaging Spectrometer (CHRIS) onboard the Project for On-Board Autonomy (PROBA) platform system provides the first high spatial resolution hyperspectral/multiangular remote sensing data from a satellite system, what represents a new source of information for Earth Observation purposes. A fully consistent radiative transfer approach is always preferred when dealing with the retrieval of surface reflectance from hyperspectral/multiangular data. However, due to the reported calibration anomalies for CHRIS data, a direct atmospheric correction based on physical radiative transfer modeling is not possible, and the method must somehow compensate for such calibration problems in specific wavelength ranges. A dedicated atmospheric correction algorithm for PROBA/CHRIS data over land is presented in this work. It consists in the combination of radiative transfer and empirical line approaches to atmospheric correction, in order to retrieve surface reflectance images free from both the atmospheric distortion and artifacts due to miscalibration. The atmospheric optical parameters and the updated set of calibration coefficients are obtained jointly in an autonomous process, without the need for any ancillary data. Results from the application of the algorithm to PROBA/CHRIS data from the two European Space Agency SPectra bARrax Campaign (SPARC) held at the Barrax study site (La Mancha, Spain) in 2003 and 2004 are presented in this work, focusing on the validation of the final surface reflectance using in situ measurements acquired simultaneously to PROBA overpasses.

EeteS—The EnMAP End-to-End Simulation Tool

The design of future Earth imaging systems, the optimization of fundamental instrument parameters, and the development and evaluation of data pre-processing and scientific-exploitation algorithms require an accurate end-to-end simulation of the entire image generation and processing chain. For this purpose, the end-to-end simulation software EeteS has been developed within the framework of the Environmental Mapping and Analysis Program (EnMAP) mission. This paper presents the EeteS simulation approach and software implementation focusing on calibration and pre-processing. The sequential processing chain of the EnMAP scene simulator consists of four independent parts-the atmospheric, spatial, spectral and radiometric modules. This forward simulator is coupled with a backward simulation branch consisting of calibration modules (non-linearity, dark current and absolute radiometric calibration) and a series of pre-processing modules (radiometric calibration, co-registration, atmospheric correction and orthorectification) forming the complete end-to-end simulation tool. In the result EeteS is capable of simulating EnMAP-like raw image scenes (L0) taking into account a variety of instrumental and environmental configurations. Furthermore, EeteS allows simulations of EnMAP reflectance images carrying out the complete L1 and L2 processing chains. Analysis of the intermediate and final EeteS simulation products has shown the accurate, reliable and consistent performance of the developed modules enabling the system to support technical decision-making processes required for the development of the EnMAP sensor. EeteS has also been used to estimate the SNR characteristics of potential EnMAP products after calibration and pre-processing. Comparing the results to SNR characteristics achieved by the already existing EO-1 Hyperion system has shown a significantly improved SNR which can be expected from future EnMAP data products.

Simulation of Optical Remote-Sensing Scenes With Application to the EnMAP Hyperspectral Mission

The simulation of remote-sensing images is a useful tool for a variety of tasks, such as the definition of future Earth Observation systems, the optimization of instrument specifications, and the development and validation of data processing algorithms. A scene simulator for optical hyperspectral and multispectral data has been implemented in the frame of the Environmental Mapping and Analysis Program (EnMAP) mission. EnMAP is a German-built hyperspectral space sensor scheduled for launch in 2012. EnMAP will measure in the 420-2450-nm spectral range at a varying spectral sampling of 6.5-10 nm. Images will cover 30 times 30 km areas at an approximate ground sampling distance of 30 m. The EnMAP scene simulator presented in this paper is able to generate realistic EnMAP-like data in an automatic way under a set of user-driven instrumental and scene parameters. Radiance and digital numbers data are generated by five sequential processing modules which are able to produce data over a range of natural environments, acquisition and illumination geometries, cloud covers, and instrument configurations. The latter include the simulation of data nonuniformity in the spatial and spectral domains, spatially coherent and noncoherent instrumental noise, and instruments modulation transfer function. Realistic surface patterns for the simulated data are provided by existing remote-sensing data in different environments, from dry geological sites to green vegetation areas. A flexible radiative transfer simulation scheme enables the generation of different illumination, observation, and atmospheric conditions. The methodology applied to the complete scene simulation and some sample results are presented and analyzed in this paper.

On the application of the MODTRAN4 atmospheric radiative transfer code to optical remote sensing

The quantification of atmospheric effects on the solar radiation measured by a spaceborne or airborne optical sensor is required for some key tasks in remote sensing, such as atmospheric correction, simulation of realistic scenarios or retrieval of atmospheric parameters. The MODTRAN4 code is an example of state‐of‐the‐art atmospheric radiative transfer code, as it provides very accurate calculations by means of a rigorous mathematical formulation and a very fine spectral resolution. However, the application of MODTRAN4 to remote sensing is not straightforward for the average user for a number of reasons: the provided output parameters do not exactly correspond to those necessary for the construction of the at‐sensor signal by combination with the surface reflectance, an advanced knowledge of radiative transfer theory and atmospheric physics is needed for the understanding of the input parameters and all their possible combinations, and the computation time may be too high for many practical applications. This work is intended to give explicit solutions to those problems. MODTRAN4 has been modified so that the proper atmospheric parameters are calculated and delivered as output. In addition, the most important execution options are investigated, and the compromise between accuracy and computation time is analysed. The performance of the proposed methodology is demonstrated by generating a look‐up table (LUT) enabling fast but accurate radiative transfer calculations for the atmospheric correction of data acquired by the Compact High Resolution Imaging Spectrometer (CHRIS) on board the Project for On‐Board Autonomy (PROBA).