Mapping vegetation variables in Google Earth Engine using Gaussian Process Regression models.   
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<p>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.</p><p>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.</p><p>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 (<span>Instituto Tecnol&#243;gico Agrario de Castilla y Le&#243;n</span>) during SENSAGRI.</p><p>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.</p>Keywords:
Earth observation
Photochemical Reflectance Index
Photochemical Reflectance Index
Non-photochemical quenching
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Chlorophyll fluorescence measured at the leaf scale through pulse amplitude modulation (PAM) has provided valuable insight into photosynthesis. At the canopy- and satellite-scale, solar-induced fluorescence (SIF) provides a method to estimate the photosynthetic activity of plants across spatiotemporal scales. However, retrieving SIF signal remotely requires instruments with high spectral resolution, making it difficult and often expensive to measure canopy-level steady-state chlorophyll fluorescence under natural sunlight. Considering this, we built a novel low-cost photodiode system that retrieves far-red chlorophyll fluorescence emission induced by a blue light emitting diode (LED) light source, for 2 h at night, above the canopy. Our objective was to determine if an active remote sensing-based night-time photodiode method could track changes in canopy-scale LED-induced chlorophyll fluorescence (LEDIF) during an imposed drought on a broadleaf evergreen shrub,
Photochemical Reflectance Index
Fluorometer
Photodiode
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Photochemical Reflectance Index
Fluorometer
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Photochemical Reflectance Index
Non-photochemical quenching
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Passive measurement of sun-induced chlorophyll fluorescence (F) represents the most promising tool to quantify changes in photosynthetic functioning on a large scale. However, the complex relationship between this signal and other photosynthesis-related processes restricts its interpretation under stress conditions. To address this issue, we conducted a field campaign by combining daily airborne and ground-based measurements of F (normalized to photosynthetically active radiation), reflectance and surface temperature and related the observed changes to stress-induced variations in photosynthesis. A lawn carpet was sprayed with different doses of the herbicide Dicuran. Canopy-level measurements of gross primary productivity indicated dosage-dependent inhibition of photosynthesis by the herbicide. Dosage-dependent changes in normalized F were also detected. After spraying, we first observed a rapid increase in normalized F and in the Photochemical Reflectance Index, possibly due to the blockage of electron transport by Dicuran and the resultant impairment of xanthophyll-mediated non-photochemical quenching. This initial increase was followed by a gradual decrease in both signals, which coincided with a decline in pigment-related reflectance indices. In parallel, we also detected a canopy temperature increase after the treatment. These results demonstrate the potential of using F coupled with relevant reflectance indices to estimate stress-induced changes in canopy photosynthesis.
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This manual has been prepared for use by the disaster management community. It introduces remote sensing and geospatial concepts, ICIMOD’s science applications and their applications in disaster preparedness. The manual’s contents were used in training sessions on using Earth observation and geospatial applications for disaster preparedness in Nepal. It provides a step-by-step guide to using free and open-source geospatial software, remote sensing data, and ICIMOD’s science applications for preparedness, management, and risk reduction of disasters. It uses examples and sample datasets from Nepal.
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Photochemical Reflectance Index
Fluorometer
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Remote sensing (RS) techniques have evolved into an important instrument to investigate forest function. New methods based on the remote detection of leaf biochemistry and photosynthesis are being developed and applied in pilot studies from airborne and satellite platforms (PRI, solar-induced fluorescence; N and chlorophyll content).
Non-destructive monitoring methods, a direct application of RS studies, are also proving increasingly attractive for the determination of stress conditions or nutrient deficiencies not only in research but also in agronomy, horticulture and urban forestry (proximal RS).
In this work I will focus on some novel techniques recently developed for the estimation of photochemistry and photosynthetic rates based (i) on the proximal measurement of steady-state chlorophyll fluorescence yield, or (ii) the remote sensing of changes in hyperspectral leaf reflectance, associated to xanthophyll de-epoxydation and energy partitioning, which is closely coupled to leaf photochemistry and photosynthesis. I will also present and describe a mathematical model of leaf steady-state fluorescence and photosynthesis recently developed in our group. Two different species were used in the experiments: Arbutus unedo, a schlerophyllous Mediterranean species, and Populus euroamericana, a broad leaf deciduous tree widely used in plantation forestry.
Results show that ambient fluorescence could provide a useful tool for testing photosynthetic processes from a distance. These results confirm also the photosynthetic reflectance index (PRI) as an efficient remote sensing reflectance index estimating short-term changes in photochemical efficiency as well as long-term changes in leaf biochemistry.
The study also demonstrated that RS techniques could provide a fast and reliable method to estimate photosynthetic pigment content and total nitrogen, beside assessing the state of photochemical process in our plants’ leaves in the field. This could have important practical applications for the management of plant cultivation systems, for the estimation of the nutrient requirements of our plants for optimal growth.
Photochemical Reflectance Index
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Leaf area index(LAI) is one of the key structural parameter for cotton canopy.The objectives of this study were to determine the relationships between spectral parameters and LAI so that the optimum regression models for estimating LAI were developed in cotton,and to analysis the sensitivity of these spectral parameters.The reflectance spectra of canopy were measured using a field radiometric spectrometer in different canopy LAI in the different growth stages of cotton.The results showed that the maximum sensitivity of reflectance to variation in leaf area index,694 nm and 1099 nm,were found in visible and near-infrared spectrum,respectively.Hence,previous established spectral parameters were modified using reflectance of these two wavebands.Furthermore,the models to retrieve LAI using wide dynamic range vegetation index(WDRVI) and ratio vegetation index(RVI) were most feasible with the maximum determination coefficients(r2)(0.8375 and 0.8324,respectively).Additional,RVI showed higher sensitivity to LAI than WDRVI consistently.
Photochemical Reflectance Index
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In this chapter, two case studies that involve the quantitative estimation of the value of information (VOI) in specific adaptation and mitigation decisions in the agriculture sector are summarized. The first case study focuses on adapting land use to sustain drinking water quality and to avoid an increase in the contamination of groundwater by agrochemicals. In the first example the Landsat archive is used to evaluate the societal benefits to adapt agricultural land management to reduce nonpoint source groundwater contamination. The second case study targets mitigating drought disasters by determining farmer eligibility for financial assistance by the U.S. Department of Agriculture (USDA). For the second example the Gravity Recovery and Climate Experiment (GRACE) provide data to assess the economic loss due to the misspecification of eligibility for drought disaster assistance and insurance that is evaluated in a specific drought policy. In each case, Earth observation data are transformed into information and is processed with other science-based indicators, as well as socioeconomic data in support of applicable communities.
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