269 research outputs found
Sensitivity of scope modelled GPP and fluorescence for different plant functional types
No full textThis study addresses the question which factors are responsible for reported positive correlations between solar induced fluorescence (SIF) and gross primary production (GPP). A sensitivity analysis of the model SCOPE, which simulates photosynthesis, fluorescence emission and radiative transfer in canopies, has been carried out for four different plant functional types (PFT): tropical rainforest, C4 crops, C3 crops, and tundra, located in distinct climate zones: tropical everwet (Af), tropical with seasonal drought (savannah, Aw), temperate (Cf), and continental tundra (Dfd). Literature values for structural and physiological parameters and climate reanalysis data were used as input. The effect of main driving variables points towards a positive relation between GPP and SIF. For all four climates, the partial derivative of SIF to GPP is higher when irradiance varies than when any other parameter varies. Climate and PFT specific differences occurred, including a hot-spot effect on SIF in the tropics, relatively strong sensitivity of SIF and GPP to carboxylation capacity in the tropics, and a temperature and humidity effect in the tropical seasonal climate
Global sensitivity analysis of the A-SCOPE model in support of future FLEX fluorescence retrievals
No full textIn support of ESA's Earth Explorer 8 candidate mission FLEX (FLuorescence EXplorer), a Photosynthesis Study has been initiated to quantitatively link fluorescence to photosynthesis. This led to the development of A-SCOPE, a graphical user interface software package that integrates multiple biochemical models into the soil-vegetation-atmosphere-transfer model SCOPE. Its latest version (v1.53) has been successfully verified and was subsequently evaluated through a global sensitivity analysis. By using the method of Saltelli [4], the relative importance of each input variable to model outputs was quantified through first order and total effect sensitivity indices. Variations in leaf area index (LAI) and chlorophyll content are mostly impacting the reflectance and fluorescence signal. Non-driving variables that can be safely set to default values have been identified and will facilitate consolidating SCOPE into an operational and invertible model
Hyperspectral imaging for precision nitrogen management: A comparative exploration of two methodological approaches to estimate optimal nitrogen rate in processing tomato
No full textHyperspectral imaging is widespread in crop nitrogen (N) monitoring for precision agriculture, although approaches that address the agronomical recommendation of the optimal N rate are still lacking. Here, two approaches are explored in defining the optimal N rate to be supplied in fertigated processing tomatoes through hyperspectral imaging. The first one, called the N uptake approach, focuses on the virtual reproduction of the critical N uptake curve through the estimation of both aboveground biomass and crop N uptake. The estimated biomass is used to derive the critical N uptake, and the optimal N rate is computed as the difference between the critical N uptake and the estimated actual N uptake. The second approach focuses on the monitoring of the Nitrogen Nutrition Index (NNI) and biomass. Again, the biomass is used to calculate the critical N uptake, which, when combined with the estimated NNI, resolves the equation to retrieve the actual crop N uptake. A modeling stage was included to estimate the N-related variables from crop canopy reflectance across the full spectrum (400–1000 nm). Canopy reflectance was measured by using an unmanned aerial vehicle at five growth stages of processing tomatoes grown under experimental plot conditions with different N rates. Three nonparametric algorithms were trained, i.e., Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Partial Least Square Regression (PLSR). Multicollinearity of spectral bands was prevented with a principal component analysis, and models were 5-fold cross-validated. Considering the pivotal role of biomass in the selected N rate estimation approaches, two distinct biomass estimation methods were explored. The direct biomass retrieval from spectral data was compared with the indirect biomass retrieval from the remotely sensed LAI applying empirical regressions. PLSR outperformed the other algorithms in estimating N uptake (Relative Root Mean Square Error, RRMSE=21.8 %), while SVR better estimated NNI (RRMSE=10.2 %) and direct biomass (RRMSE=19.4 %). The indirect estimation of biomass outperformed the direct approach when GPR is used (RRMSE 18.2 % vs. 21.4 %), although the influence of soil background at early growth stages determines an unreliable biomass estimation for both methods. The NNI approach outperformed the N uptake approach in estimating the optimal N rate, especially when the biomass is directly retrieved from GPR. The promising estimation performances in N rate estimation (R2=0.88 and RRMSE=36 %) revealed the effectiveness of hyperspectral imaging in entering the agronomical scheduling of precision N management
Space-born spectrodirectional estimation of forest properties
No full textWith the upcoming global warming forests are under threat. To forecast climate change impacts and adaptations, there is need for developing improved forest monitoring services, which are able to record, quantify and map bio-indicators of the forests’ health status across the globe. In this context, Earth observation (EO) can provide a substantial amount of up-to-date information about the biochemical and structural conditions of our forests at a local-to-global scale. Among the optical EO instruments in space, one of the most innovative instruments is the experimental Compact High Resolution Imaging Spectrometer (CHRIS) on board the PROBA-1 (Project for On Board Autonomy) satellite. CHRIS is capable of sampling reflected radiation at five viewing angles over the visible and near-infrared (VNIR) region of the solar spectrum with a relatively high spatial resolution (~17 m). The as such acquired spectrodirectional (combined multi-angular and spectroscopy) data may lead to new opportunities for space-based forest monitoring applications, yet the added value of canopy reflectance anisotropy measured over the whole VNIR spectral region is largely unknown. This is why the use of space-borne spectrodirectional data of a forested target has been investigated in this thesis
Evaluating the predictive power of sun-induced chlorophyll fluorescence to estimate net photosynthesis of vegetation canopies : a SCOPE modeling study
No full textProgress in imaging spectroscopy technology and data processing can enable derivation of the complete sun-induced chlorophyll fluorescence (SIF) emission spectrum. This opens up opportunities to fully exploit the use of the SIF spectrum as an indicator of photosynthetic activity. Simulations performed with the coupled fluorescence-photosynthesis model SCOPE were used to determine how strongly canopy-leaving SIF can be related to net photosynthesis of the canopy (NPC) for various canopy configurations. Regression analysis between SIF retrievals and NPC values produced the following general findings: (1) individual SIF bands that were most sensitive to NPC were located around the first emission peak (SIFred) for heterogeneous canopy configurations (i.e., varying biochemistry, leaf, canopy variables); (2) using two SIF retrieval bands, e.g. O2-B at 687nm and O2-A at 760nm, or the red and NIR emission peaks at 685nm and 740nm, led to stronger correlations than using only one band (3) using the O2-B and the O2-A SIF retrieval bands was at least as effective as using the two emission peaks; (4) superior correlations were achieved by using the four main SIF retrieval bands (Hα, O2-B, water vapor, O2-A); and (5) further improvements may be obtained by exploiting the full SIF profile and by using an adaptive, nonlinear regression algorithm such as Gaussian processes regression (GPR). Relationships can be due to variation in photosynthetic capacity (Vcmo), but also from variation in leaf optical and canopy structural variables such as chlorophyll content and leaf area index. Overall, modeling results suggest that sampling the SIF profile in at least both O2-B and O2-A bands enables quantification photosynthetic activity of vegetation with high accuracy
Analysis of biophysical variables in an onion crop (Allium cepa L.) with nitrogen fertilization by sentinel-2 observations
No full textThe 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
Development of atmospheric correction algorithms for very high spectral and spatial resolution images: application to SEOSAT and the FLEX/Sentinel-3 missions
No full textAdvanced high spectral and spatial resolution imager spectrometers on board new generation of Earth Observation missions bring new exciting opportunities to the remote sensing scientific community. However, this progress goes hand in hand with new challenges. The exploitation of data acquired from these family of advanced instruments requires new processing algorithms able to deal with these particularities. As part of this evolution, atmospheric correction algorithms - a mandatory processing step applied prior to the Earth surface reflectance data exploitation - must be adapted or reformulated, thereby paying special attention to how atmospheric effects disturb the acquired signal in the spectral and spatial domains.
For these reasons, this Thesis aims to develop new atmospheric correction strategies to be applied over very high spectral and spatial resolution data. Following this goal, this Thesis was conducted in the framework of two missions during their development phase: (1) the FLEX/Sentinel–3 tandem space mission (for high spectral resolution data) and, (2) the Ingenio/SEOsat space mission (for high spatial resolution data). In the context of these missions, an additional challenge is introduced when acquiring proximal remote sensing data for their validation. This is especially relevant for the FLEX mission, which is dedicated to monitor the weak Solar Induced Chlorophyll Fluorescence (SIF) signal.
Following this motivation, the main objectives of this Thesis are threefold:
The first objective involved to analyse atmospheric effects on the Ingenio/SEOsat high spatial and low spectral resolution satellite mission and to propose a new atmospheric correction strategy. This strategy was called Hybrid and combines: (1) a per–pixel atmospheric radiative transfer model inversion technique making use of auxiliary data to characterize the atmospheric state, followed by (2) an image deconvolution technique modelling the atmospheric MTF to correct for atmospheric spatial effects. The Hybrid method was applied to Sentinel–2 data, particularly over bands acquired at 10 m resolution due to its similarities with the Ingenio/SEOsat mission.
The second objective involved to define a novel atmospheric correction strategy for the FLEX/Sentinel-3 tandem mission. The proposed strategy is a two-steps method where information from Sentinel-3 instruments, OLCI and SLSTR, is first used in synergy to characterize the aerosol and water vapour presence. The high spectral resolution of FLEX data is subsequently exploited to refine the previously aerosol characterization. As part of this objective, the suitability of all the approximations assumed in the formulation proposed for the atmospheric inversion of FLEX data was validated against the FLEX mission requirements.
The third objective involved to develop a strategy that deals with the atmospheric correction of very high spectral and spatial resolution data acquired at lower atmospheric scales such as Unmanned Aerial Vehicles or systems mounted on towers. In this Thesis, it was demonstrated that even when acquiring the signal at proximal remote sensing scale, i.e., few meters from the target oxygen absorption must be compensated to properly estimate SIF within these spectral regions. For this reason, a strategy to compensate for the oxygen absorption while properly dealing with the instrumental spectral response function convolution was presented and tested using simulated data.
Altogether, this work identified challenges associated to atmospheric correction when applying to high spatial and especially to very high spectral resolution data. In this Thesis, adequate formulations have been developed to resolve these difficulties, and successful methodologies have been designed for the particular cases of SEOsat (high spatial resolution) and FLEX (high spectral resolution); two future remote sensing space missions that will be launched in the forthcoming years
Newspaper Clipping, Flowers Present Many Pictures, March 23, 1972
No full textThis newspaper clipping provides a list of flowers and what the author, Jochem V. Benson sees when they look at them.https://scholarsjunction.msstate.edu/mss-james-franklin-buchanan/1195/thumbnail.jp
Estudio integral de humedales altoandinos (andean peatlands) con Teledetección y SIG
No full textThe Chimborazo Fauna Production Reserve (RPFCH) is a high-value ecosystem located in the Ecuadorian Andes, occupied mostly by peatlands, also called bofedales. The objective of this thesis is the study of these ecosystems from an extensive field database obtained in 2016 and using optical and radar remote sensing data and topographic, environmental and climatic variables with GIS.
To this end, the best methods for mapping peatlands in the RPFCH, the estimation of carbon below the ground (SOC) in the 0-30 cm layer and the estimation of carbon stored in the vegetation calculated from biomass were analyzed. As a result, it was obtained that, comparing Sentinel-2 with Landsat-8, the best method for mapping was obtained by classifying with Random Forest and using vegetation indices with Sentinel-2, with SAVI and REDEDGE being the most important indices. With this, the peatlands in the RPFCH have been mapped and it has been estimated that the area occupied by this ecosystem in 2017 was 23,292 ha.
Field data have shown a strong correlation between the biomass and the water content of the vegetation in the area, with the water content being 1.6 times the biomass, which is indicative of a very wet vegetation.
Regarding the carbon stored in the vegetation, and using an extensive field database of 320 points, 4 machine learning models were tested with different combinations of variables and it was found that the best method was Gaussian Process Regression (GPR) with the best correlation of R2 = 0.76, with the most important variables, in order, being elevation, precipitation, Sentinel-1 VV/VH ratio, Sentinel-2 NBRI and LAI. For the estimation of temporary changes in vegetation carbon, an additional model is proposed using only information obtained from the Sentinel-2 image. With this, the loss of carbon stored by the vegetation of the ecosystem between the years 2017 and 2020 was quantified using only the Sentinel-2 bands and training GPR with the field data, estimating that the total amount of carbon of the vegetation was 71975 Mg in 2017 and went to 59362 Mg in 2020.
On the other hand, to study the COS, from 320 field data, the same 4 machine learning models were also tested with different combinations of variables and it was obtained that the best method was GPR, showing the best correlation with R2 = 0, 76, and the variables indicated before, with which the amount of carbon could be estimated and that the best variables in order of importance were: elevation, land use, temperature, distance to rivers, REDEDGE of Sentinel-2 and the band average ratio (VH/VV) of Sentinel-1. With this, the SOC map of the bofedal of the RPFCH was elaborated and the total carbon in the 30 cm layer of soil was calculated, resulting in 13639407 Mg, showing the importance of this ecosystem in terms of its carbon storage capacity.La Reserva de Producción de Fauna Chimborazo (RPFCH) es un ecosistema de alto valor situado en los andes ecuatorianos, ocupado en su mayor parte por turberas, también llamados bofedales o peatlands. El objetivo de esta tesis es el estudio de dichos ecosistemas a partir de una extensa base de datos de campo obtenida en 2016 y usando datos de teledetección óptica y radar y variables topográficas, ambientales y climáticas con SIG.
Para ello se analizaron los mejores métodos para el cartografiado de los peatlands en la RPFCH, la estimación del carbono bajo el suelo (COS) en la capa 0-30 cm y la estimación del carbono almacenado en la vegetación calculado a partir de la biomasa. Como resultado se obtuvo que, comparando Sentinel-2 con Landsat-8, el mejor método para el cartografiado se obtuvo clasificando con Random Forest y usando índices de vegetación con Sentinel-2, siendo SAVI y REDEDGE los índices de mayor importancia. Con ello se ha cartografiado los peatlands en la RPFCH y se ha estimado que el área ocupada por este ecosistema en 2017 fue de 23292 ha.
Los datos de campo han mostrado una fuerte correlación entre la biomasa y el contenido en agua de la vegetación de la zona, siendo el contenido en agua 1,6 veces la biomasa, lo que es indicativo de una vegetación muy húmeda.
En cuanto al carbono almacenado en la vegetación, y usando una extensa base de datos de campo de 320 puntos, se probaron 4 modelos de aprendizaje automático con diferentes combinaciones de variables y se obtuvo que el mejor método fue Gaussian Process Regression (GPR) con la mejor correlación de R2 = 0,76, siendo las variables más importantes, por orden, la elevación, la precipitación, la relación VV/VH de Sentinel-1, el índice NBRI y el LAI de Sentinel-2. Para la estimación de cambios temporales del carbono de la vegetación se propone un modelo adicional utilizando únicamente información obtenida de la imagen Sentinel-2. Con ello se cuantificó la pérdida de carbono almacenada por la vegetación del ecosistema entre los años 2017 y 2020 usando solo las bandas de Sentinel-2 y entrenando GPR con los datos de campo, estimando que la cantidad de carbono total de la vegetación fue de 71975 Mg en 2017 y pasó a 59362 Mg en 2020.
Por otra parte, para estudiar el COS, a partir de 320 datos de campo se probaron también los mismos 4 modelos de aprendizaje automático con diferentes combinaciones de variables y se obtuvo que el mejor método fue GPR, mostrando la mejor correlación con R2 = 0,76, y las variables señaladas antes, con lo que se pudo estimar la cantidad de carbono y que las mejores variables por orden de importancia fueron: la elevación, usos de suelo, temperatura, distancia a ríos, REDEDGE de Sentinel-2 y el band ratio del promedio (VH/VV) de Sentinel-1. Con ello se elaboró el mapa de COS del bofedal de la RPFCH y se calculó el carbono total en la capa de 30 cm de suelo, resultando ser de 13639407 Mg, mostrando la importancia de este ecosistema en cuanto a su capacidad de almacenamiento de carbono
Retrieval of Vegetation Biophysical Variables from Top-of-Atmosphere Radiance Data
No full textThe retrieval of vegetation biophysical variables from satellite and airborne optical data usually takes place after atmospheric correction (AC). However, it is also possible to retrieve them directly from top-of-atmosphere (TOA) radiance data if algorithms account for variability in the atmosphere. In this context, hybrid methods are of interest for building more efficient and robust retrieval models as they combine the advantages of physically-based radiative transfer models (RTMs) with the flexibility of machine learning (ML) regression algorithms.
For this reason, this Thesis aimed to develop Gaussian process regression (GPR)-based hybrid models for the retrieval of essential crop traits applicable to Sentinel-2 (S2) TOA data (L1C product). Another objective was the optimization of the GPR models and the further integration into GEE for large-scale mapping. To achieve this, GPR and variational heteroscedastic GPR (VHGPR) models were trained on a look-up table (LUT) of TOA radiance data and associated input variables simulated using the coupled leaf-canopy-atmosphere RTM PROSAIL-6SV. They were also trained with a bottom-of-atmosphere (BOA) LUT from PROSAIL. The models were then applied to cloud-free S2 L1C (TOA) and L2A (BOA) reflectance products for mapping leaf chlorophyll content, leaf water content, leaf dry matter content, fractional vegetation cover, leaf area index (LAI), and upscaled leaf biochemical compounds, i.e., canopy chlorophyll content, canopy water content, and canopy dry matter content.
VHGPR delivered superior accuracies and lower uncertainties than GPR for LAI estimations at BOA and TOA scales when validated against an in situ dataset over Marchfeld agricultural site. Validation using the Munich-North-Isar site showed consistent performance between BOA and TOA, with canopy-level variables outperforming leaf-level variables. Accuracy increased using TOA data instead of BOA data. A successful reduction of the training dataset by 78% was achieved by applying the Active Learning technique Euclidean distance-based diversity (EBD). The optimized EBD-GPR models demonstrated highly accurate validation results for LAI and upscaled leaf variables against the MNI site, with normalized root mean square errors (NRMSE) from 6% to 13%. However, when validated against an independent dataset from the Italian Grosseto site, the models showed moderate-to-good performances for canopy-level variables, with NRMSE ranging from 14% to 50%, but failed for leaf-level estimates. Finally, using GEE, the TOA-based mapping was applied over the entirety of Germany at 20 m spatial resolution including information about prediction uncertainty.
In summary, this Thesis demonstrated that essential crop traits can be retrieved from TOA radiance data, thus avoiding the critical AC step. The optimization and integration of ML-based hybrid models in a cloud-based computing platform proved automated fast mapping directly from S2 TOA data from local to large-scale
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