Sun-induced Fluorescence Data Research Articles

Soil moisture retrieval by a novel hybrid model based on CYGNSS and Sun-induced fluorescence data

Using Global Navigation Satellite System-Reflectometry (GNSS-R) for soil moisture (SM) retrieval has recently gained importance due to its high temporal-spatial resolution. However, the current methods, i.e., constructing a single machine learning (ML)-based model, have large model uncertainty resulting from ML networks and input schemes. Moreover, traditional Normalized Difference Vegetation Index (NDVI) cannot capture the rapid vegetation changes well. In this paper, a new SM retrieval method of constructing a hybrid model based on Bayesian model averaging (BMA) is employed to reduce the model uncertainty. Meanwhile, novel Sun-induced fluorescence (SIF) data is used as ancillary data to represent the rapid change of vegetation. We validate the proposed method at point and regional scales using in-situ data and the Global Land Data Assimilation System (GLDAS) product. The results demonstrate that our method has high accuracy and low uncertainty in SM retrieval. At the point scale, as accuracy indices, the average R (μR) of BMA increases from 0.90 to 0.93 and the average root-mean-square-error (μRMSE) decreases from 0.034 cm3/cm3 to 0.029 cm3/cm3; as indices of uncertainty, the standard deviations of R and RMSE (σR and σRMSE) decrease by 32 % and 9 % compared to the single ML-based model. For the regional scale, the μR increases from 0.79 to 0.81, the μRMSE decreases from 0.024 cm3/cm3 to 0.023 cm3/cm3, and the σR decreases by 19 %. Moreover, we take the point-scale experiment as an example for comparison to compare the performance of SIF with that of NDVI. The μR of BMA trained by SIF is 0.03 higher than that trained by NDVI and the μRMSE decreases by 0.002 cm3/cm3; σR and σRMSE decrease by 25 % and 6 %. Based on these results, the proposed method can reduce the uncertainty and the advantage of SIF has potential for improving the SM retrieval.

Evaluation of the Spatial Representativeness of In Situ SIF Observations for the Validation of Medium-Resolution Satellite SIF Products

The upcoming Fluorescence Explorer (FLEX) mission will provide sun-induced fluorescence (SIF) products at unprecedented spatial resolution. Thus, accurate calibration and validation (cal/val) of these products are key to guarantee robust SIF estimates for the assessment and quantification of photosynthetic processes. In this study, we address one specific component of the uncertainty budget related to SIF retrieval: the spatial representativeness of in situ SIF observations compared to medium-resolution SIF products (e.g., 300 m pixel size). Here, we propose an approach to evaluate an optimal sampling strategy to characterise the spatial representativeness of in situ SIF observations based on high-spatial-resolution SIF data. This approach was applied for demonstration purposes to two agricultural areas that have been extensively characterized with a HyPlant airborne imaging spectrometer in recent years. First, we determined the spatial representativeness of an increasing number of sampling points with respect to a reference area (either monocultural crop fields or hypothetical FLEX pixels characterised by different land cover types). Then, we compared different sampling approaches to determine which strategy provided the most representative reference data for a given area. Results show that between 3 and 13.5 sampling points are needed to characterise the average SIF value of both monocultural fields and hypothetical FLEX pixels of the agricultural areas considered in this study. The number of sampling points tends to increase with the standard deviation of SIF of the reference area, as well as with the number of land cover classes in a FLEX pixel, even if the increase is not always statistically significant. This study contributes to guiding cal/val activities for the upcoming FLEX mission, providing useful insights for the selection of the validation site network and particularly for the definition of the best sampling scheme for each site.

Multiple-constraint inversion of SCOPE. Evaluating the potential of GPP and SIF for the retrieval of plant functional traits

The most recent efforts to provide remote sensing (RS) estimates of plant function rely on the combination of Radiative Transfer Models (RTM) and Soil-Vegetation-Atmosphere Transfer (SVAT) models, such as the Soil-Canopy Observation Photosynthesis and Energy fluxes (SCOPE) model. In this work we used ground spectroradiometric and chamber-based CO2 flux measurements in a nutrient manipulated Mediterranean grassland in order to: 1) develop a multiple-constraint inversion approach of SCOPE able to retrieve vegetation biochemical, structural as well as key functional traits, such as chlorophyll concentration (Cab), leaf area index (LAI), maximum carboxylation rate (Vcmax) and the Ball-Berry sensitivity parameter (m); and 2) compare the potential of the of gross primary production (GPP) and sun-induced fluorescence (SIF), together with up-welling Thermal Infrared (TIR) radiance and optical reflectance factors (RF), to estimate such parameters. The performance of the proposed inversion method as well as of the different sets of constraints was assessed with contemporary measurements of water and heat fluxes and leaf nitrogen content, using pattern-oriented model evaluation.The multiple-constraint inversion approach proposed together with the combination of optical RF and diel GPP and TIR data provided reliable estimates of parameters, and improved predicted water and heat fluxes. The addition of SIF to this scheme slightly improved the estimation of m. Parameter estimates were coherent with the variability imposed by the fertilization and the seasonality of the grassland. Results revealed that fertilization had an impact on Vcmax, while no significant differences were found for m. The combination of RF, SIF and diel TIR data weakly constrained functional traits. Approaches not including GPP failed to estimate LAI; however GPP overestimated Cab in the dry period. These problems might be related to the presence of high fractions of senescent leaves in the grassland. The proposed inversion approach together with pattern-oriented model evaluation open new perspectives for the retrieval of plant functional traits relevant for land surface models, and can be utilized at various research sites where hyperspectral remote sensing imagery and eddy covariance flux measurements are simultaneously taken.

Diurnal Response of Sun-Induced Fluorescence and PRI to Water Stress in Maize Using a Near-Surface Remote Sensing Platform

Sun-induced Fluorescence (SIF) and Photochemical Reflectance Index (PRI) data were collected in the field over maize to study their diurnal responses to different water stresses at the canopy scale. An automated field spectroscopy system was used to obtain continuous and long-term measurements of maize canopy in four field plots with different irrigation treatments. This system collects visible to near-infrared spectra with a spectrometer, which provides a sub-nanometer spectral resolution in the spectral range of 480~850 nm. The red SIF (FR) and far red SIF (FFR) data were retrieved by Spectral Fitting Methods (SFM) in the O 2 -A band and O 2 -B band, respectively. In addition to PRI, Δ PRI values were derived from PRI by subtracting an early morning PRI value. Photosynthetic active radiation (PAR) data, the canopy fraction of absorbed PAR (fPAR), and the air/canopy temperature and photosystem II operating efficiency (YII) at the leaf scale were collected concurrently. In this paper, the diurnal dynamics of each parameter before and after watering at the jointing stage were compared. The results showed that (i) both FR and FFR decreased under water stress, but FR always peaked at noon, and the peak of FFR advanced with the increase in stress. Leaf folding and the increase in Non-photochemical Quenching (NPQ) are the main reasons for this trend. Leaf YII gradually decreased from 8:00 to 14:00 and then recovered. In drought, leaf YII was smaller and decreased more rapidly. Therefore, the fluorescence yield at both the leaf and canopy scale responded to water stress. (ii) As good indicators of changes in NPQ, diurnal PRI and Δ PRI data also showed specific decreases due to water stress. Δ PRI can eliminate the impact of canopy structure. Under water stress, Δ PRI decreased rapidly from 8:00 to 13:00, and the maximum range of this decrease was approximately 0.05. After 13:00, their values started to increase but could not recover to their morning level. (iii) Higher canopy-air temperature differences ( Δ T ) indicate that stomatal closure leads to an increase in leaf temperature, which maintains a higher state in the afternoon. In summary, to cope with water stress, both leaf folding and changes in physiology are activated. To monitor drought, SIF performs best around midday, and PRI is better after noon.

Spatially-explicit monitoring of crop photosynthetic capacity through the use of space-based chlorophyll fluorescence data

Plant functional traits such as photosynthetic capacity are critical parameters for terrestrial biosphere models. However, their spatial and temporal characteristics are still poorly represented. In this study, we used satellite observations of sun-induced fluorescence (SIF) to estimate top-of-canopy photosynthetic capacity (maximum carboxylation rate, Vcmax at a reference temperature of 25 °C) for crops, which was in turn utilized to simulate regional gross primary production (GPP). We first estimate the key parameter, Vcmax, in the widely-used FvCB photosynthesis model using field measurements of CO2 and water fluxes during 2007–2012 at seven crop eddy covariance flux sites over the US Corn Belt. The results showed that satellite far-red SIF retrievals have a stronger link to Vcmax at the seasonal scale (R2 = 0.70 for C4 and R2 = 0.63 for C3 crop) as compared with widely-used vegetation indices. We calibrate an empirical model linking Vcmax with SIF that was used to estimate spatially and temporally varying crop Vcmax for the US Corn Belt region. The resulting Vcmax maps are used together with meteorological data from MERRA reanalysis data and vegetation structural parameters derived from the satellite-based spectral reflectance data to constrain the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) balance model in order to estimate regional crop GPP. Our results show a substantial improvement in the seasonal and spatial patterns of cropland GPP when compared with crop yield inventory data. The evaluation with tall tower atmospheric CO2 measurements further supports our estimation of spatiotemporal Vcmax from space-borne SIF. Considering that SIF has a direct link to photosynthetic activity, our findings highlight the potential to infer regional Vcmax using remotely sensed SIF data and to use this information for a better quantification of regional cropland carbon cycles.