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ispartof: REMOTE SENSING OF ENVIRONMENT vol:284 status: published

The presence of surface water on the canopy affects radar backscatter. However, its influence on the relationship between radar backscatter and crop biophysical parameters has not been investigated. The aim of this study was to quantify the influence of surface canopy water (SCW) on the relationship between L-band radar backscatter and biophysical variables of interest in agricultural monitoring. In this study, we investigated the effect of SCW on the relationship between co- and cross-polarized radar backscatter, cross ratios (VH/VV and HV/HH), and radar vegetation index (RVI) and dry biomass, vegetation water content (VWC), plant height and leaf area index (LAI). In addition, the effect of SCW on estimated vegetation optical depth (VOD) and its relationship with internal VWC was investigated. The analysis was based on data collected during a field experiment in Florida, USA in 2018. A corn field was scanned with a truck-mounted, fully polarimetric, L-band radar along with continuous monitoring of SCW (dew, interception) and soil moisture every 15 min for 58 days. In addition, pre-dawn destructive sampling was conducted to measure internal vegetation water content and dry biomass. Results showed that the presence of SCW can increase the radar backscatter up to 2 dB and this effect was lower for cross ratios (CRs) and RVI. The Spearman's rank correlations between radar observables and biophysical parameters were, on average, 0.2 higher for dry vegetation compared to wet vegetation. The estimated VOD from wet vegetation was generally higher than those from dry vegetation, which led to different fitting parameter (so-called b) values in the linear fit between VOD and VWC. The results presented here underscore the importance of considering the influence of SCW on the retrieval of biophysical variables of interest in agricultural monitoring. In particular, they highlight the importance of overpass time, and the impact that daily patterns in dew and interception can have on the retrieval of biophysical variables of interest. Mathematical Geodesy and Positioning Water Resources

Scattering and re-absorption have been recognized as relevant aspects for the interpretation of solar induced chlorophyll fluorescence (SIF) in vegetation remote sensing. In an earlier study [Yang and Van der Tol, RSE 215, 97–108, 2018] we addressed the problem of scattering and re-absorption of near-infrared fluorescence in the vegetation canopy. In this study we analyse within-leaf re-absorption of both red and near-infrared fluorescence using the radiative transfer model Fluspect. The leaf scattering determines the ratio of backward to total leaf fluorescence emission Fb/(Fb + Ff). Fluspect reproduces this ratio with an RMSE of less than 0.1, and explains the observed dependence of the spectral shape of this ratio on chlorophyll content and other leaf properties. We further provide a theoretical evaluation of how asymmetric SIF emission affects the SIF of a whole canopy and explain why recent within-canopy scattering models for fluorescence are not valid for red SIF.

Abstract The growing availability of global measurements of sun-induced chlorophyll fluorescence (SIF) can help in improving crop monitoring, especially the monitoring of photosynthetic activity. However, variations in top-of-canopy (TOC) SIF cannot be directly interpreted as physiological changes because of the confounding effects of vegetation biochemistry (i.e. pigments, dry matter and water) and structure. In this study, we propose an approach of using radiative transfer models (RTMs) and TOC reflectance to estimate the biochemical and structural effects on TOC SIF, as a necessary step in retrieving physiological information from TOC SIF. The approach was assessed by using airborne (HyPlant) reflectance and SIF data acquired over an agricultural experimental farm in Germany on two days, before and during a heat event in summer 2015 with maximum temperatures of 27°C and 34°C, respectively. The results show that over 76% variation among different crops in SIF observations was explained by variation in vegetation biochemistry and structure. In addition, the changes of vegetation biochemistry and structure explained as much as 73% variation between the two days in far-red SIF, and 40% variation in red SIF. The remaining unexplained variation was mostly attributed to the variability in physiological status. We conclude that reflectance provides valuable information to account for biochemical and structural effects on SIF and to advance analysis of SIF observations. The combination of RTMs, reflectance and SIF opens new pathways to detect vegetation biochemical, structural and physiological changes.

Knowing the exact sampling depth of microwave radiometry is essential for quantifying the performance and appreciation of the applicability of satellite soil moisture products. We investigate in this study the sampling depth (δ SM ) of the L-band microwave emission under frozen and thawed soil conditions on the Tibetan Plateau. Two years of diurnal brightness temperature (T B p ) measurements at a time interval of 30 min are collected by the ELBARA-III radiometer deployed at a Tibetan meadow site. Vertical profiles of soil temperature and volumetric liquid water content (θ liq ) are measured simultaneously at soil depths up to 1 m below the surface. The impact of the θ liq measured at different depths on the microwave emission simulations is assessed using the τ-ω emission model, whereby the permittivity of frozen and thawed soil is estimated by the four-phase dielectric mixing model. It is found that: 1) the sampling depth for the effective temperature depends on the magnitude of θ liq , and is estimated to be, on average, about 50 and 15 cm for the cold dry and wet warm period, respectively, because of the seasonality in θ liq ; 2) the δ SM is determined at 2.5 cm for both frozen and thawed soil conditions during both cold and warm periods, which is shallower than the commonly used θ liq measurement depth (i.e. 5 cm) adopted for the in-situ monitoring networks across the globe; 3) the T B p simulations performed with the θ liq measurements taken at the estimated δ SM of 2.5 cm result in lower unbiased root mean squared errors, about 14% (3.16 K) and 22% (3.36 K) for the horizontal and vertical polarizations respectively, in comparison to the simulations with the θ liq measurements taken from 5 cm soil depth; and 4) the θ liq retrieved with the single channel algorithm from the ELBARA-III measured vertically polarized T B p are in better agreement with the θ liq measured at 2.5 cm than the one measured at 5 cm. These findings are crucial for developing strategies for the calibration/validation as well as the application of satellite based soil moisture products relying on the L-band radiometry.

In this paper, the discrete electromagnetic model developed at the Tor Vergata University of Rome (hereafter, TV-DEM) is used to simulate both active and passive L-band signals using a single set of input parameters. The simulations are compared to Aquarius observations collected from a view angle of 28.7° over the Maqu study area located near the north-eastern edge of the Tibetan Plateau. The TV-DEM parameters litter biomass, litter moisture factor, plant moisture and standard deviation of height variations are calibrated for Aquarius observations collected during the 2012 and 2013 warm seasons. The calibrated parameters are used to reproduce the brightness temperature (T b) and backscattering coefficient (σ o) for the 2014 and 2015 warm season as an independent assessment. The results show that the calibrated TV-DEM simulations capture Aquarius observations reasonably well with coefficients of determination (R 2's) varying from 0.75 to 0.86 for the T b's and from 0.36 to 0.68 (−) for the σ o's depending on the polarization and adopted matchup set (e.g. calibration or validation). The simulations, however, systematically overestimate the H polarized Aquarius observation and underestimate the V polarized observations. The calibrated TV-DEM is also utilized for soil moisture retrieval from three combinations of Aquarius data, including a set of both active and passive microwave observations. The obtained error metrics indicate that the soil moisture estimates from the active/passive data have the smallest bias (− 0.008 m 3 m − 3) and the lowest unbiased root mean squared difference (0.021 m 3 m − 3), while soil moisture retrieved using a calibrated τ (optical depth)-ω (single scattering albedo) model based algorithm provided the best R 2. These results warrant further investigation of the synergistic use of active/passive data for soil moisture retrieval as well as the use of complex physically radiative transfer model as part of well-established algorithms.

The Gravity Recovery And Climate Experiment (GRACE) satellite mission provides time-variable gravity fields that are commonly used to study regional and global terrestrial total water storage (TWS) changes. These estimates are superimposed by different error sources such as the north–south stripes in the spatial domain and spectral/spatial leakage errors, which should be reduced before use in hydrological applications. Although different filtering methods have been developed to mitigate these errors, their performances are known to vary between regions. In this study, a Kernel Fourier Integration (KeFIn) filter is proposed, which can significantly decrease leakage errors over (small) river basins through a two-step post-processing algorithm. The first step mitigates the measurement noise and the aliasing of unmodelled high-frequency mass variations, and the second step contains an efficient kernel to decrease the leakage errors. To evaluate its performance, the KeFIn filter is compared with commonly used filters based on (i) basin/gridded scaling factors and (ii) ordinary basin averaging kernels. Two test scenarios are considered that include synthetic data with properties similar to GRACE TWS estimates within 43 globally distributed river basins of various sizes and application of the filters on real GRACE data. The KeFIn filter is assessed against water flux observations through the water balance equations as well as in-situ measurements. Results of both tests indicate a remarkable improvement after applying the KeFIn filter with leakage errors reduced in 34 out of the 43 assessed river basins and an average improvement of about 23.38% in leakage error reduction compared to other filters applied in this study. International audience

Abstract We present the Fluspect-B model (generally referred to as Fluspect), which simulates leaf chlorophyll fluorescence (ChlF), reflectance and transmittance spectra. The existing PROSPECT model and its concept of a compact leaf are used as a starting point, and the differential equations for radiative transfer within the leaf are solved by an efficient doubling algorithm. Due to the simplicity of these equations, Fluspect offers a high computational speed. With incident light provided as the main input parameter, Fluspect calculates the emission of ChlF on both the illuminated and shaded side of the leaf. Other input parameters are chlorophyll and carotenoid concentrations, leaf water, dry matter and senescent material (brown pigments) content, leaf mesophyll structure parameter and ChlF quantum efficiency for the two photosystems, PS-I and PS-II. We investigated the model performance using measurements of leaf reflectance, transmittance and ChlF spectra, collected for barley and sugar beet leaves in both a laboratory and outdoors setting. The plants had been grown under various illumination conditions to increase between-leaf variability of leaf biochemical and structural properties. We retrieved the model parameters, compared them to corresponding destructive measurements and finally, used them to simulate ChlF on either side of the leaf at several light intensities. The results show that the model reproduces observed SIF accurately, especially for leaves measured under natural illumination. Most of the observed between-leaf variability of ChlF could be explained from differences in leaf biochemical and structural properties, with potential additional information held by ChlF emission efficiency parameters.