Assessing interactions between crop biophysical parameters and X-band backscattering using empirical data and model sensitivity analysis

Active microwave remote sensing data at different frequencies can provide crucial information on crop morphology and conditions, thus effectively supporting agronomic management at different scales. In this work, we used variance-based global sensitivity analysis (GSA) as a quantitative framework for investigating the sensitivity of X-band backscattering to agronomic and morphological features typical of two different crops, maize and rice. To this end, we jointly exploited empirical data on crop status and growth, high-resolution TerraSAR-X data, and microwave radiative transfer model (RTM) simulations. Phenology-informed simulations allowed us to quantify the contributions of different scattering mechanisms for the two crops under varying observation setups, to assess the sensitivity of X-band backscattering to morpho-structural crop biophysical parameters (and their interactions), and to evaluate the effects of crop biomass on backscatter across growth stages.

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Assessing interactions between crop biophysical parameters and X-band backscattering using empirical data and model sensitivity analysis

10.36227/techrxiv.16803646 ◽

2021 ◽

Author(s):

Giacomo Fontanelli ◽

Francesco Montomoli ◽

Ramion Azar ◽

Giovanni Macelloni ◽

Paolo Villa

Keyword(s):

Sensitivity Analysis ◽

Empirical Data ◽

Remote Sensing Data ◽

Microwave Remote Sensing ◽

Radiative Transfer Model ◽

Transfer Model ◽

Growth Stages ◽

Biophysical Parameters ◽

X Band ◽

Active Microwave Remote Sensing

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THE SENSITIVITY OF C-BAND HYBRID POLARIMETRIC RISAT-1 SAR DATA TO LEAF AREA INDEX OF PADDY CROP

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-5-215-2018 ◽

2018 ◽

Vol IV-5 ◽

pp. 215-222

Author(s):

H. S. Srivastava ◽

T. Sivasankar ◽

P. Patel

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Incidence Angle ◽

Remote Sensing Data ◽

Microwave Remote Sensing ◽

Biophysical Parameters ◽

Area Index ◽

Crop Cycle ◽

Paddy Crop ◽

Active Microwave Remote Sensing

Abstract. Active microwave remote sensing data has become an important source to retrieve crop biophysical parameters due to its unique sensitivity towards geometrical, structural and dielectric properties of various crop components. The temporal variability of various crop biophysical parameters during crop cycle has significant impact on the overall crop yield. In this study, two RISAT-1 hybrid polarimetric temporal SAR datasets at &sim;32&deg; incidence angle were acquired during 2015 Kharif season. The in-situ leaf area index (LAI) values from seventeen paddy fields were measured in synchrony to the satellite passes during both the campaigns. Analysis observed the decreasing trend of backscattering coefficients (&sigma;&deg;RH, &sigma;&deg;RV) with increase in LAI. Results indicate that the sensitivity of hybrid polarimetric parameters towards LAI, also depends on the change in crop structure due to crop growth. This study investigate the sensitivity of backscattering coefficients (σ&deg;RH, σ&deg;RV) and polarimetric parameters (even bounce, odd bounce and volume component) generated from m-&delta;, m-&chi; and m-&alpha; space decompositions towards LAI using empirical analysis. An increase of 0.16 in R2 (from 0.63 to 0.79) clearly indicates that the polarimetric parameters (even bounce, odd bounce and volume component) are more sensitive to LAI of paddy crop than the backscattering coefficients (&sigma;&deg;RH, &sigma;&deg;RV). It has been identified that the combined use of backscattering coefficients as well as polarimetric parameters (even bounce, odd bounce and volume component) in the model, can significantly improve the accuracy of the LAI estimation.

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Assessing Interactions Between Crop Biophysical Parameters and X-Band Backscattering Using Empirical Data and Model Sensitivity Analysis

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2021.3119146 ◽

2021 ◽

pp. 1-12

Author(s):

Giacomo Fontanelli ◽

Francesco Montomoli ◽

Ramin Azar ◽

Giovanni Macelloni ◽

Paolo Villa

Keyword(s):

Sensitivity Analysis ◽

Empirical Data ◽

Biophysical Parameters ◽

Model Sensitivity ◽

X Band

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HAMP – the microwave package on the High Altitude and LOng range research aircraft HALO

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-7-4623-2014 ◽

2014 ◽

Vol 7 (5) ◽

pp. 4623-4657

Author(s):

M. Mech ◽

E. Orlandi ◽

S. Crewell ◽

F. Ament ◽

L. Hirsch ◽

...

Keyword(s):

High Altitude ◽

Long Range ◽

Microwave Remote Sensing ◽

Passive Microwave ◽

Lower Atmosphere ◽

Radiative Transfer Model ◽

Transfer Model ◽

Model Calculations ◽

Cloud Radar ◽

Research Aircraft

Abstract. An advanced package of microwave remote sensing instrumentation has been developed for the operation on the new German High Altitude LOng range research aircraft (HALO). The HALO Microwave Package, HAMP, consists of two nadir looking instruments: a cloud radar at 36 GHz and a suite of passive microwave radiometers with 26 frequencies in different bands between 22.24 and 183.31 ± 12.5 GHz. We present a description of HAMP's instrumentation together with an illustration of its potential. To demonstrate this potential synthetic measurements for the implemented passive microwave frequencies and the cloud radar based on cloud resolving and radiative transfer model calculations were performed. These illustrate the advantage of HAMP's chosen frequency coverage, which allows for improved detection of hydrometeors both via the emission and scattering of radiation. Regression algorithms compare HAMP retrieval with standard satellite instruments from polar orbiters and show its advantages particularly for the lower atmosphere with a reduced root mean square error by 5 and 15% for temperature and humidity, respectively. HAMP's main advantage is the high spatial resolution of about 1 km which is illustrated by first measurements from test flights. Together these qualities make it an exciting tool for gaining better understanding of cloud processes, testing retrieval algorithms, defining future satellite instrument specifications, and validating platforms after they have been placed in orbit.

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Glacier algae accelerate melt rates on the western Greenland Ice Sheet

10.5194/tc-2019-58 ◽

2019 ◽

Cited By ~ 2

Author(s):

Joseph M. Cook ◽

Andrew J. Tedstone ◽

Christopher Williamson ◽

Jenine McCutcheon ◽

Andrew J. Hodson ◽

...

Keyword(s):

Sea Level ◽

Ice Sheet ◽

Algal Growth ◽

Greenland Ice Sheet ◽

Remote Sensing Data ◽

Radiation Absorption ◽

Radiative Transfer Model ◽

Transfer Model ◽

Active Growth ◽

Ice Surface

Abstract. Melting of the Greenland Ice Sheet (GrIS) is the largest single contributor to eustatic sea level and is amplified by the growth of pigmented algae on the ice surface that increase solar radiation absorption. This biological albedo reducing effect and its impact upon sea level rise has not previously been quantified. Here, we combine field spectroscopy with a novel radiative transfer model, supervised classification of UAV and satellite remote sensing data and runoff modelling to calculate biologically-driven ice surface ablation and compare it to the albedo reducing effects of local mineral dust. We demonstrate that algal growth led to an additional 5.5–8.0 Gt of runoff from the western sector of the GrIS in summer 2016, representing 6–9 % of the total. Our analysis confirms the importance of the biological albedo feedback and that its omission from predictive models leads to the systematic underestimation of Greenland’s future sea level contribution, especially because both the bare ice zones available for algal colonization and the length of the active growth season are set to expand in the future.

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Radiative Transfer Model parametrization for simulating the reflectance of meadow vegetation

Miscellanea Geographica ◽

10.2478/mgrsd-2014-0001 ◽

2014 ◽

Vol 18 (2) ◽

pp. 5-9 ◽

Cited By ~ 4

Author(s):

Anna M. Jarocińska

Keyword(s):

Radiative Transfer ◽

Field Measurements ◽

Radiative Transfer Model ◽

Model Parameters ◽

Transfer Model ◽

Biophysical Parameters ◽

Input Parameters ◽

Vegetation Characteristics ◽

Field Reflectance ◽

Lower Water Content

Abstract Natural vegetation is complex and its reflectance is not easy to model. The aim of this study was to adjust the Radiative Transfer Model parameters for modelling the reflectance of heterogeneous meadows and evaluate its accuracy dependent on the vegetation characteristics. PROSAIL input parameters and reference spectra were collected during field measurements. Two different datasets were created: in the first, the input parameters were modelled using only field measurements; in the second, three input parameters were adjusted to minimize the differences between modelled and measured spectra. Reflectance was modelled using two datasets and then verified based on field reflectance using the RMSE. The average RMSE for the first dataset was equal to 0.1058, the second was 0.0362. The accuracy of the simulated spectra was analysed dependent on the value of the biophysical parameters. Better results were obtained for meadows with higher biomass value, greater LAI and lower water content.

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FLiES-SIF ver. 1.0: Three-dimensional radiative transfer model for estimating solar induced fluorescence

10.5194/gmd-2020-19 ◽

2020 ◽

Cited By ~ 1

Author(s):

Yuma Sakai ◽

Hideki Kobayashi ◽

Tomomichi Kato

Keyword(s):

Sensitivity Analysis ◽

Chlorophyll Fluorescence ◽

Radiative Transfer ◽

Three Dimensional ◽

Radiative Transfer Model ◽

Model Description ◽

Transfer Model ◽

Plant Canopy ◽

Parallel Layer ◽

Leaf Level

Abstract. Global terrestrial ecosystems control the atmospheric CO2 concentration through gross primary production (GPP) and ecosystem respiration processes. Chlorophyll fluorescence is one of the energy release pathways of excess incident lights in the photosynthetic process. Over the last ten years, extensive studies have been revealed that canopy scale sun-induced chlorophyll fluorescence (SIF), which potentially provides a direct pathway to link leaf level photosynthesis to global GPP, can be observed from satellites. SIF is used to infer photosynthetic capacity of plant canopy, however, it is not clear how the leaf-level SIF emission contributes to the top of canopy directional SIF. Plant canopy radiative transfer models are the useful tools to understand the causality of directional canopy SIF. One dimensional (1-D) plane parallel layer models (e.g. the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model) have been widely used and are useful to understand the general mechanisms behind the temporal and seasonal variations in SIF. However, due to the lack of complexity of the actual canopy structures, three dimensional models (3-D) have a potential to delineate the realistic directional canopy SIFs. Forest Light Environmental Simulator for SIF (FLiES-SIF) version 1.0 is the 3-D Monte Carlo plant canopy radiative transfer model to understand the biological and physical mechanisms behind the SIF emission from complex forest canopies. In this model description paper, we focused on the model formulation and simulation schemes, and showed some sensitivity analysis against several major variables such as view angle and leaf area index (LAI). The simulation results show that SIF increases with LAI then saturated at LAI > 2–4 depending on the spectral wavelength. The sensitivity analysis also shows that simulated SIF radiation may decrease with LAI at higher LAI domain (LAI > 5). These phenomena are seen in certain sun and view angle conditions. This type of non-linear and non-monotonic SIF behavior to LAI is also related to spatial forest structure patterns. FLiES-SIF version 1.0 can be used to quantify the canopy SIF in various view angles including the contribution of multiple scattering which is the important component in the near infrared domain. The potential use of the model is to standardize the satellite SIF by correcting the bi-directional effect. This step will contribute to the improvement of the GPP estimation accuracy through SIF.

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Parameterization of Vegetation Scattering Albedo in the Tau-Omega Model for Soil Moisture Retrieval on Croplands

Remote Sensing ◽

10.3390/rs12182939 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2939

Author(s):

Chang-Hwan Park ◽

Thomas Jagdhuber ◽

Andreas Colliander ◽

Johan Lee ◽

Aaron Berg ◽

...

Keyword(s):

Soil Moisture ◽

Radiative Transfer ◽

Data Assimilation ◽

Brightness Temperature ◽

Microwave Remote Sensing ◽

Radiative Transfer Model ◽

Transfer Model ◽

Time Varying ◽

Vegetation Fraction ◽

Omega Model

An accurate radiative transfer model (RTM) is essential for the retrieval of soil moisture (SM) from microwave remote sensing data, such as the passive microwave measurements from the Soil Moisture Active Passive (SMAP) mission. This mission delivers soil moisture products based upon L-band brightness temperature data, via retrieval algorithms for surface and root-zone soil moisture, the latter is retrieved using data assimilation and model support. We found that the RTM based on the tau-omega (τ-ω) model can suffer from significant errors over croplands in the simulation of brightness temperature (Tb) (in average between −9.4K and +12.0K for single channel algorithm (SCA); −8K and +9.7K for dual-channel algorithm (DCA)) if the vegetation scattering albedo (omega) is set constant and temporal variations are not considered. In order to reduce this uncertainty, we propose a time-varying parameterization of omega for the widely established zeroth order radiative transfer τ-ω model. The main assumption is that omega can be expressed by a functional relationship between vegetation optical depth (tau) and the Green Vegetation Fraction (GVF). Assuming allometry in the tau-omega relationship, a power-law function was established and it is supported by correlating measurements of tau and GVF. With this relationship, both tau and omega increase during the development of vegetation. The application of the proposed time-varying vegetation scattering albedo results in a consistent improvement for the unbiased root mean square error of 16% for SCA and 15% for DCA. The reduction for positive and negative biases was 45% and 5% for SCA and 26% and 12% for DCA, respectively. This indicates that vegetation dynamics within croplands are better represented by a time-varying single scattering albedo. Based on these results, we anticipate that the time-varying omega within the tau-omega model will help to mitigate potential estimation errors in the current SMAP soil moisture products (SCA and DCA). Furthermore, the improved tau-omega model might serve as a more accurate observation operator for SMAP data assimilation in weather and climate prediction model.

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Analysis of Sentinel-2 and RapidEye for Retrieval of Leaf Area Index in a Saltmarsh Using a Radiative Transfer Model

Remote Sensing ◽

10.3390/rs11060671 ◽

2019 ◽

Vol 11 (6) ◽

pp. 671 ◽

Cited By ~ 19

Author(s):

Roshanak Darvishzadeh ◽

Tiejun Wang ◽

Andrew Skidmore ◽

Anton Vrieling ◽

Brian O’Connor ◽

...

Keyword(s):

Remote Sensing ◽

Radiative Transfer ◽

Leaf Area Index ◽

Plant Traits ◽

Remote Sensing Data ◽

Radiative Transfer Model ◽

Transfer Model ◽

Area Index ◽

Field Campaigns ◽

Sentinel 2

The Sentinel satellite fleet of the Copernicus Programme offers new potential to map and monitor plant traits at fine spatial and temporal resolutions. Among these traits, leaf area index (LAI) is a crucial indicator of vegetation growth and an essential variable in biodiversity studies. Numerous studies have shown that the radiative transfer approach has been a successful method to retrieve LAI from remote-sensing data. However, the suitability and adaptability of this approach largely depend on the type of remote-sensing data, vegetation cover and the ecosystem studied. Saltmarshes are important wetland ecosystems threatened by sea level rise among other human- and animal-induced changes. Therefore, monitoring their vegetation status is crucial for their conservation, yet few LAI assessments exist for these ecosystems. In this study, the retrieval of LAI in a saltmarsh ecosystem is examined using Sentinel-2 and RapidEye data through inversion of the PROSAIL radiative transfer model. Field measurements of LAI and some other plant traits were obtained during two succeeding field campaigns in July 2015 and 2016 on the saltmarsh of Schiermonnikoog, a barrier island of the Netherlands. RapidEye (2015) and Sentinel-2 (2016) data were acquired concurrent to the time of the field campaigns. The broadly employed PROSAIL model was inverted using two look-up tables (LUTs) generated in the spectral band’s settings of the two sensors and in which each contained 500,000 records. Different solutions from the LUTs, as well as, different Sentinel-2 spectral subsets were considered to examine the LAI retrieval. Our results showed that generally the LAI retrieved from Sentinel-2 had higher accuracy compared to RapidEye-retrieved LAI. Utilising the mean of the first 10 best solutions from the LUTs resulted in higher R2 (0.51 and 0.59) and lower normalised root means square error (NRMSE) (0.24 and 0.16) for both RapidEye and Sentinel-2 data respectively. Among different Sentinel-2 spectral subsets, the one comprised of the four near-infrared (NIR) and shortwave infrared (SWIR) spectral bands resulted in higher estimation accuracy (R2 = 0.44, NRMSE = 0.21) in comparison to using other studied spectral subsets. The results demonstrated the feasibility of broadband multispectral sensors, particularly Sentinel-2 for retrieval of LAI in the saltmarsh ecosystem via inversion of PROSAIL. Our results highlight the importance of proper parameterisation of radiative transfer models and capacity of Sentinel-2 spectral range and resolution, with impending high-quality global observation aptitude, for retrieval of plant traits at a global scale.

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A Refined Four-Stream Radiative Transfer Model for Row-Planted Crops

Remote Sensing ◽

10.3390/rs12081290 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1290 ◽

Cited By ~ 1

Author(s):

Xu Ma ◽

Tiejun Wang ◽

Lei Lu

Keyword(s):

Radiative Transfer ◽

Computer Simulations ◽

Radiative Transfer Model ◽

Transfer Model ◽

Growth Stages ◽

Canopy Reflectance ◽

Transfer Equations ◽

Radiative Transfer Equations ◽

Different Growth Stages

In modeling the canopy reflectance of row-planted crops, neglecting horizontal radiative transfer may lead to an inaccurate representation of vegetation energy balance and further cause uncertainty in the simulation of canopy reflectance at larger viewing zenith angles. To reduce this systematic deviation, here we refined the four-stream radiative transfer equations by considering horizontal radiation through the lateral “walls”, considered the radiative transfer between rows, then proposed a modified four-stream (MFS) radiative transfer model using single and multiple scattering. We validated the MFS model using both computer simulations and in situ measurements, and found that the MFS model can be used to simulate crop canopy reflectance at different growth stages with an accuracy comparable to the computer simulations (RMSE < 0.002 in the red band, RMSE < 0.019 in NIR band). Moreover, the MFS model can be successfully used to simulate the reflectance of continuous (RMSE = 0.012) and row crop canopies (RMSE < 0.023), and therefore addressed the large viewing zenith angle problems in the previous row model based on four-stream radiative transfer equations. Our results demonstrate that horizontal radiation is an important factor that needs to be considered in modeling the canopy reflectance of row-planted crops. Hence, the refined four-stream radiative transfer model is applicable to the real world.

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