scholarly journals Remote Sensing Energy Balance Model for the Assessment of Crop Evapotranspiration and Water Status in an Almond Rootstock Collection

2021 ◽  
Vol 12 ◽  
Author(s):  
Joaquim Bellvert ◽  
Héctor Nieto ◽  
Ana Pelechá ◽  
Christian Jofre-Čekalović ◽  
Lourdes Zazurca ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Water Productivity ◽  
Balance Model ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Stem Water Potential ◽  
Crop Evapotranspiration ◽  
Area Index ◽  
Mean Square Errors

One of the objectives of many studies conducted by breeding programs is to characterize and select rootstocks well-adapted to drought conditions. In recent years, field high-throughput phenotyping methods have been developed to characterize plant traits and to identify the most water use efficient varieties and rootstocks. However, none of these studies have been able to quantify the behavior of crop evapotranspiration in almond rootstocks under different water regimes. In this study, remote sensing phenotyping methods were used to assess the evapotranspiration of almond cv. “Marinada” grafted onto a rootstock collection. In particular, the two-source energy balance and Shuttleworth and Wallace models were used to, respectively, estimate the actual and potential evapotranspiration of almonds grafted onto 10 rootstock under three different irrigation treatments. For this purpose, three flights were conducted during the 2018 and 2019 growing seasons with an aircraft equipped with a thermal and multispectral camera. Stem water potential (Ψstem) was also measured concomitant to image acquisition. Biophysical traits of the vegetation were firstly assessed through photogrammetry techniques, spectral vegetation indices and the radiative transfer model PROSAIL. The estimates of canopy height, leaf area index and daily fraction of intercepted radiation had root mean square errors of 0.57 m, 0.24 m m–1 and 0.07%, respectively. Findings of this study showed significant differences between rootstocks in all of the evaluated parameters. Cadaman® and Garnem® had the highest canopy vigor traits, evapotranspiration, Ψstem and kernel yield. In contrast, Rootpac® 20 and Rootpac® R had the lowest values of the same parameters, suggesting that this was due to an incompatibility between plum-almond species or to a lower water absorption capability of the rooting system. Among the rootstocks with medium canopy vigor, Adesoto and IRTA 1 had a lower evapotranspiration than Rootpac® 40 and Ishtara®. Water productivity (WP) (kg kernel/mm water evapotranspired) tended to decrease with Ψstem, mainly in 2018. Cadaman® and Garnem® had the highest WP, followed by INRA GF-677, IRTA 1, IRTA 2, and Rootpac® 40. Despite the low Ψstem of Rootpac® R, the WP of this rootstock was also high.

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

2019 ◽  
Vol 11 (6) ◽  
pp. 671 ◽  
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.

scholarly journals Physically based retrieval of crop characteristics for improved water use estimates

2009 ◽  
Vol 13 (5) ◽  
pp. 663-674 ◽  
Author(s):  
K. Richter ◽  
W. J. Timmermans
Keyword(s):  
Sustainable Management ◽  
Test Site ◽  
Balance Model ◽  
Radiative Transfer Model ◽  
Irrigated Agriculture ◽  
Transfer Model ◽  
Retrieval Performance ◽  
Area Index ◽  
Physically Based

Abstract. The increasing scarcity of water from local to global scales requires the efficient monitoring of this valuable resource, especially in the context of a sustainable management in irrigated agriculture. In this study, a two-source energy balance model (TSEB) was applied to the Barrax test site. The inputs of leaf area index (LAI) and fractional vegetation cover (fCover) were estimated from CHRIS imagery by using the traditional scaled NDVI and a look-up table (LUT) inversion approach. The LUT was constructed by using the well established SAILH + PROSPECT radiative transfer model. Simulated fluxes were compared with tower measurements and vegetation characteristics were evaluated with in situ LAI and fCover measurements of a range of crops from the SPARC campaign 2004. Results showed a better retrieval performance for the LUT approach for canopy parameters, affecting flux predictions that were related to land use.

scholarly journals Estimation of Needleleaf Canopy and Trunk Temperatures and Longwave Contribution to Melting Snow

2017 ◽  
Vol 18 (2) ◽  
pp. 555-572 ◽  
Author(s):  
K. N. Musselman ◽  
J. W. Pomeroy
Keyword(s):  
Energy Balance ◽  
Radiative Transfer ◽  
Thermal Inertia ◽  
Longwave Radiation ◽  
Ambient Air ◽  
Balance Model ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Individual Tree ◽  
Energy Balance Approach

AbstractA measurement and modeling campaign evaluated variations in tree temperatures with solar exposure at the edge of a forest clearing and how the resulting longwave radiation contributed to spatial patterns of snowmelt energy surrounding an individual tree. Compared to measurements, both a one-dimensional (1D) energy-balance model and a two-dimensional (2D) radial trunk heat transfer model that simulated trunk surface temperatures and thermal inertia performed well (RMSE and biases better than 1.7° and ±0.4°C). The 2D model that resolved a thin bark layer better simulated daytime temperature spikes. Measurements and models agreed that trunk surfaces returned to ambient air temperature values near sunset. Canopy needle temperatures modeled with a 1D energy-balance approach were within the range of measurements. The radiative transfer model simulated substantial tree-contributed snow surface longwave irradiance to a distance of approximately one-half the tree height, with higher values on the sun-exposed sides of the tree. Trunks had very localized and substantially lower longwave energy influence on snowmelt compared to that of the canopy. The temperature and radiative transfer models provide the spatially detailed information needed to develop scaling relationships for estimating net radiation for snowmelt in sparse and discontinuous forest canopies.

scholarly journals Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection

2015 ◽  
Vol 7 (4) ◽  
pp. 4604-4625 ◽  
Author(s):  
Gaofei Yin ◽  
Jing Li ◽  
Qinhuo Liu ◽  
Weiliang Fan ◽  
Baodong Xu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Model Selection ◽  
Radiative Transfer ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Area Index

scholarly journals Physical based retrieval of crop characteristics for improved water use estimates

2009 ◽  
Vol 6 (2) ◽  
pp. 1973-2004
Author(s):  
K. Richter ◽  
W. J. Timmermans
Keyword(s):  
Sustainable Management ◽  
Test Site ◽  
Balance Model ◽  
Radiative Transfer Model ◽  
Irrigated Agriculture ◽  
Transfer Model ◽  
Retrieval Performance ◽  
Area Index ◽  
Vegetation Characteristics

Abstract. The increasing scarcity of water from local to global scales requires the efficient monitoring of this valuable resource, especially in the context of a sustainable management in irrigated agriculture. In this study, a two-source energy balance model (TSEB) was applied to the Barrax test site. The inputs of Leaf area index (LAI) and fractional vegetation cover (fCover), were estimated from CHRIS imagery by using the traditional scaled NDVI and a LUT inversion approach. The LUT was constructed using the well established SAILH+PROSPECT radiative transfer model. Simulated fluxes were compared with tower measurements and vegetation characteristics were evaluated with in situ LAI and fCover measurements of a range of crops from the SPARC campaign 2004. Results showed a better retrieval performance for the LUT approach for canopy parameters, affecting flux predictions that were related to land use.

scholarly journals Cutting out the middleman: calibrating and validating a dynamic vegetation model (ED2-PROSPECT5) using remotely sensed surface reflectance

2021 ◽  
Vol 14 (5) ◽  
pp. 2603-2633
Author(s):  
Alexey N. Shiklomanov ◽  
Michael C. Dietze ◽  
Istem Fer ◽  
Toni Viskari ◽  
Shawn P. Serbin
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Leaf Area ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Surface Reflectance ◽  
Vegetation Model ◽  
Area Index ◽  
Hemispherical Reflectance ◽  
Vegetation Models

Abstract. Canopy radiative transfer is the primary mechanism by which models relate vegetation composition and state to the surface energy balance, which is important to light- and temperature-sensitive plant processes as well as understanding land–atmosphere feedbacks. In addition, certain parameters (e.g., specific leaf area, SLA) that have an outsized influence on vegetation model behavior can be constrained by observations of shortwave reflectance, thus reducing model predictive uncertainty. Importantly, calibrating against radiative transfer outputs allows models to directly use remote sensing reflectance products without relying on highly derived products (such as MODIS leaf area index) whose assumptions may be incompatible with the target vegetation model and whose uncertainties are usually not well quantified. Here, we created the EDR model by coupling the two-stream representation of canopy radiative transfer in the Ecosystem Demography model version 2 (ED2) with a leaf radiative transfer model (PROSPECT-5) and a simple soil reflectance model to predict full-range, high-spectral-resolution surface reflectance that is dependent on the underlying ED2 model state. We then calibrated this model against estimates of hemispherical reflectance (corrected for directional effects) from the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and survey data from 54 temperate forest plots in the northeastern United States. The calibration significantly reduced uncertainty in model parameters related to leaf biochemistry and morphology and canopy structure for five plant functional types. Using a single common set of parameters across all sites, the calibrated model was able to accurately reproduce surface reflectance for sites with highly varied forest composition and structure. However, the calibrated model's predictions of leaf area index (LAI) were less robust, capturing only 46 % of the variability in the observations. Comparing the ED2 radiative transfer model with another two-stream soil–leaf–canopy radiative transfer model commonly used in remote sensing studies (PRO4SAIL) illustrated structural errors in the ED2 representation of direct radiation backscatter that resulted in systematic underestimation of reflectance. In addition, we also highlight that, to directly compare with a two-stream radiative transfer model like EDR, we had to perform an additional processing step to convert the directional reflectance estimates of AVIRIS to hemispherical reflectance (also known as “albedo”). In future work, we recommend that vegetation models add the capability to predict directional reflectance, to allow them to more directly assimilate a wide range of airborne and satellite reflectance products. We ultimately conclude that despite these challenges, using dynamic vegetation models to predict surface reflectance is a promising avenue for model calibration and validation using remote sensing data.

scholarly journals The role of radiation penetration in the energy budget of the snowpack at Summit, Greenland

2009 ◽  
Vol 3 (2) ◽  
pp. 155-165 ◽  
Author(s):  
P. Kuipers Munneke ◽  
M. R. van den Broeke ◽  
C. H. Reijmer ◽  
M. M. Helsen ◽  
W. Boot ◽  
...  
Keyword(s):  
Energy Balance ◽  
Model Simulation ◽  
Longwave Radiation ◽  
Heat Fluxes ◽  
Heat Sinks ◽  
Shortwave Radiation ◽  
Balance Model ◽  
Radiative Transfer Model ◽  
Transfer Model

Abstract. Measurements of the summer surface energy balance at Summit, Greenland, are presented (8 June–20 July 2007). These measurements serve as input to an energy balance model that searches for a surface temperature for which closure of all energy terms is achieved. A good agreement between observed and modelled surface temperatures was found, with an average difference of 0.45°C and an RMSE of 0.85°C. It turns out that penetration of shortwave radiation into the snowpack plays a small but important role in correctly simulating snow temperatures. After 42 days, snow temperatures in the first meter are 3.6–4.0°C higher compared to a model simulation without radiation penetration. Sensitivity experiments show that these results cannot be reproduced by tuning the heat conduction process alone, by varying snow density or snow diffusivity. We compared the two-stream radiation penetration calculations with a sophisticated radiative transfer model and discuss the differences. The average diurnal cycle shows that net shortwave radiation is the largest energy source (diurnal average of +61 W m−2), net longwave radiation the largest energy sink (−42 W m−2). On average, subsurface heat flux, sensible and latent heat fluxes are the remaining, small heat sinks (−5, −5 and −7 W m−2, respectively), although these are more important on a subdaily timescale.

Disentangling ecosystem transpiration from evapotranspiration observations employing simplified vegetation-substrate energy balance model

2020 ◽  
Author(s):  
Kaniska Mallick ◽  
Dennis Baldocchi ◽  
Andrew Jarvis ◽  
Ivonne Trebs ◽  
Mauro Sulis ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Energy Balance ◽  
Land Surface ◽  
Sensible Heat Flux ◽  
Energy Balance Model ◽  
Arid Ecosystems ◽  
Balance Model ◽  
Area Index ◽  
Availability Constraints

<p>Evapotranspiration (E<sub>ET</sub>) observed by eddy covariance (EC) towers is composed of physical evaporation (E<sub>E</sub>) from wet surfaces and biological transpiration (E<sub>T</sub>), that involves soil moisture uptake by roots and water vapor transfer regulated through the canopy-stomatal conductance (g<sub>C</sub>) during photosynthesis. E<sub>T</sub> plays a dominant role in the global water cycle and represents 80% of the total terrestrial E<sub>ET</sub>. Understanding the magnitude and variability of E<sub>T</sub> are critical to assess the ecophysiological responses of vegetation to drought. While separating E<sub>T</sub> signals from lumped E<sub>ET</sub> observations and/or simulating E<sub>T</sub> by terrestrial systems models is insufficiently constrained owing to the large uncertainties in disentangling g<sub>C</sub> from the aggregated canopy-substrate conductance (g<sub>cS</sub>), evaluating ecosystem E<sub>T</sub> derived through partitioning E<sub>ET</sub> observations (or model simulation) is also challenging due to the absence of any ecosystem-scale measurements of this biotic flux and g<sub>C</sub>. To date, the main methods for partitioning EC-E<sub>ET</sub> observations are largely based on regressing E<sub>ET</sub> with gross photosynthesis (P<sub>g</sub>) and atmospheric vapor pressure deficit (D<sub>A</sub>) observations. However, such methods ignore the essential feedback of the surface energy balance (SEB) and canopy temperature (T<sub>C</sub>) on g<sub>C</sub> and E<sub>T</sub>.</p><p>This study demonstrates partitioning E<sub>ET</sub> observations into E<sub>T</sub> and E<sub>E</sub> [soil evaporation (E<sub>Es</sub>) and interception evaporation (E<sub>Ei</sub>)] through an ‘analytical solution’ of g<sub>C</sub>, T<sub>C</sub> and canopy vapor pressures by employing a Shuttleworth-Gurney vegetation-substrate energy balance model with minimal complexity. The model is called TRANSPIRE (Top-down partitioning evapotRANSPIRation modEl), which ingests remote sensing land surface temperature (LST) and leaf area index (L<sub>ai</sub>) information in conjunction with meteorological, sensible heat flux (H) and E<sub>ET</sub> observations from EC tower into the SEB equations for retrieving canopy and soil temperatures (T<sub>S</sub>, T<sub>C</sub>), g<sub>C</sub>, and E<sub>T</sub>.</p><p>E<sub>T</sub> estimates from TRANSPIRE were tested and evaluated with a remote sensing based E<sub>T</sub> estimate from an analytical model (STIC1.2), where lumped E<sub>ET</sub> was partitioned by employing a moisture availability constraints across an aridity gradient in the North Australian Tropical Transect (NATT) by using time-series of 8-day MODIS Terra LST and LAI products in conjunction with EC measurements from 2011 to 2018. Both methods captured the seasonal pattern of E<sub>T</sub>/E<sub>ET</sub> ratio in a very similar way. While E<sub>T</sub> accounted for 60±10% of the annual E<sub>ET</sub> in the tropical savanna, E<sub>T</sub> in the arid mulga contributed 75±12% of the annual E<sub>ET</sub>. Seasonal variation of E<sub>T</sub> was higher in the arid, semi-arid ecosystems (50 - 90%), as compared to the humid tropical ecosystem (10 - 50%). The TRANSPIRE model reasonably captured E<sub>T</sub> variations along with soil moisture and precipitation dynamics in both sparse and homogeneous vegetation and showed the potential of partitioning E<sub>ET</sub> observations for cross-site comparison with a variety of models.</p>

scholarly journals Improving thermodynamic profile retrievals from microwave radiometers by including Radio Acoustic Sounding System (RASS) observations

2021 ◽  
Author(s):  
Irina V. Djalalova ◽  
David D. Turner ◽  
Laura Bianco ◽  
James M. Wilczak ◽  
James Duncan ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Pearson Correlation ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Iterative Approach ◽  
Linear Quadratic ◽  
Acoustic Sounding ◽  
Microwave Radiometers ◽  
Mean Square Errors

Abstract. Thermodynamic profiles are often retrieved from the multi-wavelength brightness temperature observations made by microwave radiometers (MWRs) using regression methods (linear, quadratic approaches), artificial intelligence (neural networks), or physical-iterative methods. Regression and neural network methods are tuned to mean conditions derived from a climatological dataset of thermodynamic profiles collected nearby. In contrast, physical-iterative retrievals use a radiative transfer model starting from a climatologically reasonable value of temperature and water vapor, with the model run iteratively until the derived brightness temperatures match those observed by the MWR within a specified uncertainty. In this study, a physical-iterative approach is used to retrieve temperature and humidity profiles from data collected during XPIA (eXperimental Planetary boundary layer Instrument Assessment), a field campaign held from March to May 2015 at NOAA's Boulder Atmospheric Observatory (BAO) facility. During the campaign, several passive and active remote sensing instruments as well as in-situ platforms were deployed and evaluated to determine their suitability for the verification and validation of meteorological processes. Among the deployed remote sensing instruments was a multi-channel MWR, as well as two radio acoustic sounding systems (RASS), associated with 915-MHz and 449-MHz wind profiling radars. Having the possibility to combine the information provided by the MWR and RASS systems, in this study the physical-iterative approach is tested with different observational inputs: first using data from surface sensors and the MWR in different configurations, and then including data from the RASSs. These temperature retrievals are also compared to those derived by a neural network method, assessing their relative accuracy against 58 co-located radiosonde profiles. Results show that the combination of the MWR and RASS observations in the physical-iterative approach allows for a more accurate characterization of low-level temperature inversions, and that these retrieved temperature profiles match the radiosonde observations better than all other approaches, including the neural network, in the atmospheric layer between the surface and 5 km AGL. Specifically, in this layer of the atmosphere, both root mean square errors and standard deviations of the difference between radiosonde and retrievals that combine MWR and RASS are improved by ~0.5 °C compared to the other methods. Pearson correlation coefficients are also improved.

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