scholarly journals MODIFIED MONITORING METHOD OF VEGETATION WATER CONTENT BASED ON COUPLED RADIATIVE TRANSFER MODEL

2010 ◽  
Vol 29 (3) ◽  
pp. 185-189
Author(s):  
Xiang ZHAO ◽  
Jin-Di WANG ◽  
Su-Hong LIU
Keyword(s):  
Radiative Transfer ◽  
Water Content ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Monitoring Method ◽  
Vegetation Water Content ◽  
Vegetation Water

scholarly journals Seasonal variation in vegetation water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

2015 ◽  
Vol 12 (7) ◽  
pp. 5503-5533
Author(s):  
G. Mendiguren ◽  
M. P. Martín ◽  
H. Nieto ◽  
J. Pacheco-Labrador ◽  
S. Jurdao
Keyword(s):  
Spatial Variation ◽  
Water Content ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Spectral Indices ◽  
Proximal Sensing ◽  
Area Index ◽  
Vegetation Water Content ◽  
Equivalent Water Thickness ◽  
Vegetation Water

Abstract. This study evaluates three different metrics of vegetation water content estimated from proximal sensing and MODIS satellite imagery: Fuel Moisture Content (FMC), Equivalent Water Thickness (EWT) and Canopy Water Content (CWC). Dry matter (Dm) and Leaf area Index (LAI) were also analyzed in order to connect FMC with EWT and EWT with CWC, respectively. This research took place in a Fluxnet site located in Mediterranean wooded grassland (dehesa) ecosystem in Las Majadas del Tietar (Spain). Results indicated that FMC and EWT showed lower spatial variation than CWC. The spatial variation within the MODIS pixel was not as critical as its temporal trend, so to capture better the variability, fewer plots should be sampled but more times. Due to the high seasonal Dm variability, a constant annual value would not work to predict EWT from FMC. Relative root mean square error (RRMSE) evaluated the performance of nine spectral indices to compute each variable. VARI provided the worst results in all cases. For proximal sensing, GEMI worked best for both FMC (RRMSE = 34.5%) and EWT (RRMSE = 27.43%) while NDII and GVMI performed best for CWC (RRMSE =30.27% and 31.58% respectively). For MODIS data, results were a bit better with EVI as the best predictor for FMC (RRMSE = 33.81%) and CWC (RRMSE = 27.56%) and GEMI for EWT (RRMSE = 24.6%). To explain these differences, proximal sensing measures only grasslands at nadir view angle, but MODIS includes also trees, their shades, and other artifacts at up to 20° view angle. CWC was better predicted than the other two water content variables, probably because CWC depends on LAI, which is highly correlated to the spectral indices. Finally, these empirical methods outperformed FMC and CWC products based on radiative transfer model inversion.

scholarly journals Landsat-8 and Sentinel-2 Canopy Water Content Estimation in Croplands through Radiative Transfer Model Inversion

2020 ◽  
Vol 12 (17) ◽  
pp. 2803
Author(s):  
Erik J. Boren ◽  
Luigi Boschetti
Keyword(s):  
Radiative Transfer ◽  
Water Content ◽  
A Priori ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Landsat 8 ◽  
Multiple Sources ◽  
Model Inversion ◽  
Canopy Water ◽  
Sentinel 2

Despite the potential implications of a cropland canopy water content (CCWC) thematic product, no global remotely sensed CCWC product is currently generated. The successful launch of the Landsat-8 Operational Land Imager (OLI) in 2012, Sentinel-2A Multispectral Instrument (MSI) in 2015, followed by Sentinel-2B in 2017, make possible the opportunity for CCWC estimation at a spatial and temporal scale that can meet the demands of potential operational users. In this study, we designed and tested a novel radiative transfer model (RTM) inversion technique to combine multiple sources of a priori data in a look-up table (LUT) for inverting the NASA Harmonized Landsat Sentinel-2 (HLS) product for CCWC estimation. This study directly builds on previous research for testing the constraint of the leaf parameter (Ns) in PROSPECT, by applying those constraints in PRO4SAIL in an agricultural setting where the variability of canopy parameters are relatively minimal. In total, 225 independent leaf measurements were used to train the LUTs, and 102 field data points were collected over the 2015–2017 growing seasons for validating the inversions. The results confirm increasing a priori information and regularization yielded the best performance for CCWC estimation. Despite the relatively low variable canopy conditions, the inclusion of Ns constraints did not improve the LUT inversion. Finally, the inversion of Sentinel-2 data outperformed the inversion of Landsat-8 in the HLS product. The method demonstrated ability for HLS inversion for CCWC estimation, resulting in the first HLS-based CCWC product generated through RTM inversion.

Constraining ice water content of thin Antarctic cirrus clouds using ground-based lidar and satellite data

2021 ◽  
Author(s):  
S. P. Alexander ◽  
A. R. Klekociuk
Keyword(s):  
Radiative Transfer ◽  
Water Content ◽  
Cirrus Clouds ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Cold Temperatures ◽  
Ice Water Content ◽  
Ground Based Lidar ◽  
Ice Water

AbstractWe combine observations of optically-thin cirrus clouds made by lidar at Davis, Antarctica (69°S, 78°E) during 14 – 15 June 2011 with a microphysical retrieval algorithm to constrain the ice water content (IWC) of these clouds. The cirrus were embedded in a tropopause jet which flowed around a ridge of high pressure extending southwards over Davis from the Southern Ocean. Cloud optical depths were (0.082±0.001) and sub-visual cirrus were present during 11% of the observation period. The macrophysical cirrus cloud properties obtained during this case study are consistent with those previously reported at lower latitudes. MODIS satellite imagery and AIRS surface temperature data are used as inputs into a radiative transfer model in order to constrain the IWC and ice water path of the cirrus. The derived cloud IWC is consistent with in-situ observations made at other locations but at similarly cold temperatures. The optical depths derived from the model agree with those calculated directly from the lidar data. This study demonstrates the value of a combination of ground-based lidar observations and a radiative transfer model in constraining microphysical cloud parameters which could be utilised at locations where other lidar measurements are made.

Probing clouds in planets with a simple radiative transfer model: the Jupiter case

2012 ◽  
Vol 33 (6) ◽  
pp. 1611-1624 ◽  
Author(s):  
Iñigo Mendikoa ◽  
Santiago Pérez-Hoyos ◽  
Agustín Sánchez-Lavega
Keyword(s):  
Radiative Transfer ◽  
Radiative Transfer Model ◽  
Transfer Model

scholarly journals Mapping barrier island soil moisture using a radiative transfer model of hyperspectral imagery from an unmanned aerial system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rehman S. Eon ◽  
Charles M. Bachmann
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Moisture Content ◽  
Layer Thickness ◽  
Soil Moisture Content ◽  
Hyperspectral Imagery ◽  
Barrier Island ◽  
Water Layer ◽  
Radiative Transfer Model ◽  
Transfer Model

AbstractThe advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system. Our approach uses a recently developed model known as the multilayer radiative transfer model of soil reflectance (MARMIT). MARMIT partitions contributions due to water and the sediment surface into equivalent but separate layers and describes these layers using an equivalent slab model formalism. The model water layer thickness along with the fraction of wet surface become parameters that must be optimized in a calibration step, with extinction due to water absorption being applied in the model based on equivalent water layer thickness, while transmission and reflection coefficients follow the Fresnel formalism. In this work, we evaluate the model in both field settings, using UAS hyperspectral imagery, and laboratory settings, using hyperspectral spectra obtained with a goniometer. Sediment samples obtained from four different field sites representing disparate environmental settings comprised the laboratory analysis while field validation used hyperspectral UAS imagery and coordinated ground truth obtained on a barrier island shore during field campaigns in 2018 and 2019. Analysis of the most significant wavelengths for retrieval indicate a number of different wavelengths in the short-wave infra-red (SWIR) that provide accurate fits to measured soil moisture content in the laboratory with normalized root mean square error (NRMSE)< 0.145, while independent evaluation from sequestered test data from the hyperspectral UAS imagery obtained during the field campaign obtained an average NRMSE = 0.169 and median NRMSE = 0.152 in a bootstrap analysis.

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