Retrieval of time series LAI by coupling an empirical crop growth model with a radiative transfer model

2010 ◽  
Author(s):  
Guangjian Yan ◽  
Xiaoyan Yang ◽  
Jing Li ◽  
Xihan Mu
Keyword(s):  
Time Series ◽  
Radiative Transfer ◽  
Growth Model ◽  
Crop Growth ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Crop Growth Model

Exploring continuous time series of vegetation hyperspectral reflectance and solar-induced fluorescence through radiative transfer model inversion

2020 ◽  
Author(s):  
Marco Celesti ◽  
Khelvi Biriukova ◽  
Petya K. E. Campbell ◽  
Ilaria Cesana ◽  
Sergio Cogliati ◽  
...  
Keyword(s):  
Time Series ◽  
Radiative Transfer ◽  
Near Infrared ◽  
Plant Traits ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Optimization Approach ◽  
Ancillary Data ◽  
Full Spectrum ◽  
Retrieval Scheme

<p>Remote sensing of solar-induced chlorophyll fluorescence (SIF) is of growing interest for the scientific community due to the inherent link of SIF with vegetation photosynthetic activity. An increasing number of in situ and airborne fluorescence spectrometers has been deployed worldwide to advance the understanding and usage of SIF for ecosystem studies. Particularly, a number of sites has been instrumented with the FloX (J&B Hyperspectral Devices, Germany), an automated instrument that houses two high resolution spectrometers covering the visible and near infrared spectral regions, one specifically optimized for fluorescence retrieval, the other for plant trait estimation.</p><p>In this contribution we explore the feasibility to consistently retrieve plant traits and SIF from canopy level FloX measurements through the numerical inversion of a light version of the SCOPE model. The optimization approach was specifically adapted to work with the high- frequency time series produced by the FloX. In this context, a strategy for optimal retrieval of plant traits at daily scale is discussed, together with the implementation of an emulator of the radiative transfer model in the retrieval scheme. The retrieval strategy was applied to site measurements across Europe and the US that span a variety of natural and agricultural ecosystems.</p><p>The full spectrum of canopy SIF, the fluorescence quantum efficiency, and main plant traits controlling light absorption and reabsorption were retrieved concurrently and evaluated over the growing season in comparison with site-specific ancillary data. Improvements and challenges of this method compared to other retrievals are discussed, together with the potential of applying a similar retrieval scheme to airborne datasets acquired with e.g. the HyPlant sensor, or the reconfigured “FLEX mode” data acquired with the recently launched Sentinel-3B during its commissioning phase.</p>

scholarly journals Integration of a Crop Growth Model and Deep Learning Methods to Improve Satellite-Based Yield Estimation of Winter Wheat in Henan Province, China

2021 ◽  
Vol 13 (21) ◽  
pp. 4372
Author(s):  
Yi Xie ◽  
Jianxi Huang
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Crop Yield ◽  
Growth Model ◽  
Wheat Yield ◽  
Crop Growth ◽  
Henan Province ◽  
Crop Growth Model ◽  
Area Index ◽  
Growing Region

Timely and accurate regional crop-yield estimates are crucial for guiding agronomic practices and policies to improve food security. In this study, a crop-growth model was integrated with time series of remotely sensed data through deep learning (DL) methods to improve the accuracy of regional wheat-yield estimations in Henan Province, China. Firstly, the time series of moderate-resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) were input into the long short-term memory network (LSTM) model to identify the wheat-growing region, which was further used to estimate wheat areas at the municipal and county levels. Then, the leaf area index (LAI) and grain-yield time series simulated by the Crop Environment REsource Synthesis for Wheat (CERES-Wheat) model were used to train and evaluate the LSTM, one-dimensional convolutional neural network (1-D CNN) and random forest (RF) models, respectively. Finally, an exponential model of the relationship between the field-measured LAI and MODIS NDVI was applied to obtain the regional LAI, which was input into the trained LSTM, 1-D CNN and RF models to estimate wheat yields within the wheat-growing region. The results showed that the linear correlations between the estimated wheat areas and the statistical areas were significant at both the municipal and county levels. The LSTM model provided more accurate estimates of wheat yields, with higher R2 values and lower root mean square error (RMSE) and mean relative error (MRE) values than the 1-D CNN and RF models. The LSTM model has an inherent advantage in capturing phenological information contained in the time series of the MODIS-derived LAI, which is important for satellite-based crop-yield estimates.

scholarly journals Assessment of Radiative Forcing by Light-Absorbing Particles in Snow from In Situ Observations with Radiative Transfer Modeling

2018 ◽  
Vol 19 (8) ◽  
pp. 1397-1409 ◽  
Author(s):  
S. McKenzie Skiles ◽  
Thomas H. Painter
Keyword(s):  
Time Series ◽  
Radiative Transfer ◽  
Radiative Forcing ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Full Time ◽  
Snow Hydrology ◽  
Physically Based ◽  
Rf Values ◽  
Colorado Rockies

Abstract It is well established that episodic deposition of dust on mountain snow reduces snow albedo and impacts snow hydrology in the western United States, particularly in the Colorado Rockies, which are headwaters for the Colorado River. Until recently the snow observations needed to physically quantify radiative forcing (RF) by dust on snow were lacking, and analysis of impacts used a semiempirical relationship between snow optical properties and observed surface reflectance. Here, we present a physically based daily time series of RF by dust and black carbon (BC) in snow at Senator Beck Basin Study Area, Colorado. Over the 2013 ablation season (March–May), a snow–aerosol radiative transfer model was forced with near daily measured snow property inputs (density, effective grain size, and dust/BC concentrations) and validated with coincidentally measured spectral albedo. Over the measurement period, instantaneous RF by dust and BC in snow ranged from 0.25 to 525 W m−2, with daily averages ranging from 0 to 347 W m−2. Dust dominated particulate mass, accounting for more than 90% of RF. The semiempirical RF values, which constitute the continuous long-term record, compared well to the physically based RF values; over the full time series, daily reported semiempirical RF values were 8 W m−2 higher on average, with a root-mean-square difference of 16 W m−2.

scholarly journals Atmospheric effects of volcanic eruptions as seen by famous artists and depicted in their paintings

2007 ◽  
Vol 7 (2) ◽  
pp. 5145-5172 ◽  
Author(s):  
C. S. Zerefos ◽  
V. T. Gerogiannis ◽  
D. Balis ◽  
S. C. Zerefos ◽  
A. Kazantzidis
Keyword(s):  
Time Series ◽  
Radiative Transfer ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Volcanic Eruptions ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Atmospheric Effects ◽  
Middle Latitudes ◽  
Time Period

Abstract. Paintings created by famous artists, representing sunsets throughout the period 1500–1900, provide proxy information on the aerosol optical depth following major volcanic eruptions. This is supported by a statistically significant correlation coefficient (0.8) between the measured red-to-green ratios of 327 paintings and the corresponding values of the dust veil index. A radiative transfer model was used to compile an independent time series of aerosol optical depth at 550 nm corresponding to Northern Hemisphere middle latitudes during the period 1500–1900. The estimated aerosol optical depths range from 0.05 for background aerosol conditions, to about 0.6 following the Tambora and Krakatau eruptions and cover a time period mostly outside of the instrumentation era.

scholarly journals Atmospheric effects of volcanic eruptions as seen by famous artists and depicted in their paintings

2007 ◽  
Vol 7 (15) ◽  
pp. 4027-4042 ◽  
Author(s):  
C. S. Zerefos ◽  
V. T. Gerogiannis ◽  
D. Balis ◽  
S. C. Zerefos ◽  
A. Kazantzidis
Keyword(s):  
Time Series ◽  
Radiative Transfer ◽  
Correlation Coefficient ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Volcanic Eruptions ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Atmospheric Effects ◽  
Middle Latitudes

Abstract. Paintings created by famous artists, representing sunsets throughout the period 1500–1900, provide proxy information on the aerosol optical depth following major volcanic eruptions. This is supported by a statistically significant correlation coefficient (0.8) between the measured red-to-green ratios of a few hundred paintings and the dust veil index. A radiative transfer model was used to compile an independent time series of aerosol optical depth at 550 nm corresponding to Northern Hemisphere middle latitudes during the period 1500–1900. The estimated aerosol optical depths range from 0.05 for background aerosol conditions, to about 0.6 following the Tambora and Krakatau eruptions and cover a period practically outside of the instrumentation era.

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