scholarly journals Top-of-Atmosphere Retrieval of Multiple Crop Traits Using Variational Heteroscedastic Gaussian Processes within a Hybrid Workflow

2021 ◽  
Vol 13 (8) ◽  
pp. 1589
Author(s):  
José Estévez ◽  
Katja Berger ◽  
Jorge Vicent ◽  
Juan Pablo Rivera-Caicedo ◽  
Matthias Wocher ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Solar Spectrum ◽  
Vegetation Coverage ◽  
Estimation Accuracy ◽  
Leaf Water Content ◽  
Canopy Reflectance ◽  
Retrieval Models ◽  
Area Index ◽  
Spatial And Temporal Scales ◽  
Reflectance Data

In support of cropland monitoring, operational Copernicus Sentinel-2 (S2) data became available globally and can be explored for the retrieval of important crop traits. Based on a hybrid workflow, retrieval models for six essential biochemical and biophysical crop traits were developed for both S2 bottom-of-atmosphere (BOA) L2A and S2 top-of-atmosphere (TOA) L1C data. A variational heteroscedastic Gaussian process regression (VHGPR) algorithm was trained with simulations generated by the combined leaf-canopy reflectance model PROSAILat the BOA scale and further combined with the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) atmosphere model at the TOA scale. Established VHGPR models were then applied to S2 L1C and L2A reflectance data for mapping: leaf chlorophyll content (Cab), leaf water content (Cw), fractional vegetation coverage (FVC), leaf area index (LAI), and upscaled leaf biochemical compounds, i.e., LAI ∗ Cab (laiCab) and LAI ∗ Cw (laiCw). Estimated variables were validated using in situ reference data collected during the Munich-North-Isar field campaigns within growing seasons of maize and winter wheat in the years 2017 and 2018. For leaf biochemicals, retrieval from BOA reflectance slightly outperformed results from TOA reflectance, e.g., obtaining a root mean squared error (RMSE) of 6.5 μμg/cm2 (BOA) vs. 8 μμg/cm2 (TOA) in the case of Cab. For the majority of canopy-level variables, instead, estimation accuracy was higher when using TOA reflectance data, e.g., with an RMSE of 139 g/m2 (BOA) vs. 113 g/m2 (TOA) for laiCw. Derived maps were further compared against reference products obtained from the ESA Sentinel Application Platform (SNAP) Biophysical Processor. Altogether, the consistency between L1C and L2A retrievals confirmed that crop traits can potentially be estimated directly from TOA reflectance data. Successful mapping of canopy-level crop traits including information about prediction confidence suggests that the models can be transferred over spatial and temporal scales and, therefore, can contribute to decision-making processes for cropland management.

scholarly journals Comparison of Empirical and Physical Modelling for Estimation of Biochemical and Biophysical Vegetation Properties: Field Scale Analysis across an Arctic Bioclimatic Gradient

2020 ◽  
Vol 12 (18) ◽  
pp. 3073
Author(s):  
Blair E. Kennedy ◽  
Douglas J. King ◽  
Jason Duffe
Keyword(s):  
Numerical Optimization ◽  
Vegetation Indices ◽  
Physical Modelling ◽  
Predictive Performance ◽  
High Arctic ◽  
Canopy Reflectance ◽  
Area Index ◽  
Empirical Modelling ◽  
Plant Area Index ◽  
Reflectance Data

To evaluate the potential of multi-angle hyperspectral sensors for monitoring vegetation variables in Arctic environments, empirical and physical modelling using field data was implemented for the retrieval of leaf and canopy chlorophyll content (LCC, CCC) and plant area index (PAI) measured at four sites situated across a bioclimatic gradient in the Western Canadian Arctic. Field reflectance data were acquired with an ASD FieldSpec (305–1075 nm) and used to simulate CHRIS Mode1 spectra (411–997 nm). Multi-angle measurements were taken corresponding to CHRIS view zenith angles (VZA) (−55°, −36°, 0°, +36°, +55°). Empirical modelling compared parametric regression based on vegetation indices (VIs) to non-parametric Gaussian Processes Regression (GPR). In physical modelling, PROSAIL was inverted using numerical optimization and look-up table (LUT) approaches. Cross-validation of the empirical models ranked GPR as best, followed by simple ratio (SR) with optimally selected NIR and red wavelengths, and then ROSAVI using its published wavelengths (mean r2cv = 0.62, 0.58, and 0.54, respectively across all sites, variables, and VZAs). However, the best predictive performance was achieved by SR followed by GPR and ROSAVI (NRMSEcv = 0.12, 0.16, 0.16, respectively). PROSAIL simulated the multi-angle top-of-canopy reflectance well with numerical optimization (r2 = ~0.99, RMSE = 0.004 ± 0.002), but best performing LUT models of LCC, CCC and PAI were poorer than the empirical approaches (mean r2 = 0.48, mean NRMSE = 0.22). PROSAIL performed best at the high Arctic sparsely vegetated site (r2 = 0.57–0.86 for all parameters). Overall, the best performing VZA was −55° for empirical modelling and 0° and ±55° for physical modelling; however, these were not significantly better than the other VZAs. Overall, this study demonstrates that, for Arctic vegetation, nadir narrowband reflectance data used to derive simple empirical VIs with optimally selected bands is a more efficient approach for modelling chlorophyll and PAI than more complex empirical and physical approaches.

scholarly journals Remote Sensing of Grassland Biophysical Parameters in the Context of the Sentinel-2 Satellite Mission

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Karolina Sakowska ◽  
Radoslaw Juszczak ◽  
Damiano Gianelle
Keyword(s):  
Photosynthetically Active Radiation ◽  
Solar Spectrum ◽  
Added Value ◽  
Estimation Accuracy ◽  
Least Squares Regression ◽  
Satellite Mission ◽  
Biophysical Parameters ◽  
Active Radiation ◽  
Reflectance Data ◽  
Sentinel 2

This study investigates the potential of the Sentinel-2 satellite for monitoring the seasonal changes in grassland total canopy chlorophyll content (CCC), fraction of photosynthetically active radiation absorbed by the vegetation canopy (FAPAR), and fraction of photosynthetically active radiation absorbed only by its photosynthesizing components (GFAPAR). Reflectance observations were collected on a continuous basis during growing seasons by means of a newly developed ASD-WhiteRef system. Two models using Sentinel-2 simulated data (linear regression-vegetation indices (VIs) approach and multiple regression (MR) reflectance approach) were tested to estimate vegetation biophysical parameters. To assess whether the use of full solar spectrum reflectance data is able to provide an added value in CCC and GFAPAR estimation accuracy, a third model based on partial least squares regression (PLSR) and the ASD-WhiteRef reflectance data was tested. The results showed that FAPAR remained quite stable during the reproduction and senescence stages, and no significant relationships between FAPAR and VIs were found. On the other hand, GFAPAR showed clearer seasonal trends. The comparison of the three models revealed no significant differences in the accuracies of CCC and GFAPAR predictions and demonstrated a strong contribution of SWIR bands to the explained variability of investigated parameters. The promising results highlight the potential of the Sentinel-2 satellite for retrieving biophysical parameters from space.

scholarly journals UAV Based Estimation of Forest Leaf Area Index (LAI) through Oblique Photogrammetry

2021 ◽  
Vol 13 (4) ◽  
pp. 803
Author(s):  
Lingchen Lin ◽  
Kunyong Yu ◽  
Xiong Yao ◽  
Yangbo Deng ◽  
Zhenbang Hao ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Point Cloud ◽  
Forest Canopy ◽  
Estimation Method ◽  
Vertical Direction ◽  
Estimation Accuracy ◽  
3D Point Cloud ◽  
Area Index ◽  
Better Than

As a key canopy structure parameter, the estimation method of the Leaf Area Index (LAI) has always attracted attention. To explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° and 15°) and OT30 (0° and 30°)), which were used to reconstruct 3D point cloud of forest canopy based on photogrammetry. Subsequently, the LAI values and the leaf area distribution in the vertical direction derived from five schemes were calculated based on the voxelized model. Our results show that the serious lack of leaf area in the middle and lower layers determines that the LAI estimate of O is inaccurate. For oblique photogrammetry, schemes with 30° photos always provided better LAI estimates than schemes with 15° photos (T30 better than T15, OT30 better than OT15), mainly reflected in the lower part of the canopy, which is particularly obvious in low-LAI areas. The overall structure of the single-tilt angle scheme (T15, T30) was relatively complete, but the rough point cloud details could not reflect the actual situation of LAI well. Multi-angle schemes (OT15, OT30) provided excellent leaf area estimation (OT15: R2 = 0.8225, RMSE = 0.3334 m2/m2; OT30: R2 = 0.9119, RMSE = 0.1790 m2/m2). OT30 provided the best LAI estimation accuracy at a sub-voxel size of 0.09 m and the best checkpoint accuracy (OT30: RMSE [H] = 0.2917 m, RMSE [V] = 0.1797 m). The results highlight that coupling oblique photography and nadiral photography can be an effective solution to estimate forest LAI.

scholarly journals Development of a Deep Neural Network Model for Estimating Joint Location of Occupant Indoor Activities for Providing Thermal Comfort

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 696
Author(s):  
Eun Ji Choi ◽  
Jin Woo Moon ◽  
Ji-hoon Han ◽  
Yongseok Yoo
Keyword(s):  
Neural Network ◽  
Thermal Comfort ◽  
Deep Neural Network ◽  
Mean Squared Error ◽  
Thermal Environment ◽  
The Body ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Body Parts ◽  
Joint Location

The type of occupant activities is a significantly important factor to determine indoor thermal comfort; thus, an accurate method to estimate occupant activity needs to be developed. The purpose of this study was to develop a deep neural network (DNN) model for estimating the joint location of diverse human activities, which will be used to provide a comfortable thermal environment. The DNN model was trained with images to estimate 14 joints of a person performing 10 common indoor activities. The DNN contained numerous shortcut connections for efficient training and had two stages of sequential and parallel layers for accurate joint localization. Estimation accuracy was quantified using the mean squared error (MSE) for the estimated joints and the percentage of correct parts (PCP) for the body parts. The results show that the joint MSEs for the head and neck were lowest, and the PCP was highest for the torso. The PCP for individual activities ranged from 0.71 to 0.92, while typing and standing in a relaxed manner were the activities with the highest PCP. Estimation accuracy was higher for relatively still activities and lower for activities involving wide-ranging arm or leg motion. This study thus highlights the potential for the accurate estimation of occupant indoor activities by proposing a novel DNN model. This approach holds significant promise for finding the actual type of occupant activities and for use in target indoor applications related to thermal comfort in buildings.

LAI Retrieval Based on PROSPECT-SAILH Model from Multi-Angular Data of WiDAS Imaging System

2012 ◽  
Vol 518-523 ◽  
pp. 5697-5703
Author(s):  
Zhao Yan Liu ◽  
Ling Ling Ma ◽  
Ling Li Tang ◽  
Yong Gang Qian
Keyword(s):  
Remote Sensing ◽  
Imaging System ◽  
Remote Sensing Data ◽  
Canopy Reflectance ◽  
Area Index ◽  
Sensing Data ◽  
Spectral Bands ◽  
Sensing Technology ◽  
Leaf Optical Properties ◽  
Angular Data

The aim of this study is to assess the capability of estimating Leaf Area Index (LAI) from high spatial resolution multi-angular Vis-NIR remote sensing data of WiDAS (Wide-Angle Infrared Dual-mode Line/Area Array Scanner) imaging system by inverting the coupled radiative transfer models PROSPECT-SAILH. Based on simulations from SAILH canopy reflectance model and PROSPECT leaf optical properties model, a Look-up Table (LUT) which describes the relationship between multi-angular canopy reflectance and LAI has been produced. Then the LAI can be retrieved from LUT by directly matching canopy reflectance of six view directions and four spectral bands with LAI. The inversion results are validated by field data, and by comparing the retrieval results of single-angular remote sensing data with multi-angular remote sensing data, we can found that the view angle takes the obvious impact on the LAI retrieval of single-angular data and that high accurate LAI can be obtained from the high resolution multi-angular remote sensing technology.

Beyond Cloud: A Fused Optic and SAR Based Solution to Monitor Crop Health

2021 ◽  
Author(s):  
Brianna Pagán ◽  
Adekunle Ajayi ◽  
Mamadou Krouma ◽  
Jyotsna Budideti ◽  
Omar Tafsi
Keyword(s):  
Machine Learning ◽  
Satellite Imagery ◽  
Cloud Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Learning Approaches ◽  
Area Index ◽  
Spatial And Temporal Scales ◽  
Field Level ◽  
Crop Health

<p>The value of satellite imagery to monitor crop health in near-real time continues to exponentially grow as more missions are launched making data available at higher spatial and temporal scales. Yet cloud cover remains an issue for utilizing vegetation indexes (VIs) solely based on optic imagery, especially in certain regions and climates. Previous research has proven the ability to reconstruct VIs like the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) by leveraging synthetic aperture radar (SAR) datasets, which are not inhibited by cloud cover. Publicly available data from SAR missions like Sentinel-1 at relatively decent spatial resolutions present the opportunity for more affordable options for agriculture users to integrate satellite imagery in their day to day operations. Previous research has successfully reconstructed optic VIs (i.e. from Sentinel-2) with SAR data (i.e. from Sentinel-1) leveraging various machine learning approaches for a limited number of crop types. However, these efforts normally train on individual pixels rather than leveraging information at a field level. </p><p>Here we present Beyond Cloud, a product which is the first to leverage computer vision and machine learning approaches in order to provide fused optic and SAR based crop health information. Field level learning is especially well-suited for inherently noisy SAR datasets. Several use cases are presented over agriculture fields located throughout the United Kingdom, France and Belgium, where cloud cover limits optic based solutions to as little as 2-3 images per growing season. Preliminary efforts for additional features to the product including automated crop and soil type detection are also discussed. Beyond Cloud can be accessed via a simple API which makes integration of the results easy for existing dashboards and smart-ag tools. Overall, these efforts promote the accessibility of satellite imagery for real agriculture end users.</p><p> </p>

scholarly journals Improving Spatial Soil Moisture Representation through the Integration of SMAP and PROBA-V Products

2018 ◽  
Vol 10 (10) ◽  
pp. 3459
Author(s):  
Shu-Di Fan ◽  
Yue-Ming Hu ◽  
Lu Wang ◽  
Zhen-Hua Liu ◽  
Zhou Shi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Factor Model ◽  
Original Method ◽  
Vegetation Coverage ◽  
Estimation Accuracy ◽  
Modified Model ◽  
The North ◽  
Moderate Resolution Imaging Spectroradiometer

To increase the spatial resolution of Soil Moisture Active Passive (SMAP), this study modifies the downscaling factor model based on the Temperature Vegetation Drought Index (TVDI) using data from the Project for On-Board Autonomy (PROBA-V). In the modified model, TVDI parameters were derived from the temperature-vegetation space and the Enhanced Vegetation Index (EVI). This study was conducted in the north China region using SMAP, PROBA-V, and Moderate Resolution Imaging Spectroradiometer satellite images. The 9-km spatial resolution SMAP data was downscaled to 0.3-km spatial resolution soil moisture using a modified downscaling method. Downscaling accuracies from the original and modified downscaling factor models were compared based on field observations. The results show that both methods generated similar spatial distributions in which soil moisture estimates increased as vegetation coverage increased from built-up areas to forest. However, based on the root mean square error between observations and estimations, the modified model demonstrated an increased estimation accuracy of 4.2% for soil moisture compared to the original method. This study also implies that downscaled soil moisture shows promise as a data source for subsequent watershed scale studies.

scholarly journals Effects of Forest Changes on Summer Surface Temperature in Changbai Mountain, China

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1551
Author(s):  
Jiaqi Zhang ◽  
Xiangjin Shen ◽  
Yanji Wang ◽  
Ming Jiang ◽  
Xianguo Lu
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Land Surface ◽  
Climate Models ◽  
Regional Climate ◽  
Changbai Mountain ◽  
Vegetation Coverage ◽  
Area Index ◽  
Forest Coverage ◽  
Cooling Effects

The area and vegetation coverage of forests in Changbai Mountain of China have changed significantly during the past decades. Understanding the effects of forests and forest coverage change on regional climate is important for predicting climate change in Changbai Mountain. Based on the satellite-derived land surface temperature (LST), albedo, evapotranspiration, leaf area index, and land-use data, this study analyzed the influences of forests and forest coverage changes on summer LST in Changbai Mountain. Results showed that the area and vegetation coverage of forests increased in Changbai Mountain from 2003 to 2017. Compared with open land, forests could decrease the summer daytime LST (LSTD) and nighttime LST (LSTN) by 1.10 °C and 0.07 °C, respectively. The increase in forest coverage could decrease the summer LSTD and LSTN by 0.66 °C and 0.04 °C, respectively. The forests and increasing forest coverage had cooling effects on summer temperature, mainly by decreasing daytime temperature in Changbai Mountain. The daytime cooling effect is mainly related to the increased latent heat flux caused by increasing evapotranspiration. Our results suggest that the effects of forest coverage change on climate should be considered in climate models for accurately simulating regional climate change in Changbai Mountain of China.

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