65. Potential of temporal series of Sentinel-2 images to define zones of vine water restriction

2021 ◽  
Author(s):  
E. Fornieles Lopez ◽  
G. Brunel ◽  
N. Devaux ◽  
F. Rancon ◽  
L. Pichon ◽  
...  
Keyword(s):  
Water Restriction ◽  
Temporal Series ◽  
Sentinel 2

scholarly journals Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

2019 ◽  
Vol 11 (15) ◽  
pp. 1836 ◽  
Author(s):  
Hassan Bazzi ◽  
Nicolas Baghdadi ◽  
Dino Ienco ◽  
Mohammad El Hajj ◽  
Mehrez Zribi ◽  
...  
Keyword(s):  
Time Series ◽  
Water Resource Management ◽  
Vegetation Index ◽  
Scale Up ◽  
Principal Component ◽  
Radar Data ◽  
Temporal Series ◽  
Optical Images ◽  
Combined Use ◽  
Sentinel 2

Mapping irrigated plots is essential for better water resource management. Today, the free and open access Sentinel-1 (S1) and Sentinel-2 (S2) data with high revisit time offers a powerful tool for irrigation mapping at plot scale. Up to date, few studies have used S1 and S2 data to provide approaches for mapping irrigated plots. This study proposes a method to map irrigated plots using S1 SAR (synthetic aperture radar) time series. First, a dense temporal series of S1 backscattering coefficients were obtained at plot scale in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations over a study site located in Catalonia, Spain. In order to remove the ambiguity between rainfall and irrigation events, the S1 signal obtained at plot scale was used conjointly to S1 signal obtained at a grid scale (10 km × 10 km). Later, two mathematical transformations, including the principal component analysis (PCA) and the wavelet transformation (WT), were applied to the several SAR temporal series obtained in both VV and VH polarization. Irrigated areas were then classified using the principal component (PC) dimensions and the WT coefficients in two different random forest (RF) classifiers. Another classification approach using one dimensional convolutional neural network (CNN) was also performed on the obtained S1 temporal series. The results derived from the RF classifiers with S1 data show high overall accuracy using the PC values (90.7%) and the WT coefficients (89.1%). By applying the CNN approach on SAR data, a significant overall accuracy of 94.1% was obtained. The potential of optical images to map irrigated areas by the mean of a normalized differential vegetation index (NDVI) temporal series was also tested in this study in both the RF and the CNN approaches. The overall accuracy obtained using the NDVI in RF classifier reached 89.5% while that in the CNN reached 91.6%. The combined use of optical and radar data slightly enhanced the classification in the RF classifier but did not significantly change the accuracy obtained in the CNN approach using S1 data.

Relation of Rubisco to Maize Grain Yield Improvement: Effect of Water Restriction

Crop Science ◽  
1992 ◽  
Vol 32 (3) ◽  
pp. 718-722 ◽  
Author(s):  
E. Martínez‐Barajas ◽  
C. Villanueva‐Verduzco ◽  
J. Molina‐Galán ◽  
H. Loza‐Tavera ◽  
E. Sánchez‐de‐Jiménez
Keyword(s):  
Grain Yield ◽  
Water Restriction ◽  
Yield Improvement ◽  
Maize Grain Yield ◽  
Effect Of Water ◽  
Improvement Effect ◽  
Maize Grain

Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images

CATENA ◽  
2021 ◽  
Vol 205 ◽  
pp. 105442
Author(s):  
Xianglin He ◽  
Lin Yang ◽  
Anqi Li ◽  
Lei Zhang ◽  
Feixue Shen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Sentinel 2

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

scholarly journals Supporting Physiological Trait for Indirect Selection for Grain Yield in Drought-Stressed Popcorn

Plants ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 1510
Author(s):  
Samuel Henrique Kamphorst ◽  
Gabriel Moreno Bernardo Gonçalves ◽  
Antônio Teixeira do Amaral Júnior ◽  
Valter Jário de Lima ◽  
Kátia Fabiane Medeiros Schmitt ◽  
...  
Keyword(s):  
Grain Yield ◽  
Multiple Regression ◽  
Morphological Traits ◽  
Vegetation Index ◽  
Agronomic Traits ◽  
Indirect Selection ◽  
Leaf Angle ◽  
Water Restriction ◽  
Leaf Greenness ◽  
Selection Of

The identification of traits associated with drought tolerance in popcorn is a contribution to support selection of superior plants under soil water deficit. The objective of this study was to choose morphological traits and the leaf greenness index, measured on different dates, to estimate grain yield (GY) and popping expansion (PE), evaluated in a set of 20 popcorn lines with different genealogies, estimated by multiple regression models. The variables were divided into three groups: morpho-agronomic traits—100-grain weight (GW), prolificacy (PR), tassel length (TL), number of tassel branches, anthesis-silking interval, leaf angle (FA) and leaf rolling (FB); variables related to the intensity of leaf greenness during the grain-filling period, at the leaf level, measured by a portable chlorophyll meter (SPAD) and at the canopy level, calculated as the normalized difference vegetation index (NDVI). The inbred lines were cultivated under two water conditions: well-watered (WW), maintained at field capacity, and water stress (WS), for which irrigation was stopped before male flowering. The traits GY (55%) and PE (28%) were most affected by water restriction. Among the morpho-agronomic traits, GW and PR were markedly reduced (>10%). Under dry conditions, the FA in relation to the plant stalk tended to be wider, the FB curvature greater and leaf senescence accelerated (>15% at 22 days after male flowering). The use of multiple regression for the selection of predictive traits proved to be a useful tool for the identification of groups of adequate traits to efficiently predict the economically most important features of popcorn (GY and PE). The SPAD index measured 17 days after male flowering proved useful to select indirectly for GY, while, among the morphological traits, TL stood out for the same purpose. Of all traits, PR was most strongly related with PE under WS, indicating its use in breeding programs. The exploitation of these traits by indirect selection is expected to induce increments in GY and PE.

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