scholarly journals Normalizing the Local Incidence Angle in Sentinel-1 Imagery to Improve Leaf Area Index, Vegetation Height, and Crop Coefficient Estimations

Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 680
Author(s):  
Gregoriy Kaplan ◽  
Lior Fine ◽  
Victor Lukyanov ◽  
V. S. Manivasagam ◽  
Josef Tanny ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Incidence Angle ◽  
Crop Coefficient ◽  
Vegetation Monitoring ◽  
Area Index ◽  
Crop Height ◽  
Vegetation Height ◽  
Sar Imagery ◽  
Processing Tomatoes

Public domain synthetic-aperture radar (SAR) imagery, particularly from Sentinel-1, has widened the scope of day and night vegetation monitoring, even when cloud cover limits optical Earth observation. Yet, it is challenging to combine SAR images acquired at different incidence angles and from ascending and descending orbits because of the backscatter dependence on the incidence angle. This study demonstrates two transformations that facilitate collective use of Sentinel-1 imagery, regardless of the acquisition geometry, for agricultural monitoring of several crops in Israel (wheat, processing tomatoes, and cotton). First, the radar backscattering coefficient (σ0) was multiplied by the local incidence angle (θ) of every pixel. This transformation improved the empirical prediction of the crop coefficient (Kc), leaf area index (LAI), and crop height in all three crops. The second method, which is based on the radar brightness coefficient (β0), proved useful for estimating Kc, LAI, and crop height in processing tomatoes and cotton. Following the suggested transformations, R2 increased by 0.0172 to 0.668, and RMSE improved by 5 to 52%. Additionally, the models based on the suggested transformations were found to be superior to the models based on the dual-polarization radar vegetation index (RVI). Consequently, vegetation monitoring using SAR imagery acquired at different viewing geometries became more effective.

scholarly journals Estimating Processing Tomato Water Consumption, Leaf Area Index, and Height Using Sentinel-2 and VENµS Imagery

2021 ◽  
Vol 13 (6) ◽  
pp. 1046
Author(s):  
Gregoriy Kaplan ◽  
Lior Fine ◽  
Victor Lukyanov ◽  
V. S. Manivasagam ◽  
Nitzan Malachy ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Water Consumption ◽  
Optical Sensors ◽  
Crop Coefficient ◽  
Accurate Estimation ◽  
Area Index ◽  
Crop Height ◽  
Processing Tomato ◽  
Sentinel 2

Crop monitoring throughout the growing season is key for optimized agricultural production. Satellite remote sensing is a useful tool for estimating crop variables, yet continuous high spatial resolution earth observations are often interrupted by clouds. This paper demonstrates overcoming this limitation by combining observations from two public-domain spaceborne optical sensors. Ground measurements were conducted in the Hula Valley, Israel, over four growing seasons to monitor the development of processing tomato. These measurements included continuous water consumption measurements using an eddy-covariance tower from which the crop coefficient (Kc) was calculated and measurements of Leaf Area Index (LAI) and crop height. Satellite imagery acquired by Sentinel-2 and VENµS was used to derive vegetation indices and model Kc, LAI, and crop height. The conjoint use of Sentinel-2 and VENµS imagery facilitated accurate estimation of Kc (R2 = 0.82, RMSE = 0.09), LAI (R2 = 0.79, RMSE = 1.2), and crop height (R2 = 0.81, RMSE = 7 cm). Additionally, our empirical models for LAI estimation were found to perform better than the SNAP biophysical processor (R2 = 0.53, RMSE = 2.3). Accordingly, Sentinel-2 and VENµS imagery was demonstrated to be a viable tool for agricultural monitoring.

GREENHOUSE TUNNEL VENTILATION DEPENDENCE ON TOMATO CROP HEIGHT AND LEAF AREA INDEX

2008 ◽  
pp. 1285-1292 ◽  
Author(s):  
H. Fatnassi ◽  
T. Boulard ◽  
C. Leyronas ◽  
M. Bardin ◽  
P. Nicot
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Tomato Crop ◽  
Area Index ◽  
Tunnel Ventilation ◽  
Crop Height

Estimation of wetland vegetation height and leaf area index using airborne laser scanning data

2015 ◽  
Vol 48 ◽  
pp. 550-559 ◽  
Author(s):  
Shezhou Luo ◽  
Cheng Wang ◽  
Feifei Pan ◽  
Xiaohuan Xi ◽  
Guicai Li ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Laser Scanning ◽  
Wetland Vegetation ◽  
Airborne Laser Scanning ◽  
Area Index ◽  
Vegetation Height ◽  
Airborne Laser

Image based remote sensing method for modeling black-eyed beans (Vigna unguiculata) Leaf Area Index (LAI) and Crop Height (CH) over Cyprus

2013 ◽  
Vol 5 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Giorgos Papadavid ◽  
Dionysia Fasoula ◽  
Michael Hadjimitsis ◽  
P. Skevi Perdikou ◽  
Diofantos Hadjimitsis
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Remotely Sensed ◽  
Theoretical Background ◽  
Crop Canopy ◽  
Remotely Sensed Data ◽  
Area Index ◽  
Crop Height ◽  
Statistical Relationships

AbstractIn this paper, Leaf Area Index (LAI) and Crop Height (CH) are modeled to the most known spectral vegetation index — NDVI — using remotely sensed data. This approach has advantages compared to the classic approaches based on a theoretical background. A GER-1500 field spectro-radiometer was used in this study in order to retrieve the necessary spectrum data for estimating a spectral vegetation index (NDVI), for establishing a semiempirical relationship between black-eyed beans’ canopy factors and remotely sensed data. Such semi-empirical models can be used then for agricultural and environmental studies. A field campaign was undertaken with measurements of LAI and CH using the Sun-Scan canopy analyzer, acquired simultaneously with the spectroradiometric (GER1500) measurements between May and June of 2010. Field spectroscopy and remotely sensed imagery have been combined and used in order to retrieve and validate the results of this study. The results showed that there are strong statistical relationships between LAI or CH and NDVI which can be used for modeling crop canopy factors (LAI, CH) to remotely sensed data. The model for each case was verified by the factor of determination. Specifically, these models assist to avoid direct measurements of the LAI and CH for all the dates for which satellite images are available and support future users or future studies regarding crop canopy parameters.

scholarly journals Relationship Between Spatio-Temporal Leaf Area Index and Crop Coefficient When Monitoring Coffee Plots in Brazil Using the Sentine-2

2019 ◽  
Vol 11 (15) ◽  
pp. 187
Author(s):  
Carolina Jaramillo-Giraldo ◽  
Williams Pinto Marques Ferreira ◽  
Humberto Paiva Fonseca ◽  
Marcelo de Freitas Ribeiro ◽  
Laís Maria Rodrigues Silva ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Temporal Variability ◽  
Crop Coefficient ◽  
Area Index ◽  
Directional Information ◽  
Production Area ◽  
Spatio Temporal ◽  
The Relationship ◽  
Sentinel 2

Robust monitoring techniques for perennial crops have become increasingly possible due to technological advances in the area of Remote Sensing (RS), and the products are available through the European Space Agency (ESA) initiative. RS data provides valuable opportunities for detailed assessments of crop conditions at plot level using high spatial, spectral, and temporal resolution. This study addresses the monitoring of coffee at the plot level using RS, analyzing the relationship between the spatio-temporal variability of the Leaf Area Index (LAI) and the crop coefficient (Kc); the Kc being a biophysical variable that integrates the potential hydrological characteristics of an agroecosystem compared to the reference crop. Daily and one-year Kc were estimated using the relation of crop evapotranspiration and reference. ESA Sentinel-2 images were pre-analyzed and atmospherically corrected, and Top-of-the-Atmosphere (TOA) reflections converted to Top-of-the-Canopy (TOC) reflectance. The TOCs resampled at the 10m resolution, and with the angles corresponding to the directional information at the time of the acquisition, the LAI was estimated using the trained neural network available in the Sentinel Application Platform (SNAP). During 75% of the monitored days, Kc ranged between 1.2 and 1.3 and, the LAI analyzed showed high spatial and temporal variability at the plot level. Based on the relationship between the biophysical variables, the LAI variable can substitute the Kc and be used to monitor the water conditions at the production area as well as analyze spatial variability inside that area. Sentinel-2 products could be more useful in monitoring coffee in the farm production area.

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