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

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.

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66. Estimating processing tomato water consumption, leaf area index and height using Sentinel-2 and Venµs imagery

Precision agriculture ’21 ◽

10.3920/978-90-8686-916-9_66 ◽

2021 ◽

Author(s):

G. Kaplan ◽

L. Fine ◽

V. Lukyanov ◽

V.S. Manivasagam ◽

N. Malachi ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Water Consumption ◽

Area Index ◽

Processing Tomato ◽

Sentinel 2

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

Journal of Agricultural Science ◽

10.5539/jas.v11n15p187 ◽

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

Land ◽

10.3390/land10070680 ◽

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.

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Measuring Canopy Geometric Structure Using Optical Sensors Mounted on Terrestrial Vehicles: A Case Study in Vineyards

Agriculture ◽

10.3390/agriculture11030208 ◽

2021 ◽

Vol 11 (3) ◽

pp. 208

Author(s):

Daniel Queirós da Silva ◽

André Silva Aguiar ◽

Filipe Neves dos Santos ◽

Armando Jorge Sousa ◽

Danilo Rabino ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Optical Sensors ◽

Precision Agriculture ◽

Real Data ◽

Relevant Information ◽

Normal Operation ◽

Area Index ◽

Sensory Data ◽

The Right

Smart and precision agriculture concepts require that the farmer measures all relevant variables in a continuous way and processes this information in order to build better prescription maps and to predict crop yield. These maps feed machinery with variable rate technology to apply the correct amount of products in the right time and place, to improve farm profitability. One of the most relevant information to estimate the farm yield is the Leaf Area Index. Traditionally, this index can be obtained from manual measurements or from aerial imagery: the former is time consuming and the latter requires the use of drones or aerial services. This work presents an optical sensing-based hardware module that can be attached to existing autonomous or guided terrestrial vehicles. During the normal operation, the module collects periodic geo-referenced monocular images and laser data. With that data a suggested processing pipeline, based on open-source software and composed by Structure from Motion, Multi-View Stereo and point cloud registration stages, can extract Leaf Area Index and other crop-related features. Additionally, in this work, a benchmark of software tools is made. The hardware module and pipeline were validated considering real data acquired in two vineyards—Portugal and Italy. A dataset with sensory data collected by the module was made publicly available. Results demonstrated that: the system provides reliable and precise data on the surrounding environment and the pipeline is capable of computing volume and occupancy area from the acquired data.

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Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices

Agricultural Water Management ◽

10.1016/j.agwat.2021.106906 ◽

2021 ◽

Vol 252 ◽

pp. 106906

Author(s):

Guomin Shao ◽

Wenting Han ◽

Huihui Zhang ◽

Shouyang Liu ◽

Yi Wang ◽

...

Keyword(s):

Random Forest ◽

Leaf Area Index ◽

Leaf Area ◽

Vegetation Indices ◽

Crop Coefficient ◽

Random Forest Algorithm ◽

Maize Crop ◽

Area Index

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Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2

Land ◽

10.3390/land10050505 ◽

2021 ◽

Vol 10 (5) ◽

pp. 505

Author(s):

Gregoriy Kaplan ◽

Offer Rozenstein

Keyword(s):

Water Vapor ◽

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Near Infrared ◽

Linear Regression Models ◽

Area Index ◽

Estimation Performance ◽

Red Edge ◽

Sentinel 2

Satellite remote sensing is a useful tool for estimating crop variables, particularly Leaf Area Index (LAI), which plays a pivotal role in monitoring crop development. The goal of this study was to identify the optimal Sentinel-2 bands for LAI estimation and to derive Vegetation Indices (VI) that are well correlated with LAI. Linear regression models between time series of Sentinel-2 imagery and field-measured LAI showed that Sentinel-2 Band-8A—Narrow Near InfraRed (NIR) is more accurate for LAI estimation than the traditionally used Band-8 (NIR). Band-5 (Red edge-1) showed the lowest performance out of all red edge bands in tomato and cotton. A novel finding was that Band 9 (Water vapor) showed a very high correlation with LAI. Bands 1, 2, 3, 4, 5, 11, and 12 were saturated at LAI ≈ 3 in cotton and tomato. Bands 6, 7, 8, 8A, and 9 were not saturated at high LAI values in cotton and tomato. The tomato, cotton, and wheat LAI estimation performance of ReNDVI (R2 = 0.79, 0.98, 0.83, respectively) and two new VIs (WEVI (Water vapor red Edge Vegetation Index) (R2 = 0.81, 0.96, 0.71, respectively) and WNEVI (Water vapor narrow NIR red Edge Vegetation index) (R2 = 0.79, 0.98, 0.79, respectively)) were higher than the LAI estimation performance of the commonly used NDVI (R2 = 0.66, 0.83, 0.05, respectively) and other common VIs tested in this study. Consequently, reNDVI, WEVI, and WNEVI can facilitate more accurate agricultural monitoring than traditional VIs.

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A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT Polar System

Remote Sensing ◽

10.3390/rs10050763 ◽

2018 ◽

Vol 10 (5) ◽

pp. 763 ◽

Cited By ~ 20

Author(s):

Manuel Campos-Taberner ◽

Francisco García-Haro ◽

Lorenzo Busetto ◽

Luigi Ranghetti ◽

Beatriz Martínez ◽

...

Keyword(s):

Remote Sensing ◽

Leaf Area Index ◽

Leaf Area ◽

Area Index ◽

Polar System ◽

Critical Comparison ◽

Landsat 7 ◽

Sentinel 2

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Crop Yield Modelling Applying Leaf Area Index Estimated from Sentinel-2 and Proba-V Data at JECAM site in Poland

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8519120 ◽

2018 ◽

Cited By ~ 1

Author(s):

Katarzyna Dabrowska-Zielinska ◽

Maciej Bartold ◽

Radoslaw Gurdak ◽

Martyna Gatkowska ◽

Wojciech Kiryla ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Crop Yield ◽

Area Index ◽

Sentinel 2

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An automated procedure for estimating the leaf area index (LAI) of woodland ecosystems using digital imagery, MATLAB programming and its application to an examination of the relationship between remotely sensed and field measurements of LAI

Functional Plant Biology ◽

10.1071/fp08045 ◽

2008 ◽

Vol 35 (10) ◽

pp. 1070 ◽

Cited By ~ 46

Author(s):

Sigfredo Fuentes ◽

Anthony R. Palmer ◽

Daniel Taylor ◽

Melanie Zeppel ◽

Rhys Whitley ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Gap Analysis ◽

Digital Camera ◽

Field Measurements ◽

Accurate Estimation ◽

Image Capture ◽

Area Index ◽

Modis Lai ◽

Matlab Programming

Leaf area index (LAI) is one of the most important variables required for modelling growth and water use of forests. Functional–structural plant models use these models to represent physiological processes in 3-D tree representations. Accuracy of these models depends on accurate estimation of LAI at tree and stand scales for validation purposes. A recent method to estimate LAI from digital images (LAID) uses digital image capture and gap fraction analysis (Macfarlane et al. 2007b) of upward-looking digital photographs to capture canopy LAID (cover photography). After implementing this technique in Australian evergreen Eucalyptus woodland, we have improved the method of image analysis and replaced the time consuming manual technique with an automated procedure using a script written in MATLAB 7.4 (LAIM). Furthermore, we used this method to compare MODIS LAI values with LAID values for a range of woodlands in Australia to obtain LAI at the forest scale. Results showed that the MATLAB script developed was able to successfully automate gap analysis to obtain LAIM. Good relationships were achieved when comparing averaged LAID and LAIM (LAIM = 1.009 – 0.0066 LAID; R2 = 0.90) and at the forest scale, MODIS LAI compared well with LAID (MODIS LAI = 0.9591 LAID – 0.2371; R2 = 0.89). This comparison improved when correcting LAID with the clumping index to obtain effective LAI (MODIS LAI = 1.0296 LAIe + 0.3468; R2 = 0.91). Furthermore, the script developed incorporates a function to connect directly a digital camera, or high resolution webcam, from a laptop to obtain cover photographs and LAI analysis in real time. The later is a novel feature which is not available on commercial LAI analysis softwares for cover photography. This script is available for interested researchers.

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