Relationship Between Spatio-Temporal Leaf Area Index and Crop Coefficient When Monitoring Coffee Plots in Brazil Using the Sentine-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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Estimating Processing Tomato Water Consumption, Leaf Area Index, and Height Using Sentinel-2 and VENµS Imagery

Remote Sensing ◽

10.3390/rs13061046 ◽

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.

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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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On the relationship of NDVI with leaf area index in a deciduous forest site

Remote Sensing of Environment ◽

10.1016/j.rse.2004.10.006 ◽

2005 ◽

Vol 94 (2) ◽

pp. 244-255 ◽

Cited By ~ 283

Author(s):

Quan Wang ◽

Samuel Adiku ◽

John Tenhunen ◽

André Granier

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Deciduous Forest ◽

Forest Site ◽

Area Index ◽

Relationship Of ◽

The Relationship

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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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Bulb Development in Onion (Allium cepa L.) III. Effects of the Size of Adjacent Plants, Shading by Neutral and Leaf Filters, Irrigation and Nitrogen Regime and the Relationship between the Red: Far-red Spectral Ratio in the Canopy and Leaf Area Index

Annals of Botany ◽

10.1093/oxfordjournals.aob.a087199 ◽

1986 ◽

Vol 58 (2) ◽

pp. 207-219 ◽

Cited By ~ 6

Author(s):

M. F. MONDAL ◽

J. L. BREWSTER ◽

G. E. L. MORRIS ◽

HEATHER A. BUTLER

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Allium Cepa ◽

Spectral Ratio ◽

Area Index ◽

Allium Cepa L ◽

The Relationship

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Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2019.06.007 ◽

2019 ◽

Vol 154 ◽

pp. 189-201 ◽

Cited By ~ 30

Author(s):

Jie Wang ◽

Xiangming Xiao ◽

Rajen Bajgain ◽

Patrick Starks ◽

Jean Steiner ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Aboveground Biomass ◽

Landsat Images ◽

Area Index ◽

Sentinel 2

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Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations

Remote Sensing ◽

10.3390/rs12111843 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1843 ◽

Cited By ~ 1

Author(s):

Andrew Revill ◽

Anna Florence ◽

Alasdair MacArthur ◽

Stephen Hoad ◽

Robert Rees ◽

...

Keyword(s):

Spatial Heterogeneity ◽

Leaf Area Index ◽

Leaf Area ◽

Growth Stages ◽

Two Stage ◽

Area Index ◽

Field Surveys ◽

Red Edge ◽

Calibration Approach ◽

Sentinel 2

Leaf area index (LAI) estimates can inform decision-making in crop management. The European Space Agency’s Sentinel-2 satellite, with observations in the red-edge spectral region, can monitor crops globally at sub-field spatial resolutions (10–20 m). However, satellite LAI estimates require calibration with ground measurements. Calibration is challenged by spatial heterogeneity and scale mismatches between field and satellite measurements. Unmanned Aerial Vehicles (UAVs), generating high-resolution (cm-scale) LAI estimates, provide intermediary observations that we use here to characterise uncertainty and reduce spatial scaling discrepancies between Sentinel-2 observations and field surveys. We use a novel UAV multispectral sensor that matches Sentinel-2 spectral bands, flown in conjunction with LAI ground measurements. UAV and field surveys were conducted on multiple dates—coinciding with different wheat growth stages—that corresponded to Sentinel-2 overpasses. We compared chlorophyll red-edge index (CIred-edge) maps, derived from the Sentinel-2 and UAV platforms. We used Gaussian processes regression machine learning to calibrate a UAV model for LAI, based on ground data. Using the UAV LAI, we evaluated a two-stage calibration approach for generating robust LAI estimates from Sentinel-2. The agreement between Sentinel-2 and UAV CIred-edge values increased with growth stage—R2 ranged from 0.32 (stem elongation) to 0.75 (milk development). The CIred-edge variance between the two platforms was more comparable later in the growing season due to a more homogeneous and closed wheat canopy. The single-stage Sentinel-2 LAI calibration (i.e., direct calibration from ground measurements) performed poorly (mean R2 = 0.29, mean NRMSE = 17%) when compared to the two-stage calibration using the UAV data (mean R2 = 0.88, mean NRMSE = 8%). The two-stage approach reduced both errors and biases by >50%. By upscaling ground measurements and providing more representative model training samples, UAV observations provide an effective and viable means of enhancing Sentinel-2 wheat LAI retrievals. We anticipate that our UAV calibration approach to resolving spatial heterogeneity would enhance the retrieval accuracy of LAI and additional biophysical variables for other arable crop types and a broader range of vegetation cover types.

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