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Assessing Multiple Years’ Spatial Variability of Crop Yields Using Satellite Vegetation Indices
by
Barbanti, Lorenzo
, Ali, Abid
, Martelli, Roberta
, Lupia, Flavio
in
Agricultural production
/ Agriculture
/ Biomass
/ Chlorophyll
/ Coriandrum sativum
/ correspondence analysis
/ Crop growth
/ crop rotation
/ Crop yield
/ Crops
/ durum wheat
/ field spatial variability
/ georeferencing
/ Geostatistics
/ grain yield
/ grain yield prediction
/ Helianthus annuus
/ Italy
/ Landsat
/ landsat imagery
/ Landsat satellites
/ Mediterranean climate
/ normalized difference vegetation index
/ Normalized difference vegetative index
/ prediction
/ Remote sensing
/ Satellite imagery
/ Sea level
/ Seasons
/ soil
/ Spatial discrimination
/ Spatial resolution
/ Spatial variability
/ spectral vegetation indices
/ Sunflowers
/ Temporal resolution
/ Vegetation
/ Vegetation index
/ Wheat
2019
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Assessing Multiple Years’ Spatial Variability of Crop Yields Using Satellite Vegetation Indices
by
Barbanti, Lorenzo
, Ali, Abid
, Martelli, Roberta
, Lupia, Flavio
in
Agricultural production
/ Agriculture
/ Biomass
/ Chlorophyll
/ Coriandrum sativum
/ correspondence analysis
/ Crop growth
/ crop rotation
/ Crop yield
/ Crops
/ durum wheat
/ field spatial variability
/ georeferencing
/ Geostatistics
/ grain yield
/ grain yield prediction
/ Helianthus annuus
/ Italy
/ Landsat
/ landsat imagery
/ Landsat satellites
/ Mediterranean climate
/ normalized difference vegetation index
/ Normalized difference vegetative index
/ prediction
/ Remote sensing
/ Satellite imagery
/ Sea level
/ Seasons
/ soil
/ Spatial discrimination
/ Spatial resolution
/ Spatial variability
/ spectral vegetation indices
/ Sunflowers
/ Temporal resolution
/ Vegetation
/ Vegetation index
/ Wheat
2019
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Assessing Multiple Years’ Spatial Variability of Crop Yields Using Satellite Vegetation Indices
by
Barbanti, Lorenzo
, Ali, Abid
, Martelli, Roberta
, Lupia, Flavio
in
Agricultural production
/ Agriculture
/ Biomass
/ Chlorophyll
/ Coriandrum sativum
/ correspondence analysis
/ Crop growth
/ crop rotation
/ Crop yield
/ Crops
/ durum wheat
/ field spatial variability
/ georeferencing
/ Geostatistics
/ grain yield
/ grain yield prediction
/ Helianthus annuus
/ Italy
/ Landsat
/ landsat imagery
/ Landsat satellites
/ Mediterranean climate
/ normalized difference vegetation index
/ Normalized difference vegetative index
/ prediction
/ Remote sensing
/ Satellite imagery
/ Sea level
/ Seasons
/ soil
/ Spatial discrimination
/ Spatial resolution
/ Spatial variability
/ spectral vegetation indices
/ Sunflowers
/ Temporal resolution
/ Vegetation
/ Vegetation index
/ Wheat
2019
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Assessing Multiple Years’ Spatial Variability of Crop Yields Using Satellite Vegetation Indices
Journal Article
Assessing Multiple Years’ Spatial Variability of Crop Yields Using Satellite Vegetation Indices
2019
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Overview
Assessing crop yield trends over years is a key step in site specific management, in view of improving the economic and environmental profile of agriculture. This study was conducted in a 11.07 ha area under Mediterranean climate in Northern Italy to evaluate the spatial variability and the relationships between six remotely sensed vegetation indices (VIs) and grain yield (GY) in five consecutive years. A total of 25 satellite (Landsat 5, 7, and 8) images were downloaded during crop growth to obtain the following VIs: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Green Normalized Difference Vegetation Index (GNDVI), Green Chlorophyll Index (GCI), and Simple Ratio (SR). The surveyed crops were durum wheat in 2010, sunflower in 2011, bread wheat in 2012 and 2014, and coriander in 2013. Geo-referenced GY and VI data were used to generate spatial trend maps across the experimental field through geostatistical analysis. Crop stages featuring the best correlations between VIs and GY at the same spatial resolution (30 m) were acknowledged as the best periods for GY prediction. Based on this, 2–4 VIs were selected each year, totalling 15 VIs in the five years with r values with GY between 0.729** and 0.935**. SR and NDVI were most frequently chosen (six and four times, respectively) across stages from mid vegetative to mid reproductive growth. Conversely, SAVI never had correlations high enough to be selected. Correspondence analysis between remote VIs and GY based on quantile ranking in the 126 (30 m size) pixels exhibited a final agreement between 64% and 86%. Therefore, Landsat imagery with its spatial and temporal resolution proved a good potential for estimating final GY over different crops in a rotation, at a relatively small field scale.
Publisher
MDPI AG
Subject
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