Assessing the Within-Field Heterogeneity Using Rapid-Eye NDVI Time Series Data

Jasper Mohr (), Andreas Tewes, Hella Ahrends and Thomas Gaiser
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Jasper Mohr: Crop Science Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany
Andreas Tewes: Research Center Jülich, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
Hella Ahrends: Department of Agricultural Sciences, University of Helsinki, Koetilantie 5, FI-00014 Helsinki, Finland
Thomas Gaiser: Crop Science Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany

Agriculture, 2023, vol. 13, issue 5, 1-13

Abstract: (1) Background: The relation between the sub-field heterogeneity of soil properties and high-resolution satellite time series data might help to explain spatiotemporal patterns of crop growth, but detailed field studies are seldom. (2) Methods: Normalized Difference Vegetation Index (NDVI) data derived from satellite time series images were used to identify changes in the spatial distribution of winter triticale (×Triticosecale), winter rye (Secale cereale) and winter barley (Hordeum vulgare) growth (2015 to 2020) for a field in north-eastern Germany. NDVI patterns (quartiles) that remained persistent over time were identified and it was tested if spatially heterogeneous soil characteristics such as water holding capacity and altitude could explain them. (3) Results: A statistically significant relationship between elevation and soil classes with NDVI values was found in most cases. The lowest NDVI quartiles, considered as representing the poorest growth conditions, were generally found in the depressions with the lowest water holding capacity. These areas showed temporally stable spatial patterns, especially during the pre-harvest period. Over the six-year period, up to 80% of the grid cells with the lowest NDVI values were spatially consistent over time. Differences in the climatic water balance were rather low but could contribute to explaining spatial patterns, such as the lower clustering of values in the wettest year. (4) Conclusions: High-resolution satellite NDVI time series are a valuable information source for precise land management in order to optimize crop management with respect to yield and ecosystem services.

Keywords: NDVI; satellite; time series; growth patterns; high resolution (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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