Winter wheat, winter rape and poppy crop growth evaluation with the help of remote and proximal sensing measurements

Z. Jelínek¹*, K. Starý¹, J. Kumhálová¹, J. Lukáš² and J. Mašek³

¹Czech University of Life Sciences Prague, Faculty of Engineering, Department of Machinery Utilization, Kamýcká 129, CZ165 00 Prague, Czech Republic
²Crop Research Institute, v.v.i., Drnovská 507, CZ161 00 Prague, Czech Republic
³Czech University of Life Sciences, Faculty of Engineering, Department of Agricultural Machines, Kamýcká 129, CZ165 00 Prague, Czech Republic
*Correspondence: jelinekzdenek@tf.czu.cz

Abstract:

Monitoring of agricultural crops with the help of remote and proximal sensors during the growing season plays important role for site-specific management decisions. Winter wheat, winter rape and poppy are representatives of typical agricultural crops from the family Poacea, Brassicaceae and Papaveraceae, growing in relative dry area of Rakovník district in the Czech Republic. Ten Sentinel 2 satellite images acquired during vegetation season of the crops were downloaded and processed. Crops were monitored with the help of unmanned aerial vehicles (UAV) equipped with consumer grade Red Green Blue (RGB) camera and multispectral (MS) MicaSense RedEdge MX camera. In-field variability was assessed by computing RGB-based vegetation indices Triangular Greenness Index (TGI), Green Leaf Index (GLI) and Visible Atmospherically Resistant Index (VARI) and commonly used vegetation indices as Normalised Difference Vegetation Index (NDVI) and Green NDVI (GNDVI). The results derived from satellite and UAV images were supported with in-situ measurements of hand-held GreenSeeker and Chlorophyll Meter Content sensors. The study showed the usability of individual vegetation indices, especially the TGI index for chlorophyll content estimation, and VARI index for green vegetation fraction detection and leaf area index estimation, in comparison with selected hand-held devices. The results showed as well that leaf properties and canopy structure of typical characteristics of selected families can significantly influence the spectral response of the crops detected in different phenological stages.

Key words:

Comparison of selected remote sensing sensors for crop yield variability estimation

K. Křížová¹* and J. Kumhálová²

¹Czech University of Life Sciences in Prague, Faculty of Engineering, Department of Agricultural Machines, Kamýcká 129, CZ165 21 Prague, Czech Republic
²Czech University of Life Sciences in Prague, Faculty of Engineering, Department of Machinery Utilization, Kamýcká 129, CZ165 21 Prague, Czech Republic
*Correspondence: krizovak@tf.czu.cz

Abstract:

Currently, spectral indices are very common tool how to describe various characteristics of vegetation. In fact, these are mathematical operations which are calculated using specific bands of electromagnetic spectrum. Nevertheless, remote sensing sensors can differ due to the variations in bandwidth of the particular spectral channels. Therefore, the main aim of this study is to compare selected sensors in terms of their capability to predict crop yield by NDVI utilization. The experiment was performed at two locations (Prague-Ruzyně and Vendolí) in the year 2015 for both locations and in 2007 for Prague-Ruzyně only, when winter barley or spring barley grew on the plots. The cloud-free satellite images were chosen and normalised difference vegetation indices (NDVI) were calculated for each image. Landsat satellite images with moderate spatial resolution (30 m per pixel) were chosen during the crop growth for selected years. The other data sources were commercial satellite images with very high spatial resolution – QuickBird (QB) (0.6 m per pixel) in 2007 and WorldView-2 (WV-2) (2 m per pixel) in 2015 for Prague-Ruzyně location; and SPOT-7 (6 m per pixel) satellite image in 2015 for Vendolí location. GreenSeeker handheld crop sensor (GS) was used for collecting NDVI data for both locations in 2015 only. NDVI calculated at each of images was compared with the yield data. The data sources were compared with each other at the same term of crop growth stage. The results showed that correlation between GS and yield was relatively weak at Ruzyně. Conversely, significant relation was found at Vendolí location. The satellite images showed stronger relation with yield than GS. Landsat satellite images had higher values of correlation coefficient (in 30 m spatial resolution) at Ruzyně in both selected years. However, at Vendolí location, SPOT-7 satellite image has significantly better results compared to Landsat image. It is necessary to do more research to define which sensor measurements are most useful for selected applications in agriculture management.

Key words:

crop yield, Greenseeker, NDVI, Remote sensing, satellite images