Skip to main content

LUP Student Papers

LUND UNIVERSITY LIBRARIES

Time series analysis for spring barley phenology monitoring using Sentinel 2 – A case study of Southern-central Sweden

Aslanis, Pavlos LU (2020) In Student thesis series INES NGEM01 20201
Dept of Physical Geography and Ecosystem Science
Abstract
Monitoring crop phenology at parcel scale aligns with the concept of precision agriculture (PA) and can provide invaluable information to agronomic management systems. Satellite time series data are commonly deployed for detecting crop growth stages, while the recent advancements in remote sensing (RS) technologies such as the launch of Sentinel 2 (S2) are providing unprecedented opportunities for crop monitoring. In this thesis, the focus is on spring barley parcels with available in-situ crop growth stage measurements (recorded in Zadoks scale) located in south-central Sweden over the period of 2017 – 2019. More specifically, the aim was to detect three specific crop growth stages of spring barley that are crucial for applying external... (More)
Monitoring crop phenology at parcel scale aligns with the concept of precision agriculture (PA) and can provide invaluable information to agronomic management systems. Satellite time series data are commonly deployed for detecting crop growth stages, while the recent advancements in remote sensing (RS) technologies such as the launch of Sentinel 2 (S2) are providing unprecedented opportunities for crop monitoring. In this thesis, the focus is on spring barley parcels with available in-situ crop growth stage measurements (recorded in Zadoks scale) located in south-central Sweden over the period of 2017 – 2019. More specifically, the aim was to detect three specific crop growth stages of spring barley that are crucial for applying external inputs (e.g. fungicides applications) named according to the Zadoks Scale: (i) first node detectable (31DC) (ii) flag leaf ligule just visible (39DC) and (iii) first spikelet of inflorescence just visible (51DC).
This thesis describes a simple empirical approach based on Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index 2 (EVI2) S2 time series. TIMESAT 4.0 was deployed to reconstruct the S2 NDVI and EVI2 trajectories using the Double Logistic (DL) smoothing function. Moreover, the available in-situ crop growth stage measurements were utilized to optimize the dynamic thresholds (% of the amplitude of the season) for detecting the different crop growth stages of interest (31DC, 39DC, 51DC). Two types of thresholds were conceptualized and optimized: the (i) local threshold and the (ii) global threshold. The optimal local threshold for each crop growth stage of interest refers to the threshold that created an agreement between the vegetation index (NDVI and EVI2) results and the in-situ measurements for each spring barley field individually. The global threshold refers to the optimal threshold that resulted in the smallest Root Mean Square Error (RMSE) between in-situ measurements and S2 derived results when applied on all the studied spring barley parcels.
The optimal local thresholds showed high variability especially for the crop growth stage 31DC, where the standard deviation (SD) of the local threshold values was 16.1% (NDVI) and 22.1% (EVI2). The variability of the optimal local thresholds showed a decreasing trend with latter crop growth stages, where for the stage 51DC the SD was 5.9% (NDVI) and 3.1% (EVI2). According to the results of the global threshold optimization, the optimal thresholds for the stages 31DC, 39DC and 51DC based on NDVI were 74%, 92%, and 99% respectively, where for EVI2 the optimal global thresholds were 70% (31DC), 91% (39DC) and 99% (51DC). When applying the optimized global thresholds, the RMSE of the retrieved dates for the different crop growth stages against the in-situ measurements was smaller than 7.6 days (for both NDVI and EVI2). EVI2 consistently outperformed NDVI regarding all the crop growth stages of interest (31DC, 39DC, 51DC) where it resulted in lower RMSE (5.1 days, 4.8 days, 4.2 days) and higher coefficient of determination (R-square; 0.43, 0.45, 0.69) compared to the NDVI induced RMSE (6.9 days, 7.6 days, 7.2 days) and R-square (0.33, 0.27, 0.39). The results showed the feasibility of using S2 data in crop phenology studies and demonstrated its potential uses and inaccuracies regarding the detection of three specific crop growth stages of spring barley that are of interest in agronomic decision making. (Less)
Popular Abstract
Precision agriculture is used in farming management to optimize crop yield and minimize negative environmental impact. Precision agriculture applications that relate to crop growth monitoring and crop growth stage detection are crucial for applying different external inputs (e.g. fungicides, fertilizers) when and where needed, which ultimately increases the profitability of the crops and minimizes any adverse environmental effects.
Optical satellites provide data in different parts of the electromagnetic spectrum (i.e. bands) with 2-3 days time interval, which are commonly exploited in the form of Vegetation Indexes (VIs). Vegetation Indexes are widely recognized for enabling vegetation monitoring, where time series of VIs can provide... (More)
Precision agriculture is used in farming management to optimize crop yield and minimize negative environmental impact. Precision agriculture applications that relate to crop growth monitoring and crop growth stage detection are crucial for applying different external inputs (e.g. fungicides, fertilizers) when and where needed, which ultimately increases the profitability of the crops and minimizes any adverse environmental effects.
Optical satellites provide data in different parts of the electromagnetic spectrum (i.e. bands) with 2-3 days time interval, which are commonly exploited in the form of Vegetation Indexes (VIs). Vegetation Indexes are widely recognized for enabling vegetation monitoring, where time series of VIs can provide invaluable information about crop growth over large areas in a quicker and less expensive manner than the traditional method of field measurements. Sentinel 2 (S2) is a satellite constellation with frequent revisit and high spatial resolution that has been characterized as promising for precision agriculture applications.
Field measurements of crop growth stages of spring barley over the growing seasons of 2017 – 2019 enabled this thesis to focus on specific crop growth stages that are of high importance in agronomic management (i.e. fungicides application) instead of detecting the general phenological development of the crops. The overall objective of this project was to assess the ability and potential use of S2 for detecting three specific crop growth stages of spring barley. Moreover, the aim was to also compare the performance of two widely used Vegetation Indexes (Normalized Difference Vegetation Index; NDVI and Enhanced Vegetation Index 2; EVI2) for detecting the crop growth stages of interest.
To achieve that, the software (TIMESAT 4.0) was used to process time series of Sentinel 2 data. The available field measurements were used to optimize, validate, and ultimately compare the detection of the crop growth stages of interest for both NDVI and EVI2. The results of the optimization of the crop growth stage detection were aligned with past studies. In general, both VIs showed varying agreement with the field measurements, where the comparison of the VIs suggests that EVI2 was generally more accurate than NDVI for detecting the crop growth stages of interest.
Finally, the uncertainty related to the field measurements in conjunction with the small sample size of spring barley parcels that were used in the analysis does not allow any confident conclusions. Nonetheless, in this thesis, the potential of S2 to monitor crop growth at parcel scale and thus for depicting the within parcel spatial variability of the crop growth was shown. (Less)
Please use this url to cite or link to this publication:
author
Aslanis, Pavlos LU
supervisor
organization
course
NGEM01 20201
year
type
H2 - Master's Degree (Two Years)
subject
keywords
spring barley, crop phenology, crop growth stages, TIMESAT, double logistic, threshold optimization, Sentinel 2
publication/series
Student thesis series INES
report number
526
language
English
additional info
External supervisor: Johannes Albertson, Swedish University of Agricultural Sciences
id
9023819
date added to LUP
2020-07-03 13:43:30
date last changed
2020-11-15 03:41:56
@misc{9023819,
  abstract     = {{Monitoring crop phenology at parcel scale aligns with the concept of precision agriculture (PA) and can provide invaluable information to agronomic management systems. Satellite time series data are commonly deployed for detecting crop growth stages, while the recent advancements in remote sensing (RS) technologies such as the launch of Sentinel 2 (S2) are providing unprecedented opportunities for crop monitoring. In this thesis, the focus is on spring barley parcels with available in-situ crop growth stage measurements (recorded in Zadoks scale) located in south-central Sweden over the period of 2017 – 2019. More specifically, the aim was to detect three specific crop growth stages of spring barley that are crucial for applying external inputs (e.g. fungicides applications) named according to the Zadoks Scale: (i) first node detectable (31DC) (ii) flag leaf ligule just visible (39DC) and (iii) first spikelet of inflorescence just visible (51DC).
This thesis describes a simple empirical approach based on Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index 2 (EVI2) S2 time series. TIMESAT 4.0 was deployed to reconstruct the S2 NDVI and EVI2 trajectories using the Double Logistic (DL) smoothing function. Moreover, the available in-situ crop growth stage measurements were utilized to optimize the dynamic thresholds (% of the amplitude of the season) for detecting the different crop growth stages of interest (31DC, 39DC, 51DC). Two types of thresholds were conceptualized and optimized: the (i) local threshold and the (ii) global threshold. The optimal local threshold for each crop growth stage of interest refers to the threshold that created an agreement between the vegetation index (NDVI and EVI2) results and the in-situ measurements for each spring barley field individually. The global threshold refers to the optimal threshold that resulted in the smallest Root Mean Square Error (RMSE) between in-situ measurements and S2 derived results when applied on all the studied spring barley parcels.
The optimal local thresholds showed high variability especially for the crop growth stage 31DC, where the standard deviation (SD) of the local threshold values was 16.1% (NDVI) and 22.1% (EVI2). The variability of the optimal local thresholds showed a decreasing trend with latter crop growth stages, where for the stage 51DC the SD was 5.9% (NDVI) and 3.1% (EVI2). According to the results of the global threshold optimization, the optimal thresholds for the stages 31DC, 39DC and 51DC based on NDVI were 74%, 92%, and 99% respectively, where for EVI2 the optimal global thresholds were 70% (31DC), 91% (39DC) and 99% (51DC). When applying the optimized global thresholds, the RMSE of the retrieved dates for the different crop growth stages against the in-situ measurements was smaller than 7.6 days (for both NDVI and EVI2). EVI2 consistently outperformed NDVI regarding all the crop growth stages of interest (31DC, 39DC, 51DC) where it resulted in lower RMSE (5.1 days, 4.8 days, 4.2 days) and higher coefficient of determination (R-square; 0.43, 0.45, 0.69) compared to the NDVI induced RMSE (6.9 days, 7.6 days, 7.2 days) and R-square (0.33, 0.27, 0.39). The results showed the feasibility of using S2 data in crop phenology studies and demonstrated its potential uses and inaccuracies regarding the detection of three specific crop growth stages of spring barley that are of interest in agronomic decision making.}},
  author       = {{Aslanis, Pavlos}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Student thesis series INES}},
  title        = {{Time series analysis for spring barley phenology monitoring using Sentinel 2 – A case study of Southern-central Sweden}},
  year         = {{2020}},
}