TY  - JOUR
A1  - Dhillon, Maninder Singh
A1  - Dahms, Thorsten
A1  - Kübert-Flock, Carina
A1  - Liepa, Adomas
A1  - Rummler, Thomas
A1  - Arnault, Joel
A1  - Steffan-Dewenter, Ingolf
A1  - Ullmann, Tobias
T1  - Impact of STARFM on crop yield predictions: fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany
JF  - Remote Sensing
N2  - Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km\(^2\)), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R\(^2\) of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R\(^2\) of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R\(^2\) of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R\(^2\) = 0.88) and OSR (R\(^2\) = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R\(^2\) of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively.
KW  - MOD13Q1
KW  - precision agriculture
KW  - fusion
KW  - sustainable agriculture
KW  - decision making
KW  - winter wheat
KW  - oil-seed rape
KW  - crop models
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-311092
SN  - 2072-4292
VL  - 15
IS  - 6
ER  - 
TY  - JOUR
A1  - Rokhafrouz, Mohammad
A1  - Latifi, Hooman
A1  - Abkar, Ali A.
A1  - Wojciechowski, Tomasz
A1  - Czechlowski, Mirosław
A1  - Naieni, Ali Sadeghi
A1  - Maghsoudi, Yasser
A1  - Niedbała, Gniewko
T1  - Simplified and hybrid remote sensing-based delineation of management zones for nitrogen variable rate application in wheat
JF  - Agriculture
N2  - Enhancing digital and precision agriculture is currently inevitable to overcome the economic and environmental challenges of the agriculture in the 21st century. The purpose of this study was to generate and compare management zones (MZ) based on the Sentinel-2 satellite data for variable rate application of mineral nitrogen in wheat production, calculated using different remote sensing (RS)-based models under varied soil, yield and crop data availability. Three models were applied, including (1) a modified “RS- and threshold-based clustering”, (2) a “hybrid-based, unsupervised clustering”, in which data from different sources were combined for MZ delineation, and (3) a “RS-based, unsupervised clustering”. Various data processing methods including machine learning were used in the model development. Statistical tests such as the Paired Sample T-test, Kruskal–Wallis H-test and Wilcoxon signed-rank test were applied to evaluate the final delineated MZ maps. Additionally, a procedure for improving models based on information about phenological phases and the occurrence of agricultural drought was implemented. The results showed that information on agronomy and climate enables improving and optimizing MZ delineation. The integration of prior knowledge on new climate conditions (drought) in image selection was tested for effective use of the models. Lack of this information led to the infeasibility of obtaining optimal results. Models that solely rely on remote sensing information are comparatively less expensive than hybrid models. Additionally, remote sensing-based models enable delineating MZ for fertilizer recommendations that are temporally closer to fertilization times.
KW  - precision agriculture
KW  - management zones
KW  - remote sensing
KW  - Sentinel-2
KW  - clustering
KW  - winter wheat
KW  - drought
KW  - digital agriculture
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-250033
SN  - 2077-0472
VL  - 11
IS  - 11
ER  -