## 7 Conclusions  
In this study, spatiotemporal analysis of GPP values for seven major regions in Turkiye was conducted. We evaluated different remote sensing products for GPP, since there are inadequate measurement systems, i.e., flux towers and tower networks, in the study area. While many studies in the literature have focused on the global-scale analysis of GPP values, local-scale analyses are rarely conducted. Moreover, many studies in the literature have focused on annual GPP sums, whereas this study presents monthly analyses of GPP. The key conclusions drawn from this study can be listed as follows:  
* GPP values show high similarity among datasets at low values, but this similarity decreases at high GPP values. The inputs used by the algorithms differ and more meticulous regional calibration and validation are necessary. When conducting a monthly trend analysis of GPP values, only the MODIS dataset exhibited a decreasing trend at certain months. The factors causing this trend should be further investigated. Additionally, significant increasing trends are detected in GPP values during summer months. There are many potential mechanisms that underlie these trends, which should be studied further to understand the changing carbon cycle of Turkiye. * The ITA and EMD methods could be promising alternatives to MMK as they provide additional insight into how time series change. This comparison in the field of GPP is a first, and future studies may benefit from these methods for trend analysis. The ITA and EMD methods have already made substantial contributions to the literature in terms of visualizing trends. While ITA can easily detect trends at low and high values, EMD stands out for answering how the trend follows a nonlinear path over time. Optimizing the hyperparameters of the EMD method could lead to the more rational extraction of trend component information. We hope that the present analysis helps motivate more studies on carbon cycling and its trends, in Turkiye and other regions that are under-represented in flux networks, to improve our mechanistic understanding of the carbon cycle across larger regions of the globe. Also, researchers are encouraged to use data with higher spatial resolution in future studies, to work with data classified according to plant functional type, and to analyze variables that may affect the trends in GPP values. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs16111994/s1](https://www.mdpi.com/article/10.3390/rs16111994/s1)]([https://www.mdpi.com/article/10.3390/rs16111994/s1](https://www.mdpi.com/article/10.3390/rs16111994/s1)), Figure S1. MODIS GPP algorithm flow chart. Figure S2. The MuSyQ GPP algorithm flowchart. Figure S3: Histograms and time series of MODIS datasets (a) Antalya, (b) Erzincan, (c) Izmir, (d) Kirklareli, (e) Konya, (f) Samsun, (g) Sanliurfa. Figure S4. Histograms and time series of MuSyQ datasets (a) Antalya, (b) Erzincan, (c) Izmir, (d) Kirklareli, (e) Konya, (f) Samsun, (g) Sanliurfa. Figure S5. Histograms and time series of PMLV2 datasets (a) Antalya, (b) Erzincan, (c) Izmir, (d) Kirklareli, (e) Konya, (f) Samsun, (g) Sanliurfa. Figure S6. Histograms and time series of TL-LUE datasets (a) Antalya, (b) Erzincan, (c) Izmir, (d) Kirklareli, (e) Konya, (f) Samsun, (g) Sanliurfa. Figure S7. Histograms and time series of GOSIF datasets (a) Antalya, (b) Erzincan, (c) Izmir, (d) Kirklareli, (e) Konya, (f) Samsun, (g) Sanliurfa. Figure S8. Histograms and time series of FLUXCOM datasets (a) Antalya, (b) Erzincan, (c) Izmir, (d) Kirklareli, (e) Konya, (f) Samsun, (g) Sanliurfa. Figure S9. ITA trend analysis results (horizontal and vertical axes show GPP values in g C m\({}^{-2}\) d\({}^{-1}\)), (a) Antalya, (b) Erzincan, (c) Izmir, (d) Kirklareli, (e) Konya, (f) Samsun, (g) Sanliurfa. Figure S10. EMD trend analysis results (the horizontal axis shows time; the vertical axis shows GPP values), (a) Antalya, (b) Erzincan, (c) Izmir, (d) Kirklareli, (e) Konya, (f) Samsun, (g) Sanliurfa.