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- Bouras, El houssaine, et al.
(författare)
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Wheat Yield Estimation at High Spatial Resolution through the Assimilation of Sentinel-2 Data into a Crop Growth Model
- 2023
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Ingår i: Remote Sensing. - 2072-4292. ; 15:18
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Tidskriftsartikel (refereegranskat)abstract
- Monitoring crop growth and estimating crop yield are essential for managing agricultural production, ensuring food security, and maintaining sustainable agricultural development. Combining the mechanistic framework of a crop growth model with remote sensing observations can provide a means of generating realistic and spatially detailed crop growth information that can facilitate accurate crop yield estimates at different scales. The main objective of this study was to develop a robust estimation methodology of within-field winter wheat yield at a high spatial resolution (20 m × 20 m) by combining a light use efficiency-based model and Sentinel-2 data. For this purpose, Sentinel-2 derived leaf area index (LAI) time series were assimilated into the Simple Algorithm for Yield Estimation (SAFY) model using an ensemble Kalman filter (EnKF). The study was conducted on rainfed winter wheat fields in southern Sweden. LAI was estimated using vegetation indices (VIs) derived from Sentinel-2 data with semi-empirical models. The enhanced two-band vegetation index (EVI2) was found to be a useful VI for LAI estimation, with a coefficient of determination (R2) and a root mean square error (RMSE) of 0.80 and 0.65 m2/m2, respectively. Our findings demonstrate that the assimilation of LAI derived from Sentinel-2 into the SAFY model using EnKF enhances the estimation of within-field spatial variability of winter wheat yield by 70% compared to the baseline simulation without the assimilation of remotely sensed data. Additionally, the assimilation of LAI improves the accuracy of winter wheat yield estimation by decreasing the RMSE by 53%. This study demonstrates an approach towards practical applications of freely accessible Sentinel-2 data and a crop growth model through data assimilation for fine-scale mapping of crop yield. Such information is critical for quantifying the yield gap at the field scale, and to aid the optimization of management practices to increase crop production.
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| 2. |
- Kelly, Julia, et al.
(författare)
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Wildfire impacts on the carbon budget of a managed Nordic boreal forest
- 2024
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Ingår i: Agricultural and Forest Meteorology. - 0168-1923. ; 351
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Tidskriftsartikel (refereegranskat)abstract
- Wildfire is one of the most important disturbances affecting boreal forests. Most previous research on boreal forest fires has occurred in North American forests which have different fire regimes, tree species and are less intensively managed than their Eurasian counterparts. Recent extreme fire years have highlighted the vulnerability of the Nordic boreal forest to climatic shifts that are increasing forest fire frequency and severity. The Ljusdal fire (2018) was one of the largest wildfires in recorded history in Sweden. We established eddy covariance flux towers to track the impacts of this fire on the carbon balance of two Pinus sylvestris sites subject to different fire severities and forest management strategies 1–4 years post-fire. The ‘SLM’ site was a mature stand that experienced low-severity fire (trees survived) followed by salvage-logging and reseeding, whilst the ‘HY’ site was 10 years old when it experienced high-severity fire (all trees killed) then was replanted with seedlings. During the study period, both sites were net carbon sources at the annual scale. It took up to 4 years after the fire until the first day of net CO2 uptake was recorded at each site. We estimated that it will take 13 years (8, 21; mean ± 95 % confidence intervals) after the fire until the sites reach a neutral annual carbon balance. It will take up to 32 years (19, 53) at HY and 46 years (31, 70) at SLM to offset the carbon lost during and after the fire and salvage-logging. In addition, our measurements showed that more carbon was emitted in the first 4 years after the fire compared to the carbon lost from combustion during the fire. Quantifying carbon fluxes during the initial years after fire is therefore crucial for estimating the net impact of wildfire on the carbon budget of boreal forests.
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| 3. |
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| 4. |
- Thapa, Shangharsha, et al.
(författare)
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Assessing forest phenology : A multi-scale comparison of near-surface (UAV, spectral reflectance sensor, phenocam) and satellite (MODIS, sentinel-2) remote sensing
- 2021
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 13:8
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Tidskriftsartikel (refereegranskat)abstract
- The monitoring of forest phenology based on observations from near-surface sensors such as Unmanned Aerial Vehicles (UAVs), PhenoCams, and Spectral Reflectance Sensors (SRS) over satellite sensors has recently gained significant attention in the field of remote sensing and vegetation phenology. However, exploring different aspects of forest phenology based on observations from these sensors and drawing comparatives from the time series of vegetation indices (VIs) still remains a challenge. Accordingly, this research explores the potential of near-surface sensors to track the temporal dynamics of phenology, cross-compare their results against satellite observations (MODIS, Sentinel-2), and validate satellite-derived phenology. A time series of Normalized Difference Vegetation Index (NDVI), Green Chromatic Coordinate (GCC), and Normalized Difference of Green & Red (VIgreen) indices were extracted from both near-surface and satellite sensor platforms. The regression analysis between time series of NDVI data from different sensors shows the high Pearson’s correlation coefficients (r > 0.75). Despite the good correlations, there was a remarkable offset and significant differences in slope during green-up and senescence periods. SRS showed the most distinctive NDVI profile and was different to other sensors. PhenoCamGCC tracked green-up of the canopy better than the other indices, with a well-defined start, end, and peak of the season, and was most closely correlated (r > 0.93) with the satellites, while SRS-based VIgreen accounted for the least correlation (r = 0.58) against Sentinel-2. Phenophase transition dates were estimated and validated against visual inspection of the PhenoCam data. The Start of Spring (SOS) and End of Spring (EOS) could be predicted with an accuracy of <3 days with GCC, while these metrics from VIgreen and NDVI resulted in a slightly higher bias of (3–10) days. The observed agreement between UAVNDVI vs. satelliteNDVI and PhenoCamGCC vs. satelliteGCC suggests that it is feasible to use PhenoCams and UAVs for satellite data validation and upscaling. Thus, a combination of these near-surface vegetation metrics is promising for a holistic understanding of vegetation phenology from canopy perspective and could serve as a good foundation for analysing the interoperability of different sensors for vegetation dynamics and change analysis.
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