Evaluation of Three Techniques for Correcting the Spatial Scaling Bias of Leaf Area Index

Jiang, Jiale; Ji, Xusheng; Yao, Xia; Tian, Yongchao; Zhu, Yan; Cao, Weixing; Cheng, Tao

doi:10.3390/rs10020221

“…Integrating radar data 40,41 and blending lower resolution time series 42,43 are two mitigation options to increase data frequency in areas with persistent cloud cover. Attention should be paid to correct the spatial scaling bias when fusing LAI data 44,45 because it does not correlate linearly with spatial resolution 46,47 . Given the unprecedented revisit cycle of current Earth Observation systems, data fusion capabilities, and the prospects of future missions, our modelling suggests that the time is ripe for a shift towards the use of data-intensive metrics for empirical yield estimation.…”

Section: Discussionmentioning

confidence: 99%

High temporal resolution of leaf area data improves empirical estimation of grain yield

Waldner

¹

,

Horan

²

,

Yang

³

et al. 2019

Sci Rep

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Empirical yield estimation from satellite data has long lacked suitable combinations of spatial and temporal resolutions. Consequently, the selection of metrics, i.e., temporal descriptors that predict grain yield, has likely been driven by practicality and data availability rather than by systematic targetting of critically sensitive periods as suggested by knowledge of crop physiology. The current trend towards hyper-temporal data raises two questions: How does temporality affect the accuracy of empirical models? Which metrics achieve optimal performance? We followed an in silico approach based on crop modelling which can generate any observation frequency, explore a range of growing conditions and reduce the cost of measuring yields in situ. We simulated wheat crops across Australia and regressed six types of metrics derived from the resulting time series of Leaf Area Index (LAI) against wheat yields. Empirical models using advanced LAI metrics achieved national relevance and, contrary to simple metrics, did not benefit from the addition of weather information. This suggests that they already integrate most climatic effects on yield. Simple metrics remained the best choice when LAI data are sparse. As we progress into a data-rich era, our results support a shift towards metrics that truly harness the temporal dimension of LAI data.

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“…Integrating radar data 40,41 and blending lower resolution time series 42,43 are two mitigation options to increase data frequency in areas with persistent cloud cover. Attention should be paid to correct the spatial scaling bias when fusing LAI data 44,45 because it does not correlate linearly with spatial resolution 46,47 . Given the unprecedented revisit cycle of current Earth Observation systems, data fusion capabilities, and the prospects of future missions, our modelling suggests that the time is ripe for a shift towards the use of data-intensive metrics for empirical yield estimation.…”

Section: Discussionmentioning

confidence: 99%

High temporal resolution of leaf area data improves empirical estimation of grain yield

Waldner¹,

Horan²,

Chen³

et al. 2019

Preprint

View full text Add to dashboard Cite

Empirical yield estimation from satellite data has long lacked suitable combinations of spatial and temporal resolutions. Consequently, the selection of metrics, i.e., temporal descriptors that predict grain yield, has likely been driven by practicality and data availability rather than by systematic targetting of critically sensitive periods as suggested by knowledge of crop physiology. The current trend towards hyper-temporal data raises two questions: How does temporality affect the accuracy of empirical models? Which metrics achieve optimal performance? We followed an in silico approach based on crop modelling which can generate any observation frequency, explore a range of growing conditions and reduce the cost of measuring yields in situ. We simulated wheat crops across Australia and regressed six types of metrics derived from the resulting time series of Leaf Area Index (LAI) against wheat yields. Empirical models using advanced LAI metrics achieved national relevance and, contrary to simple metrics, did not benefit from the addition of weather information. This suggests that they already integrate most climatic effects on yield. Simple metrics remained the best choice when LAI data are sparse. As we progress into a data-rich era, our results support a shift towards metrics that truly harness the temporal dimension of LAI data.

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“…Due to the lack of sub-meter imagery, the fine images used in this study are pan-sharpened PMS/GF-1 bands. Although pan-sharpened PMS/GF-1 data has been widely used as a real and reliable product in many applications [53]- [58], it is important to use the original high-resolution data to do the fusion. Our results indicate the great potential of HISTIF in the applications of fusing Landsat-8 or Sentinel-2 and WorldView-2 or other high-resolution imagery.…”

Section: Advantages and Limitations Of Histif And User Guidementioning

confidence: 99%

HISTIF: A New Spatiotemporal Image Fusion Method for High-Resolution Monitoring of Crops at the Subfield Level

Jiang

¹

,

Zhang

²

,

Yao

³

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

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Satellite-based time-series crop monitoring at the subfield level is essential to the efficient implementation of precision crop management. Existing spatiotemporal image fusion techniques can be helpful, but they were often proposed to generate medium-resolution images. This study proposed a HIghresolution SpatioTemporal Image Fusion method (HISTIF) consisting of filtering for cross-scale spatial matching (FCSM) and multiplicative modulation of temporal change (MMTC). In FCSM, we considered both point spread function effect and georegistration errors between fine and coarse resolution images. Subsequently, MMTC used pixel-based multiplicative factors to estimate the temporal change between reference and prediction dates without image classification. The performance of HISTIF was evaluated using both simulated and real datasets with one from real Gaofen-1 (GF-1) and simulated Landsat-like/Sentinellike images, and the other from real GF-1 and real Landsat/Sentinel-2 data on two sites. HISTIF was compared with the existing methods Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF) and Fit-FC. The results demonstrated that HISTIF produced substantial reduction in the fusion error from crossscale spatial mismatch and accurate reconstruction in spatial details within fields, regardless of simulated or real data. The images predicted by STARFM exhibited pronounced blocky artifacts. While the images predicted by HISTIF and Fit-FC both showed clear within-field variability patterns, HISTIF was able to reduce the spectral distortion more significantly than Fit-FC. Furthermore, HISTIF exhibited the most stable performance across sensors. The findings suggest that HISTIF could be beneficial for the frequent and detailed monitoring of crop growth at the subfield level.

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Evaluation of Three Techniques for Correcting the Spatial Scaling Bias of Leaf Area Index

Cited by 16 publications

References 40 publications

High temporal resolution of leaf area data improves empirical estimation of grain yield

High temporal resolution of leaf area data improves empirical estimation of grain yield

High temporal resolution of leaf area data improves empirical estimation of grain yield

HISTIF: A New Spatiotemporal Image Fusion Method for High-Resolution Monitoring of Crops at the Subfield Level

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