Maize Leaf Area Index Retrieval from Synthetic Quad Pol SAR Time Series Using the Water Cloud Model

Bériaux, Emilie; Waldner, François; Collienne, François; Bogaert, Patrick Van; Defourny, Pierre

doi:10.3390/rs71215818

Cited by 58 publications

(37 citation statements)

References 50 publications

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“…Summer crops also achieved good classification results. Corn, with an F1-score of~75%, was characterized by a rather insensitive VV curve, and on the contrary, a clear increase in VH due to volume scattering during the vegetative growth phase (250-300 days) until the plants reached their maximum height [46], after which it remained rather constant. Rice achieved an F1-score of~85%, mainly due to its characteristic time signature due to flooding at the time of sowing, which led to very low backscatter due to specular reflection [47].…”

Section: Crop Classification Results With Regard To Its Temporal Signmentioning

confidence: 97%

Crop Classification Based on Temporal Signatures of Sentinel-1 Observations over Navarre Province, Spain

2020

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Crop classification provides relevant information for crop management, food security assurance and agricultural policy design. The availability of Sentinel-1 image time series, with a very short revisit time and high spatial resolution, has great potential for crop classification in regions with pervasive cloud cover. Dense image time series enable the implementation of supervised crop classification schemes based on the comparison of the time series of the element to classify with the temporal signatures of the considered crops. The main objective of this study is to investigate the performance of a supervised crop classification approach based on crop temporal signatures obtained from Sentinel-1 time series in a challenging case study with a large number of crops and a high heterogeneity in terms of agro-climatic conditions and field sizes. The case study considered a large dataset on the Spanish province of Navarre in the framework of the verification of Common Agricultural Policy (CAP) subsidies. Navarre presents a large agro-climatic diversity with persistent cloud cover areas, and therefore, the technique was implemented both at the provincial and regional scale. In total, 14 crop classes were considered, including different winter crops, summer crops, permanent crops and fallow. Classification results varied depending on the set of input features considered, obtaining Overall Accuracies higher than 70% when the three (VH, VV and VH/VV) channels were used as the input. Crops exhibiting singularities in their temporal signatures were more easily identified, with barley, rice, corn and wheat achieving F1-scores above 75%. The size of fields severely affected classification performance, with ~14% better classification performance for larger fields (>1 ha) in comparison to smaller fields (<0.5 ha). Results improved when agro-climatic diversity was taken into account through regional stratification. It was observed that regions with a higher diversity of crop types, management techniques and a larger proportion of fallow fields obtained lower accuracies. The approach is simple and can be easily implemented operationally to aid CAP inspection procedures or for other purposes.

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Section: Crop Classification Results With Regard To Its Temporal Signmentioning

confidence: 97%

Crop Classification Based on Temporal Signatures of Sentinel-1 Observations over Navarre Province, Spain

2020

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“…This confirms that biweekly composites cannot adequately characterise crop productivity if crop critical periods are smoothed by the compositing algorithm 8 and implies that the use of maximum LAI or Early/Late Windows LAI was driven by practicality and data availability rather than by systematic targetting of critically sensitive periods suggested by crop physiology. 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 .…”

Section: Discussionmentioning

confidence: 99%

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

Waldner¹,

Horan²,

Chen³

et al. 2019

Preprint

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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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“…A new LAI estimation approach was developed though the coupling of two existing models, the WCM and the Ulaby soil moisture model. Beriaux et al (2015) used the water cloud model and a Bayesian fusion method to estimate maize LAI, and the result showed that this method has great potential in improving the accuracy and reliability of LAI retrieval using C-band SAR data. Crop biomass estimation Plant biomass plays an important role in ecosystem mechanisms.…”

Section: Crop Height Estimationmentioning

confidence: 99%

Research advances of SAR remote sensing for agriculture applications: A review

Liu

Chen

Shao

et al. 2019

Journal of Integrative Agriculture

153

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Synthetic aperture radar (SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical structures and dielectric properties of the targets and has a certain penetration ability to some agricultural targets. The capabilities of SAR for agriculture applications can be organized into three main categories: crop identification and crop planting area statistics, crop and cropland parameter extraction, and crop yield estimation. According to the above concepts, this paper systematically analyses the recent progresses, existing problems and future directions in SAR agricultural remote sensing. In recent years, with the remarkable progresses in SAR remote sensing systems, the available SAR data sources have been greatly enriched. The accuracies of the crop classification and parameter extraction by SAR data have been improved progressively. But the development of modern agriculture has put forwarded higher requirements for SAR remote sensing. For instance, the spatial resolution and revisiting cycle of the SAR sensors, the accuracy of crop classification, the whole phenological period monitoring of crop growth status, the soil moisture inversion under the condition of high vegetation coverage, the integrations of SAR remote sensing retrieval information with hydrological models and/or crop growth models, and so on, still need to be improved. In the future, the joint use of optical and SAR remote sensing data, the application of multi-band multi-dimensional SAR, the precise and high efficient modeling of electromagnetic scattering and parameter extraction of crop and farmland composite scene, the development of light and small SAR systems like those onboard unmanned aerial vehicles and their applications will be active research areas in agriculture remote sensing. This paper concludes that SAR remote sensing has great potential and will play a more significant role in the various fields of agricultural remote sensing.

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Maize Leaf Area Index Retrieval from Synthetic Quad Pol SAR Time Series Using the Water Cloud Model

Cited by 58 publications

References 50 publications

Crop Classification Based on Temporal Signatures of Sentinel-1 Observations over Navarre Province, Spain

Crop Classification Based on Temporal Signatures of Sentinel-1 Observations over Navarre Province, Spain

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

Research advances of SAR remote sensing for agriculture applications: A review

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