Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion

Yin, Gaofei; Verger, Aleixandre; Qu, Yonghua; Zhao, Wei; Xu, Baodong; Zeng, Yelu; Liu, Ke; Li, Jing; Liu, Qinhuo

doi:10.3390/rs11030244

Cited by 15 publications

(12 citation statements)

References 72 publications

(88 reference statements)

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“…Denser leaf layers increase these effects on the canopy level. In this context, the multi-spectral Landsat missions and especially its latest satellite Landsat 8 [6] have shown potential to estimate crop LAI [7][8][9], forest LAI [10] and in combination with Radiative Transfer Models (RTMs)' generic LAI [11]. However, the missions' orbits defines the revisit time as 16 days, which may be insufficient to track fast vegetation changes such as spring leaf flush.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Workflow Feature Selection on Forest LAI Prediction with Sentinel-2A MSI, Landsat 7 ETM+ and Landsat 8 OLI

et al. 2020

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The European Space Agency (ESA)’s Sentinel-2A (S2A) mission is providing time series that allow the characterisation of dynamic vegetation, especially when combined with the National Aeronautics and Space Administration (NASA)/United States Geological Survey (USGS) Landsat 7 (L7) and Landsat 8 (L8) missions. Hybrid retrieval workflows combining non-parametric Machine Learning Regression Algorithms (MLRAs) and vegetation Radiative Transfer Models (RTMs) were proposed as fast and accurate methods to infer biophysical parameters such as Leaf Area Index (LAI) from these data streams. However, the exact design of optimal retrieval workflows is rarely discussed. In this study, the impact of five retrieval workflow features on LAI prediction performance of MultiSpectral Instrument (MSI), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) observations was analysed over a Dutch beech forest site for a one-year period. The retrieval workflow features were the (1) addition of prior knowledge of leaf chemistry (two alternatives), (2) the choice of RTM (two alternatives), (3) the addition of Gaussian noise to RTM produced training data (four and five alternatives), (4) possibility of using Sun Zenith Angle (SZA) as an additional MLRA training feature (two alternatives), and (5) the choice of MLRA (six alternatives). The features were varied in a full grid resulting in 960 inversion models in order to find the overall impact on performance as well as possible interactions among the features. A combination of a Terrestrial Laser Scanning (TLS) time series with litter-trap derived LAI served as independent validation. The addition of absolute noise had the most significant impact on prediction performance. It improved the median prediction Root Mean Square Error (RMSE) by 1.08 m2 m−2 when 5% noise was added compared to inversions with 0% absolute noise. The choice of the MLRA was second most important in terms of median prediction performance, which differed by 0.52 m2 m−2 between the best and worst model. The best inversion model achieved an RMSE of 0.91 m2 m−2 and explained 84.9% of the variance of the reference time series. The results underline the need to explicitly describe the used noise model in future studies. Similar studies should be conducted in other study areas, both forest and crop systems, in order to test the noise model as an integral part of hybrid retrieval workflows.

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Section: Introductionmentioning

confidence: 99%

Assessment of Workflow Feature Selection on Forest LAI Prediction with Sentinel-2A MSI, Landsat 7 ETM+ and Landsat 8 OLI

et al. 2020

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“…For this reason, a Gaussian process regression model was implemented to estimate the amount of water to be applied at each time depending on the developmental stage of the crop. This methodology has also been used to improve the accuracy in leaf area index (LAI) retrieval and to provide uncertainty estimates directly through Gaussian probabilities [46,47].…”

Section: Discussionmentioning

confidence: 99%

Irrigation Protocols in Different Water Availability Scenarios for ‘Crimson Seedless’ Table Grapes under Mediterranean Semi-Arid Conditions

Temnani

Conesa

Ruiz

et al. 2020

Water

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For three consecutive years (2015–2017), two deficit irrigation (DI) strategies were used in a 12-year old vineyard (cv. ‘Crimson Seedless’) to implement a sustainable irrigation protocol according to the available water for the farmer. Four different irrigation treatments were assessed: (i) Control (CTL), irrigated to satisfy the maximum crop water requirements throughout the entire growing season; two DI treatments irrigated as CTL except during post-veraison, when the vines were irrigated at 50% CTL: (ii) Regulated Deficit Irrigation (RDI); and (iii) Partial Root Drying (PRD), alternating the wet and dry sides of the root zone, and (iv) irrigated according to the criteria followed by the farmer (FARM), and conditioned by the availability of water each season. The DI strategies resulted in a 50% increase in water use efficiency in the first two years and 81% during the third year. Weekly deficit irrigation protocols are proposed, which specify a maximum difference of 0.22 MPa of midday stem water potential with respect to well-watered vines for a range of irrigation water availabilities between 4000 and 7000 m3 ha−1. An applied water prediction model based on the Gaussian regression using day of the year and maximum temperature of the day is also proposed.

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“…This can be done in situ by using commercial instruments, digital hemispherical photography (DHP), or more recently by WSNs [6,23]. Alternatively, LAI information can be derived via remote sensing from satellite or airborne images, enabling large-scale monitoring [20,32]. A different widespread vegetation index assessed by remote sensing is the normalized difference vegetation index (NDVI) that is derived from characteristic absorptions of specific wavelengths of vegetative reflectance.…”

Section: Crop Parametersmentioning

confidence: 99%

“…Possible future work, therefore, includes more sophisticated models and techniques of machine learning to further improve the potential of the proposed approach. For example, Gaussian Process Regression (GPR) could be considered, which is already applied in the context of remote sensing to combine in situ with space-borne LAI retrieval in a promising way [20,32]. Finally, the data set collected in the longterm deployment used in this work has been released for the community.…”

Section: Limitations Of This Studymentioning

confidence: 99%

Towards a Low-cost RSSI-based Crop Monitoring

Bauer

Aschenbruck

2020

ACM Trans. Internet Things

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The continuous monitoring of crop growth is crucial for site-specific and sustainable farm management in the context of precision agriculture. With the help of precise in situ information, agricultural practices, such as irrigation, fertilization, and plant protection, can be dynamically adapted to the changing needs of individual sites, thereby supporting yield increases and resource optimization. Nowadays, IoT technology with networked sensors deployed in greenhouses and farmlands already contributes to in situ information. In addition to existing soil sensors for moisture or nutrient monitoring, there are also (mainly optical) sensors to assess growth developments and vital conditions of crops. This article presents a novel and complementary approach for a low-cost crop sensing that is based on temporal variations of the signal strength of low-power IoT radio communication. To this end, the relationship between crop growth, represented by the leaf area index (LAI), and the attenuation of signal propagation of low-cost radio transceivers is investigated. Real-world experiments in wheat fields show a significant correlation between LAI and received signal strength indicator (RSSI) time series. Moreover, influencing meteorological factors are identified and their effects are analyzed. Including these factors, a multiple linear model is finally developed that enables an RSSI-based LAI estimation with great potential.

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Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion

Cited by 15 publications

References 72 publications

Assessment of Workflow Feature Selection on Forest LAI Prediction with Sentinel-2A MSI, Landsat 7 ETM+ and Landsat 8 OLI

Assessment of Workflow Feature Selection on Forest LAI Prediction with Sentinel-2A MSI, Landsat 7 ETM+ and Landsat 8 OLI

Irrigation Protocols in Different Water Availability Scenarios for ‘Crimson Seedless’ Table Grapes under Mediterranean Semi-Arid Conditions

Towards a Low-cost RSSI-based Crop Monitoring

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