Accurate LAI retrieval method based on PROBA/CHRIS data

Fan, Wenjie; Xu, Xiru; Liu, Xichuan; Yan, Bonan; Cui, Yaokui

doi:10.5194/hess-14-1499-2010

Cited by 26 publications

(7 citation statements)

References 23 publications

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“…HyspIRI's thermal infrared and hyperspectral sensors will be able to contemporaneously observe hydrologic and hydro-meteorological processes, including ground water seepage, nitrogen load, water quality, and evapotranspiration (Banks et al, 1996;Bendjoudi et al, 2002;Byers & Chmura, 2014;Chen et al, 2002;Meijerink, 2002;Moffett, 2010;Mohamed et al, 2004;Xin, 2004). Along with some canopy structure from hyperspectral data (Fan et al, 2010), a more complete model of wetland energy and water exchanges with the atmosphere may be obtained. Contemporaneous observations of water surface temperature could provide some insight into coastal freshwater discharges and hence yield further information tying wetland processes to open water systems.…”

Section: Resultsmentioning

confidence: 99%

“…Recent analysis of full spectrum field spectrometer data indicates that hyperspectral first derivative reflectance spectra improve predictions of wetland vegetation biomass over simulated broadband spectra under low inundations conditions or wetland canopy structure (Fan, Xu, Liu, & Cui, 2010). Furthermore, biomass studies of grasslands demonstrate the greater capacity of hyperspectral imagery to reduce the saturation problems in biomass estimation compared to multispectral sensors (Mutanga & Skidmore, 2004), suggesting that hyperspectral data will improve biophysical models in marshes as well, which are typically dominated by grass species like S. alterniflora.…”

Section: Tidal Marsh Vegetationmentioning

confidence: 99%

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Prospective HyspIRI global observations of tidal wetlands

Turpie

Klemas

Byrd

et al. 2015

Remote Sensing of Environment

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a b s t r a c tTidal wetlands are highly productive and act as critical habitat for a wide variety of plants, fish, shellfish, and other wildlife. These ecotones between aquatic and terrestrial environments also provide protection from storm damage, run-off filtering, and recharge of aquifers. Many wetlands along coasts have been exposed to stress-inducing alterations globally, including dredge and fill operations, hydrologic modifications, pollutants, impoundments, fragmentation by roads/ditches, and sea level rise. For wetland protection and sensible coastal development, there is a need to monitor these ecosystems at global and regional scales. Recent advances in satellite sensor design and data analysis are providing practical methods for monitoring natural and man-made changes in wetlands. However, available satellite remote sensors have been limited to mapping primarily wetland location and extent. This paper describes how the HyspIRI hyperspectral and thermal infrared sensors can be used to study and map key ecological properties, such as species composition, biomass, hydrology, and evapotranspiration of tidal salt and brackish marshes and mangroves, and perhaps other major wetland types, including freshwater marshes and wooded/shrub wetlands.

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Section: Resultsmentioning

confidence: 99%

Section: Tidal Marsh Vegetationmentioning

confidence: 99%

Prospective HyspIRI global observations of tidal wetlands

Turpie

Klemas

Byrd

et al. 2015

Remote Sensing of Environment

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“…The LAI of winter wheat in UAV hyperspectral remote sensing images was retrieved by the directional second derivative (DSD) method [45], while the LNC of winter wheat was retrieved based on the novel angular insensitivity vegetation index (AIVI) [46]. The DSD is an algorithm that can effectively eliminate the soil background effect and bi-direction effect (view direction and solar direction), and then retrieve the LAI.…”

Section: E Remote Sensing Retrieval Methodsmentioning

confidence: 99%

Integration of Crop Growth Model and Random Forest for Winter Wheat Yield Estimation From UAV Hyperspectral Imagery

Yang

et al. 2021

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

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Accurate and timely crop yield estimation is critical for food security and sustainable development. The rapid development of unmanned aerial vehicles (UAVs) offers a new approach to acquire high spatio-temporal resolution imagery of farmland at a low cost. In order to realize the full potential of UAV platform and sensor, machine learning has been introduced to estimate crop yield, but the shortages of field measurements have troubled researchers. In this study, the CW-RF model, a new wheat yield estimation model suitable for the North China Plain, was established using random forest, and the crop growth model (the CERES-Wheat model) was chosen to simulate abundant training samples for random forest at field plot scale. According to CERES-Wheat model simulation, the leaf area index (LAI) and leaf nitrogen content (LNC) at the wheat jointing and heading stages were selected as the most sensitive parameters, and were retrieved from UAV hyperspectral imagery using the directional second derivative (DSD) and angular insensitivity vegetation index (AIVI) methods respectively. Then the retrieved LAI and LNC results were input into the CW-RF model to estimate winter wheat yield. The field validation in Luohe, Henan showed that the root mean squared error (RMSE) of the retrieved LAI and LNC were 6.27% and 12.17% at jointing stages, 9.21% and 13.64% at heading stages, respectively. The RMSE of estimated yield was 1,008.08 kg/ha, and the mean absolute percent error (MAPE) of estimated yield was 9.36%, demonstrating the available of the CW-RF model in wheat yield estimation at field plot scale. Apart from Luohe, validations in some other fields (e.g. Xiaotangshan, Beijing), prove the wide applicability of the CW-RF model. In addition, the UAV hyperspectral data were found to significantly improve the retrieval accuracy, and further improve CW-RF model estimation accuracy. In conclusion, this study showed that the CERES-Wheat model simulation can be important data source for machine learning-based wheat yield estimation model at field plot scale, and the hyperspectral sensor mounted on a UAV is a feasible remote sensing data acquisition mode for winter wheat growth monitoring and yield estimation.Index Terms-wheat yield estimation, the CERES-Wheat model, random forest, unmanned aerial vehicle (UAV), hyperspectral remote sensing

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“…After topographic radiation correction, the effective LAI was retrieved using a hybrid model of canopy reflectance [33,34]. A lookup table was built to facilitate effective LAI retrieval.…”

Section: Fapar Retrievalmentioning

confidence: 99%

Study of the Remote Sensing Model of FAPAR over Rugged Terrains

et al. 2016

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Mountainous areas with rugged terrains are widely distributed around the world.Remotely sensed values of the fraction of absorbed photosynthetically active radiation (FAPAR) suffer from the effect of rugged terrain. In this study, the effect of rugged terrain was incorporated into the FAPAR model based on recollision probability (FAPAR-P), which was improved in two aspects: calculating the sky viewing factor to correct for the fraction of diffuse sky radiation to the total radiation, and correcting the interception probability according to the slope and aspect of each pixel. The newly developed model is called FAPAR-PR (FAPAR-P Model for Rugged Terrain Area). Two study areas were chosen to validate the proposed model: the Dayekou watershed in Gansu Province, and Weichang in Hebei Province, China. The FAPAR values derived from the models were compared with FAPAR values measured in situ using photon flux sensors and the SunScan canopy analysis system (Delta-T Devices Ltd., Cambridge, UK). The validation results show that the FAPAR-PR model is applicable to rugged terrain areas, and it achieves a high level of accuracy. The FAPAR retrieval at different scales was also conducted to estimate the effect of terrain on the FAPAR-P and FAPAR-PR models. In our chosen study area, the effect of rugged terrain was significant in fine resolution pixels, but it was not obvious at larger scales, as the effects of slope and aspect were partly eliminated by the upscaling of the digital elevation model.

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Accurate LAI retrieval method based on PROBA/CHRIS data

Cited by 26 publications

References 23 publications

Prospective HyspIRI global observations of tidal wetlands

Prospective HyspIRI global observations of tidal wetlands

Integration of Crop Growth Model and Random Forest for Winter Wheat Yield Estimation From UAV Hyperspectral Imagery

Study of the Remote Sensing Model of FAPAR over Rugged Terrains

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