A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

Dorigo, Wouter; Zurita-Milla, Raúl; Wit, Allard de; Brazile, Jason; Singh, Ranvir; Schaepman, Michael E.

doi:10.1016/j.jag.2006.05.003

Cited by 504 publications

(344 citation statements)

References 157 publications

(261 reference statements)

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“…High-resolution LAI maps can provide important information for the ecological and crop growth studies, as well as the validation of LAI products [9][10][11][12][13][14][15][16][17][18]. Existing empirical methods often require enough measurements to represent the LAI-VI relationship throughout the study area.…”

Section: Discussionmentioning

confidence: 99%

“…It describes the size of the interface available for energy and mass exchange between the canopy and atmosphere, which governs the photosynthesis, transpiration, and rain interception processes [4][5][6][7][8]. LAI maps estimated from high-resolution satellite imagery provide valuable information for the climate, ecological, and crop models [9][10][11][12], as well as estimating crop vegetation status, biomass production, and yield [13][14][15]. In addition, the high-resolution LAI map plays an important role in the on-going validation of medium-resolution LAI products [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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A Prior Knowledge-Based Method to Derivate High-Resolution Leaf Area Index Maps with Limited Field Measurements

Shi

Wang

et al. 2016

Remote Sensing

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High-resolution leaf area index (LAI) maps from remote sensing data largely depend on empirical models, which link field LAI measurements to the vegetation index. The existing empirical methods often require the field measurements to be sufficient for constructing a reliable model. However, in many regions of the world, there are limited field measurements available. This paper presents a prior knowledge-based (PKB) method to derivate LAI with limited field measurements, in an effort to improve the accuracy of empirical model. Based on the assumption that the experimental sites with the same vegetation type can be represented by similar models, a priori knowledge for crops was extracted from the published models in various cropland sites. The knowledge, composed of an initial guess of each model parameter with the associated uncertainty, was then combined with the local field measurements to determine a semi-empirical model using the Bayesian inversion method. The proposed method was evaluated at a cropland site in the Huailai region of Hebei Province, China. Compared with the regression method, the proposed PKB method can effectively improve the accuracy of empirical model and LAI estimation, when the field measurements were limited. The results demonstrate that a priori knowledge extracted from the universal sites can provide important auxiliary information to improve the representativeness of the empirical model in a given study area.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Prior Knowledge-Based Method to Derivate High-Resolution Leaf Area Index Maps with Limited Field Measurements

Shi

Wang

et al. 2016

Remote Sensing

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“…Furthermore to the monitoring of changes, LULC has been studied profusely (Schubert, Sanders, Smith, & Wright, 2008) with the general objective of treating areas of particular interest from an economical or environmental point of view. In these cases, planning and management play an important role at the time of exploiting the resources but are always subject to the quality of the products extracted from remote sensing (Dorigo et al, 2007;Kennedy et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Automatic environmental quality assessment for mixed-land zones using lidar and intelligent techniques

García-Gutiérrez

Gonçalves-Seco

Santos

2011

Expert Systems with Applications

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Human impact on the natural environment is an evident global fact. Natural, industrial and touristic areas coexist in a more than delicate balance. In Andalusia, in the south of Spain, the Regional Ministry for the Environment is responsible for the control and preservation of natural resources. This task bears a high cost in time and money. Remote sensing and the use of intelligent techniques are excellent tools to reduce such costs. This work explores the joint use of the lidar sensor, which provides a great quantity of information describing three dimensional space, and the application of intelligent techniques for rapid and efficient land use and land cover classification with the objective of differentiating urban land from natural ground close to protected areas of Huelva province. For this, seven types of land use and land cover have been studied for a riparian area next to the mouth of the rivers Tinto and Odiel, extracting 33 distinct features from the lidar point cloud. Subsequently, a supervised learning algorithm is applied to construct a model which, with a resolution of 4 m 2 , obtained relative precision between 71% and 100%and an average total precision of 85%.

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“…Remote sensing techniques for estimating vegetation characteristics from reflective optical measurements are either based on statistical-empirical or physical approaches, both having their advantages and disadvantages [6]. Statistical-empirical methods are typically based on a regression function between measured biochemical or biophysical properties and spectral measurements in the form of a vegetation index (VI).…”

Section: Introductionmentioning

confidence: 99%

“…Statistical-empirical methods are typically based on a regression function between measured biochemical or biophysical properties and spectral measurements in the form of a vegetation index (VI). A VI is a combination of a limited number of spectral bands, selected and combined in a way that enhances spectral features related to the variable of interest while reducing undesired effects caused by variations in soil reflectance, sun and view geometry, atmospheric composition, and other leaf or canopy properties [6]. Classical VIs such as the NDVI [7] and SAVI [8] have been based on broad spectral bands in the visible and near infrared (NIR) part of the spectrum and are particularly suitable to monitor structural vegetation variables such as LAI and fraction of green cover [9].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Automated Canopy Characterization from Hyperspectral Data by a Novel Two Step Radiative Transfer Model Inversion Approach

Dorigo

Richter

Baret³

et al. 2009

Remote Sensing

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Automated, image based methods for the retrieval of vegetation biophysical and biochemical variables are often hampered by the lack of a priori knowledge about land cover and phenology, which makes the retrieval a highly underdetermined problem. This study addresses this problem by presenting a novel approach, called CRASh, for the concurrent retrieval of leaf area index, leaf chlorophyll content, leaf water content and leaf dry matter content from high resolution solar reflective earth observation data. CRASh, which is based on the inversion of the combined PROSPECT+SAILh radiative transfer model (RTM), explores the benefits of combining semi-empirical and physically based approaches. The approach exploits novel ways to address the underdetermined problem in the context of an automated retrieval from mono-temporal high resolution data. To regularize the inverse problem in the variable domain, RTM inversion is coupled with an OPEN ACCESSRemote Sens. 2009, 1 1140 automated land cover classification. Model inversion is based on a two step lookup table (LUT) approach: First, a range of possible solutions is selected from a previously calculated LUT based on the analogy between measured and simulated reflectance. The final solution is determined from this subset by minimizing the difference between the variables used to simulate the spectra contained in the reduced LUT and a first guess of the solution. This first guess of the variables is derived from predictive semi-empirical relationships between classical vegetation indices and the single variables. Additional spectral regularization is obtained by the use of hyperspectral data. Results show that estimates obtained with CRASh are significantly more accurate than those obtained with a tested conventional RTM inversion and semi-empirical approach. Accuracies obtained in this study are comparable to the results obtained by various authors for better constrained inversions that assume more a priori information. The completely automated and image-based nature of the approach facilitates its use in operational chains for upcoming high resolution airborne and spaceborne imaging spectrometers.

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A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

Cited by 504 publications

References 157 publications

A Prior Knowledge-Based Method to Derivate High-Resolution Leaf Area Index Maps with Limited Field Measurements

A Prior Knowledge-Based Method to Derivate High-Resolution Leaf Area Index Maps with Limited Field Measurements

Automatic environmental quality assessment for mixed-land zones using lidar and intelligent techniques

Enhanced Automated Canopy Characterization from Hyperspectral Data by a Novel Two Step Radiative Transfer Model Inversion Approach

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