Satellite-driven modelling of Net Primary Productivity (NPP): Theoretical analysis

Prieto-Blanco, A.; North, Peter; Barnsley, Michael J.; Fox, Nigel

doi:10.1016/j.rse.2008.09.002

Cited by 41 publications

(18 citation statements)

References 60 publications

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“…Simulations were carried out similar to the approach taken by Prieto-Blanco et al [9]. We used two models in conjunction to simulate a set of the ground BRF observations and to generate the LUTs.…”

Section: Radiative Transfer Modelingmentioning

confidence: 99%

“…where F a,i is the mean fraction of radiant energy absorbed by canopy at band i, calculated from FLIGHT output, and W i are the weights, see also [9].…”

Section: Generation Of Look-up Tablesmentioning

confidence: 99%

“…The observations are simulated from a wider range of conditions that are not present in the conifer and broadleaf LUTs (soils, leaf area distribution). Further errors due to sensor noise and calibration are not considered here, but have been examined in previous studies [9,65] and should be considered prior to application to particular instruments. The tables of "observed" ground reflectances were constructed to contain complex errors for needleleaf and broadleaf forests.…”

Section: Simulation Of Observationsmentioning

confidence: 99%

“…Numerical solution of this inverse problem adjusts the model parameters such that model-predicted values closely match the measured values [7,8]. The match between model output and data is usually based on minimizing the sum of least squares [9].…”

Section: Introductionmentioning

confidence: 99%

“…Solutions of the model can be constrained to a range of realistic input parameters, optimization is fast and the complexity of the analytical model is retained [14]. In the present study we adopt a LUT-based inversion using the FLIGHT radiative transfer model [9,15].…”

Section: Introductionmentioning

confidence: 99%

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Statistical Distances and Their Applications to Biophysical Parameter Estimation: Information Measures, M-Estimates, and Minimum Contrast Methods

2013

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Radiative transfer models predicting the bidirectional reflectance factor (BRF) of leaf canopies are powerful tools that relate biophysical parameters such as leaf area index (LAI), fractional vegetation cover f V and the fraction of photosynthetically active radiation absorbed by the green parts of the vegetation canopy (f APAR ) to remotely sensed reflectance data. One of the most successful approaches to biophysical parameter estimation is the inversion of detailed radiative transfer models through the construction of Look-Up Tables (LUTs). The solution of the inverse problem requires additional information on canopy structure, soil background and leaf properties, and the relationships between these parameters and the measured reflectance data are often nonlinear. The commonly used approach for optimization of a solution is based on minimization of the least squares estimate between model and observations (referred to as cost function or distance; here we will also use the terms "statistical distance" or "divergence" or "metric", which are common in the statistical literature). This paper investigates how least-squares minimization and alternative distances affect the solution to the inverse problem. The paper provides a comprehensive list of different cost functions from the statistical literature, which can be divided into three major classes: information measures, M-estimates and minimum contrast methods. We found that, for the conditions investigated, Least Square Estimation (LSE) is not an optimal statistical distance for the estimation of biophysical parameters. Our results indicate that other statistical distances, such as the two power measures, Hellinger, Pearson chi-squared measure, Arimoto and Koenker-Basset distances result in better estimates of biophysical parameters than LSE; in some cases the parameter estimation was improved by 15%.Remote Sens. 2013, 5 1356

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Section: Radiative Transfer Modelingmentioning

confidence: 99%

“…where F a,i is the mean fraction of radiant energy absorbed by canopy at band i, calculated from FLIGHT output, and W i are the weights, see also [9].…”

Section: Generation Of Look-up Tablesmentioning

confidence: 99%

Section: Simulation Of Observationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical Distances and Their Applications to Biophysical Parameter Estimation: Information Measures, M-Estimates, and Minimum Contrast Methods

2013

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Fine‐grained forest net primary productivity monitoring: Software system integrating multisource data and smart optimization

Zou,

Luo,

Sun

et al. 2024

Softw Pract Exp

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Net primary productivity (NPP) is essential for sustainable resource management and conservation, and it serves as a primary monitoring target in smart forestry systems. The predominant method for NPP inversion involves data collection through terrestrial and satellite sensing systems, followed by parameter estimation using models such as the Carnegie‐Ames‐Stanford Approach (CASA). While this method benefits from low costs and extensive monitoring capabilities, the data derived from multisource sensing systems display varied spatial scale characteristics, and the NPP inversion models cannot detect the impact of data heterogeneity on the outcomes sensitively, reducing the accuracy of fine‐grained NPP inversion. Therefore, this paper proposes a modular system for fine‐grained data processing and NPP inversion. Regarding data processing, a two‐stage spatial‐spectral fusion model based on non‐negative matrix factorization (NMF) is proposed to enhance the spatial resolution of remote sensing data. A spatial interpolation model based on stacking generalization with residual correction is introduced to get raster meteorological data compatible with remote sensing images. Furthermore, we optimize the CASA model with the kernel method to enhance model sensitivity and enrich the spatial details of the inversion results with high resolution. Through validation using real datasets, the proposed fusion and interpolation models have significant advantages over mainstream methods. Furthermore, the correlation coefficient () between the estimated NPP using our improved inversion model and the field‐measured NPP is 0.69, demonstrating the feasibility of this platform in detailed forest NPP monitoring tasks.

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Regional Degraded Trend through Assessing of Steppe NPP on Remotely-Sensed Images in China

Zhu

et al. 2012

Advances in Intelligent and Soft Computing

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Satellite-driven modelling of Net Primary Productivity (NPP): Theoretical analysis

Cited by 41 publications

References 60 publications

Statistical Distances and Their Applications to Biophysical Parameter Estimation: Information Measures, M-Estimates, and Minimum Contrast Methods

Statistical Distances and Their Applications to Biophysical Parameter Estimation: Information Measures, M-Estimates, and Minimum Contrast Methods

Fine‐grained forest net primary productivity monitoring: Software system integrating multisource data and smart optimization

Regional Degraded Trend through Assessing of Steppe NPP on Remotely-Sensed Images in China

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