Scaling of FAPAR from the Field to the Satellite

Wang, Yiting; Xie, Donghui; Liu, Song; Hu, Ronggui; Li, Yahui; Yan, Guangjian

doi:10.3390/rs8040310

Cited by 16 publications

(14 citation statements)

References 49 publications

(65 reference statements)

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“…Subsequently, 20 m bands were resampled by the nearest neighbor method to the same pixel size as the bands of the visible band and near infrared (NIR) (8), i.e., 10 m. Then, all the biological predictors [67] were calculated, which are standardly implemented in the freely available software SNAP [66], including the vegetation index NDVI. Biological predictors were selected on the basis of several elaborated studies focused on plant physiology or biomass monitoring [91][92][93][94] or precision agriculture [95,96] as well as based on our previous experiences and testing [71]. The original spectral bands from the red edge bands (spectral bands 5, 6, and 7), were chosen as further input predictors.…”

Section: Methods Usedmentioning

confidence: 99%

Selecting Relevant Biological Variables Derived from Sentinel-2 Data for Mapping Changes from Grassland to Arable Land Using Random Forest Classifier

Šandera

Štych

2020

Land

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Permanent grassland is one of the monitored categories of land use, land use change, and forestry (LULUCF) within the climate concept and greenhouse gas reduction policy (Regulation (EU) 2018/841). Mapping the conditions and changes of permanent grasslands is thus very important. The area of permanent grassland is strongly influenced by agricultural subsidy policies. Over the course of history, it is possible to trace different shares of permanent grassland within agricultural land and areas with significant changes from grassland to arable land. The need for monitoring permanent grassland and arable land has been growing in recent years. New subsidy policies determining farm management are beginning to affect land use, especially in countries that have joined the EU in recent waves. The large amount of freely available satellite data enables this monitoring to take place, mainly owing to data products of the Copernicus program. There are a large number of parameters (predictors) that can be calculated from satellite data, but finding the right combination is very difficult. This study presents a methodical, systematic procedure using the random forest classifier and its internal metric of mean decrease accuracy (MDA) to select the most suitable predictors to detect changes from permanent grassland to arable land. The relevance of suitable predictors takes into account the date of the satellite image, the overall accuracy of change detection, and the time required for calculations. Biological predictors, such as leaf area index (LAI), fraction absorbed photosynthetically active radiation (FAPAR), normalized difference vegetation index (NDVI), etc. were tested in the form of a time series from the Sentinel-2 satellite, and the most suitable ones were selected. FAPAR, canopy water content (CWC), and LAI seemed to be the most suitable. The proposed change detection procedure achieved a very high accuracy of more than 95% within the study site with an area of 8766 km2.

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Section: Methods Usedmentioning

confidence: 99%

Selecting Relevant Biological Variables Derived from Sentinel-2 Data for Mapping Changes from Grassland to Arable Land Using Random Forest Classifier

Šandera

Štych

2020

Land

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“…Within each site, sampling points were selected uniformly at 3-5 m intervals along the diagonal direction, and FPAR was measured sequentially. The SunScan Canopy Analyzer [28,29] was used to measure FPAR at each sampling point by measuring the incident and reflected PAR above and below the canopy layer [30] for crops (i.e., maize and wheat) in the study area. For high trees, the fraction of intercepted PAR was measured as the approximation of FPAR by measuring the incident PAR in nearby open areas and under the canopy.…”

Section: B Data Setmentioning

confidence: 99%

“…For high trees, the fraction of intercepted PAR was measured as the approximation of FPAR by measuring the incident PAR in nearby open areas and under the canopy. To match the satellite data in time and space, the measured FPAR was temporally normalized to the time of satellite overpass using the method in [30], and those within the same Landsat pixel were averaged to the 30-m resolution. Finally, a total of 63 in situ FPAR measurements were acquired to validate the Landsat FPAR retrievals.…”

Section: B Data Setmentioning

confidence: 99%

Generalized Fine-Resolution FPAR Estimation Using Google Earth Engine: Random Forest or Multiple Linear Regression

Wang

Zhan

Yan

et al. 2023

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

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Accurate estimation of fine-resolution FPAR is urgently needed for modeling land surface processes at finer scales. While traditional methods can hardly balance universality, efficiency, and accuracy, methods using coarse-resolution products as a reference are promising for operational fineresolution FPAR estimation. However, current methods confront major problems of underrepresentation of FPAR-reflectance relations within coarse-resolution FPAR products, particularly for densely vegetated areas. To overcome this limitation, this article has developed an enhanced scaling method that proposes an outlier removal procedure and a method weighting the selected samples and models FPAR through weighted multiple linear regression (MLR) between the coarse-resolution FPAR product and the aggregated fine-resolution surface reflectance. Meanwhile, a random forest regression (RFR) method has also been implemented for comparison. Both methods were particularly applied to Landsat 8 OLI and MODIS FPAR data on the Google Earth Engine. Their performance was tested on a regional scale for an entire year. The results of the enhanced scaling method were closer to the in situ measurements (RMSE = 0.058 and R² = 0.768) and were more consistent with the MODIS FPAR (RMSE = 0.091 and R² =0.894) than those of the RFR, particularly over densely vegetated pixels. This indicates that a well-designed simple MLRbased method can outperform the more sophisticated RFR method. The enhanced scaling method is also less sensitive to the number of training samples than the RFR method. Moreover, both methods are insensitive to land cover maps, and their computation efficiency depends on the number of images to be estimated.

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“…As temporal mismatching between in situ data and satellite observation could be critical [66], we calculated fAPAR as averages from 10:00 a.m. to 11 a.m. to guarantee temporal matching between ground data and the satellites overpass. For the purpose of validating fAPAR products, only photosynthesizing materials (leaves, needles, or other green elements) should be accounted for in the calculation (green fAPAR) [41].…”

Section: Estimation Of Fapar From Apogee (Fapar Apogee )mentioning

confidence: 99%

Validation of PROBA-V GEOV1 and MODIS C5 & C6 fAPAR Products in a Deciduous Beech Forest Site in Italy

et al. 2017

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Abstract:The availability of new fAPAR satellite products requires simultaneous efforts in validation to provide users with a better comprehension of product performance and evaluation of uncertainties. This study aimed to validate three fAPAR satellite products, GEOV1, MODIS C5, and MODIS C6, against ground references to determine to what extent the GCOS requirements on accuracy (maximum 10% or 5%) can be met in a deciduous beech forest site in a gently and variably sloped mountain site. Three ground reference fAPAR, differing for temporal (continuous or campaign mode) and spatial sampling (single points or Elementary Sampling Units-ESUs), were collected using different devices: (1) Apogee (defined as benchmark in this study); (2) PASTIS; and (3) Digital cameras for collecting hemispherical photographs (DHP). A bottom-up approach for the upscaling process was used in the present study. Radiometric values of decametric images (Landsat-8) were extracted over the ESUs and used to develop empirical transfer functions for upscaling the ground measurements. The resulting high-resolution ground-based maps were aggregated to the spatial resolution of the satellite product to be validated considering the equivalent point spread function of the satellite sensors, and a correlation analysis was performed to accomplish the accuracy assessment. PASTIS sensors showed good performance as fAPAR PASTIS appropriately followed the seasonal trends depicted by fAPAR APOGEE (benchmark) (R 2 = 0.84; RMSE = 0.01). Despite small dissimilarities, mainly attributed to different sampling schemes and errors in DHP classification process, the agreement between fAPAR PASTIS and fAPAR DHP was noticeable considering all the differences between both approaches. The temporal courses of the three satellite products were found to be consistent with both Apogee and PASTIS, except at the end of the summer season when ground data were more affected by senescent leaves, with both MODIS C5 and C6 displaying larger short-term variability due to their shorter temporal composite period. MODIS C5 and C6 retrievals were obtained with the backup algorithm in most cases. The three green fAPAR satellite products under study showed good agreement with ground-based maps of canopy fAPAR at 10 h, with RMSE values lower than 0.06, very low systematic differences, and more than 85% of the pixels within GCOS requirements. Among them, GEOV1 fAPAR showed up to 98% of the points lying within Remote Sens. 2017, 9, 126; doi:10.3390/rs9020126 www.mdpi.com/journal/remotesensing Remote Sens. 2017, 9, 126 2 of 28 the GCOS requirements, and slightly lower values (mean bias = −0.02) as compared with the ground canopy fAPAR, which is expected to be only slightly higher than green fAPAR in the peak season.

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Scaling of FAPAR from the Field to the Satellite

Cited by 16 publications

References 49 publications

Selecting Relevant Biological Variables Derived from Sentinel-2 Data for Mapping Changes from Grassland to Arable Land Using Random Forest Classifier

Selecting Relevant Biological Variables Derived from Sentinel-2 Data for Mapping Changes from Grassland to Arable Land Using Random Forest Classifier

Generalized Fine-Resolution FPAR Estimation Using Google Earth Engine: Random Forest or Multiple Linear Regression

Validation of PROBA-V GEOV1 and MODIS C5 & C6 fAPAR Products in a Deciduous Beech Forest Site in Italy

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