Landsat-8 and Sentinel-2 Canopy Water Content Estimation in Croplands through Radiative Transfer Model Inversion

Despite the potential implications of a cropland canopy water content (CCWC) thematic product, no global remotely sensed CCWC product is currently generated. The successful launch of the Landsat-8 Operational Land Imager (OLI) in 2012, Sentinel-2A Multispectral Instrument (MSI) in 2015, followed by...

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Main Authors: Erik J. Boren, Luigi Boschetti
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/12/17/2803
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spelling doaj-e83a11b1524544b295847c42297803ad2020-11-25T02:47:10ZengMDPI AGRemote Sensing2072-42922020-08-01122803280310.3390/rs12172803Landsat-8 and Sentinel-2 Canopy Water Content Estimation in Croplands through Radiative Transfer Model InversionErik J. Boren0Luigi Boschetti1Department of Forest, Rangeland and Fire Sciences, College of Natural Resources, University of Idaho, Moscow, ID 83844, USADepartment of Forest, Rangeland and Fire Sciences, College of Natural Resources, University of Idaho, Moscow, ID 83844, USADespite the potential implications of a cropland canopy water content (CCWC) thematic product, no global remotely sensed CCWC product is currently generated. The successful launch of the Landsat-8 Operational Land Imager (OLI) in 2012, Sentinel-2A Multispectral Instrument (MSI) in 2015, followed by Sentinel-2B in 2017, make possible the opportunity for CCWC estimation at a spatial and temporal scale that can meet the demands of potential operational users. In this study, we designed and tested a novel radiative transfer model (RTM) inversion technique to combine multiple sources of <i>a priori</i> data in a look-up table (LUT) for inverting the NASA Harmonized Landsat Sentinel-2 (HLS) product for CCWC estimation. This study directly builds on previous research for testing the constraint of the leaf parameter (<i>N<sub>s</sub></i>) in PROSPECT, by applying those constraints in PRO4SAIL in an agricultural setting where the variability of canopy parameters are relatively minimal. In total, 225 independent leaf measurements were used to train the LUTs, and 102 field data points were collected over the 2015–2017 growing seasons for validating the inversions. The results confirm increasing <i>a priori</i> information and regularization yielded the best performance for CCWC estimation. Despite the relatively low variable canopy conditions, the inclusion of <i>N<sub>s </sub></i>constraints did not improve the LUT inversion. Finally, the inversion of Sentinel-2 data outperformed the inversion of Landsat-8 in the HLS product. The method demonstrated ability for HLS inversion for CCWC estimation, resulting in the first HLS-based CCWC product generated through RTM inversion.https://www.mdpi.com/2072-4292/12/17/2803radiative transfer inversionharmonized Landsat sentinel productPRO4SAILcanopy water content
collection DOAJ
language English
format Article
sources DOAJ
author Erik J. Boren
Luigi Boschetti
spellingShingle Erik J. Boren
Luigi Boschetti
Landsat-8 and Sentinel-2 Canopy Water Content Estimation in Croplands through Radiative Transfer Model Inversion
Remote Sensing
radiative transfer inversion
harmonized Landsat sentinel product
PRO4SAIL
canopy water content
author_facet Erik J. Boren
Luigi Boschetti
author_sort Erik J. Boren
title Landsat-8 and Sentinel-2 Canopy Water Content Estimation in Croplands through Radiative Transfer Model Inversion
title_short Landsat-8 and Sentinel-2 Canopy Water Content Estimation in Croplands through Radiative Transfer Model Inversion
title_full Landsat-8 and Sentinel-2 Canopy Water Content Estimation in Croplands through Radiative Transfer Model Inversion
title_fullStr Landsat-8 and Sentinel-2 Canopy Water Content Estimation in Croplands through Radiative Transfer Model Inversion
title_full_unstemmed Landsat-8 and Sentinel-2 Canopy Water Content Estimation in Croplands through Radiative Transfer Model Inversion
title_sort landsat-8 and sentinel-2 canopy water content estimation in croplands through radiative transfer model inversion
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2020-08-01
description Despite the potential implications of a cropland canopy water content (CCWC) thematic product, no global remotely sensed CCWC product is currently generated. The successful launch of the Landsat-8 Operational Land Imager (OLI) in 2012, Sentinel-2A Multispectral Instrument (MSI) in 2015, followed by Sentinel-2B in 2017, make possible the opportunity for CCWC estimation at a spatial and temporal scale that can meet the demands of potential operational users. In this study, we designed and tested a novel radiative transfer model (RTM) inversion technique to combine multiple sources of <i>a priori</i> data in a look-up table (LUT) for inverting the NASA Harmonized Landsat Sentinel-2 (HLS) product for CCWC estimation. This study directly builds on previous research for testing the constraint of the leaf parameter (<i>N<sub>s</sub></i>) in PROSPECT, by applying those constraints in PRO4SAIL in an agricultural setting where the variability of canopy parameters are relatively minimal. In total, 225 independent leaf measurements were used to train the LUTs, and 102 field data points were collected over the 2015–2017 growing seasons for validating the inversions. The results confirm increasing <i>a priori</i> information and regularization yielded the best performance for CCWC estimation. Despite the relatively low variable canopy conditions, the inclusion of <i>N<sub>s </sub></i>constraints did not improve the LUT inversion. Finally, the inversion of Sentinel-2 data outperformed the inversion of Landsat-8 in the HLS product. The method demonstrated ability for HLS inversion for CCWC estimation, resulting in the first HLS-based CCWC product generated through RTM inversion.
topic radiative transfer inversion
harmonized Landsat sentinel product
PRO4SAIL
canopy water content
url https://www.mdpi.com/2072-4292/12/17/2803
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AT luigiboschetti landsat8andsentinel2canopywatercontentestimationincroplandsthroughradiativetransfermodelinversion
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