A global canopy water content product from AVHRR/Metop

Garcı́a-Haro, Francisco Javier; Campos-Taberner, Manuel; Moreno, A.; Tagesson, Håkan Torbern; Camacho, Fernando; Martínez, Beatriz; Sánchez, S. Roig; Piles, María; Camps‐Valls, Gustau; Yebra, Marta

doi:10.1016/j.isprsjprs.2020.02.007

Cited by 31 publications

(21 citation statements)

References 63 publications

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“…The vegetation water content (VWC) is a valuable indicator of vegetation drought stress [ 1 ], forest fire risk [ 2 , 3 ], and regional water resource assessment [ 4 ]. The most accurate method to evaluate the water status of vegetation involves traditional physiological measurements; however, this method is time consuming, laborious, and cannot meet the requirements of large-scale, real-time monitoring [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Estimation of leaf water content from hyperspectral data of different plant species by using three new spectral absorption indices

Yang

Jin

et al. 2021

PLoS ONE

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The leaf equivalent water thickness (EWT, g cm−2) and fuel moisture content (FMC, %) are key variables in ecological and environmental monitoring. Although a variety of hyperspectral vegetation indices have been developed to estimate the leaf EWT and FMC, most of these indices are defined considered two or three specific bands for a specific plant species, which limits their applicability. In this study, we proposed three new spectral absorption indices (SAI970, SAI1200, and SAI1660) for various plant types by considering the symmetry of the spectral absorption at 970 nm, 1200 nm and 1660 nm and spectral heterogeneity of different leaves. The indices were calculated considering the absorption peak and shoulder bands of each leaf instead of the same specific bands for all leaves. A pooled dataset of three tree species (camphor (VX), capricorn (VJ), and red-leaf plum (VL)) was used to test the performance of the SAIs in terms of the leaf EWT and FMC estimation. The results indicated that, first, SAI1200 was more suitable for estimating the EWT than FMC, whereas SAI970 and SAI1660 were more suitable for estimating the FMC. Second, SAI1200 achieved the most accurate estimation of the EWT with a cross-validation coefficient of determination (Rcv2) of 0.845 and relative cross-validation root mean square error (rRMSEcv) of 8.90%. Third, SAI1660 outperformed the other indices in estimating the FMC at the leaf level, with an Rcv2 of 0.637 and rRMSEcv of 8.56%. Fourth, SAI970 achieved a moderate accuracy in estimating the EWT (Rcv2 of 0.25 and rRMSEcv of 19.68%) and FMC (Rcv2 of 0.275 and rRMSEcv of 12.10%) at the leaf level. These results can enrich the application of the SAIs and demonstrate the potential of using SAI1200 to determine the leaf EWT and SAI1660 to obtain the leaf FMC among various plant types.

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Section: Introductionmentioning

confidence: 99%

Estimation of leaf water content from hyperspectral data of different plant species by using three new spectral absorption indices

Yang

Jin

et al. 2021

PLoS ONE

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“…This aspect confirms the relevance of the Sentinel-2 near infrared, and red bands to categorise vegetation, as these two bands well address differences in leaf area index (LAI) and leaf pigmentation, respectively 52 . In a similar way, the fact that Band 11 (SWIR) also scored relatively high underlines the effectiveness of the proposed scheme, as this band is known for providing independent information related to crop water and/or protein content 55 . Several recent studies using Sentinel-2 data have highlighted the importance of this band for crop type identification 50,56 .…”

Section: Discussionmentioning

confidence: 58%

Understanding deep learning in land use classification based on Sentinel-2 time series

Campos-Taberner

Garcı́a-Haro

Martínez

et al. 2020

Sci Rep

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143

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The use of deep learning (DL) approaches for the analysis of remote sensing (RS) data is rapidly increasing. DL techniques have provided excellent results in applications ranging from parameter estimation to image classification and anomaly detection. Although the vast majority of studies report precision indicators, there is a lack of studies dealing with the interpretability of the predictions. This shortcoming hampers a wider adoption of DL approaches by a wider users community, as model’s decisions are not accountable. In applications that involve the management of public budgets or policy compliance, a better interpretability of predictions is strictly required. This work aims to deepen the understanding of a recurrent neural network for land use classification based on Sentinel-2 time series in the context of the European Common Agricultural Policy (CAP). This permits to address the relevance of predictors in the classification process leading to an improved understanding of the behaviour of the network. The conducted analysis demonstrates that the red and near infrared Sentinel-2 bands convey the most useful information. With respect to the temporal information, the features derived from summer acquisitions were the most influential. These results contribute to the understanding of models used for decision making in the CAP to accomplish the European Green Deal (EGD) designed in order to counteract climate change, to protect biodiversity and ecosystems, and to ensure a fair economic return for farmers.

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“…The VIs including Normalized Difference Infrared Index (NDII SWIR2 ) [73], Normalized Difference Tillage Index (NDTI) [74], Normalized Difference Vegetation Index (NDVI) [75], and Ratio Vegetation Index (RVI) [76] are listed in Table 1. The composition of NDII SWIR2 and NDTI both contain short wave infrared band, which indicates the vegetation water content and dry matter situation well [54,[77][78][79]. NDVI composed of Red and NIR bands with minimal water absorption is also sensitive to vegetation coverage thus a good indicator of vegetation biophysical variable [54,80].…”

Section: Comparison Experimentsmentioning

confidence: 99%

A Semi-Empirical Retrieval Method of Above-Ground Live Forest Fuel Loads by Combining SAR and Optical Data

2022

Remote Sensing

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Forest fuel load is the key factor for fire risk assessment, firefighting, and carbon emissions estimation. Remote sensing technology has distinct advantages in fuel load estimation due to its sensitivity to biomass and adequate spatiotemporal observations for large scales. Many related works applied empirical methods with individual satellite observation data to estimate fuel load, which is highly conditioned on local data and limited by saturation problems. Here, we combined optical data (i.e., Landsat 7 ETM+) and spaceborne Synthetic Aperture Radar (SAR) data (i.e., ALOS PALSAR) in a proposed semi-empirical retrieval model to estimate above-ground live forest fuel loads (FLAGL). Specifically, optical data was introduced into water cloud model (WCM) to compensate for vegetation coverage information. For comparison, we also evaluated the performance of single spaceborne L-band SAR data (i.e., ALOS PALSAR) in fuel load estimation with common WCM. The above two comparison experiments were both validated by field measurements (i.e., BioSAR-2008) and leave-one-out cross-validation (LOOCV) method. WCM with single SAR data could achieve reasonable performance (R2 = 0.64 or higher and RMSEr = 35.3% or lower) but occurred an underestimation problem especially in dense forests. The proposed method performed better with R2 increased by 0.05–0.13 and RMSEr decreased by 5.8–12.9%. We also found that the underestimation problem (i.e., saturation problem) was alleviated even when vegetation coverage reached 65% or the total FLAGL reached about 183 Tons/ha. We demonstrated our FLAGL estimation method by validation in an open-access dataset in Sweden.

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A global canopy water content product from AVHRR/Metop

Cited by 31 publications

References 63 publications

Estimation of leaf water content from hyperspectral data of different plant species by using three new spectral absorption indices

Estimation of leaf water content from hyperspectral data of different plant species by using three new spectral absorption indices

Understanding deep learning in land use classification based on Sentinel-2 time series

A Semi-Empirical Retrieval Method of Above-Ground Live Forest Fuel Loads by Combining SAR and Optical Data

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