Peiqi Yang , Christiaan van der Tol , Jing Liu , Zhigang Liu
{"title":"Separation of the direct reflection of soil from canopy spectral reflectance","authors":"Peiqi Yang , Christiaan van der Tol , Jing Liu , Zhigang Liu","doi":"10.1016/j.rse.2024.114500","DOIUrl":null,"url":null,"abstract":"<div><div>Separation of soil effects from top-of-canopy (TOC) reflectance is crucial for quantitative remote sensing of vegetation. Soil affects TOC reflectance via the soil-vegetation interaction and the direct reflection by soil. Various vegetation indices have been developed semi-empirically to mitigate the interferences caused by soil for specific applications, such estimating biomass and monitoring vegetation phenology. However, a practical approach to separate soil effects from the entire TOC spectral reflectance is still lacking. In this study, we investigate the radiative transfer process in a vegetation canopy with soil contamination and develop three methods to estimate the contribution of soil's direct reflection to TOC reflectance. Theoretical analysis reveals that the soil's direct reflection can be quantified and separated from TOC reflectance due to the distinct spectral characteristics of soil and vegetation. We identify three key features: a) Bands in the visible region where the reflectance of soil-uncontaminated green vegetation approaches zero due to strong pigment absorption. b) Two bands in the visible region where the vegetation reflectance is similar, but soil reflectance is distinguishable. c) Soil reflectance within the range of 400 nm to 1000 nm exhibits a near-linear dependence on wavelength. Using these features, we develop three methods to quantify the contribution of soil's direct reflection to TOC reflectance. For given soil reflectance, feature a) or b) alone allows estimating the fraction of soil that directly contributes to TOC reflectance, and thus the soil's direct reflection. Using all three features enables estimation of the soil's direct reflection without knowing soil reflectance.</div><div>The proposed methods, along with certain assumptions made during their development, are tested and evaluated using field and synthetic datasets of soil, leaf, and canopy. The evaluation of the three methods demonstrates that the estimation of the soil's direct reflection can be achieved through: i) Using TOC reflectance at approximately 675 nm and soil spectral reflectance, termed the red-band-based method (Method-RBB). ii) Using TOC reflectance at approximately 675 nm and 438 nm, along with soil spectral reflectance, termed as the two-band-based method (Method-TBB). iii) Using TOC reflectance at approximately 675 nm and 438 nm, assuming linear dependence of soil reflectance on wavelength in the visible and near-infrared region, termed as the linear-assumption-based method (Method-LAB). Our evaluation indicates that the linearity from 400 nm to 1000 nm holds true for a wide range of soil types. The conditions outlined in features a) and b) are valid for green vegetation with moderate to high leaf chlorophyll content: when leaf chlorophyll content exceeds 20 μg cm<sup>−2</sup>, the leaf albedo at 675 nm is generally below 0.15, and the difference in leaf albedo at 675 nm and 438 nm is sufficiently small. The results reveal that when leaf albedo at 675 nm is less than 0.15 and NDVI is less than 0.8, all three methods perform satisfactorily, exhibiting an R<sup>2</sup> value of approximately 0.9 between the true and estimated contribution of soil's direct reflection. The R<sup>2</sup> values are 0.92 for both Method-RBB and Method-LAB, while Method-TBB has an R<sup>2</sup> of 0.95. The performance of Method-RBB is particularly sensitive to leaf albedo at the red band, which correlates with leaf chlorophyll content. Canopies exhibiting higher red-band leaf albedo usually indicate lower chlorophyll content and less resemblance to typical green vegetation. The accuracy of Method-TBB diminishes as the differences in leaf albedo between the selected two bands increase. Similarly, deviations from the linear dependence of soil reflectance on wavelength negatively impact the accuracy of Method-LAB. Overall, these proposed methods work reasonably well for sparse canopies and healthy vegetation. Method-TBB exhibits the highest level of accuracy, followed by Method-RBB, while Method-LAB is more convenient to use as it does not require prior knowledge of soil reflectance. The proposed methods offer practical ways to estimate the contribution of soil's direct reflection to TOC reflectance. Utilizing TOC reflectance after the soil adjustment facilitates more direct monitoring of canopy structural characteristics, and biochemical and physiological information of leaves.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"316 ","pages":"Article 114500"},"PeriodicalIF":11.1000,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing of Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0034425724005261","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Separation of soil effects from top-of-canopy (TOC) reflectance is crucial for quantitative remote sensing of vegetation. Soil affects TOC reflectance via the soil-vegetation interaction and the direct reflection by soil. Various vegetation indices have been developed semi-empirically to mitigate the interferences caused by soil for specific applications, such estimating biomass and monitoring vegetation phenology. However, a practical approach to separate soil effects from the entire TOC spectral reflectance is still lacking. In this study, we investigate the radiative transfer process in a vegetation canopy with soil contamination and develop three methods to estimate the contribution of soil's direct reflection to TOC reflectance. Theoretical analysis reveals that the soil's direct reflection can be quantified and separated from TOC reflectance due to the distinct spectral characteristics of soil and vegetation. We identify three key features: a) Bands in the visible region where the reflectance of soil-uncontaminated green vegetation approaches zero due to strong pigment absorption. b) Two bands in the visible region where the vegetation reflectance is similar, but soil reflectance is distinguishable. c) Soil reflectance within the range of 400 nm to 1000 nm exhibits a near-linear dependence on wavelength. Using these features, we develop three methods to quantify the contribution of soil's direct reflection to TOC reflectance. For given soil reflectance, feature a) or b) alone allows estimating the fraction of soil that directly contributes to TOC reflectance, and thus the soil's direct reflection. Using all three features enables estimation of the soil's direct reflection without knowing soil reflectance.
The proposed methods, along with certain assumptions made during their development, are tested and evaluated using field and synthetic datasets of soil, leaf, and canopy. The evaluation of the three methods demonstrates that the estimation of the soil's direct reflection can be achieved through: i) Using TOC reflectance at approximately 675 nm and soil spectral reflectance, termed the red-band-based method (Method-RBB). ii) Using TOC reflectance at approximately 675 nm and 438 nm, along with soil spectral reflectance, termed as the two-band-based method (Method-TBB). iii) Using TOC reflectance at approximately 675 nm and 438 nm, assuming linear dependence of soil reflectance on wavelength in the visible and near-infrared region, termed as the linear-assumption-based method (Method-LAB). Our evaluation indicates that the linearity from 400 nm to 1000 nm holds true for a wide range of soil types. The conditions outlined in features a) and b) are valid for green vegetation with moderate to high leaf chlorophyll content: when leaf chlorophyll content exceeds 20 μg cm−2, the leaf albedo at 675 nm is generally below 0.15, and the difference in leaf albedo at 675 nm and 438 nm is sufficiently small. The results reveal that when leaf albedo at 675 nm is less than 0.15 and NDVI is less than 0.8, all three methods perform satisfactorily, exhibiting an R2 value of approximately 0.9 between the true and estimated contribution of soil's direct reflection. The R2 values are 0.92 for both Method-RBB and Method-LAB, while Method-TBB has an R2 of 0.95. The performance of Method-RBB is particularly sensitive to leaf albedo at the red band, which correlates with leaf chlorophyll content. Canopies exhibiting higher red-band leaf albedo usually indicate lower chlorophyll content and less resemblance to typical green vegetation. The accuracy of Method-TBB diminishes as the differences in leaf albedo between the selected two bands increase. Similarly, deviations from the linear dependence of soil reflectance on wavelength negatively impact the accuracy of Method-LAB. Overall, these proposed methods work reasonably well for sparse canopies and healthy vegetation. Method-TBB exhibits the highest level of accuracy, followed by Method-RBB, while Method-LAB is more convenient to use as it does not require prior knowledge of soil reflectance. The proposed methods offer practical ways to estimate the contribution of soil's direct reflection to TOC reflectance. Utilizing TOC reflectance after the soil adjustment facilitates more direct monitoring of canopy structural characteristics, and biochemical and physiological information of leaves.
期刊介绍:
Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing.
The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques.
RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.