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Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 3852

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Special Issue Editors

Dr. Nikolay V. Shabanov

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Guest Editor

Space Research Institute of the Russian Academy of Sciences, Moscow, Russia
Interests: parameterization of the effect of vegetation spatial heterogeneity with stochastic radiative transfer theory; BRDF modeling; development of the algorithms for retrievals of vegetation biophysical parameters from satellite data; satellite data fusion; assimilation of satellite data in land climate models

Dr. Zhangyan Jiang

E-Mail Website
Guest Editor

National Oceanic and Atmospheric Administration, Washington, DC, USA
Interests: vegetation; remote sensing; environment; vegetation mapping; spatial analysis; satellite image analysis; satellite image processing; physical geography; mapping; geospatial science

Special Issue Information

Dear Colleagues,

Biophysical parameters allow for natural describing the state and functioning of vegetation canopy. The twenty-first century has seen remarkable advances both in technologies and algorithms for acquiring and processing remotely sensed data to generate a core set of such parameters. Extensive archives of atmospherically corrected optical and radar data, advanced models of photon–vegetation interactions, data bases for ground sampling, and laser/radar and local UAV measurements of canopy parameters have established a necessary basis for high-accuracy assessment of canopy conditions. However, we still face significant uncertainties in the value estimation of core parameters, their applications remain limited and the fundamental laws of the environment still need to be revealed. Thus, it is time to rethink traditional approaches in terms of newly available technologies and the synergistic use of core parameters to advance vegetation canopy monitoring, which is essential for long-term monitoring of the earth system. This Special Issue therefore calls for the submission of diverse, innovative and high-quality research considering the following topics:

Algorithms for retrievals of biophysical (landcover, LAI, NPP, chlorophyll content, GSV and biomass) and radiometric (surface reflectance, VI, albedo and energy budget) parameters;
Photon–vegetation interaction (radiative transfer, geometrical optics, machine learning and semi-empirical models) theory;
Building networks of vegetation parameter inventory and validation networks;
Modeling and measuring ecosystems’ structure and landscape scaling issues;
Phenology/phenology change monitoring;
Monitoring ecosystem change including, clear-cuts, post-fire and infestation recovery, forest succession and climate-driven vegetation cover changes;
Forecasting canopy dynamics;
Incorporation of biophysical parameters into climate and biogeochemical models.

Dr. Nikolay V. Shabanov
Dr. Zhangyan Jiang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

biophysical and radiometric parameters
vegetation parameters inventory
ecosystems’ structure and landscape scaling
phenology/phenology change
ecosystem change
climate–vegetation interaction

Published Papers (3 papers)

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Research

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20 pages, 7498 KiB

Open AccessArticle

Utility of Leaf Area Index for Monitoring Phenology of Russian Forests

by Nikolay V. Shabanov, Vyacheslav A. Egorov, Tatiana S. Miklashevich, Ekaterina A. Stytsenko and Sergey A. Bartalev

Remote Sens. 2023, 15(22), 5419; https://doi.org/10.3390/rs15225419 - 19 Nov 2023

Viewed by 829

Abstract

Retrievals of land surface phenology metrics depend on the choice of base variables selected to quantify the seasonal “greenness” profile of vegetation. Commonly used variables are vegetation indices, which curry signal not only from vegetation but also from the background of sparse foliage, they saturate over the dense foliage and are also affected by sensor bandwidth, calibration, and illumination/view geometry, thus introducing bias in the estimation of phenometrics. In this study we have intercompared the utility of LAI and other biophysical variables (FPAR) and radiometric parameters (NDVI and EVI2) for phenometrics retrievals. This study was implemented based on MODIS products at a resolution of 230 m over the entire extent of Russian forests. Free from artifacts of radiometric parameters, LAI exhibits a better utilization of its dynamic range during the course of seasonal variations and better sensitivity to the actual foliage “greenness” changes and its dependence on forest species. LAI-based retrievals feature a more conservative estimate of the duration of the growing season, including late spring (9.3 days) and earlier fall (8.9 days), compared to those retrieved using EVI2. In this study, we have tabulated typical values of the key phenometrics of 12 species in Russian forests. We have also demonstrated the presence of the latitudinal dependence of phenometrics over the extent of Russian forests. Full article

(This article belongs to the Special Issue Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications)

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18 pages, 8502 KiB

Open AccessArticle

Accuracy Assessment of Atmospheric Correction of KMSS-2 Meteor-M #2.2 Data over Northern Eurasia

by Dmitry Plotnikov, Pavel Kolbudaev, Alexey Matveev, Andrey Proshin and Ivan Polyanskiy

Remote Sens. 2023, 15(18), 4395; https://doi.org/10.3390/rs15184395 - 07 Sep 2023

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Abstract

Atmospheric correction of satellite remote sensing data is a prerequisite for a large variety of applications, including time series analysis and quantitative assessment of the Earth’s vegetation cover. It was earlier reported that an atmospherically corrected KMSS-M (Meteor-M #2) dataset was produced for Russia and neighboring countries. The methodology adopted for atmospheric correction was based on localized histogram matching of target KMSS-M and MODIS reference gap-free and date-matching imagery. In this paper, we further advanced the methodology and quantitatively assessed Level-2 surface reflectance analysis-ready datasets, operatively produced for KMSS-2 instruments over continental scales. Quantitative assessment was based on accuracy, precision, and uncertainty (APU) metrics produced for red and near-infrared bands of the KMSS-2 instrument based on a reference derived from a MODIS MOD09 reconstructed surface reflectance. We compared error distributions at 5%, 20%, and 50% levels of cloudiness and indicated that the cloudiness factor has little impact on the robustness of the atmospheric correction regardless of the band. Finally, the spatial and temporal gradients of accuracy metrics were investigated over northern Eurasia and across different seasons. It was found that for the vast majority of observations, accuracy falls within the −0.010–0.035 range, while precision and uncertainty were below 0.06 for any band. With the successful launch of the most recent Meteor-M #2.3 with a new KMSS-2 instrument onboard, the efficiency and interoperability of the constellation are expected to increase. Full article

(This article belongs to the Special Issue Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications)

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Review

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33 pages, 5284 KiB

Open AccessReview

Advancing Skyborne Technologies and High-Resolution Satellites for Pasture Monitoring and Improved Management: A Review

by Michael Gbenga Ogungbuyi, Caroline Mohammed, Iffat Ara, Andrew M. Fischer and Matthew Tom Harrison

Remote Sens. 2023, 15(19), 4866; https://doi.org/10.3390/rs15194866 - 08 Oct 2023

Cited by 1 | Viewed by 1597

Abstract

The timely and accurate quantification of grassland biomass is a prerequisite for sustainable grazing management. With advances in artificial intelligence, the launch of new satellites, and perceived efficiency gains in the time and cost of the quantification of remote methods, there has been growing interest in using satellite imagery and machine learning to quantify pastures at the field scale. Here, we systematically reviewed 214 journal articles published between 1991 to 2021 to determine how vegetation indices derived from satellite imagery impacted the type and quantification of pasture indicators. We reveal that previous studies have been limited by highly spatiotemporal satellite imagery and prognostic analytics. While the number of studies on pasture classification, degradation, productivity, and management has increased exponentially over the last five years, the majority of vegetation parameters have been derived from satellite imagery using simple linear regression approaches, which, as a corollary, often result in site-specific parameterization that become spurious when extrapolated to new sites or production systems. Few studies have successfully invoked machine learning as retrievals to understand the relationship between image patterns and accurately quantify the biophysical variables, although many studies have purported to do so. Satellite imagery has contributed to the ability to quantify pasture indicators but has faced the barrier of monitoring at the paddock/field scale (20 hectares or less) due to (1) low sensor (coarse pixel) resolution, (2) infrequent satellite passes, with visibility in many locations often constrained by cloud cover, and (3) the prohibitive cost of accessing fine-resolution imagery. These issues are perhaps a reflection of historical efforts, which have been directed at the continental or global scales, rather than at the field level. Indeed, we found less than 20 studies that quantified pasture biomass at pixel resolutions of less than 50 hectares. As such, the use of remote sensing technologies by agricultural practitioners has been relatively low compared with the adoption of physical agronomic interventions (such as ‘no-till’ practices). We contend that (1) considerable opportunity for advancement may lie in fusing optical and radar imagery or hybrid imagery through the combination of optical sensors, (2) there is a greater accessibility of satellite imagery for research, teaching, and education, and (3) developers who understand the value proposition of satellite imagery to end users will collectively fast track the advancement and uptake of remote sensing applications in agriculture. Full article

(This article belongs to the Special Issue Remote Sensing for Quantitative Parameters Retrieval: Methods and Applications)

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