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Remote Sensing for Advancing Nature-Based Climate Solutions

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

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 17308

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Special Issue Editors

Prof. Dr. Sheng Wang

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Guest Editor

Institute for Sustainability, Energy, and Environment (ISEE), University of Illinois Urbana-Champaign (UIUC), Urbana, IL 61801, USA
Interests: remote sensing; agriculture; imaging spectroscopy; machine learning; eco-hydrology

Prof. Dr. Suxia Liu

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Guest Editor

Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: remote sensing; eco-hydrology; soil moisture; climate change; land use cover change; GRACE

Prof. Dr. Yongqiang Zhou

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Guest Editor

State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Interests: dissolved organic carbon; remote sensing; carbon dioxide; methane; lake; eutrophication
Special Issues, Collections and Topics in MDPI journals

Dr. Raphaél Payet-Burin

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Guest Editor

1. Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA 02319, USA 2. COWI A/S, Parallelvej 2, 2800 Kgs. Lyngby, Denmark
Interests: water–energy–food nexus; hydroeconomic optimization; water and food security

Special Issue Information

Dear Colleagues,

As one of the world’s most urgent missions, the goal of carbon neutrality has been pledged by nations, public organizations, and private sectors in efforts to reduce greenhouse gas (GHG, e.g., CO₂, CH₄, and N₂O) emissions and increase carbon sequestration. Nature-based climate solutions target managing, conserving, or restoring natural or agricultural ecosystems, and can bring significant benefits for the removal of carbon from the atmosphere as well as improving ecosystem resilience. These solutions include reforestation, soil conservation, sustainable agriculture management, wetland restoration, and resource optimization. To support decision-making for implementing Nature-based climate solutions, remote sensing can play an essential role in providing cost-effective approaches for ecosystem monitoring.

With recent advances in remote sensing and big data analytics, a variety of sensing data (e.g., hyperspectral, multispectral, thermal, LiDAR, microwave, and gravity) from spaceborne, unmanned/manned airborne, and proximal sensors have been utilized to enhance our capabilities for ecosystem monitoring. These state-of-the-art remote sensing technologies provide great opportunities for advancing our understanding of GHG emissions, carbon sequestration and fluxes, and anthropogenic influences in natural and agricultural ecosystems across scales from single plants, landscapes, and regions to the entire globe.

This Special Issue, “Remote Sensing for Advancing Nature-Based Climate Solutions”, aims to publish research, review, and data articles on developing remote sensing technologies to advance Nature-based solutions for climate change mitigation. We welcome a wide range of contributions on topics including, but not limited to:

Ecosystem energy, carbon, water and nutrient fluxes;
Regenerative agriculture;
Water–energy–food nexus;
Water quantity and quality;
Soil organic carbon;
Air quality and pollution;
Land use and land cover change;
Climate change mitigation and adaptation.

Prof. Dr. Sheng Wang
Prof. Dr. Suxia Liu
Prof. Dr. Yongqiang Zhou
Dr. Raphaél Payet-Burin

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

Spaceborne, airborne, and proximal sensing
Hyperspectral, multispectral, SIF, thermal, LiDAR, microwave, gravity
Machine learning, radiative transfer modeling
Ecosystem monitoring and modeling
Carbon cycling and greenhouse gas emission
Ecohydrology, agriculture, water resources
UN Sustainable Development Goals

Published Papers (8 papers)

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Research

18 pages, 4152 KiB

Open AccessArticle

Significant Improvement in Soil Organic Carbon Estimation Using Data-Driven Machine Learning Based on Habitat Patches

by Wenping Yu, Wei Zhou, Ting Wang, Jieyun Xiao, Yao Peng, Haoran Li and Yuechen Li

Remote Sens. 2024, 16(4), 688; https://doi.org/10.3390/rs16040688 - 15 Feb 2024

Viewed by 634

Abstract

Soil organic carbon (SOC) is generally thought to act as a carbon sink; however, in areas with high spatial heterogeneity, using a single model to estimate the SOC of the whole study area will greatly reduce the simulation accuracy. The earth surface unit division is important to consider in building different models. Here, we divided the research area into different habitat patches using partitioning around a medoids clustering (PAM) algorithm; then, we built an SOC simulation model using machine learning algorithms. The results showed that three habitat patches were created. The simulation accuracy for Habitat Patch 1 (R² = 0.55; RMSE = 2.89) and Habitat Patch 3 (R² = 0.47; RMSE = 3.94) using the XGBoost model was higher than that for the whole study area (R² = 0.44; RMSE = 4.35); although the R² increased by 25% and 6.8%, the RMSE decreased by 33.6% and 9.4%, and the field sample points significantly declined by 70% and 74%. The R² of Habitat Patch 2 using the RF model increased by 17.1%, and the RMSE also decreased by 10.5%; however, the sample points significantly declined by 58%. Therefore, using different models for corresponding patches will significantly increase the SOC simulation accuracy over using one model for the whole study area. This will provide scientific guidance for SOC or soil property monitoring with low field survey costs and high simulation accuracy. Full article

(This article belongs to the Special Issue Remote Sensing for Advancing Nature-Based Climate Solutions)

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16 pages, 3342 KiB

Open AccessArticle

Land Use Transitions and the Associated Impacts on Carbon Storage in the Poyang Lake Basin, China

by Yiming Wang, Zengxin Zhang and Xi Chen

Remote Sens. 2023, 15(11), 2703; https://doi.org/10.3390/rs15112703 - 23 May 2023

Cited by 6 | Viewed by 1314

Abstract

Carbon storage plays an important role in the global carbon cycle and climate change mitigation. Understanding the relationship between land use change and carbon storage can significantly contribute to carbon neutrality and sustainable development. However, most previous studies only analyze the carbon storage change due to land use change, while few studies quantitatively evaluate the contributions of various land use transitions (LUTs) to carbon storage change, which cannot provide enough information for land use management. In the context of rapid urbanization and ecological conservation, the Poyang Lake basin (PYLB) has experienced dramatic land use change, which has significantly affected local carbon storage. Therefore, this study used the InVEST model to evaluate carbon storage in the PYLB from 1990 to 2020. Then, the Geo-information Tupu method was used to quantify the contributions of various LUTs to carbon storage change and identify the key LUTs. The results showed that carbon storage in PYLB decreased by 17.26 Tg from 1990 to 2020. The carbon gain was mainly attributed to transitions from ‘farmland to forestland’ (36.87%), ‘grassland to forestland’ (22.58%), and ‘farmland to water’ (15.89%). In contrast, the transitions from ‘farmland to built-up land’, ‘forestland to built-up land’, and ‘forestland to grassland’ contributed 39.94%, 28.06%, and 13.25% to carbon loss, respectively. Massive carbon loss caused by built-up land expansion should attract attention. This study can provide references for the formulation and optimization of land use policies to achieve carbon neutrality and sustainable development in the PYLB. Full article

(This article belongs to the Special Issue Remote Sensing for Advancing Nature-Based Climate Solutions)

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17 pages, 5133 KiB

Open AccessArticle

Spatio-Temporal Evolution and Prediction of Carbon Storage in Guilin Based on FLUS and InVEST Models

by Yunlin He, Jiangming Ma, Changshun Zhang and Hao Yang

Remote Sens. 2023, 15(5), 1445; https://doi.org/10.3390/rs15051445 - 04 Mar 2023

Cited by 15 | Viewed by 3132

Abstract

In the context of sustainable development and dual-carbon construction, to quantify the carbon storage and its spatial-temporal distribution characteristics of Guilin City and predict the carbon storage of Guilin City in 2035 under different future scenarios, this study set four future scenarios based on SDGs and the sustainable development plan of Guilin City: natural development, economic priority, ecological priority, and sustainable development. At the same time, FLUS and InVEST models and GeoDa 1.20and ArcGIS software were used to establish a coupling model of land use change and ecosystem carbon storage to simulate and predict the distribution and change of ecosystem carbon storage based on land use change in the future. The results showed that: (1) From 2005 to 2020, forest land was the main type of land use in Guilin, and cropland and impervious continued to expand. In 2035, the forest land under four different future scenarios will be an important transformation type; (2) From 2005 to 2020, the carbon storage in the northwest of Guilin was relatively high, and the carbon loss area was larger than the carbon increase area. The carbon storage in the ecological priority scenario in 2035 is the highest, reaching 874.76 × 10⁶ t. The aboveground carbon storage (ACG) is the main carbon pool in Guilin. Most of the regions with high carbon storage are located in the northwest and northeast of Guilin. No matter what scenario, the carbon storage in the main urban area is maintained at a low level; (3) In 2035, the distribution of carbon storage in Guilin has a strong spatial positive correlation, with more hot spots than cold spots. The high-value areas of carbon storage are concentrated in the northwest and east, whereas the low-value areas are concentrated in the urban area of Guilin. Full article

(This article belongs to the Special Issue Remote Sensing for Advancing Nature-Based Climate Solutions)

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17 pages, 12155 KiB

Open AccessArticle

Assessing the Potential of 10-m Resolution TVDI Based on Downscaled LST to Monitor Soil Moisture in Tang River Basin, China

by Lin Cheng, Suxia Liu, Xingguo Mo, Shi Hu, Haowei Zhou, Chaoshuai Xie, Sune Nielsen, Henrik Grosen and Peter Bauer-Gottwein

Remote Sens. 2023, 15(3), 744; https://doi.org/10.3390/rs15030744 - 27 Jan 2023

Cited by 3 | Viewed by 2165

Abstract

Soil moisture is a key parameter in hydrological research and drought management. The inversion of soil moisture based on land surface temperature (LST) and NDVI triangular feature spaces has been widely used in various studies. Remote sensing provides regional LST data with coarse spatial resolutions which are insufficient for field scale (tens of meters). In this study, we bridged the data gap by adopting a Data Mining Sharpener algorithm to downscale MODIS thermal data with Vis-NIR imagery from Sentinel-2. To evaluate the downscaling algorithm, an unmanned aerial system (UAS) equipped with a thermal sensor was used to capture the ultra-fine resolution LST at three sites in the Tang River Basin in China. The obtained fine-resolution LST data were then used to calculate the Temperature Vegetation Dryness Index (TVDI) for soil moisture monitoring. Results indicated that downscaled LST data from satellites showed spatial patterns similar to UAS-measured LST, although discrepancies still existed. Based on the fine-resolution LST data, a 10-m resolution TVDI map was generated. Significant negative correlations were observed between the TVDI and in-situ soil moisture measurements (Pearson’s r of

- 0.67

and

- 0.71

). Overall, the fine-resolution TVDI derived from the downscaled LST has a high potential for capturing spatial soil moisture variation. Full article

(This article belongs to the Special Issue Remote Sensing for Advancing Nature-Based Climate Solutions)

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20 pages, 5930 KiB

Open AccessArticle

Optimizing the Land Use and Land Cover Pattern to Increase Its Contribution to Carbon Neutrality

by Kai Wang, Xiaobing Li, Xin Lyu, Dongliang Dang, Huashun Dou, Mengyuan Li, Siyu Liu and Wanyu Cao

Remote Sens. 2022, 14(19), 4751; https://doi.org/10.3390/rs14194751 - 23 Sep 2022

Cited by 19 | Viewed by 3289

Abstract

Land use and land cover (LULC) contribute to both carbon storage and carbon emissions. Therefore, regulating the LULC is an important means of achieving carbon neutrality under global environmental change. Here, the West Liaohe River Basin, a semiarid watershed, was taken as a case study. Based on the assessment of the carbon storage and emissions induced by LULC from 2000–2020, we set up three different coupled shared socioeconomic pathway (SSP) and representative concentration pathway (RCP) scenarios (SSP119, SSP245, and SSP585), from 2030–2060, to optimize the LULC. Then, the LULC patterns under each scenario were simulated using the patch-generating land use simulation (PLUS) model, and the corresponding changes in carbon storage and emissions were compared and analyzed. It was found that, since 2000, with the expansion of forest, cropland, and construction land, as well as the degradation of grassland, the carbon storage and emissions induced by LULC have significantly increased, but the increase in storage was lower than that of emissions. The scenario simulations revealed that, when we optimize the LULC, mainly including the protection and expansion of ecological land such as forest and grassland in the western and southern edges of the basin, as well as the control and management of cropland land and construction land in the northeast and central parts of the basin, there will be a significant increase in the carbon storage and a significant reduction in carbon emissions from 2030–2060. This indicates that zone-based management measures with rational LULC regulation can contribute to the achievement of carbon neutrality in the study area. Supported by the results of this study, a direct decision-making basis for land use policy regulation to promote regional sustainable development can be undertaken in the basin. This study also provides a reference for low-carbon development in other regions. Full article

(This article belongs to the Special Issue Remote Sensing for Advancing Nature-Based Climate Solutions)

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16 pages, 5916 KiB

Open AccessArticle

Impact of Saline-Alkali Land Greening on the Local Surface Temperature—A Multiscale Assessment Based on Remote Sensing

by Bingxia Xin, Lingxue Yu, Guangshuai Li, Yue Jiao, Tingxiang Liu, Shuwen Zhang and Zhongying Lei

Remote Sens. 2022, 14(17), 4246; https://doi.org/10.3390/rs14174246 - 28 Aug 2022

Cited by 1 | Viewed by 1520

Abstract

In recent years, the conversion of saline-alkali land to rice fields has become the most dominant land use change feature in western Jilin, leading to significant surface greening. Saline–alkali land and paddy fields have distinct surface biophysical properties; however, there is a lack of systematic assessment of the moderating effect of planting rice on saline–alkali land on regional climate by changing surface properties. In this paper, multiscale data on the surface temperature of saline–alkali land and paddy fields were obtained using 1 km MODIS product, 30 m Landsat 8 satellite imagery and centimeter-scale UAV imagery in Da’an City, western Jilin as the study area, and the various characteristics of the surface temperature of saline-alkali land and paddy fields in different months of the year and at different times of the day were analyzed. Furthermore, the effect of rice cultivation in saline–alkali land on the local surface temperature was assessed using a space-for-time approach. The results based on satellite observations including both MODIS and Landsat showed that the surface temperature of saline–alkali land was significantly higher than that of paddy fields during the crop growing season, especially in July and August. The high temporal resolution MODIS LST data also indicated the paddy fields cool the daytime surface temperature, while warming the nighttime surface temperature, which was in contrast for saline–alkali land during the growing season. High-resolution UAV observations in July confirmed that the cooling effect of paddy fields was most significant at the middle of day. From the biophysical perspective, the reclamation of saline–alkali land into paddy fields leads to an increase in leaf area index, followed by a significant increase in evapotranspiration. Meanwhile, rice cultivation in saline–alkali land reduces surface albedo and increases surface net radiation. The trade-off relationship between the two determines the seasonal difference in the surface temperature response of saline–alkali land for rice cultivation. At the same time, the daily cycle of crop evapotranspiration and the thermal insulation effect of paddy fields at night are the main reasons for the intraday difference in surface temperature between saline–alkali land and paddy field. Based on the multiscale assessment of the impact of rice cultivation in saline-alkali land on surface temperature, this study provides a scientific basis for predicting future regional climate change and comprehensively understanding the ecological and environmental benefits of saline–alkali land development. Full article

(This article belongs to the Special Issue Remote Sensing for Advancing Nature-Based Climate Solutions)

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26 pages, 4511 KiB

Open AccessArticle

A Multivariate Drought Index for Seasonal Agriculture Drought Classification in Semiarid Regions

by K. Bageshree, Abhishek and Tsuyoshi Kinouchi

Remote Sens. 2022, 14(16), 3891; https://doi.org/10.3390/rs14163891 - 11 Aug 2022

Cited by 8 | Viewed by 2327

Abstract

Drought assessment in any region primarily hinges on precipitation deficiency, which is subsequently propagated to various components and sectors, leading to different drought types. In countries such as India, an intricate relationship between various governing factors, drought types, and their quantification methodologies make it elusive to timely initiate government relief measures. This also prevents comprehensive inclusion of the integrated effect of the principal drivers of drought, resulting in ambiguous categorization of severity, where groundwater storage variability is often neglected despite its significant role in irrigation. Here, we developed a multivariate Joint Drought Index (JDI) combining satellite and model-based standardized indices of precipitation and evapotranspiration (SPEI), soil moisture (SSI), groundwater (SGI), and surface runoff (SRI) with different temporal scales by employing two robust methods, principal component analysis (PCA) and Gaussian copula, and applied the index to highly drought-prone Marathwada region from central India. Our novel approach of using different scale combinations of integrated indices for two primary seasons (Kharif and Rabi) provides more realistic drought intensities than multiple univariate indices, by incorporating the response from each index, representing the seasonal drought conditions corroborating with the seasonal crop yields. JDI, with both methods, successfully identified two major drought events in 2015 and 2018, while effectively capturing the groundwater drought. Moreover, despite the high correlation between JDI using PCA and copula, we observed a significant difference in the intensities reported by these methods, where copula detected exceptional drought conditions more frequently than PCA. JDI effectively detected the onset, duration, and termination of drought, where the improved accuracy of drought detection can play a critical role in policy formation and socioeconomic security of the related stakeholders. Seasonal agriculture drought categorization for holistic quantification of drought conditions as presented in this study should provide broad methodological implications on drought monitoring and mitigation measures, especially for agriculture-dominated regions in semiarid climates. Full article

(This article belongs to the Special Issue Remote Sensing for Advancing Nature-Based Climate Solutions)

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15 pages, 15759 KiB

Open AccessArticle

Modeling Potential Impacts on Regional Climate Due to Land Surface Changes across Mongolia Plateau

by Guangshuai Li, Lingxue Yu, Tingxiang Liu, Yue Jiao and Jiaxin Yu

Remote Sens. 2022, 14(12), 2947; https://doi.org/10.3390/rs14122947 - 20 Jun 2022

Cited by 6 | Viewed by 1682

Abstract

Although desertification has greatly increased across the Mongolian Plateau during the last decades of the 20th century, recent satellite records documented increasing vegetation growth since the 21st century in some areas of the Mongolian Plateau. Compared to the study of desertification, the opposite characteristics of land use and vegetation cover changes and their different effects on regional land–atmosphere interaction factors still lack enough attention across this vulnerable region. Using long-term time-series multi-source satellite records and regional climate model, this study investigated the climate feedback to the observed land surface changes from the 1990s to the 2010s in the Mongolia Plateau. Model simulation suggests that vegetation greening induced a local cooling effect, while the warming effect is mainly located in the vegetation degradation area. For the typical vegetation greening area in the southeast of Inner Mongolia, latent heat flux increased over 2 W/m² along with the decrease of sensible heat flux over 2 W/m², resulting in a total evapotranspiration increase by 0.1~0.2 mm/d and soil moisture decreased by 0.01~0.03 mm/d. For the typical vegetation degradation area in the east of Mongolia and mid-east of Inner Mongolia, the latent heat flux decreased over 2 W/m² along with the increase of sensible heat flux over 2 W/m² obviously, while changes in moisture cycling were spatially more associated with variations of precipitation. It means that precipitation still plays an important role in soil moisture for most areas, and some areas would be at potential risk of drought with the asynchronous increase of evapotranspiration and precipitation. Full article

(This article belongs to the Special Issue Remote Sensing for Advancing Nature-Based Climate Solutions)

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