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Quantitative Remote Sensing of Vegetation and Its Applications
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Quantitative Remote Sensing of Vegetation and Its Applications

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 5728

Special Issue Editors

Prof. Dr. Kun Jia

E-Mail Website
Guest Editor
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: remote sensing of vegetation; land cover/land use; remote sensing of ecological environment; agriculture remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Linqing Yang

E-Mail Website
Guest Editor
Wilkes Center for Climate Science and Policy, School of Biological Sciences, University of Utah, Salt Lake City, UT 84112, USA
Interests: remote sensing of vegetation; modeling; forest ecophysiology; carbon science and climate change; climate change mitigation

Special Issue Information

Dear Colleagues,

Vegetation is the basic component of the terrestrial ecosystem and it plays an important role in energy exchange as well as biogeochemical and hydrological cycling processes on Earth’s surface. Quantitative remote sensing of vegetation can provide spatially and temporally continuous monitoring of Earth’s system parameter data and deliver invaluable insights into diverse fields such as agriculture, forestry, and environment. The past decades have witnessed great progress in satellite remote sensing data processing and the retrieval of Earth’s system parameter, as well as their applications. The advances in monitoring methodologies/technologies, such as empirical statistical models, radiative transfer models, artificial intelligence, and cloud computing technology, have improved the quality and accuracy of remote sensing products. Furthermore, remote sensing products play an increasingly critical role in resolving global environmental issues and climate change mitigation.

This aim of this Special Issue is to advance novel techniques/approaches for retrieving and estimating vegetation structure and function parameters at various spatial (e.g., leaf, canopy, stand, landscape, and regional levels) and temporal scales using remote sensing data across various ecosystems and vegetation types, as well as their applications such as in delineating the responses of vegetation structure and the function of climate change and disturbance in key ecological issues.

Potential topics for this Special Issue may include, but are not limited to, the following:

  • Satellite-based vegetation monitoring, estimation, and modeling: techniques (artificial intelligence, multi-sensor data fusion, etc.), evaluation, and future missions;
  • Applications of new sensors/algorithms to biochemical/biophysical parameters, such as FVC, LAI, vegetation productivity, biomass, pigments;
  • Novel data fusion of spectral, LiDAR, or Radar data obtained from different platforms;
  • New product development or evaluation of uncertainty in current products;
  • Vegetation degradation and structure variation monitoring using remote sensing;
  • Evaluations of ecosystem vulnerability and resilience to climate change;
  • Remote sensing applications in global environmental issues;
  • Remote sensing applications in efforts to mitigate climate change, such as nature-based climate solutions.

Prof. Dr. Kun Jia
Dr. Linqing Yang
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

  • remote sensing
  • biochemical/biophysical parameters
  • vegetation dynamics
  • multi-sensor data fusion
  • algorithm development
  • artificial intelligence
  • accuracy validation
  • inter-comparison and evaluation
  • products and applications

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Published Papers (5 papers)

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Research

19 pages, 5540 KiB  
Article
A Probabilistic Statistical Risk Assessment Method for Soil Erosion Using Remote Sensing Data: A Case Study of the Dali River Basin
by Hao Zhao, Yuhui Cheng, Xiwang Zhang, Shiqi Yu, Mengwei Chen and Chengqiang Zhang
Remote Sens. 2024, 16(18), 3491; https://doi.org/10.3390/rs16183491 - 20 Sep 2024
Viewed by 907
Abstract
Soil erosion risk assessment enables the identification of areas requiring priority treatment and avoids wasting human and material resources. The factor scoring method used in existing studies has high subjectivity, and the method of expressing erosion risk according to the soil erosion intensity [...] Read more.
Soil erosion risk assessment enables the identification of areas requiring priority treatment and avoids wasting human and material resources. The factor scoring method used in existing studies has high subjectivity, and the method of expressing erosion risk according to the soil erosion intensity ignores the random nature of the occurrence of erosion; therefore, neither method accurately reflects the risk of soil erosion. In order to address this issue, this study proposes a soil erosion risk assessment method that integrates the outcome and the probability of occurrence of soil erosion by means of a probabilistic statistical model. Subsequently, experimental research is conducted in the Dali River Basin. On the basis of long time-series data, using mathematical statistics as a tool and drawing on the empirical frequency formula, the probabilistic statistical risk assessment model is combined with the Modified Universal Soil Loss Equation (RUSLE) model to account for the probability of regional soil erosion at different intensity levels in the long time-series, which is combined with the intensity of erosion to carry out soil erosion risk assessment. The results of our study show the following: (1) The central and southwestern regions of the Dali River Basin (DRB) present medium and high levels of soil erosion risk, with the proportion of low-risk areas increasing annually, accounting for 78.97% of the DRB in 2020, while extremely high-risk areas account for only 0.40% of the DRB. (2) The major components impacting soil erosion risk in the DRB, as revealed by the geodetector, are the normalized difference vegetation index (NDVI) and slope, where the interaction between the two dominated the spatial variation in soil erosion risk. (3) Comparing the soil erosion risk and its status in the coming years, the proposed assessment method based on the occurrence probability can reveal the future soil erosion risk better than the traditional assessment method. Full article
(This article belongs to the Special Issue Quantitative Remote Sensing of Vegetation and Its Applications)
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27 pages, 10360 KiB  
Article
Soil Moisture-Derived SWDI at 30 m Based on Multiple Satellite Datasets for Agricultural Drought Monitoring
by Jing Ning, Yunjun Yao, Joshua B. Fisher, Yufu Li, Xiaotong Zhang, Bo Jiang, Jia Xu, Ruiyang Yu, Lu Liu, Xueyi Zhang, Zijing Xie, Jiahui Fan and Luna Zhang
Remote Sens. 2024, 16(18), 3372; https://doi.org/10.3390/rs16183372 - 11 Sep 2024
Viewed by 1229
Abstract
As a major agricultural hazard, drought frequently occurs due to a reduction in precipitation resulting in a continuously propagating soil moisture (SM) deficit. Assessment of the high spatial-resolution SM-derived drought index is crucial for monitoring agricultural drought. In this study, we generated a [...] Read more.
As a major agricultural hazard, drought frequently occurs due to a reduction in precipitation resulting in a continuously propagating soil moisture (SM) deficit. Assessment of the high spatial-resolution SM-derived drought index is crucial for monitoring agricultural drought. In this study, we generated a downscaled random forest SM dataset (RF-SM) and calculated the soil water deficit index (RF-SM-SWDI) at 30 m for agricultural drought monitoring. The results showed that the RF-SM dataset exhibited better consistency with in situ SM observations in the detection of extremes than did the SM products, including SMAP, SMOS, NCA-LDAS, and ESA CCI, for different land cover types in the U.S. and yielded a satisfactory performance, with the lowest root mean square error (RMSE, below 0.055 m3/m3) and the highest coefficient of determination (R2, above 0.8) for most observation networks, based on the number of sites. A vegetation health index (VHI), derived from a Landsat 8 optical remote sensing dataset, was also generated for comparison. The results illustrated that the RF-SM-SWDI and VHI exhibited high correlations (R ≥ 0.5) at approximately 70% of the stations. Furthermore, we mapped spatiotemporal drought monitoring indices in California. The RF-SM-SWDI provided drought conditions with more detailed spatial information than did the short-term drought blend (STDB) released by the U.S. Drought Monitor, which demonstrated the expected response of seasonal drought trends, while differences from the VHI were observed mainly in forest areas. Therefore, downscaled SM and SWDI, with a spatial resolution of 30 m, are promising for monitoring agricultural field drought within different contexts, and additional reliable factors could be incorporated to better guide agricultural management practices. Full article
(This article belongs to the Special Issue Quantitative Remote Sensing of Vegetation and Its Applications)
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17 pages, 6746 KiB  
Article
Satellite-Based PT-SinRH Evapotranspiration Model: Development and Validation from AmeriFlux Data
by Zijing Xie, Yunjun Yao, Yufu Li, Lu Liu, Jing Ning, Ruiyang Yu, Jiahui Fan, Yixi Kan, Luna Zhang, Jia Xu, Kun Jia and Xiaotong Zhang
Remote Sens. 2024, 16(15), 2783; https://doi.org/10.3390/rs16152783 - 30 Jul 2024
Cited by 1 | Viewed by 745
Abstract
The Priestley–Taylor model of the Jet Propulsion Laboratory (PT-JPL) evapotranspiration (ET) model is relatively simple and has been widely used based on meteorological and satellite data. However, soil moisture (SM) constraints include a vapor pressure deficit (VPD) that causes large uncertainty. In this [...] Read more.
The Priestley–Taylor model of the Jet Propulsion Laboratory (PT-JPL) evapotranspiration (ET) model is relatively simple and has been widely used based on meteorological and satellite data. However, soil moisture (SM) constraints include a vapor pressure deficit (VPD) that causes large uncertainty. In this study, we proposed a PT-SinRH model by introducing a sine function of air relative humidity (RH) to replace RHVPD to characterize SM constraints, which can improve the accuracy of ET estimations. The PT-SinRH model is validated by eddy covariance (EC) data from 2000–2020. These data were collected by AmeriFlux at 28 sites on the conterminous United States (CONUS), and the land cover types of the sites vary from croplands to wetlands, grasslands, shrub lands and forests. The validation results from daily scale-based on-site and satellite data inputs showed that the PT-SinRH model estimates fit the observations with a coefficient of determination (R2) of 0.55, root-mean-square error (RMSE) of 17.5 W/m2, bias of −1.2 W/m2 and Kling–Gupta efficiency (KGE) of 0.70. Additionally, the PT-SinRH model based on reanalysis and satellite data inputs has an R2 of 0.49, an RMSE of 20.3 W/m2, a bias of −8.6 W/m2 and a KGE of 0.55. The PT-SinRH model showed better accuracy when using the site-measured meteorological data than when using reanalysis meteorological data as inputs. Additionally, compared with the PT-JPL model, the results demonstrate that our approach, i.e., PT-SinRH, improved ET estimates, increasing the R2 and KGE by 0.02 and decreasing the RMSE by about 0.6 W/m2. This simple but accurate method permits us to investigate the decadal variation in regional ET over the land. Full article
(This article belongs to the Special Issue Quantitative Remote Sensing of Vegetation and Its Applications)
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27 pages, 35655 KiB  
Article
An Improved Gap-Filling Method for Reconstructing Dense Time-Series Images from LANDSAT 7 SLC-Off Data
by Yue Li, Qiang Liu, Shuang Chen and Xiaotong Zhang
Remote Sens. 2024, 16(12), 2064; https://doi.org/10.3390/rs16122064 - 7 Jun 2024
Viewed by 1318
Abstract
Over recent decades, Landsat satellite data has evolved into a highly valuable resource across diverse fields. Long-term satellite data records with integrity and consistency, such as the Landsat series, provide indispensable data for many applications. However, the malfunction of the Scan Line Corrector [...] Read more.
Over recent decades, Landsat satellite data has evolved into a highly valuable resource across diverse fields. Long-term satellite data records with integrity and consistency, such as the Landsat series, provide indispensable data for many applications. However, the malfunction of the Scan Line Corrector (SLC) on the Landsat 7 satellite in 2003 resulted in stripping in subsequent images, compromising the temporal consistency and data quality of Landsat time-series data. While various methods have been proposed to improve the quality of Landsat 7 SLC-off data, existing gap-filling methods fail to enhance the temporal resolution of reconstructed images, and spatiotemporal fusion methods encounter challenges in managing large-scale datasets. Therefore, we propose a method for reconstructing dense time series from SLC-off data. This method utilizes the Neighborhood Similar Pixel Interpolator to fill in missing values and leverages the time-series information to reconstruct high-resolution images. Taking the blue band as an example, the surface reflectance verification results show that the Mean Absolute Error (MAE) and BIAS reach minimum values of 0.0069 and 0.0014, respectively, with the Correlation Coefficient (CC) and Structural Similarity Index Metric (SSIM) reaching 0.93 and 0.94. The proposed method exhibits advantages in repairing SLC-off data and reconstructing dense time-series data, enabling enhanced remote sensing applications and reliable Earth’s surface reflectance data reconstruction. Full article
(This article belongs to the Special Issue Quantitative Remote Sensing of Vegetation and Its Applications)
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17 pages, 10875 KiB  
Article
An Improved Gross Primary Production Model Considering Atmospheric CO2 Fertilization: The Qinghai–Tibet Plateau as a Case Study
by Jie Li, Kun Jia, Linlin Zhao, Guofeng Tao, Wenwu Zhao, Yanxu Liu, Yunjun Yao and Xiaotong Zhang
Remote Sens. 2024, 16(11), 1856; https://doi.org/10.3390/rs16111856 - 23 May 2024
Cited by 1 | Viewed by 979
Abstract
Involving the effect of atmospheric CO2 fertilization is effective for improving the accuracy of estimating gross primary production (GPP) using light use efficiency (LUE) models. However, the widely used LUE model, the remote sensing-driven Carnegie–Ames–Stanford Approach (CASA) model, scarcely considers the effects [...] Read more.
Involving the effect of atmospheric CO2 fertilization is effective for improving the accuracy of estimating gross primary production (GPP) using light use efficiency (LUE) models. However, the widely used LUE model, the remote sensing-driven Carnegie–Ames–Stanford Approach (CASA) model, scarcely considers the effects of atmospheric CO2 fertilization, which causes GPP estimation uncertainties. Therefore, this study proposed an improved method for estimating GPP by integrating the atmospheric CO2 concentration into the CASA model and generated a long time series GPP dataset with high precision for the Qinghai–Tibet Plateau. The CASA model was improved by considering the impact of atmospheric CO2 on vegetation productivity and discerning variations in CO2 gradients within the canopy and leaves. A 500 m monthly GPP dataset for the Qinghai–Tibet Plateau from 2003 to 2020 was generated. The results showed that the improved GPP estimation model achieved better performances on estimating GPP (R2 = 0.68, RMSE = 406 g C/m2/year) than the original model (R2 = 0.67, RMSE = 499.32 g C/m2/year) and MODIS GPP products (R2 = 0.49, RMSE = 522.56 g C/m2/year). The GPP on the Qinghai–Tibet Plateau increased significantly with the increase in atmospheric CO2 concentration and the gradual accumulation of dry matter. The improved method can also be used for other regions and the generated GPP dataset is valuable for further understanding the ecosystem carbon cycles on the Qinghai–Tibet Plateau. Full article
(This article belongs to the Special Issue Quantitative Remote Sensing of Vegetation and Its Applications)
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