Combining Microwave and Optical Remote Sensing to Characterize Global Vegetation Water Status DOI
Xin Wang,

Zhengxiang Zhang,

Shan Lu

et al.

IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2023, Volume and Issue: 61, P. 1 - 19

Published: Jan. 1, 2023

Vegetation water status, an important physiological characteristic of vegetation, lacked a global-scale estimate method. In this study, global vegetation moisture relative index (VMRI) was established based on the optical depth (VOD) and leaf area compared to live fuel content (LFMC) in-situ measurements environmental factors (soil from different depths, precipitation, vapor pressure deficit, ratio actual potential evapotranspiration, self-calibrating Palmer drought severity index). Validation using LFMC indicated that VMRI could characterize status (R median = 0.37) establishment method eliminate influence aboveground biomass in VOD. The results correlated comparison between showed positive significant correlations most regions. Besides, more with shrublands grasslands (e.g., R xmlns:xlink="http://www.w3.org/1999/xlink">mean 0.38 multi-depth soil moisture) than forests savannas 0.15), water-limited regions 0.33) were higher those non-water-limited 0.18). Moreover, deeper provided information above 60°N. Furthermore, trends displayed synchronization, about 60% pixels showing same trend 85% same-trend decreasing Particularly, interannual variations time-lagged responses drought. Overall, provides new measurement-independent estimation for affected by multiple at scale.

Language: Английский

Progress and Limitations in Forest Carbon Stock Estimation Using Remote Sensing Technologies: A Comprehensive Review DOI Open Access
Weifeng Xu,

Yu-Hao Cheng,

Mengyuan Luo

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(3), P. 449 - 449

Published: March 2, 2025

Forests play a key role in carbon sequestration and oxygen production. They significantly contribute to peaking neutrality goals. Accurate estimation of forest stocks is essential for precise understanding the capacity ecosystems. Remote sensing technology, with its wide observational coverage, strong timeliness, low cost, stock research. However, challenges data acquisition processing include variability, signal saturation dense forests, environmental limitations. These factors hinder accurate estimation. This review summarizes current state research on from two aspects, namely remote methods, highlighting both advantages limitations various sources models. It also explores technological innovations cutting-edge field, focusing deep learning techniques, optical vegetation thickness impact forest–climate interactions Finally, discusses including issues related quality, model adaptability, stand complexity, uncertainties process. Based these challenges, paper looks ahead future trends, proposing potential breakthroughs pathways. The aim this study provide theoretical support methodological guidance researchers fields.

Language: Английский

Citations

1
Asymmetric response of primary productivity to precipitation anomalies in Southwest China DOI
Guanyu Dong, Lei Fan, Rasmus Fensholt

et al.

Agricultural and Forest Meteorology, Journal Year: 2023, Volume and Issue: 331, P. 109350 - 109350

Published: Feb. 2, 2023

Language: Английский

Citations

17
Assessment of five SMAP soil moisture products using ISMN ground-based measurements over varied environmental conditions DOI Creative Commons

Chuanxiang Yi,

Xiaojun Li, Jiangyuan Zeng

et al.

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 619, P. 129325 - 129325

Published: March 1, 2023

Language: Английский

Citations

17
Cooling wisdom of ‘water towns’: How urban river networks can shape city climate? DOI
Dachuan Shi, Jiyun Song,

Qilong Zhong

et al.

Remote Sensing of Environment, Journal Year: 2023, Volume and Issue: 300, P. 113925 - 113925

Published: Nov. 27, 2023

Language: Английский

Citations

15
Soil salinization poses greater effects than soil moisture on field crop growth and yield in arid farming areas with intense irrigation DOI
Jingxiao Zhang, Jiabing Cai, Di Xu

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 451, P. 142007 - 142007

Published: March 28, 2024

Language: Английский

Citations

5
Synergistic retrieval of mangrove vital functional traits using field hyperspectral and satellite data DOI Creative Commons
Bolin Fu, Yan Wu, Shurong Zhang

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 131, P. 103963 - 103963

Published: June 13, 2024

Language: Английский

Citations

5
A new global C-band vegetation optical depth product from ASCAT: Description, evaluation, and inter-comparison DOI Creative Commons
Xiangzhuo Liu, Jean‐Pierre Wigneron, Wolfgang Wagner

et al.

Remote Sensing of Environment, Journal Year: 2023, Volume and Issue: 299, P. 113850 - 113850

Published: Oct. 14, 2023

Language: Английский

Citations

12
Understanding and extending the geographical detector model under a linear regression framework DOI Creative Commons
Hang Zhang, Guanpeng Dong, Jinfeng Wang

et al.

International Journal of Geographical Information Science, Journal Year: 2023, Volume and Issue: 37(11), P. 2437 - 2453

Published: Oct. 9, 2023

The Geographical Detector Model (GDM) is a popular statistical toolkit for geographical attribution analysis. Despite the striking resemblance of q-statistic in GDM to R-squared linear regression models, their explicit connection has not yet been established. This study proves that reduces into under framework. Under and moderate-to-strong spatial autocorrelation, Monte Carlo simulation results show tends underestimate importance variables. In addition, an almost perfect power law relationship present between percentage bias degree autocorrelations, indicating presence fast uplifting response increasing levels autocorrelations. We propose integrated approach variable quantification by bringing together econometrics model game theory based-Shapley value method. By applying our proposed methodology case land desertification African, it found human activity affect both directly indirectly. However, such effects appear be underestimated or undistinguished classic GDM.

Language: Английский

Citations

11
Estimating vegetation water content from Sentinel-1 C-band SAR data over savanna and grassland ecosystems DOI Creative Commons
Paulo N. Bernardino, Rafael S. Oliveira, Koenraad Van Meerbeek

et al.

Environmental Research Letters, Journal Year: 2024, Volume and Issue: 19(3), P. 034019 - 034019

Published: Feb. 12, 2024

Abstract Studying vegetation water content (VWC) dynamics is essential for understanding plant growth, and carbon cycles, ecosystem stability. However, acquiring field-based VWC estimates, consistently through space time, challenging due to time resource constraints. This study investigates the potential of Sentinel-1 C-band Synthetic Aperture Radar (SAR) data estimating in natural ecosystems central Brazil. We assessed (i) how well SAR can capture variations over three different types (i.e. dry waterlogged grasslands, savannas) (ii) studied respond seasonal periods terms content. Field from 82 plots, distributed across revisited four seasons, were used calibrate validate a model estimation. The calibrated model, with an R 2 0.52 RMSE 0.465 kg m −2 , was then applied backscatter generate monthly maps grassland savanna at 30 spatial resolution between April 2015 September 2023. These maps, combined rainfall evapotranspiration data, provided insights into shortage during season community scale. More specifically, savannas showed be better able retain higher levels season, probably holding capacity woody component together its deep-root system ability access deeper groundwater. research demonstrates monitoring ecosystems, allowing future studies assess ecosystems’ response drought events changes their functioning, ultimately supporting land management decisions.

Language: Английский

Citations

4
Vegetation Water Content Retrieval from Spaceborne GNSS-R and Multi-Source Remote Sensing Data Using Ensemble Machine Learning Methods DOI Creative Commons
Yongfeng Zhang, Jinwei Bu, Xiaoqing Zuo

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(15), P. 2793 - 2793

Published: July 30, 2024

Vegetation water content (VWC) is a crucial parameter for evaluating vegetation growth, climate change, natural disasters such as forest fires, and drought prediction. Spaceborne global navigation satellite system reflectometry (GNSS-R) has become valuable tool soil moisture (SM) biomass remote sensing (RS) due to its higher spatial resolution compared with microwave measurements. Although previous studies have confirmed the enormous potential of spaceborne GNSS-R monitoring, utilization this technology fuse multiple RS parameters retrieve VWC not yet mature. For purpose, paper constructs local high-spatiotemporal-resolution retrieval model that integrates key information, bistatic radar cross section (BRCS), effective scattering area, CYGNSS variables, surface auxiliary based on five ensemble machine learning (ML) algorithms (i.e., bagging tree (BT), gradient boosting decision (GBDT), extreme (XGBoost), random (RF), light (LightGBM)). We extensively tested performance different models using SMAP ancillary data validation data, results show root mean square errors (RMSEs) BT, XGBoost, RF, LightGBM in are better than 0.50 kg/m2. Among them, BT RF performed best localized retrieval, RMSE values Conversely, XGBoost exhibits worst performance, an 0.85 In terms RMSE, demonstrates improvements 70.00%, 52.00%, 32.00% over LightGBM, GBDT models, respectively.

Language: Английский

Citations

4