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Recent Advances in Remote Sensing Modeling and Retrieving for Mountain Ecological Parameters

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 23062

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Dr. Huaan Jin

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Research Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
Interests: remote sensing; vegetation dynamics trend analysis; climate change impacts on vegetation greening; intercomparison and validation of multiple satellite products

Dr. Jinhu Bian

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Research Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
Interests: remote sensing images processing in mountainous areas; spatiotemporal fusion methods for mountain remote sensing images
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Prof. Dr. Jianguang Wen

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State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: remote sensing modeling and parameters quantitative retrieval; remote sensing experiments
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Prof. Dr. Tao He

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School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: atmospheric physics; precipitation; climate modeling; climate variability; fluorescence; nanomaterials; optics and lasers; material characterization; air quality; environment
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Prof. Dr. Ainong Li

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Dear Colleagues,

Mountainous areas occupy about 24% of the global land surface area, have rich natural resources, and are a key ecological security barrier for human social development. Mountainous areas play important ecological service functions in maintaining biodiversity, regulating climate, and conserving water resources. Remote sensing technology is an important means for monitoring mountain ecosystems, especially over global/regional scales. Remote sensing modeling effectively builds a bridge between mountain ecological parameters and spectral radiative signals. In addition, the retrieval results and products of ecological parameters from satellite data are used to analyze spatio-temporal ecosystem evolution processes and provide necessary inputs of mountain land surface models. It is worth noting that data processing and quantitative applications of the remotely sensed observations face serious challenges (geometric and spectral distortions, evident ill-posed inversion, complex energy balance, etc.) due to the complex topography and the redistribution of material and energy over rugged surfaces. This requires improvements and advances in methodologies for remote sensing modeling and the retrieval of mountain ecological parameters.

However, at present the relevant studies are dispersed, and there is little systematic integration in the field of modeling and retrieving for mountain ecological parameters based on remotely sensed observations. Furthermore, the well-developed technologies from other disciplines, especially machine learning and deep learning methods, are seldom introduced in this field. In this context, this Special Issue aims to collect recent advances, the latest methodologies, and state-of-the-art technologies for remote sensing data preprocessing, forward canopy reflectance modeling, retrieval of ecological parameters, as well as product generation and validation over rugged surfaces. We hope that this Issue can integrate the latest advances and provide recent important research findings, and further serve for the innovative development in this field.

The topics of interest may include:

Multisource and multiscale data fusion technology over mountainous areas;
Topographic and atmospheric correction methodology over mountainous areas;
Radiative transfer modeling and its application on complex terrains;
Retrieval of biophysical parameters on complex terrains;
Application of machine learning and deep learning technologies over rugged surfaces;
Integrated multiscale remote sensing experiments over mountainous areas;
Generation of high-resolution satellite products and validation over mountainous areas.

Dr. Huaan Jin
Dr. Jinhu Bian
Prof. Dr. Jianguang Wen
Prof. Dr. Tao He
Prof. Dr. Ainong Li
Guest Editors

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Keywords

ecological parameters
topographic correction
topographic effects
mountain experiment
forward modeling
machine learning
deep learning
retrieval
validation

Published Papers (9 papers)

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Research

18 pages, 3320 KiB

Open AccessArticle

Performance of Multiple Models for Estimating Rodent Activity Intensity in Alpine Grassland Using Remote Sensing

by Guang Dong, Wei Xian, Huaiyong Shao, Qiufang Shao and Jiaguo Qi

Remote Sens. 2023, 15(5), 1404; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15051404 - 02 Mar 2023

Cited by 1 | Viewed by 1611

Abstract

Rodents are a vital part of the natural succession chain of the alpine grassland ecosystem, and rodent activities have an important impact on alpine grassland ecology. Moderate rodent population activities positively improve soil permeability, promote nutrient cycling, and promote biodiversity. However, too much rodent population or excessive activity intensity will bring negative effects on the ecological environment. Therefore, it is of great significance to accurately grasp the rodent activity intensity (RAI) in alpine grassland to cope with the changes in rodent populations and maintain the stability of the alpine grassland ecosystem. The Zoige alpine grassland was used as the study area in this study. In addition, UAV was sent to sample the rodent activity area in the alpine grassland. With the aid of field survey data, the surface information of rodent activity in the experimental area was identified, and the RAI index in the sample plot was calculated. Then, based on Sentinel-2A satellite remote sensing multi-spectral data and spectral index, multiple linear regression (MLR), multi-layer perceptron neural networks (MPL neural nets), random forest (RF), and support vector regression (SVR) were used to construct four models for RAI and Sentinel-2 datasets. The accuracy of the four models was compared and analyzed. The results showed that the RF model had the highest prediction accuracy (R² = 0.8263, RWI = 0.8210, LCCC = 0.8916, RMSE = 0.0840, MAE = 0.0549), followed by the SVR model, the MLP neural nets model, and the MLR model. Overall, the nonlinear relationship between rodent activity intensity and satellite remote sensing images is obvious. Machine learning with strong nonlinear fitting ability can better characterize the RAI in alpine grassland. The RF model, with the best accuracy, can quantitatively estimate RAI in the alpine grassland, providing theoretical and technical support for monitoring RAI and rodent control in the alpine grassland. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing Modeling and Retrieving for Mountain Ecological Parameters)

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15 pages, 14061 KiB

Open AccessArticle

Generalized Additive Model Reveals Nonlinear Trade-Offs/Synergies between Relationships of Ecosystem Services for Mountainous Areas of Southwest China

by Qi Huang, Li Peng, Kexin Huang, Wei Deng and Ying Liu

Remote Sens. 2022, 14(12), 2733; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14122733 - 07 Jun 2022

Cited by 9 | Viewed by 2179

Abstract

Ecosystem services (ESs) are an essential link between ecosystems and human well-being, and trade-offs/synergies happen in ESs at different temporal and spatial scales. It is crucial to explore patterns of trade-offs/synergies among ESs, and their nonlinear relationships with changes in ESs. The primary objective of this study was to evaluate five ESs in 2000 and 2018: namely, water yield, food production, carbon sequestration, soil conservation, and habitat quality in mountainous regions of Southwest China. The mean values of the five ESs increased by 365.8 m³/ha, 13.92 t/hm², 497.09 TgC/yr², 138.48 t/km², and 0.002, respectively. Using spatial statistics and analysis, an ES trade-off synergy model (ESTD) was constructed for the five ESs change values. Overall, soil conservation has a trade-off with all five ESs, except habitat quality; this trade-off is increasing slightly. Water yield is in synergy with all ESs except soil conservation, with decreasing synergy; habitat quality is in synergy with all ESs except food production, with increasing synergy. Finally, the nonlinear relationship between the value of the change in the ES and ESTD was analyzed using a generalized additive model. Changes in water yield showed the greatest impact on ESTD except for food production, wherein changes in all three ESs had minimal impacts on ESTD. Food production dominates its trade-offs/synergies relationship with soil conservation; carbon sequestration is the dominant player in its trade-offs/synergies relationship with soil conservation. Habitat quality has a secondary position of influence, except in the trade-offs/synergies involving food production. By exploring the drivers of trade-offs/synergies among ESs, this study can provide guidance for the effective implementation of policies related to ecological protection and restoration. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing Modeling and Retrieving for Mountain Ecological Parameters)

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24 pages, 11065 KiB

Open AccessArticle

SGOT: A Simplified Geometric-Optical Model for Crown Scene Components Modeling over Rugged Terrain

by Guyue Hu and Ainong Li

Remote Sens. 2022, 14(8), 1821; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14081821 - 10 Apr 2022

Cited by 2 | Viewed by 1900

Abstract

Topography affects the fraction of scene components of the canopy and background, resulting in the observed reflectance distortion. Modeling the canopy reflectance over rugged terrain needs to account for topographic effects. For this purpose, the existing models greatly increased the mathematical complexity while improving description of terrain and crown structure, which dramatically decreased the computational efficiency so as to limit their universal application. In this study, we developed a simplified geometric-optical model (SGOT) for simulating the scene components over rugged terrain. The geotropism of tree growth was considered to make SGOT physically sound. The internal structure of crown was simplified to make SGOT mathematically simpler. Scene component observations derived from Persistence of Vision Ray-tracer (POV-Ray) on surfaces with different normal directions and simulations were made using Geometric-Optical and Mutual Shadowing Coupled with Topography Model (GOMST) and Geometric-Optical for Sloping Terrains Model GOST; models were combined to test the SGOT model. In addition, topographic factors and crown density effect on the scene components modeling were analyzed. The results indicated that SGOT has good accuracy (R² for the areal proportions of sunlit crown (Kc), sunlit background (Kg), shaded crown (Kt), and shaded background (Kz) are 0.853, 0.857, 0.914, and 0.838, respectively) compared with POV-Ray simulation, and performs better than GOMST, especially in scenes with high crown density. Moreover, SGOT outperformed the compared models in computational efficiency (4% faster than GOMST and 29.5% faster than GOST). Finally, the simulations of the scene components distribution in different topographic factors and crown density were further discussed. SGOT and GOST can both capture scene component variations caused by terrain better than GOMST, but comparatively, SGOT provides a more efficient tool to simulate the crown scene components because of its physical soundness and mathematical simplicity, and consequently, it will facilitate the modeling of canopy reflectance over mountainous regions. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing Modeling and Retrieving for Mountain Ecological Parameters)

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21 pages, 6919 KiB

Open AccessArticle

Extending the GOSAILT Model to Simulate Sparse Woodland Bi-Directional Reflectance with Soil Reflectance Anisotropy Consideration

by Juan Cheng, Jianguang Wen, Qing Xiao, Shengbiao Wu, Dalei Hao and Qinhuo Liu

Remote Sens. 2022, 14(4), 1001; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14041001 - 18 Feb 2022

Cited by 2 | Viewed by 1916

Abstract

Anisotropic canopy reflectance plays a crucial role in estimating vegetation biophysical parameters, whereas soil reflectance anisotropy affects canopy reflectance. However, woodland canopy bidirectional reflectance distribution function (BRDF) models considering soil anisotropy are far from universal, especially for the BRDF models of mountain forest. In this study, a mountain forest canopy model, named geometric-optical and mutual shadowing and scattering from arbitrarily inclined-leaves model coupled with topography (GOSAILT), was extended to consider the soil anisotropic reflectance characteristics by introducing the simple soil directional (SSD) reflectance model. The modified GOSAILT model (named GOSAILT-SSD) was evaluated using unmanned aerial vehicle (UAV) field observations and discrete anisotropic radiative transfer (DART) simulations. Then, the effects of Lambertian soil assumption on simulating the vi-directional reflectance factor (BRF) were evaluated across different fractions of vegetation cover (Cv), view zenith angles (VZA), solar zenith angles (SZA), and spectral bands with the GOSAILT-SSD model. The evaluation results, with the DART simulations, show that the performance of the GOSAILT-SSD model in simulating canopy BRF is significantly improved, with decreasing RMSE, from 0.027 to 0.017 for the red band and 0.051 to 0.037 for the near-infrared (NIR) band. Meanwhile, the GOSAILT-SSD simulations show high consistency with UAV multi-angular observations (R² = 0.97). Besides, it is also found that the BRF simulation errors caused by Lambertian soil assumption are too large to be neglected, with a maximum relative bias of about 45% for the red band. This inappropriate assumption results in a remarkable BRF underestimation near the hot spot direction and an obvious BRF overestimation for large VZA in the solar principal plane (PP). Meanwhile, this simulation bias decreases with the increase of fraction of vegetation cover. This study provides an effective technique to improve the capability of the mountain forest canopy BRDF model by considering the soil anisotropic characteristics for advancing the modeling of radiative transfer (RT) processes over rugged terrain. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing Modeling and Retrieving for Mountain Ecological Parameters)

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22 pages, 11664 KiB

Open AccessArticle

Comprehensive Assessment of Performances of Long Time-Series LAI, FVC and GPP Products over Mountainous Areas: A Case Study in the Three-River Source Region, China

by Wenqi Zhang, Huaan Jin, Ainong Li, Huaiyong Shao, Xinyao Xie, Guangbin Lei, Xi Nan, Guyue Hu and Wenjie Fan

Remote Sens. 2022, 14(1), 61; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14010061 - 23 Dec 2021

Cited by 7 | Viewed by 2664

Abstract

Vegetation biophysical products offer unique opportunities to examine long-term vegetation dynamics and land surface phenology (LSP). It is important to understand the time-series performances of various global biophysical products for global change research. However, few endeavors have been dedicated to assessing the performances of long-term change characteristics or LSP extraction derived from different satellite products, especially in mountainous areas with highly fragmented and rugged surfaces. In this paper, we assessed the time-series characteristics and LSP detections of Global LAnd Surface Satellite (GLASS) leaf area index (LAI), fractional vegetation cover (FVC), and gross primary production (GPP) products across the Three-River Source Region (TRSR). The performances of products’ temporal agreements and their statistical relationship as a function of topographic indices and heterogeneous pixels, respectively, were investigated through intercomparison among three products during the period 2000 to 2018. The results show that the phenological differences between FVC and two other products are beyond 10 days over more than 35% of the pixels in TRSR. The long-term trend of FVC diverges significantly from GPP and LAI for 13.96% of the total pixels, and the percentages of mismatched pixels between FVC and two other products are 33.24% in the correlation comparison. Moreover, good agreements are observed between GPP and LAI, both in terms of LSP and interannual variations. Finally, the LSP and long-term dynamics of the three products exhibit poor performances on heterogeneous surfaces and complex topographic areas, which reflects the potential impacts of environmental factors and algorithmic imperfections on the quality and performances of different products. Our study highlights the spatiotemporal disparities in detections of surface vegetation activity in mountainous areas by using different biophysical products. Future global change studies may require multiple high-quality satellite products with long-term stability as data support. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing Modeling and Retrieving for Mountain Ecological Parameters)

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25 pages, 2741 KiB

Open AccessArticle

Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform

by Yanpeng Yang, Dong Yang, Xufeng Wang, Zhao Zhang and Zain Nawaz

Remote Sens. 2021, 13(24), 5064; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245064 - 14 Dec 2021

Cited by 32 | Viewed by 4069

Abstract

The Qilian Mountains (QLM) are an important ecological barrier in western China. High-precision land cover data products are the basic data for accurately detecting and evaluating the ecological service functions of the QLM. In order to study the land cover in the QLM and performance of different remote sensing classification algorithms for land cover mapping based on the Google Earth Engine (GEE) cloud platform, the higher spatial resolution remote sensing images of Sentinel-1 and Sentinel-2; digital elevation data; and three remote sensing classification algorithms, including the support vector machine (SVM), the classification regression tree (CART), and the random forest (RF) algorithms, were used to perform supervised classification of Sentinel-2 images of the QLM. Furthermore, the results obtained from the classification process were compared and analyzed by using different remote sensing classification algorithms and feature-variable combinations. The results indicated that: (1) the accuracy of the classification results acquired by using different remote sensing classification algorithms were different, and the RF had the highest classification accuracy, followed by the CART and the SVM; (2) the different feature variable combinations had different effects on the overall accuracy (OA) of the classification results and the performance of the identification and classification of the different land cover types; and (3) compared with the existing land cover products for the QLM, the land cover maps obtained in this study had a higher spatial resolution and overall accuracy. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing Modeling and Retrieving for Mountain Ecological Parameters)

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21 pages, 7137 KiB

Open AccessArticle

Evaluation and Intercomparison of Topographic Correction Methods Based on Landsat Images and Simulated Data

by Yichuan Ma, Tao He, Ainong Li and Sike Li

Remote Sens. 2021, 13(20), 4120; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13204120 - 14 Oct 2021

Cited by 13 | Viewed by 2501

Abstract

Topographic effects in medium and high spatial resolution remote sensing images greatly limit the application of quantitative parameter retrieval and analysis in mountainous areas. Many topographic correction methods have been proposed to reduce such effects. Comparative analyses on topographic correction algorithms have been carried out, some of which drew different or even contradictory conclusions. Performances of these algorithms over different terrain and surface cover conditions remain largely unknown. In this paper, we intercompared ten widely used topographic correction algorithms by adopting multi-criteria evaluation methods using Landsat images under various terrain and surface cover conditions as well as images simulated by a 3D radiative transfer model. Based on comprehensive analysis, we found that the Teillet regression-based models had the overall best performance in terms of topographic effects’ reduction and overcorrection; however, correction bias may be introduced by Teillet regression models when surface reflectance in the uncorrected images do not follow a normal distribution. We recommend including more simulated images for a more in-depth evaluation. We also recommend that the pros and cons of topographic correction methods reported in this paper should be carefully considered for surface parameters retrieval and applications in mountain regions. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing Modeling and Retrieving for Mountain Ecological Parameters)

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19 pages, 5157 KiB

Open AccessArticle

Extraction of Abandoned Land in Hilly Areas Based on the Spatio-Temporal Fusion of Multi-Source Remote Sensing Images

by Shan He, Huaiyong Shao, Wei Xian, Shuhui Zhang, Jialong Zhong and Jiaguo Qi

Remote Sens. 2021, 13(19), 3956; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193956 - 02 Oct 2021

Cited by 12 | Viewed by 3050

Abstract

Hilly areas are important parts of the world’s landscape. A marginal phenomenon can be observed in some hilly areas, leading to serious land abandonment. Extracting the spatio-temporal distribution of abandoned land in such hilly areas can protect food security, improve people’s livelihoods, and serve as a tool for a rational land plan. However, mapping the distribution of abandoned land using a single type of remote sensing image is still challenging and problematic due to the fragmentation of such hilly areas and severe cloud pollution. In this study, a new approach by integrating Linear stretch (Ls), Maximum Value Composite (MVC), and Flexible Spatiotemporal DAta Fusion (FSDAF) was proposed to analyze the time-series changes and extract the spatial distribution of abandoned land. MOD09GA, MOD13Q1, and Sentinel-2 were selected as the basis of remote sensing images to fuse a monthly 10 m spatio-temporal data set. Three pieces of vegetation indices (VIs: ndvi, savi, ndwi) were utilized as the measures to identify the abandoned land. A multiple spatio-temporal scales sample database was established, and the Support Vector Machine (SVM) was used to extract abandoned land from cultivated land and woodland. The best extraction result with an overall accuracy of 88.1% was achieved by integrating Ls, MVC, and FSDAF, with the assistance of an SVM classifier. The fused VIs image set transcended the single source method (Sentinel-2) with greater accuracy by a margin of 10.8–23.6% for abandoned land extraction. On the other hand, VIs appeared to contribute positively to extract abandoned land from cultivated land and woodland. This study not only provides technical guidance for the quick acquirement of abandoned land distribution in hilly areas, but it also provides strong data support for the connection of targeted poverty alleviation to rural revitalization. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing Modeling and Retrieving for Mountain Ecological Parameters)

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25 pages, 63108 KiB

Open AccessArticle

Comparing Three Remotely Sensed Approaches for Simulating Gross Primary Productivity over Mountainous Watersheds: A Case Study in the Wanglang National Nature Reserve, China

by Xinyao Xie, Ainong Li, Huaan Jin, Jinhu Bian, Zhengjian Zhang and Xi Nan

Remote Sens. 2021, 13(18), 3567; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183567 - 08 Sep 2021

Cited by 8 | Viewed by 2020

Abstract

Light Use Efficiency (LUE), Vegetation Index (VI)-based, and process-based models are the main approaches for spatially continuous gross primary productivity (GPP) estimation. However, most current GPP models overlook the effects of topography on the vegetation photosynthesis process. Based on the structures of a two-leaf LUE model (TL-LUE), a VI-based model (temperature and greenness, TG), and a process-based model (Boreal Ecosystem Productivity Simulator, BEPS), three models, named mountain TL-LUE (MTL-LUE), mountain TG (MTG), and BEPS-TerrainLab, have been proposed to improve GPP estimation over mountainous areas. The GPP estimates from the three mountain models have been proven to align more closely with tower-based GPP than those from the original models at the site scale, but their abilities to characterize the spatial variation of GPP at the watershed scale are not yet known. In this work, the GPP estimates from three LUE models (i.e., MOD17, TL-LUE, and MTL-LUE), two VI-based models (i.e., TG and MTG), and two process-based models (i.e., BEPS and BEPS-TerrainLab) were compared for a mountainous watershed. At the watershed scale, the annual GPP estimates from MTL-LUE, MTG, and BTL were found to have a higher spatial variation than those from the original models (increasing the spatial coefficient of variation by 6%, 8%, and 22%), highlighting that incorporating topographic information into GPP models might improve understanding of the high spatial heterogeneity of the vegetation photosynthesis process over mountainous areas. Obvious discrepancies were also observed in the GPP estimates from MTL-LUE, MTG, and BTL, with determination coefficients ranging from 0.02–0.29 and root mean square errors ranging from 399–821 gC m⁻²yr⁻¹. These GPP discrepancies mainly stem from the different (1) structures of original LUE, VI, and process models, (2) assumptions associated with the effects of topography on photosynthesis, (3) input data, and (4) values of sensitive parameters. Our study highlights the importance of considering surface topography when modeling GPP over mountainous areas, and suggests that more attention should be given to the discrepancy of GPP estimates from different models. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing Modeling and Retrieving for Mountain Ecological Parameters)

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