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Quantitative Remote Sensing Product and Validation Technology

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 22373

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Prof. Dr. Jianguang Wen

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Guest Editor

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
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Qinhuo Liu

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Guest Editor

State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: quantitative remote sensing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Shaomin Liu

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Guest Editor

School of Geography, Beijing Normal University, Beijing 100875, China
Interests: remote sensing experiment and validation

Special Issue Information

Dear Colleagues,

Operational quantitative satellite remote sensing products have provided spatially-and-temporally continuous data at the global/regional scale to enable the science community to monitor the earth surface and understand its mechanism. Over the past few years, great progresses in satellite remote sensing data processing and the Earth system parameters retrieval have been achieved, which constitutes a significant application in the science research especially in ecological-and-environmental monitoring and climate change. Besides, great efforts have also been made in quantitative remote sensing product validation, as it is critical to ensure the application quality and improve the performance of remote sensing products. Nevertheless, there is still a great challenge in developing high temporal-and-spatial-resolution remote sensing products and their credible validation.

On 26–29 June 2021, the national quantitative remote sensing forum was held in Wuhan, China, with more than 130 reports on quantitative remote sensing products and validation technology. This shows that great progress has been achieved in the quantitative remote sensing, including the processing of remote sensing data, the retrieval and product generation of Earth system key parameters, the application of long-time series quantitative remote sensing products, and the validation of remote sensing product.

This Special Issue is to collect recent advances, the latest methodologies, and state-of-the-art technologies for quantitative remote sensing products and validation. We hope that this Issue can integrate the latest advances and attracts important research developments in the quantitative remote sensing product generation, and theory and technology in the product validation.

The topics of interest may include:

Quantitative retrieval theory and technology;
Next-generation satellite data processing for quantitative remote sensing production;
Remote sensing product validation theory and technology;
Scale effects and scale transfer model development;
Multiscale remote sensing experiments for remote sensing product validation;
Quantitative remote sensing production analysis and application.

Prof. Dr. Jianguang Wen
Prof. Dr. Qinhuo Liu
Prof. Dr. Shaomin Liu
Guest Editors

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Keywords

Quantitative remote sensing production
Satellite remote sensing data processing
Remote sensing retrieval
Scale effects
Remote sensing experiments
Remote sensing product validation

Published Papers (11 papers)

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Research

23 pages, 23000 KiB

Open AccessArticle

A High Spatiotemporal Enhancement Method of Forest Vegetation Leaf Area Index Based on Landsat8 OLI and GF-1 WFV Data

by Xin Luo, Lili Jin, Xin Tian, Shuxin Chen and Haiyi Wang

Remote Sens. 2023, 15(11), 2812; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15112812 - 29 May 2023

Viewed by 1168

Abstract

The leaf area index (LAI) is a crucial parameter for analyzing terrestrial ecosystem carbon cycles and global climate change. Obtaining high spatiotemporal resolution forest stand vegetation LAI products over large areas is essential for an accurate understanding of forest ecosystems. This study takes the northwestern part of the Inner Mongolia Autonomous Region (the northern section of the Greater Khingan Mountains) in northern China as the research area. It also generates the LAI time series product of the 8-day and 30 m forest stand vegetation growth period from 2013 to 2017 (from the 121st to the 305th day of each year). The Simulated Annealing-Back Propagation Neural Network (SA-BPNN) model was used to estimate LAI from Landsat8 OLI, and the multi-period GaoFen-1 WideField-View satellite images (GF-1 WFV) and the spatiotemporal adaptive reflectance fusion mode (STARFM) was used to predict high spatiotemporal resolution LAI by combining inversion LAI and Global LAnd Surface Satellite-derived vegetation LAI (GLASS LAI) products. The results showed the following: (1) The SA-BPNN estimation model has relatively high accuracy, with R² = 0.75 and RMSE = 0.38 for the 2013 LAI estimation model, and R² = 0.74 and RMSE = 0.17 for the 2016 LAI estimation model. (2) The fused 30 m LAI product has a good correlation with the LAI verification of the measured sample site (R² = 0.8775) and a high similarity with the GLASS LAI product. (3) The fused 30 m LAI product has a high similarity with the GLASS LAI product, and compared with the GLASS LAI interannual trend line, it accords with the growth trend of plants in the seasons. This study provides a theoretical and technical reference for forest stand vegetation growth period LAI spatiotemporal fusion research based on high-score data, and has an important role in exploring vegetation primary productivity and carbon cycle changes in the future. Full article

(This article belongs to the Special Issue Quantitative Remote Sensing Product and Validation Technology)

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18 pages, 4113 KiB

Open AccessArticle

Evaluation of the High-Resolution MuSyQ LAI Product over Heterogeneous Land Surfaces

by Dandan Li, Yajun Huang, Yao Xiao, Min He, Jianguang Wen, Yuanqing Li and Mingguo Ma

Remote Sens. 2023, 15(5), 1238; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15051238 - 23 Feb 2023

Cited by 2 | Viewed by 1232

Abstract

In recent years, the retrieval and validation of remotely-sensed leaf area index (LAI) products over complex land surfaces have received much attention due to the high-precision land surface model simulations and applications in global climate change. However, most of these related researches mainly focus on coarse resolution products. This is because few products have been specifically designed for solving the problems derived from complex land surfaces in mountain areas until now. MuSyQ LAI is a new product derived from Gaofen-1 (GF-1) satellite data. This product is characterized with a temporal resolution of 10 days and a spatial resolution of 16 m. As is well known, high-resolution products have less uncertainties because of the homogeneities of sub-pixel. Therefore, to evaluate the precision and uncertainty of MuSyQ LAI, an up-scaling strategy was employed here to validate MuSyQ LAI for three mountain regions in Southwest China. The validation strategy can be divided into three parts. First, a regression model was built by in situ LAI measured by LAI-2200 and the normalized difference vegetation index (NDVI) from unmanned aerial vehicle (UAV) images to obtain a 0.5 m resolution LAI map. Second, an up-scaled LAI map with a spatial resolution consistent with MuSyQ LAI was calculated by the pixel-averaging method from the UAV-based LAI map. Third, the MuSyQ LAI was validated by the up-scaled UAV-based LAI in pixel scale. Simultaneously, the sources of uncertainty were analyzed and compared from the view of data source, retrieval model, and scale effects. The results suggested that MuSyQ LAI in the study areas are significantly underestimated by 53.69% due to the complex terrain and heterogeneous land cover. There are three main reasons for the underestimation. The differences between GF-1 reflectance and UAV-based reflectance employed to estimate LAI are the largest factors for the validation results, even accounting for 61.47% of the total bias. Subsequently, the scale effects led to about 28.44% bias. Last but not least, the models employed to retrieve LAI contributed merely 10.09% uncertainties to the total bias. In conclusion, the accuracy of MuSyQ LAI still has a large space to be improved from the view of reflectance over complex terrain. This study is quite important for applications of MuSyQ LAI products and also provides a reference for the improvement and application of other high-resolution remotely sensed LAI products. Full article

(This article belongs to the Special Issue Quantitative Remote Sensing Product and Validation Technology)

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18 pages, 5032 KiB

Open AccessArticle

Evaluation of Linear Kernel-Driven BRDF Models over Snow-Free Rugged Terrain

by Wenzhe Zhu, Dongqin You, Jianguang Wen, Yong Tang, Baochang Gong and Yuan Han

Remote Sens. 2023, 15(3), 786; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030786 - 30 Jan 2023

Cited by 4 | Viewed by 1425

Abstract

Semi-empirical kernel-driven models have been widely used to characterize anisotropic reflectance due to their simple form and physically meaningful approximation. Recently, several kernel-driven models have been coupled with topographic effects to improve the fitting of bidirectional reflectance over rugged terrains. However, extensive evaluations of the various models’ performances are required before their subsequent application in remote sensing. Three typical kernel-driven BRDF models over snow-free rugged terrains such as the RTLSR, TCKD, and the KDST-adjusted TCKD (KDST-TCKD) were investigated in this paper using simulated and observed BRFs. Against simulated data, the fitting error (NIR/Red RMSE) of the RTLSR gradually increases from 0.0358/0.0342 to 0.0471/0.0516 with mean slopes (α) increases from 9.13° to 33.40°. However, the TCKD and KDST-TCKD models perform an overall better fitting accuracy: the fitting errors of TCKD gradually decreased from 0.0366/0.0337 to 0.0252/0.0292, and the best fit from the KDST-TCDK model with NIR/Red RMSE decreased from 0.0192/0.0269 to 0.0169/0.0180. When compared to the sandbox data (α from 8.4° to 30.36°), the NIR/Red RMSE of the RTLSR model ranges from 0.0147/0.0085 to 0.0346/0.0165, for the TCKD model from 0.0144/0.0086 to 0.0298/0.0154, and for the KDST-TCKD model from 0.0137/0.0082 to 0.0234/0.0149. Using MODIS data, the TCKD and KDST-TCKD models show more significant improvements compared to the RTLSR model in rugged terrains. Their RMSE differences are within 0.003 over a relatively flat terrain (α < 10°). When α is large (20°–30° and >30°), the RMSE of the TCKD model has a decrease of around 0.01 compared to that of the RTLSR; for KDST-TCKD, it is approximately 0.02, and can even reach 0.0334 in the savannas. Therefore, the TCKD and KDST-TCKD models have an overall better performance than the RTLSR model in rugged terrains, especially in the case of large mean slopes. Among them, the KDST-TCKD model performs the best due to its consideration of topographic effects, geotropic growth, and component spectra. Full article

(This article belongs to the Special Issue Quantitative Remote Sensing Product and Validation Technology)

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23 pages, 5043 KiB

Open AccessArticle

PrecipGradeNet: A New Paradigm and Model for Precipitation Retrieval with Grading of Precipitation Intensity

by Danfeng Zhang, Yuqing He, Xiaoqing Li, Lu Zhang and Na Xu

Remote Sens. 2023, 15(1), 227; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15010227 - 31 Dec 2022

Cited by 1 | Viewed by 1281

Abstract

Near-real-time precipitation retrieval plays an important role in the study of the evolutionary process of precipitation and the prevention of disasters caused by heavy precipitation. Compared with ground-based precipitation observations, the infrared precipitation estimations from geostationary satellites have great advantages in terms of geographical coverage and temporal resolution. However, precipitation retrieved from multispectral infrared data still faces challenges in terms of accuracy, especially in extreme cases. In this paper, we propose a new paradigm for satellite multispectral infrared data retrieval of precipitation and construct a new model called PrecipGradeNet. This model uses FY-4A L1 FDI data as the input, IMERG precipitation data as the training target, and improves the precipitation retrieval accuracy by grading the precipitation intensity through Res-UNet, a semantic segmentation network. To evaluate the precipitation retrieval of the model, we compare the retrieval results with the FY-4A L2 QPE operational product to the IMERG precipitation. IMERG is considered as the ground truth. We evaluate the precipitation retrieval from the precipitation fall area identification, the precipitation intensity interval discrimination, and the precipitation quantification. Experimental results show that PrecipGradeNet has better overall performance compared with the FY-4A QPE product in precipitation fall area identification with POD increased by 48% and CSI and HSS improved by 21% and 14%. PrecipGradeNet also has better performance in light precipitation with POD increased by 114% and CSI and HSS improved by 64% and 52%, and better overall precipitation quantification, with RMSE and CC improved by 16% and 15%. In addition, PrecipGradeNet avoids the overall bias in the low and extreme high precipitation cases. Therefore, the new paradigm proposed in this paper has the potential to improve the retrieval accuracy of satellite precipitation estimation products. This study suggests that the application of semantic segmentation methods may provide a new path to correct the intensity bias of the satellite-based precipitation products. Full article

(This article belongs to the Special Issue Quantitative Remote Sensing Product and Validation Technology)

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19 pages, 7606 KiB

Open AccessArticle

Forest Carbon Flux Simulation Using Multi-Source Data and Incorporation of Remotely Sensed Model with Process-Based Model

by Yong Su, Wangfei Zhang, Bingjie Liu, Xin Tian, Shuxin Chen, Haiyi Wang and Yingwu Mao

Remote Sens. 2022, 14(19), 4766; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194766 - 23 Sep 2022

Cited by 3 | Viewed by 1994

Abstract

Forest carbon flux is critical to climate change, and the accurate modeling of forest carbon flux is an extremely challenging task. The remote sensing model (the MODIS MOD_17 gross primary productivity (GPP) model (MOD_17)) has strong practicability and is widely used around the world. The ecological process (the Biome-BioGeochemical Cycles Multilayer Soil Module model (Biome-BGCMuSo)) model can describe most of the vegetation’s environmental and physiological processes on fine time scales. Nevertheless, complex parameters and calibrations pose challenges to the application and development of models. In this study, we optimized all the input parameters of the MOD_17 model for the calibration of the Biome-BGCMuSo model to obtain GPP with continuous spatiality. To determine the contribution of input parameters to the GPP of different forest types, an Extended Fourier Amplitude Sensitivity Test (EFAST) was performed on the Biome-BGCMuSo model firstly. Then, we selected the sample points of each forest type and its different ecological gradients (30 for each type), using the GPP simulation value of the optimized MOD_17 model corresponding to the time and space scale to calibrate the Biome-BGCMuSo model, to drive the calibrated Biome-BGCMuSo, and we simulated the different forest types’ net primary productivity (NPP). According to dendrochronological measurements, the NPP simulation results were verified on the whole regional scale. The results showed that the GPP values of different forest types were highly sensitive to C:N_leaf (C:N of leaf), SLA1 (canopy average specific leaf area in phenological phase 1), and FLNR (fraction of leaf N in Rubisco). The coefficient of determination (R²) between the simulated forest NPP and the measured NPP was 0.64, and the root-mean-square (RMSE) was 26.55 g/C/m²/year. Our study aims to reduce uncertainty in forest carbon fluxes simulated by the Biome-BGCMuSo model, providing feedback for understanding forest ecosystem carbon cycling, vegetation productivity, and climate change. Full article

(This article belongs to the Special Issue Quantitative Remote Sensing Product and Validation Technology)

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22 pages, 8068 KiB

Open AccessArticle

Tree Species Classification of Backpack Laser Scanning Data Using the PointNet++ Point Cloud Deep Learning Method

by Bingjie Liu, Shuxin Chen, Huaguo Huang and Xin Tian

Remote Sens. 2022, 14(15), 3809; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153809 - 07 Aug 2022

Cited by 21 | Viewed by 4381

Abstract

To investigate forest resources, it is necessary to identify the tree species. However, it is a challenge to identify tree species using 3D point clouds of trees collected by light detection and ranging (LiDAR). PointNet++, a point cloud deep learning network, can effectively classify 3D objects. It is important to establish high-quality individual tree point cloud datasets when applying PointNet++ to identifying tree species. However, there are different data processing methods to produce sample datasets, and the processes are tedious. In this study, we suggest how to select the appropriate method by designing comparative experiments. We used the backpack laser scanning (BLS) system to collect point cloud data for a total of eight tree species in three regions. We explored the effect of tree height on the classification accuracy of tree species by using different point cloud normalization methods and analyzed the effect of leaf point clouds on classification accuracy by separating the leaves and wood of individual tree point clouds. Five downsampling methods were used: farthest point sampling (FPS), K-means, random, grid average sampling, and nonuniform grid sampling (NGS). Data with different sampling points were designed for the experiments. The results show that the tree height feature is unimportant when using point cloud deep learning methods for tree species classification. For data collected in a single season, the leaf point cloud has little effect on the classification accuracy. The two suitable point cloud downsampling methods we screened were FPS and NGS, and the deep learning network could provide the most accurate tree species classification when the number of individual tree point clouds was in the range of 2048–5120. Our study further illustrates that point-based end-to-end deep learning methods can be used to classify tree species and identify individual tree point clouds. Combined with the low-cost and high-efficiency BLS system, it can effectively improve the efficiency of forest resource surveys. Full article

(This article belongs to the Special Issue Quantitative Remote Sensing Product and Validation Technology)

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24 pages, 3455 KiB

Open AccessArticle

Comparison of Long-Term Albedo Products against Spatially Representative Stations over Snow

by Ruben Urraca, Christian Lanconelli, Fabrizio Cappucci and Nadine Gobron

Remote Sens. 2022, 14(15), 3745; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153745 - 04 Aug 2022

Cited by 4 | Viewed by 1530

Abstract

Multiple satellite products are available to monitor the spatiotemporal dynamics of surface albedo. They are extensively assessed over snow-free surfaces but less over snow. However, snow albedo is critical for climate monitoring applications, so a better understating of the accuracy of these products over snow is needed. This work analyzes long-term (+20 years) products (MCD43C3 v6/v6.1, GLASS-AVHRR, C3S v1/v2) by comparing them against the 11 most spatially representative stations from FLUXNET and BSRN during the snow-free and snow-covered season. Our goal is to understand how the performance of these products is affected by different snow cover conditions to use this information in an upcoming product inter-comparison that extends the analysis spatially and temporally. MCD43C3 has the smallest bias during the snow season (−0.017), and more importantly, the most stable bias with different snow cover conditions. Both v6 and v6.1 have similar performance, with v6.1 just increasing slightly the coverage at high latitudes. On the contrary, the quality of both GLASS-AVHRR and C3S-v1/v2 albedo decreases over snow. Particularly, the bias of both products varies strongly with the snow cover conditions, underestimating albedo over snow and overestimating snow-free albedo. GLASS bias strongly increases during the melting season, which is most likely due to an artificially extended snow season. C3S-v2 has the largest negative bias overall over snow during both the AVHRR (−0.141) and SPOT/VGT (−0.134) period. In addition, despite the improvements from v1 to v2, C3S-v2 still is not consistent enough during the transition from AVHRR to SPOT/VGT. Full article

(This article belongs to the Special Issue Quantitative Remote Sensing Product and Validation Technology)

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22 pages, 34631 KiB

Open AccessArticle

Validation and Analysis of MISR and POLDER Aerosol Products over China

by Sunxin Jiao, Mingyang Li, Meng Fan, Zhongbin Li, Benben Xu, Jinhua Tao and Liangfu Chen

Remote Sens. 2022, 14(15), 3697; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153697 - 02 Aug 2022

Viewed by 1640

Abstract

Multi-angle polarization measurement is an important technical means of satellite remote sensing applied to aerosol monitoring. By adding angle information and polarization measurements, aerosol optical and microphysical properties can be more comprehensively and accurately retrieved. The accuracy of aerosol retrieval can reflect the advantages and specific accuracy improvement of multi-angle polarization. In this study, the Multi-angle Imaging SpectroRadiometer (MISR) V23 aerosol products and the Polarization and Directionality of the Earth’s Reflectance (POLDER) GRASP “high-precision” archive were evaluated with the Aerosol Robotic Network (AERONET) observations over China. Validation of aerosol optical depth (AOD), absorbing aerosol optical depth (AAOD), and the Ångström exponent (AE) properties was conducted. Our results show that the AOD inversion accuracy of POLDER-3/GRASP is higher with the correlation coefficient (R) of 0.902, slope of 0.896, root mean square error (RMSE) of 0.264, mean absolute error (MAE) of 0.190, and about 40.71% of retrievals within the expected error (EE,

\pm (0.05 + 0.2 \times A O D_{A E R O N E T})

) lines. For AAOD, the performance of two products is poor, with better results for POLDER-3/GRASP data. POLDER-3/GRASP AE also has higher R of 0.661 compared with that of MISR AE (0.334). According to the validation results, spatiotemporal distribution, and comparison with other traditional scalar satellite data, the performance of multi-angle polarization observations is better and is suitable for the retrieval of aerosol properties. Full article

(This article belongs to the Special Issue Quantitative Remote Sensing Product and Validation Technology)

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26 pages, 5724 KiB

Open AccessArticle

Integrated Validation of Coarse Remotely Sensed Evapotranspiration Products over Heterogeneous Land Surfaces

by Yuan Zhang, Shaomin Liu, Lisheng Song, Xiang Li, Zhenzhen Jia, Tongren Xu, Ziwei Xu, Yanfei Ma, Ji Zhou, Xiaofan Yang, Xinlei He, Yunjun Yao and Guangcheng Hu

Remote Sens. 2022, 14(14), 3467; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143467 - 19 Jul 2022

Cited by 6 | Viewed by 1537

Abstract

Validation of remotely sensed evapotranspiration (RS_ET) products is important because their accuracy is critical for various scientific applications. In this study, an integrated validation framework was proposed for evaluating RS_ET products with coarse spatial resolution extending from homogenous to heterogeneous land surfaces. This framework was applied at the pixel and river basin scales, using direct and indirect validation methods with multisource validation datasets, which solved the spatial mismatch between ground measurements and remotely sensed products. The accuracy, rationality of spatiotemporal variations, and error sources of RS_ET products and uncertainties during the validation process were the focuses in the framework. The application of this framework is exemplified by validating five widely used RS_ET products (i.e., GLEAM, DTD, MOD16, ETMonitor, and GLASS) in the Heihe River Basin from 2012 to 2016. Combined with the results from direct (as the priority method) and indirect validation (as the auxiliary method), DTD showed the highest accuracy (1-MAPE) in the vegetation growing season (75%), followed by ETMonitor (71%), GLASS (68%), GLEAM (54%), and MOD16 (44%). Each product reasonably reflected the spatiotemporal variations in the validation dataset. ETMonitor exhibited the highest consistency with the ground truth ET at the basin scale (ETMap) (R = 0.69), followed by GLASS (0.65), DTD (0.63), MOD16 (0.62), and GLEAM (0.57). Error sources of these RS_ET products were mainly due to the limitations of the algorithms and the coarse spatial resolution of the input data, while the uncertainties in the validation process amounted to 15–28%. This work is proposed to effectively validate and improve the RS_ET products over heterogeneous land surfaces. Full article

(This article belongs to the Special Issue Quantitative Remote Sensing Product and Validation Technology)

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18 pages, 4417 KiB

Open AccessArticle

High Spatiotemporal Rugged Land Surface Temperature Downscaling over Saihanba Forest Park, China

by Xiaoying Ouyang, Youjun Dou, Jinxin Yang, Xi Chen and Jianguang Wen

Remote Sens. 2022, 14(11), 2617; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112617 - 30 May 2022

Cited by 7 | Viewed by 2027

Abstract

Satellite-derived rugged land surface temperature (LST) is an important parameter indicating the status of the Earth’s surface energy budget and its seasonal/temporal dynamic change. However, existing LST products from rugged areas are more prone to error when supporting applications in mountainous areas and Earth surface processes that occur at high spatial and temporal resolutions. This research aimed to develop a method for generating rugged LST with a high temporal and spatial resolution by using an improved ensemble LST model combining three regressors, including a random forest, a ridge, and a support vector machine. Different combinations of high-resolution input parameters were also considered in this study. The input datasets included Moderate Resolution Imaging Spectroradiometer (MODIS) LST datasets (MxD11A1) for nighttime, temporal Sentinel-2 Multispectral Instrument (MSI) datasets, and digital elevation model (DEM) datasets. The 30 m rugged LST datasets derived were compared against an in situ LST dataset obtained at Saihanba Forest Park (SFP) sites and an ASTER-derived 90 m LST, respectively. The results with in situ measurements demonstrated significant LST details, with an R² higher than 0.95 and RMSE around 3.00 K for both Terra/MOD- and Aqua/MYD-based LST datasets, and with slightly better results being obtained from the Aqua/MYD-based LST than that from Terra/MOD. The inter-comparison results with ASTER LST showed that over 80% of the pixels of the difference image for the two datasets were within 2 K. In light of the complex topography and distinct atmospheric conditions, these comparison results are encouraging. The 30 m LST from the method proposed in this study also depicts the seasonality of rugged surfaces. Full article

(This article belongs to the Special Issue Quantitative Remote Sensing Product and Validation Technology)

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37 pages, 12461 KiB

Open AccessArticle

Upscaling Evapotranspiration from a Single-Site to Satellite Pixel Scale

by Xiang Li, Shaomin Liu, Xiaofan Yang, Yanfei Ma, Xinlei He, Ziwei Xu, Tongren Xu, Lisheng Song, Yuan Zhang, Xiao Hu, Qian Ju and Xiaodong Zhang

Remote Sens. 2021, 13(20), 4072; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13204072 - 12 Oct 2021

Cited by 15 | Viewed by 2576

Abstract

It is of great significance for the validation of remotely sensed evapotranspiration (ET) products to solve the spatial-scale mismatch between site observations and remote sensing estimations. To overcome this challenge, this paper proposes a comprehensive framework for obtaining the ground truth ET at the satellite pixel scale (1 × 1 km resolution in MODIS satellite imagery). The main idea of this framework is to first quantitatively evaluate the spatial heterogeneity of the land surface, then combine the eddy covariance (EC)-observed ET (ET_EC) to be able to compare and optimize the upscaling methods (among five data-driven and three mechanism-driven methods) through direct validation and cross-validation, and finally use the optimal method to obtain the ground truth ET at the satellite pixel scale. The results showed that the ET_EC was superior over homogeneous underlying surfaces with a root mean square error (RMSE) of 0.34 mm/d. Over moderately and highly heterogeneous underlying surfaces, the Gaussian process regression (GPR) method performed better (the RMSEs were 0.51 mm/d and 0.60 mm/d, respectively). Finally, an integrated method (namely, using the ET_EC for homogeneous surfaces and the GPR method for moderately and highly heterogeneous underlying surfaces) was proposed to obtain the ground truth ET over fifteen typical underlying surfaces in the Heihe River Basin. Furthermore, the uncertainty of ground truth ET was quantitatively evaluated. The results showed that the ground truth ET at the satellite pixel scale is relatively reliable with an uncertainty of 0.02–0.41 mm/d. The upscaling framework proposed in this paper can be used to obtain the ground truth ET at the satellite pixel scale and its uncertainty, and it has great potential to be applied in more regions around the globe for remotely sensed ET products’ validation. Full article

(This article belongs to the Special Issue Quantitative Remote Sensing Product and Validation Technology)

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