The 532 nm green laser light commonly used for airborne laser bathymetry (ALB) can penetrate clear shallow water but is sensitive to turbidity, which could lead to water surface uncertainty. In this study, water surface uncertainty was quantitatively assessed using a photon-counting LiDAR (PCL) with high receiver sensitivity to analyze the effect of turbidity. The qualitative results showed that the water surface heights are generally underestimated, and the surface detection accuracy in turbid water is superior to that in clear water. These findings were confirmed by statistical analysis of representative data in quantitative empirical experiments. The diffuse attenuation coefficient as a metric of water turbidity ranged from 0.14 to 4.80 m -1 for clear to turbid water. The corresponding underestimated deviation ranged from 0.37 to 0.08 m, and the root mean square error (RMSE) was ranged from 0.39 to 0.06 m. In addition, the radiative transfer mechanism underlying the underestimation of water surface heights at different levels as water turbidity varies was determined by comparing the simulated and measured results. On the one hand, there is a high exponential relationship between the underestimation deviation and the diffuse attenuation coefficient when considering only the water optical properties. On the other hand, the presence of direct reflection component from the surface actually has an inhibiting effect on the underestimation. The present study provides reliable evidence for further understanding the interaction of green lasers with the air-water interfaces.

It is important to explore the characteristics and rules of atmospheric aerosol in the East Asian Sea for monitoring and evaluating atmospheric environmental quality. Based on Aerosol Robot Network (AERONET), Visible Infrared Imaging Radiometer (VIIRS), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data, the temporal and spatial variation characteristics and differences of aerosol parameters and types in the East Asian Sea were studied by using figure classification method (FIGCM), aerosol optical depth (AOD)440-Angstrom exponent (AE)440–870 method (AA1M), and AOD550-AE490-670 method (AA2M). The results show that the seasonal variation trend of aerosol characteristics and types is obvious in East Asia Sea. AOD, volume concentration (Cv), and aerosol effective radius (reff) in the Bohai-Yellow Sea and the Sea of Japan in autumn are lower than those in other seasons, and the occurrence frequency of ocean-type aerosols is high. Different from the Bohai-Yellow Sea and Sea of Japan, human activities in winter, summer, and autumn seriously affect the air quality in the East China Sea and South China Sea. Especially at the Taipei CWB site, from aerosol parameters and high biomass burning/urban industrial (BB/UI) aerosol, human activity is an important factor for high pollution at the Taipei CWB site. Aerosol types of AA1M, FIGCM, AA2M, and CALIPSO were compared at Anmyon and Yonsei University sites in the Bohai-Yellow Sea in March 2020. The results show that aerosol types based on threshold classification methods generally have higher mixed aerosol results, and the marine (MA) results of AA1M, FIGCM, and AA2M are close to the clean marine aerosol results of CALIPSO. Comparing the results of AA 2 M and CALIPSO on a spatial scale, it is found that the clean marine aerosol proportion identified by CALIPSO (0.38, 0.48, 0.82) is consistent with the MA proportion identified by AA 2 M (0.43, 0.46, 0.97) in the East China Sea, South China Sea, and Western Pacific Ocean.

Improving the accuracy of nearshore bathymetric measurements is essential for understanding coastal environments, resource management, and navigation. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is the first laser satellite that uses the photon-counting technique. The ICESat-2 is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), which enables higher-accuracy measurements of water, ice, and land elevation on Earth. Two-media photogrammetric bathymetry is a type of nearshore bathymetric technology that uses the geometrical characteristics of light rays. With this technique, the accuracy and reliability mainly depend on eliminating systematic errors and ensuring accurate spatial photogrammetric positioning relative to the object being measured. To improve the bathymetric accuracy of two-media photogrammetry, we integrated high-accuracy elevation data from photon datasets as constraining and control parameters. The improved method effectively eliminated systematic errors in two-media photogrammetry during the established joint-block adjustment model. To improve its accuracy and reliability, we employed multispectral WorldView-2 stereo images in our experiments. Furthermore, the bathymetric results were validated and assessed using in situ and photon data. The experimental results show that the highest accuracy achieved with the bathymetric measurements in our study area was a root mean square error (RMSE) of 0.96 m and a mean absolute error of 0.57 m. Using the proposed fusion method, the bathymetric accuracy (as measured using the RMSE) was 1 m higher than that of two-media photogrammetry without the photon datasets.

Forest canopy height is an important indicator of the forest ecosystem, and an accurate assessment of forest canopy height on a large scale is of great significance for forest resource quantification and carbon sequestration. The retrieval of canopy height based on remote sensing provides a possibility for studying forest ecosystems. This study proposes a new method for estimating forest canopy height based on remote sensing. In this method, the GEDI satellite and ICESat-2 satellite, which are different types of space-borne lidar products, are used to cooperate with the Landsat 9 image and SRTM terrain data, respectively. Two forest canopy height-retrieval models based on multi-source remote sensing integration are obtained using a random forest regression (RFR) algorithm. The study, conducted at a forest site in the northeastern United States, synthesized various remote sensing data sets to produce a robust canopy height model. First, we extracted relative canopy height products, multispectral features, and topographic data from GEDI, ICESat-2, Landsat 9, and SRTM images, respectively. The importance of each variable was assessed, and the random forest algorithm was used to analyze each variable statistically. Then, the random forest regression algorithm was used to combine these variables and construct the forest canopy height model. Validation with airborne laser scanning (ALS) data shows that the GEDI and ICESat-2 models using a single data source achieve better accuracy than the Landsat 9 model. Notably, the combination of GEDI, Landsat 9, and SRTM data (R = 0.92, MAE = 1.91 m, RMSE = 2.78 m, and rRMSE = 12.64%) and a combination of ICESat-2, Landsat 9, and SRTM data (R = 0.89, MAE = 1.84 m, RMSE = 2.54 m, and rRMSE = 10.75%). Compared with the least accurate Landsat 9 model, R increased by 29.58%, 93.48%, MAE by 44.64%, 46.20%, RMSE by 42.80%, 49.40%, and the rRMSE was increased by 42.86% and 49.32%, respectively. These results fully evaluate and discuss the practical performance and benefits of multi-source data retrieval of forest canopy height by combining space-borne lidar data with Landsat 9 data, which is of great significance for understanding forest structure and dynamics. The study provides a reliable methodology for estimating forest canopy height and valuable insights into forest resource management and its contribution to global climate change.

Canopy height is a crucial indicator for assessing the structure and function of the forest ecosystems. It plays a significant role in carbon sequestration, sink enhancement, and promoting green development. This study aimed to evaluate the accuracy of GEDI L2A version 2 data in estimating ground elevation and canopy height by comparing it with airborne laser scanning (ALS) data. Among the six algorithms provided by the GEDI L2A data, algorithm a2 demonstrated higher accuracy than the others in detecting ground elevation and canopy height. Additionally, a relatively strong correlation (R-squared = 0.35) was observed between rh95 for GEDI L2A and RH90 for ALS. To enhance the accuracy of canopy height estimation, this study proposed three backpropagation (BP) neural network inversion models based on GEDI, Landsat 8 OLI, and Landsat 9 OLI-2 data. Multiple sets of relative heights and vegetation indices were extracted from the GEDI and Landsat datasets. The random forest (RF) algorithm was employed to select feature variables with a cumulative importance score of 90% for training the BP neural network inversion models. Validation against RH90 of ALS revealed that the GEDI model outperformed the OLI or OLI-2 data models in terms of accuracy. Moreover, the quality improvement of OLI-2 data relative to OLI data contributed to enhanced inversion accuracy. Overall, the models based on a single dataset exhibited relatively low accuracy. Hence, this study proposed the GEDI and OLI and GEDI and OLI-2 models, which combine the two types of data. The results demonstrated that the combined model integrating GEDI and OLI-2 data exhibited the highest performance. Compared to the weakest OLI data model, the inversion accuracy R-squared improved from 0.38 to 0.74, and the MAE, RMSE, and rRMSE decreased by 1.21 m, 1.81 m, and 8.09%, respectively. These findings offer valuable insights for the remote sensing monitoring of forest sustainability.

The atmosphere over the ocean is an important research field that involves multiple aspects such as climate change, atmospheric pollution, weather forecasting, and marine ecosystems. It is of great significance for global sustainable development. Satellites provide a wide range of measurements of marine aerosol optical properties and are very important to the study of aerosol characteristics over the ocean. In this study, aerosol optical depth (AOD) data from seventeen AERONET (Aerosol Robotic Network) stations were used as benchmark data to comprehensively evaluate the data accuracy of six aerosol optical thickness products from 2013 to 2020, including MODIS (Moderate-resolution Imaging Spectrometer), VIIRS (Visible Infrared Imaging Radiometer Suite), MISR (Multi-Angle Imaging Spectrometer), OMAERO (OMI/Aura Multi-wavelength algorithm), OMAERUV (OMI/Aura Near UV algorithm), and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) in the East Asian Ocean. In the East Asia Sea, VIIRS AOD products generally have a higher correlation coefficient (R), expected error within ratio (EE within), lower root mean square error (RMSE), and median bias (MB) than MODIS AOD products. The retrieval accuracy of AOD data from VIIRS is the highest in spring. MISR showed a higher EE than other products in the East Asian Ocean but also exhibited systematic underestimation. In most cases, the OMAERUV AOD product data are of better quality than OMAERO, and OMAERO overestimates AOD throughout the year. The CALIPSO AOD product showed an apparent underestimation of the AOD in different seasons (EE Below = 58.98%), but when the AOD range is small (0 < AOD < 0.1), the CALIPSO data accuracy is higher compared with other satellite products under small AOD range. In the South China Sea, VIIRS has higher data accuracy than MISR, while in the Bohai-Yellow Sea, East China Sea, Sea of Japan, and the western Pacific Ocean, MISR has the best data accuracy. MODIS and VIIRS show similar trends in R, EE within, MB, and RMSE under the influence of AOD, Angstrom exponent (AE), and precipitable water. The study on the temporal and spatial distribution of AOD in the East Asian Ocean shows that the annual variation of AOD is different in different sea areas, and the ocean in the coastal area is greatly affected by land-based pollution. In contrast, the AOD values in the offshore areas are lower, and the aerosol type is mainly clean marine type aerosol. These findings can help researchers in the East Asian Ocean choose the most accurate and reliable satellite AOD data product to better study atmospheric aerosols’ impact and trends.

Horizontal and vertical distributions of aerosol properties in the Taklimakan Desert (TD), North central region of China (NCR),North China Plain(NCP), and Yangtze River Delta (YRD) were investigated by statistical analysis using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) L3 data from 2007 to 2020, to identify the similarities and differences in atmospheric aerosols in different regions, and evaluate the impact of pollution control policies developed in China in 2013 on aerosol properties in the study area. The aerosol optical depth (AOD) distribution had substantial seasonal and spatial distribution characteristics. AOD had high annual averages in TD (0.38), NCP (0.49), and YRD (0.52). However, these rates showed a decline post-implementation of the long-term pollution control policies; AOD values declined by 5%, 13.8%, 15.5%, and 23.7% in TD, NCR, NCP, and YRD respectively when comparing 2014–2018 to 2007–2013, and by 7.8%, 11.5%, 16%, and 10.4% when comparing 2019–2020 to 2014–2018. The aerosol extinction coefficient showed a clear regional pattern and a tendency to decrease gradually as height increased. Dust and polluted dust were responsible for the changes in AOD and extinction coefficients between TD and NCR and NCP and YRD, respectively. In TD, with change of longitude, dust aerosol first increased and then decreased gradually, peaking in the middle. Similarly in NCP, polluted dust aerosol first increased and then decreased, with a maximum value in the middle. The elevated smoke aerosols of NCP and YRD were significantly higher than those observed in TD and NCR. The high aerosol extinction coefficient values (>0.1 km−1) were mainly distributed below 4 km, and the relatively weak aerosol extinction coefficients (>0.001 km−1) were mainly distributed between 5–8 km, indicating that the high-altitude long-range transport of TD and NCR dust aerosols affects NCP and YRD.

Aiming at the bathymetric error caused by the uncertainty of water surface elevation in dual-medium stereo photogrammetry, a technical route of shallow water depth measurement based on fusion of active and passive optical satellite remote sensing data is proposed, and the bathymetric model of integrating active ICESat-2 laser altimetry data and passive WorldView-3 stereo image pair data is studied. Firstly, the sea surface elevation is extracted from ICESat-2 laser altimetry data as the starting position for water surface elevation correction of WorldView-3 stereo image pair data. Then the appropriate water depth correction is added using the quadratic polynomial of the area network parity. A shallow water bathymetry experiment is carried out on the fusion of active and passive optical satellite remote sensing data in South China Sea. The results show that the root mean square error (RMSE) of the active and passive fusion bathymetry model is less than 0.70 m. The active-passive fusion bathymetric model realizes high-precision shallow water bathymetry without measured control points.

Unmanned aerial vehicle (UAV)-borne laser scanning systems using photon-counting technology are applied to high-resolution water surface mapping with high efficiency. Affected by vast noise photons in raw data, the detection of surface photons from a weak reflective target like water still faces challenges in low signal-to-noise ratio (SNR) application scenarios. Noise filtering of raw data and surface detection from possible signals are two essential steps for water surface detection. In this letter, a water surface height retrieval algorithm is investigated for characterizing terrain and surface height. The proposed algorithm implements multilevel filtering to minimize noise photons and subsequently extracts the topmost boundary points as water surface photons using a modified alpha-shape to derive the water level elevation. Noise filtering results show that the multilevel filtering approach is effective in preserving signal photons integrity at low SNR. Moreover, the accuracy assessment further substantiates the robustness of the methodology in calm waters, and the root mean square error (RMSE) for the estimated water surface height was 0.02 m compared with percentile heights. Our algorithm provides an efficient solution for high-resolution water surface mapping in UAV-borne photon-counting LiDAR (PCL).

The Chlorophyll-a (Chl-a) concentration is an important indicator of water environmental conditions; thus, the simultaneous monitoring of large-area water bodies can be realized through the remote sensing-based retrieval of Chl-a concentrations. Together with hyperspectral remote sensing data, a BP neural network model was used to invert chlorophyll-a concentration, with remote sensing reflectance as the input factor. Given the presence of many bands in the hyperspectral data, selecting an appropriate band reflectance as the input factor is crucial to improving inversion accuracy. In this study, a Pearson correlation analysis method was proposed to select bands. A normality test was performed on the reflectance of each band of the Zhuhai-1 hyperspectral remote sensing data, and the significance index was p < 0.05. The absolute kurtosis value was less than 10, and the absolute skewness value was less than 3, indicating that the Pearson method was applicable. Pearson correlation analysis was utilised to calculate the correlation coefficient between the reflectance data and chlorophyll-a concentration. Five reflectance data with high correlation were selected as the input factors, and chlorophyll-a concentration was adopted as the output factor. An error backpropagation network model was constructed to predict chlorophyll-a concentration, and a Garson function was added to clarify the connection weights of the input factors in the model construction process. Model 12 was determined as the optimal model on the basis of the criteria of the coefficient of determination, the average relative variance, and the minimum mean square error. The chlorophyll-a concentration was predicted for July and November 2020 in the study area, and the results showed that the predicted values had a small error compared with the measured values. The root-mean-square error and mean relative error of the chlorophyll-a concentration predicted and measured values were 2.12 μg/L and 9.66%, respectively. Significant spatial differences in the Chl-a concentration were observed in the study area due to the influence of islands and land; the Chl-a concentration in July was generally higher than that in November. The results of these studies provide a reference for monitoring the water environment in the study area.

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针对双介质立体摄影测量中水面高程不确定性带来的测深误差,本文提出了主被动光学卫星遥感数据融合浅海水深测量的技术路线,探讨了主动ICESat-2激光测高数据与被动WorldView-3立体像对数据融合的测深模型.首先通过ICESat-2激光测高数据提取海面高程作为WorldView-3立体像对数据水面高程纠正的起算位置,然后利用区域网平差的二次多项式加入适当的水深改正量.在我国南海岛礁开展了主被动光学卫星遥感数据融合进行浅海水深测量试验.结果表明,主被动融合测深模型的均方根误差(RMSE)小于0.70 m,实现了高精度、无实测控制点的浅海水深测量.

南海珊瑚岛礁总体处于退化状态,卫星遥感可以实现珊瑚礁区底质演变的监测.本文以西沙群岛海域赵述岛为研究区域,以Landsat-8卫星遥感影像为研究数据,对浅海地貌变化进行遥感分析.将影像进行水深校正和波段组合,使用最大似然法进行珊瑚礁地貌分类,可以清晰的识别灰沙岛、礁坪、礁前坡、泻湖坡、暗滩和海面六种地貌类型,以WorldView-2卫星影像为验证数据,总体分类精度为94.81％,高于直接使用最大似然法分类的92.30％,再结合决策树分类对分类结果中灰沙岛地貌进行改正,总体分类精度提升到95.07％;基于2014—2018年的Landsat-8数据,通过转移矩阵完成珊瑚礁地貌类型的时空变化分析,结果表明赵述岛珊瑚礁退化和转移明显.

针对轻小型单光子激光雷达水深测量折射改正计算的问题,提出了一种光线跟踪的激光水面入射角计算方法,推导出了非稳态条件下卵形扫描模式的激光反射光线向量及激光天底角计算公式,建立了轻小型单光子激光雷达系统的水深折射改正模型.以中科院上海技术物理研究所研制的无人机激光测深系统为例,在海南省万宁市加井岛附近浅海水域开展试验,并用船载单波束测深数据进行精度验证.结果表明,修正后的测深中误差为0.24 m,有效保证了单光子激光雷达测深数据的可靠性.

The distribution of the surface suspended sand concentration (SSSC) in the Yangtze River estuary is extremely complex. Therefore, effective methods are needed to improve the efficiency and accuracy of SSSC inversion. Hyperspectral remote sensing technology provides an effective technical means of accurately monitoring and quantitatively inverting SSSC. In this study, a new framework for the accurate inversion of the SSSC in the Yangtze River estuary using hyperspectral remote sensing is proposed. First, we quantitatively simulated water bodies with different SSSCs using sediment samples from the Yangtze River estuary, and analyzed the spectral characteristics of water bodies with different SSSCs. On this basis, we compared six spectral transformation forms, and selected the first derivative (FD) transformation as the optimal spectral transformation form. Subsequently, we compared two feature band extraction methods: the successive projections algorithm (SPA) and the competitive adaptive reweighted sampling (CARS) method. Then, the partial least squares regression (PLSR) model and back propagation (BP) neural network model were constructed. The BP neural network model was determined as the best inversion model. The new FD-CARS-BP framework was applied to the airborne hyperspectral data of the Yangtze estuary, with R2 of 0.9203, RPD of 4.5697, RMSE of 0.0339 kg/m3, and RMSE% of 8.55%, which are markedly higher than those of other framework combination forms, further verifying the effectiveness of the FD-CARS-BP framework in the quantitative inversion process of SSSC in the Yangtze estuary.

Sub-bottom sediment classifications have been widely used in marine science and engineering to obtain high-resolution information on types of sediments; however, these are often plagued by inaccuracies. Classification difficulties arise from the inability to effectively filter multiple reflections, extract representative lithology characteristic parameters, identify sub-bottom layer interfaces, extract image samples, control sample quality, optimise characteristic parameters, etc. To generate a highly accurate sub-bottom profile sediment map, a five-step classification method that considers two key lithology characteristic parameters of sub-bottom profile acoustic data was proposed. First, multiple reflections were filtered from the sea surface and sub-bottom layer interfaces of the primary signal. Second, two key characteristic parameters (relative backscattering intensity difference and attenuation compensation residual) were calculated. These reflect the relative differences in backscattering intensity and the attenuation compensation between adjacent interfaces based on the sound intensity attenuation model of a sub-bottom profile. Third, a combined method based on the sediment quality factor and peak trough of the echo signal loss level curve was employed to identify the actual interfaces between layers. An additional technique was proposed to determine the image sample width and preferred characteristic parameters. The resulting high-quality image samples and preferred characteristic parameters not only resulted in a faster convergence rate and increased ability of self-aggregation and identification, but also ensured that the training results met the convergence accuracy requirement. Ultimately, the preferred image samples were trained to classify the overall sub-bottom map of a selected test area of approximately 36 km2 in Bahai Bay, China. Compared with traditional methods, a considerably higher sediment identification accuracy was obtained. The experimental results indicate that the contribution rate of the two key lithology characteristic parameters was 65.49% according to principal component analysis, and the internal and external compatibilities were 97.98% and 84.76% for the training image samples, respectively. The total identification accuracy for the sub-bottom profile map was 98.2%. The two key characteristic parameters accurately captured the acoustic characteristics of sub-bottom sediments, significantly improving sediment classification. These results show that this method could be used to help refine the distributional estimates of submarine mineral resources.

The seabed sediment classification map has been widely applied in marine science and engineering for providing high-resolution and high-accuracy sediment type. However, some factors degrade the resolution and identification of sediment type, which include calculation of initial incidence angle of beam, backscattering intensity extraction method and coordinate calculation for the echo samples, echo intensity correction, wavelet-BP neural network (WBPNN) improvement and so on. To generate a highly accurate seabed sediment map, a five-step sediment classification method using multi-beam acoustic backscatter data was proposed. First, initial incidence angle of beam was calculated accurately taking into account vessel attitude and beam assigned angle, and echo samples were extracted in the way of grid within the beam footprint, meanwhile, echo samples coordinate was calculated according to the geometrical relationship between echo samples and pointing echo sample in beam, footprint. Second, samples intensities were corrected based on Time Varying Gain (TVG), Angle Varying Gain (AVG). Third, a critical step was proposed for determining the image sample width and preferred characteristic parameters based on the relevant principles. In addition, an improving the wavelet-BP neural network with self-adaptive learning rate and momentum factor, and the network parameters initialization was introduced. Ultimately, the selected image samples were trained to classify the seabed sediment map of a selected test area in Jiaozhou Bay. The experimental results indicated that sample identification accuracy and image identification accuracy reached 93.3% and 98.8% respectively, the proposed method can significantly improve the resolution of the seabed sediment map and identification accuracy of sediment classification.

Mastering the fluctuation of water levels and the water storage capacity of plateau lakes is greatly important for monitoring the water balance of the Tibetan Plateau and predicting regional and global climate change. The water level of plateau lakes is difficult to measure, and the ground measured data of long-time series are difficult to obtain. Ngoring Lake is considered in this study, using spaceborne single-photon lidar ICESat-2/ATL13 inland lake standard data products, the water level values provided by Hydroweb laboratory, and the image data of an optical remote sensing satellite. A new method is proposed in the absence of measured data. The method uses multisource remote sensing data to estimate the long-term changes in the water levels, surface area, and water storage capacity of Ngoring Lake in the past three decades. The results show that the water level values of ICESat-2 and Hydroweb on overlapping observation days are highly correlated, with R-2 = 0.9776, MAE = 0.420 m, RMSE = 0.077 m, and the average absolute height difference is 0.049 m. The fusion of multiple altimetry data can obtain more continuous long-time series water-level observation results. From 1992 to 2021, the water body information of Ngoring Lake basin fluctuated greatly and showed different variation characteristics in different time periods. The lowest water level in January 1997 was approximately 4268.49 m, and it rose to its highest in October 2009, approximately 4272.44 m. The change in the water level in the basin was mainly affected by natural factors, such as precipitation, air temperature, and human activities. The analysis shows that ICESat-2 can be combined with other remote sensing data to realize the long-time series dynamic monitoring of plateau lakes, showing great advantages in the comprehensive observation of plateau lakes in no man's land.

Suspended sediment concentration (SSC) is an important indicator in water monitoring. Airborne hyperspectral remote sensing can well acquire the spectral features of ground objects to accurately extract water quality parameters, but also has the problems of serious information overlap, invalid information, and redundant bands. In this paper, a successive projections algorithm (SPA) based method for feature bands extraction of suspended sediment concentration from airborne hyperspectral is proposed. The distribution of the feature bands extracted based on the SPA method and the Correlation Coefficient (CC) approach is compared and analyzed, and the SSC retrieval models based on the three-band combination model and the Multiple Linear Regression (MLR) model are constructed using the bands extracted from the study and applied to the North Channel of the Yangtze Estuary. The results show that the SSC retrieval model based on the feature bands extracted by the SPA method has higher accuracy.

The Amazon Basin is the world's largest flowing basin and plays an important role in the global hydrological cycle. We combined 20 years of Gravity Recovery and Climate Experiment (GRACE), Swarm, and GRACE Follow-On (GRACE-FO) satellite data to investigate variations of the terrestrial water storage (TWS) from April 2002 to December 2021. We also analyzed the effects of precipitation, surface temperature, and evapotranspiration on TWS changes. Compared with different methods of processing GRACE data, we found that the combined filtering method of P4M6 + Gaussian 300 km in GRACE/GRACE-FO data provided the most accurate results. The long-term trend of TWS was an increase of approximately 0.23 +/- 0.11 cm/a in the Amazon Basin, and the central and eastern regions had the highest increase rate, whereas the southeastern region showed a decreasing trend. In the last 20 years, maximum TM variations occurred in April and minimum TWS variations occurred in October. In spring, the TWS in the Amazon Basin changes considerably from north to south (increasing in the north and decreasing in the south), opposite to that in winter. In the Amazon Basin, precipitation and surface temperature are the important factors affecting the TWS changes, unlike evapotranspiration. In October 2020, the anomalous decreases in TWS changes correlate with insufficient precipitation and rising surface temperatures, which results in a drought.