Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019

Zhang, Qiang; Yuan, Qiangqiang; Li, Jie; Wang, Yuan; Sun, Fujun; Zhang, Liangpei

doi:10.5194/essd-13-1385-2021

Cited by 51 publications

(18 citation statements)

References 54 publications

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“…For this condition, the spatiotemporal characteristic of soil moisture may be considered [60]. Thus, the potential applications of spatial-temporal Kriging [61] or spatiotemporal deep learning method [62] may be investigated to fill the SMAP data missing.…”

Section: Comparison Of Gap-filling Approaches For a Complete Smap Sm Datamentioning

confidence: 99%

Spatial Gap-Filling of SMAP Soil Moisture Pixels Over Tibetan Plateau via Machine Learning Versus Geostatistics

Tong¹,

Wang²,

Magagi³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Soil moisture is a key variable in ecology, environment, agriculture, and hydrology. The Soil Moisture Active Passive (SMAP) satellite provides global soil moisture products with reliable accuracy since 2015. However, significant gaps of SMAP soil moisture appeared over Tibetan Plateau. Considering the important role of the Tibetan Plateau in global climate and environment, it is essential to develop methods to infill the gaps to generate seamless SMAP soil moisture data. To address this issue, we proposed two methods, machine learning and geostatistics technique. For the machine learning technique, we train a Random Forest algorithm which aims to match the output of available SMAP L3 soil moisture using a series of input variables such as SMAP brightness temperature (TBH, TBV) in ascending orbits (6:00 PM local time), surface temperature, MODIS NDVI, land cover, DEM and other auxiliary data. Then, the established RF estimators were applied to the SMAP brightness temperature from descending orbits (6:00 AM local time) to reconstruct complete soil moisture data over the Tibetan Plateau. For the geostatistics technique, the Ordinary Kriging was applied to the available SMAP L3 soil moisture pixels to interpolate complete soil moisture data. To cross-validate the performances of the algorithms, we assume certain areas with available SMAP SM values as missing, and then compared the gap-filling results with the actual ones. The cross-validations show that the gap-filling results from two algorithms were highly correlated to the official SMAP SM products with high coefficients of determination (R 2 RF = 0.97, R 2 OK = 0.85) and low RMSE (RMSERF = 0.015 cm 3 /cm 3 , RMSEOK = 0.036 cm 3 /cm 3 ). Furthermore, the gap-filling soil moisture data present a better correlation with the SMOS soil moisture data (R = 0.55 ~ 0.7) than the GLDAS simulations (R = 0.18 ~ 0.62). The reconstructed soil moisture from RF (R = 0.71) and OK (R = 0.55) algorithms are well related to the Maqu network measurements. Thus, the machine learning and geostatistics algorithms have the potential to reproduce the missing SMAP soil moisture products over the Tibetan Plateau.

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Section: Comparison Of Gap-filling Approaches For a Complete Smap Sm Datamentioning

confidence: 99%

Spatial Gap-Filling of SMAP Soil Moisture Pixels Over Tibetan Plateau via Machine Learning Versus Geostatistics

Tong¹,

Wang²,

Magagi³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…It usually takes the most credible soil moisture products as the standard reference dataset and trains other data to obtain the non-linear function that can be applied to achieve more precise prediction. Previous studies have used machine learning algorithms, such as Neural Network (NN) [25], General Regression Neural Network (GRNN) [26], convolutional neural network (CNN) [27], Random Forest (RF) [28], etc., and adopted auxiliary data, including of the Normalized Difference Vegetation Index (NDVI) [29], Microwave Vegetation Index (MVI) [30], Land Surface temperature (LST) [31], Leaf Area Index (LAI) [32], Albedo, etc., to train multi-source soil moisture data to obtain longer homogeneous time series products. The Artificial Neural Network (ANN) is an effective approach to establish a nonlinear model and widely applied in microwave remote sensing soil moisture retrieval [24,33,34].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network-Based Microwave Satellite Soil Moisture Reconstruction over the Qinghai–Tibet Plateau, China

Wang

2021

Remote Sensing

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Soil moisture is a key parameter for land-atmosphere interaction system; however, fewer existing spatial-temporally continuous and high-quality observation records impose great limitations on the application of soil moisture on long term climate change monitoring and predicting. Therefore, this study selected the Qinghai–Tibet Plateau (QTP) of China as research region, and explored the feasibility of using Artificial Neural Network (ANN) to reconstruct soil moisture product based on AMSR-2/AMSR-E brightness temperature and SMAP satellite data by introducing auxiliary variables, specifically considering the sensitivity of different combination of input variables, number of neurons in hidden layer, sample ratio, and precipitation threshold in model building. The results showed that the ANN model had the highest accuracy when all variables were used as inputs, it had a network containing 12 neurons in a hidden layer, it had a sample ratio 80%-10%-10% (training-validation-testing), and had a precipitation threshold of 8.75 mm, respectively. Furthermore, validation of the reconstructed soil moisture product (named ANN-SM) in other period were conducted by comparing with SMAP (April 2019 to July 2021) for all grid cells and in situ soil moisture sites (August 2010 to March 2015) of QTP, which achieved an ideal accuracy. In general, the proposed method is capable of rebuilding soil moisture products by adopting different satellite data and our soil moisture product is promising for serving the studies of long-term global and regional dynamics in water cycle and climate.

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“…Indeed, the increasing accuracy and a variety of spatial scales of SM datasets will provide much assistance in ET estimates. However, many of these remote sensing techniques can only provide SM estimates for the surface [27,[33][34][35][36][37], which cannot satisfy the transpiration water requirements for the vegetation with deeper roots, such as trees and shrubs. Therefore, in order to investigate the SM effect on vegetation T estimation, effective SM data from the deeper sources should first be obtained.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Evapotranspiration and Its Components across China Based on a Modified Priestley–Taylor Algorithm Using Monthly Multi-Layer Soil Moisture Data

et al. 2021

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Although soil moisture (SM) is an important constraint factor of evapotranspiration (ET), the majority of the satellite-driven ET models do not include SM observations, especially the SM at different depths, since its spatial and temporal distribution is difficult to obtain. Based on monthly three-layer SM data at a 0.25° spatial resolution determined from multi-sources, we updated the original Priestley Taylor–Jet Propulsion Laboratory (PT-JPL) algorithm to the Priestley Taylor–Soil Moisture Evapotranspiration (PT-SM ET) algorithm by incorporating SM control into soil evaporation (Es) and canopy transpiration (T). Both algorithms were evaluated using 17 eddy covariance towers across different biomes of China. The PT-SM ET model shows increased R2, NSE and reduced RMSE, Bias, with more improvements occurring in water-limited regions. SM incorporation into T enhanced ET estimates by increasing R2 and NSE by 4% and 18%, respectively, and RMSE and Bias were respectively reduced by 34% and 7 mm. Moreover, we applied the two ET algorithms to the whole of China and found larger increases in T and Es in the central, northeastern, and southern regions of China when using the PT-SM algorithm compared with the original algorithm. Additionally, the estimated mean annual ET increased from the northwest to the southeast. The SM constraint resulted in higher transpiration estimate and lower evaporation estimate. Es was greatest in the northwest arid region, interception was a large fraction in some rainforests, and T was dominant in most other regions. Further improvements in the estimation of ET components at high spatial and temporal resolution are likely to lead to a better understanding of the water movement through the soil–plant–atmosphere continuum.

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Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019

Cited by 51 publications

References 54 publications

Spatial Gap-Filling of SMAP Soil Moisture Pixels Over Tibetan Plateau via Machine Learning Versus Geostatistics

Spatial Gap-Filling of SMAP Soil Moisture Pixels Over Tibetan Plateau via Machine Learning Versus Geostatistics

Artificial Neural Network-Based Microwave Satellite Soil Moisture Reconstruction over the Qinghai–Tibet Plateau, China

Estimation of Evapotranspiration and Its Components across China Based on a Modified Priestley–Taylor Algorithm Using Monthly Multi-Layer Soil Moisture Data

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