Generation of MODIS-like land surface temperatures under all-weather conditions based on a data fusion approach

Long, Di; Yan, Liping; Bai, Lanqiang; Zhang, Caijin; Li, Xueying; Lei, Huimin; Yang, Hanbo; Tian, Fuqiang; Zeng, Chao; Meng, Xianyong; Shi, Chunxiang

doi:10.1016/j.rse.2020.111863

Cited by 140 publications

(49 citation statements)

References 65 publications

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“…It can be applied for monitoring droughts and floods in agriculture (Samaniego et al, 2018) and geologic hazards (Long et al, 2014). To obtain the global and high-frequency soil moisture products, many active or passive satellite sensors have been launched, such as the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), Advanced Microwave Scanning Radiometer 2 (AMSR2), and Soil Moisture Active and Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS) among others (McColl et al, 2017;Ma et al, 2019). Nevertheless, the acquired daily soil moisture products are always incomplete in global land (see Fig.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Yuan

et al. 2021

Earth Syst. Sci. Data

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Abstract. High-quality and long-term soil moisture products are significant for hydrologic monitoring and agricultural management. However, the acquired daily Advanced Microwave Scanning Radiometer 2 (AMSR2) soil moisture products are incomplete in global land (just about 30 %–80 % coverage ratio), due to the satellite orbit coverage and the limitations of soil moisture retrieval algorithms. To solve this inevitable problem, we develop a novel spatio-temporal partial convolutional neural network (CNN) for AMSR2 soil moisture product gap-filling. Through the proposed framework, we generate the seamless daily global (SGD) AMSR2 long-term soil moisture products from 2013 to 2019. To further validate the effectiveness of these products, three verification methods are used as follows: (1) in situ validation, (2) time-series validation, and (3) simulated missing-region validation. Results show that the seamless global daily soil moisture products have reliable cooperativity with the selected in situ values. The evaluation indexes of the reconstructed (original) dataset are a correlation coefficient (R) of 0.685 (0.689), root-mean-squared error (RMSE) of 0.097 (0.093), and mean absolute error (MAE) of 0.079 (0.077). The temporal consistency of the reconstructed daily soil moisture products is ensured with the original time-series distribution of valid values. The spatial continuity of the reconstructed regions is in accordance with the spatial information (R: 0.963–0.974, RMSE: 0.065–0.073, and MAE: 0.044–0.052). This dataset can be downloaded at https://doi.org/10.5281/zenodo.4417458 (Zhang et al., 2021).

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Section: Introductionmentioning

confidence: 99%

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

Zhang

Yuan

et al. 2021

Earth Syst. Sci. Data

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“…Chen et al (2017) developed a two‐stage approach to calibrate a hydrological model against snow cover fraction, snow water equivalent, total water storage, and streamflow in the Upper Brahmaputra River basin in the Tibetan Plateau. Similarly, several recent studies are also suggested using multiple data sets in model calibration and validation (Dembélé et al, 2020; Henn et al, 2018; Ko et al, 2019; Long et al, 2020). In our study, we used streamflow in the model calibration combined with an observation based rain‐snow temperature threshold data product.…”

Section: Resultsmentioning

confidence: 99%

Snow as an Important Natural Reservoir for Runoff and Soil Moisture in Northeast China

Feng

Liu

et al. 2020

JGR Atmospheres

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As one of the major agricultural regions in the world, water scarcity problems in Northeast China have drawn much attention recently. Because of cold and long winter period, snow is an important component in the hydrological system. Yet few studies have been conducted to systematically assess its role. This study quantified the effects of snow on runoff and soil moisture in the entire region in a 30-year time period (1982-2011) for the first time. A water and energy budget-based distributed biosphere hydrological model with improved snow physics after calibration and validation is employed. A Standardized Snow depth Index (SSdI) is also proposed to quantify snow variations. Result shows that snow contributes 11% to runoff annually on average and 66% and 33% in April and May (main months for crop planting). Soil moisture could decrease by at least 20% in March-May if there would be no snow, and the major agriculture area suffers more than other regions. We also found that SSdI is indicative of standardized soil moisture index and standardized runoff index in spring, particularly useful for supporting water management in agriculture. These results indicate that snow performs like an important reservoir: redistribute water resources among months. This study provides unique insights into the importance of snow in the entire region. The results improve the awareness of the importance of snow to water resources management and indicate that it is worth paying attention to snow in water resources management in this region. Plain Language Summary Because of cold and long winter period in Northeast China, snow plays a role as natural reservoir to store water in winter and to release water to ameliorate water shortage in spring. In addition, snow can influence agriculture through affecting soil moisture. However, studies on the role of snow in the region are limited so far. Here, we evaluate the importance of snow as a reservoir in entire Northeast China for the first time. We found on average snow contributes 11% to the annual runoff and 66% and 33% in April and May (the main crop planting months). Soil moisture in the region can decrease by at least 20% in March-May if there would be no snow, and the major agriculture area suffers more than other regions. Therefore, snow is an important source of water resources in this region and worth paying attention to in water resources management.

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“…They found that the spatiotemporal correlations using EOF reconstruction were most accurate for a large number of stations and missing data. Long et al (2020) [ 25 ] combined MODIS and SEVIRI LST in order to generate a complete daytime data set, and Zhao et al (2020) [ 26 ] also used MODIS to obtain all-weather conditions data. However, high-spatial-resolution data sets allow in-depth analysis of the LST at an urban and intra-urban scale.…”

Section: Methodsmentioning

confidence: 99%

Exploratory Analysis of Urban Climate Using a Gap-Filled Landsat 8 Land Surface Temperature Data Set

Cheval

Dumitrescu

Amihăesei

2020

Sensors

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The Landsat 8 satellites have retrieved land surface temperature (LST) resampled at a 30-m spatial resolution since 2013, but the urban climate studies frequently use a limited number of images due to the problems related to missing data over the city of interest. This paper endorses a procedure for building a long-term gap-free LST data set in an urban area using the high-resolution Landsat 8 imagery. The study is applied on 94 images available through 2013–2018 over Bucharest (Romania). The raw images containing between 1.1% and 58.4% missing LST data were filled in using the Data INterpolating Empirical Orthogonal Functions (DINEOF) algorithm implemented in the sinkr R packages. The resulting high-spatial-resolution gap-filled land surface temperature data set was used to explore the LST climatology over Bucharest (Romania) an urban area, at a monthly, seasonal, and annual scale. The performance of the gap-filling method was checked using a cross-validation procedure, and the results pledge for the development of an LST-based urban climatology.

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Generation of MODIS-like land surface temperatures under all-weather conditions based on a data fusion approach

Cited by 140 publications

References 65 publications

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

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

Snow as an Important Natural Reservoir for Runoff and Soil Moisture in Northeast China

Exploratory Analysis of Urban Climate Using a Gap-Filled Landsat 8 Land Surface Temperature Data Set

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