scholarly journals Long-Term Spatiotemporal Variations in Soil Moisture in North East China Based on 1-km Resolution Downscaled Passive Microwave Soil Moisture Products

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3527 ◽  
Author(s):  
Xiangjin Meng ◽  
Kebiao Mao ◽  
Fei Meng ◽  
Xinyi Shen ◽  
Tongren Xu ◽  
...  
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Spatial Resolution ◽  
Soil Depth ◽  
Passive Microwave ◽  
Agricultural Drought ◽  
East China ◽  
North East ◽  
Moderate Resolution Imaging Spectroradiometer

It is very important to analyze and monitor agricultural drought to obtain high temporal-spatial resolution soil moisture products. To overcome the deficiencies of passive microwave soil moisture products with low resolution, we construct a spatial fusion downscaling model (SFDM) using Moderate Resolution Imaging Spectroradiometer (MODIS) data. To eliminate the inconsistencies in soil depth and time among different microwave soil moisture products (Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) and its successor (AMSR2) and the Soil Moisture Ocean Salinity (SMOS)), a time series reconstruction of the difference decomposition (TSRDD) method is developed to create long-term multisensor soil moisture datasets. Overall, the downscaled soil moisture (SM) products were consistent with the in situ measurements (R > 0.78) and exhibited a low root mean square error (RMSE < 0.10 m3/m3), which indicates good accuracy throughout the time series. The downscaled SM data at a 1-km spatial resolution were used to analyze the spatiotemporal patterns and monitor abnormal conditions in the soil water content across North East China (NEC) between 2002 and 2018. The results showed that droughts frequently appeared in western North East China and southwest of the Greater Khingan Range, while drought centers appeared in central North East China. Waterlogging commonly appeared in low-terrain areas, such as the Songnen Plain. Seasonal precipitation and temperature exhibited distinct interdecadal characteristics that were closely related to the occurrence of extreme climatic events. Abnormal SM levels were often accompanied by large meteorological and natural disasters (e.g., the droughts of 2008, 2015, and 2018 and the flooding events of 2003 and 2013). The spatial distribution of drought in this region during the growing season shows that the drought-affected area is larger in the west than in the east and that the semiarid boundary extends eastward and southward.

scholarly journals A fine-resolution soil moisture dataset for China in 2002–2018

2020 ◽  
Author(s):  
Xiangjin Meng ◽  
Kebiao Mao ◽  
Fei Meng ◽  
Jiancheng Shi ◽  
Jiangyuan Zeng ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Matching Problem ◽  
Change Models ◽  
Fine Resolution ◽  
Microwave Data

Abstract. Soil moisture is an important parameter required for agricultural drought monitoring and climate change models. Passive microwave remote sensing technology has become an important means to quickly obtain soil moisture over large areas, but the coarse spatial resolution of microwave data imposes great limitations on the application of these data. We provide a unique soil moisture dataset (0.05°, monthly) for China from 2002–2018 based on reconstruction model-based downscaling techniques using soil moisture data from different passive microwave products (including the AMSR-E/2 Level 3 products and the SMOS-INRA-CESBIO (SMOS-IC) products) calibrated with a consistent model in combination with ground observation data. This new fine-resolution soil moisture dataset with a high spatial resolution overcomes the multisource data time matching problem between optical and microwave data sources and eliminates the difference between the different sensor observation errors. The validation analysis indicates that the accuracy of the new dataset is satisfactory (bias: −0.024, −0.030 and −0.016 m3/m3, unbiased root mean square error (ubRMSE): 0.051, 0.048 and 0.042, correlation coefficient (R): 0.82, 0.88, and 0.90 on monthly, seasonal and annual scales, respectively). The new dataset was used to analyze the spatiotemporal patterns of soil water content across China from 2002 to 2018. In the past 17 years, China's soil moisture has shown cyclical fluctuations and a downward trend (slope = −0.167, R = 0.750) and can be summarized as wet in the south and dry in the north, with increases in the west and decreases in the east. The reconstructed dataset can be widely used to significantly improve hydrologic and drought monitoring and can serve as an important input for ecological and other geophysical models. The data are published in the Zenodo at http://doi.org/10.5281/zenodo.4049958 (Meng et al., 2020).

scholarly journals A fine-resolution soil moisture dataset for China in 2002–2018

2021 ◽  
Vol 13 (7) ◽  
pp. 3239-3261 ◽  
Author(s):  
Xiangjin Meng ◽  
Kebiao Mao ◽  
Fei Meng ◽  
Jiancheng Shi ◽  
Jiangyuan Zeng ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Matching Problem ◽  
Change Models ◽  
Fine Resolution ◽  
Microwave Data

Abstract. Soil moisture is an important parameter required for agricultural drought monitoring and climate change models. Passive microwave remote sensing technology has become an important means to quickly obtain soil moisture across large areas, but the coarse spatial resolution of microwave data imposes great limitations on the application of these data. We provide a unique soil moisture dataset (0.05∘, monthly) for China from 2002 to 2018 based on reconstruction model-based downscaling techniques using soil moisture data from different passive microwave products – including AMSR-E and AMSR2 (Advanced Microwave Scanning Radiometer for Earth Observing System) JAXA (Japan Aerospace Exploration Agency) Level 3 products and SMOS-IC (Soil Moisture and Ocean Salinity designed by the Institut National de la Recherche Agronomique, INRA, and Centre d’Etudes Spatiales de la BIOsphère, CESBIO) products – calibrated with a consistent model in combination with ground observation data. This new fine-resolution soil moisture dataset with a high spatial resolution overcomes the multisource data time matching problem between optical and microwave data sources and eliminates the difference between the different sensor observation errors. The validation analysis indicates that the accuracy of the new dataset is satisfactory (bias: −0.057, −0.063 and −0.027 m3 m−3; unbiased root mean square error (ubRMSE): 0.056, 0.036 and 0.048; correlation coefficient (R): 0.84, 0.85 and 0.89 on monthly, seasonal and annual scales, respectively). The new dataset was used to analyze the spatiotemporal patterns of soil water content across China from 2002 to 2018. In the past 17 years, China's soil moisture has shown cyclical fluctuations and a slight downward trend and can be summarized as wet in the south and dry in the north, with increases in the west and decreases in the east. The reconstructed dataset can be widely used to significantly improve hydrologic and drought monitoring and can serve as an important input for ecological and other geophysical models. The data are published in Zenodo at https://doi.org/10.5281/zenodo.4738556 (Meng et al., 2021a).

scholarly journals Long-Term and High-Resolution Global Time Series of Brightness Temperature from Copula-Based Fusion of SMAP Enhanced and SMOS Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1842 ◽  
Author(s):  
Christof Lorenz ◽  
Carsten Montzka ◽  
Thomas Jagdhuber ◽  
Patrick Laux ◽  
Harald Kunstmann
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
High Resolution ◽  
Brightness Temperature ◽  
Spatial Resolution ◽  
Prediction Models ◽  
Weather Prediction ◽  
Brightness Temperatures ◽  
Climate Analysis

Long and consistent soil moisture time series at adequate spatial resolution are key to foster the application of soil moisture observations and remotely-sensed products in climate and numerical weather prediction models. The two L-band soil moisture satellite missions SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) are able to provide soil moisture estimates on global scales and in kilometer accuracy. However, the SMOS data record has an appropriate length of 7.5 years since late 2009, but with a coarse resolution of ∼25 km only. In contrast, a spatially-enhanced SMAP product is available at a higher resolution of 9 km, but for a shorter time period (since March 2015 only). Being the fundamental observable from passive microwave sensors, reliable brightness temperatures (Tbs) are a mandatory precondition for satellite-based soil moisture products. We therefore develop, evaluate and apply a copula-based data fusion approach for combining SMAP Enhanced (SMAP_E) and SMOS brightness Temperature (Tb) data. The approach exploits both linear and non-linear dependencies between the two satellite-based Tb products and allows one to generate conditional SMAP_E-like random samples during the pre-SMAP period. Our resulting global Copula-combined SMOS-SMAP_E (CoSMOP) Tbs are statistically consistent with SMAP_E brightness temperatures, have a spatial resolution of 9 km and cover the period from 2010 to 2018. A comparison with Service Soil Climate Analysis Network (SCAN)-sites over the Contiguous United States (CONUS) domain shows that the approach successfully reduces the average RMSE of the original SMOS data by 15%. At certain locations, improvements of 40% and more can be observed. Moreover, the median NSE can be enhanced from zero to almost 0.5. Hence, CoSMOP, which will be made freely available to the public, provides a first step towards a global, long-term, high-resolution and multi-sensor brightness temperature product, and thereby, also soil moisture.

scholarly journals Long-term trends in agricultural droughts over Netherlands and Germany: how extreme was the year 2018?

2021 ◽  
Author(s):  
Yafei Huang ◽  
Jonas Weis ◽  
Harry Vereecken ◽  
Harrie-Jan Hendricks Franssen
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Potential Evapotranspiration ◽  
Sandy Loam ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Agricultural Drought ◽  
Drought Indices ◽  
Moisture Index

Abstract. Droughts can have important impacts on environment and economy like in the year 2018 in parts of Europe. Droughts can be analyzed in terms of meteorological drought, agricultural drought, hydrological drought and social-economic drought. In this paper, we focus on meteorological and agricultural drought and analyzed drought trends for the period 1965–2019 and assessed how extreme the drought year 2018 was in Germany and the Netherlands. The analysis was made on the basis of the following drought indices: standardized precipitation index (SPI), standardized soil moisture index (SSI), potential precipitation deficit (PPD) and ET deficit. SPI and SSI were computed at two time scales, the period April-September and a 12-months period. In order to analyze drought trends and the ranking of the year 2018, HYDRUS 1-D simulations were carried out for 31 sites with long-term meteorological observations and soil moisture, potential evapotranspiration (ET) and actual ET were determined for five soil types (clay, silt, loam, sandy loam and loamy sand). The results show that the year 2018 was severely dry, which was especially related to the highest potential ET in the time series 1965–2019, for most of the sites. For around half of the 31 sites the year 2018 had the lowest SSI, and largest PPD and ET-deficit in the 1965–2019 time series, followed by 1976 and 2003. The trend analysis reveals that meteorological drought (SPI) hardly shows significant trends over 1965–2019 over the studied domain, but agricultural droughts (SSI) are increasing, at several sites significantly, and at even more sites PPD and ET deficit show significant trends. The increasing droughts over Germany and Netherlands are mainly driven by increasing potential ET and increasing vegetation water demand.

Distribution of soil moisture content and its effect on the potential growth of the over-story species in North-East Chalkidiki

2001 ◽  
Vol 66 ◽  
Author(s):  
M. Aslanidou ◽  
P. Smiris
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Soil Depth ◽  
Moisture Distribution ◽  
Taxus Baccata ◽  
Mixed Stands ◽  
Potential Growth ◽  
North East ◽  
Soil Moisture Distribution

This  study deals with the soil moisture distribution and its effect on the  potential growth and    adaptation of the over-story species in north-east Chalkidiki. These  species are: Quercus    dalechampii Ten, Quercus  conferta Kit, Quercus  pubescens Willd, Castanea  sativa Mill, Fagus    moesiaca Maly-Domin and also Taxus baccata L. in mixed stands  with Fagus moesiaca.    Samples of soil, 1-2 kg per 20cm depth, were taken and the moisture content  of each sample    was measured in order to determine soil moisture distribution and its  contribution to the growth    of the forest species. The most important results are: i) available water  is influenced by the soil    depth. During the summer, at a soil depth of 10 cm a significant  restriction was observed. ii) the    large duration of the dry period in the deep soil layers has less adverse  effect on stands growth than in the case of the soil surface layers, due to the fact that the root system mainly spreads out    at a soil depth of 40 cm iii) in the beginning of the growing season, the  soil moisture content is    greater than 30 % at a soil depth of 60 cm, in beech and mixed beech-yew  stands, is 10-15 % in    the Q. pubescens  stands and it's more than 30 % at a soil depth of 60 cm in Q. dalechampii    stands.

scholarly journals Improving Spatial Soil Moisture Representation through the Integration of SMAP and PROBA-V Products

2018 ◽  
Vol 10 (10) ◽  
pp. 3459
Author(s):  
Shu-Di Fan ◽  
Yue-Ming Hu ◽  
Lu Wang ◽  
Zhen-Hua Liu ◽  
Zhou Shi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Factor Model ◽  
Original Method ◽  
Vegetation Coverage ◽  
Estimation Accuracy ◽  
Modified Model ◽  
The North ◽  
Moderate Resolution Imaging Spectroradiometer

To increase the spatial resolution of Soil Moisture Active Passive (SMAP), this study modifies the downscaling factor model based on the Temperature Vegetation Drought Index (TVDI) using data from the Project for On-Board Autonomy (PROBA-V). In the modified model, TVDI parameters were derived from the temperature-vegetation space and the Enhanced Vegetation Index (EVI). This study was conducted in the north China region using SMAP, PROBA-V, and Moderate Resolution Imaging Spectroradiometer satellite images. The 9-km spatial resolution SMAP data was downscaled to 0.3-km spatial resolution soil moisture using a modified downscaling method. Downscaling accuracies from the original and modified downscaling factor models were compared based on field observations. The results show that both methods generated similar spatial distributions in which soil moisture estimates increased as vegetation coverage increased from built-up areas to forest. However, based on the root mean square error between observations and estimations, the modified model demonstrated an increased estimation accuracy of 4.2% for soil moisture compared to the original method. This study also implies that downscaled soil moisture shows promise as a data source for subsequent watershed scale studies.

scholarly journals Long-term changes of tropospheric NO<sub>2</sub> over megacities derived from multiple satellite instruments

2013 ◽  
Vol 13 (8) ◽  
pp. 4145-4169 ◽  
Author(s):  
A. Hilboll ◽  
A. Richter ◽  
J. P. Burrows
Keyword(s):  
Time Series ◽  
Los Angeles ◽  
Middle East ◽  
Spatial Resolution ◽  
Western Europe ◽  
The United States ◽  
Central China ◽  
Full Time ◽  
Tropospheric No2

Abstract. Tropospheric NO2, a key pollutant in particular in cities, has been measured from space since the mid-1990s by the GOME, SCIAMACHY, OMI, and GOME-2 instruments. These data provide a unique global long-term dataset of tropospheric pollution. However, the observations differ in spatial resolution, local time of measurement, viewing geometry, and other details. All these factors can severely impact the retrieved NO2 columns. In this study, we present three ways to account for instrumental differences in trend analyses of the NO2 columns derived from satellite measurements, while preserving the individual instruments' spatial resolutions. For combining measurements from GOME and SCIAMACHY into one consistent time series, we develop a method to explicitly account for the instruments' difference in ground pixel size (40 × 320 km2 vs. 30 × 60 km2). This is especially important when analysing NO2 changes over small, localised sources like, e.g. megacities. The method is based on spatial averaging of the measured earthshine spectra and extraction of a spatial pattern of the resolution effect. Furthermore, two empirical corrections, which summarise all instrumental differences by including instrument-dependent offsets in a fitted trend function, are developed. These methods are applied to data from GOME and SCIAMACHY separately, to the combined time series, and to an extended dataset comprising also GOME-2 and OMI measurements. All approaches show consistent trends of tropospheric NO2 for a selection of areas on both regional and city scales, for the first time allowing consistent trend analysis of the full time series at high spatial resolution. Compared to previous studies, the longer study period leads to significantly reduced uncertainties. We show that measured tropospheric NO2 columns have been strongly increasing over China, the Middle East, and India, with values over east-central China tripling from 1996 to 2011. All parts of the developed world, including Western Europe, the United States, and Japan, show significantly decreasing NO2 amounts in the same time period. On a megacity level, individual trends can be as large as +27.2 ± 3.9% yr−1 and +20.7 ± 1.9% yr−1 in Dhaka and Baghdad, respectively, while Los Angeles shows a very strong decrease of −6.00 ± 0.72% yr−1. Most megacities in China, India, and the Middle East show increasing NO2 columns of +5 to 10% yr−1, leading to a doubling to tripling within the study period.

scholarly journals Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology

2019 ◽  
Vol 11 (2) ◽  
pp. 717-739 ◽  
Author(s):  
Alexander Gruber ◽  
Tracy Scanlon ◽  
Robin van der Schalie ◽  
Wolfgang Wagner ◽  
Wouter Dorigo
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Scientific Literature ◽  
Passive Microwave ◽  
Climate Data ◽  
Regular Grid ◽  
Change Initiative ◽  
Merging Algorithm ◽  
Merging Algorithms

Abstract. The European Space Agency's Climate Change Initiative for Soil Moisture (ESA CCI SM) merging algorithm generates consistent quality-controlled long-term (1978–2018) climate data records for soil moisture, which serves thousands of scientists and data users worldwide. It harmonises and merges soil moisture retrievals from multiple satellites into (i) an active-microwave-based-only product, (ii) a passive-microwave-based-only product and (iii) a combined active–passive product, which are sampled to daily global images on a 0.25∘ regular grid. Since its first release in 2012 the algorithm has undergone substantial improvements which have so far not been thoroughly reported in the scientific literature. This paper fills this gap by reviewing and discussing the science behind the three major ESA CCI SM merging algorithms, versions 2 (https://doi.org/10.5285/3729b3fbbb434930bf65d82f9b00111c; Wagner et al., 2018), 3 (https://doi.org/10.5285/b810601740bd4848b0d7965e6d83d26c; Dorigo et al., 2018) and 4 (https://doi.org/10.5285/dce27a397eaf47e797050c220972ca0e; Dorigo et al., 2019), and provides an outlook on the expected improvements planned for the next algorithm, version 5.

scholarly journals Recharge estimation and soil moisture dynamics in a Mediterranean karst aquifer

2014 ◽  
Vol 11 (7) ◽  
pp. 8803-8844 ◽  
Author(s):  
F. Ries ◽  
J. Lange ◽  
S. Schmidt ◽  
H. Puhlmann ◽  
M. Sauter
Keyword(s):  
Soil Moisture ◽  
Groundwater Recharge ◽  
Unsaturated Soil ◽  
Soil Depth ◽  
Climatic Gradient ◽  
Soil Moisture Dynamics ◽  
Soil Zone ◽  
Moisture Dynamics ◽  
Hydrus 1D

Abstract. Knowledge of soil moisture dynamics in the unsaturated soil zone provides valuable information on the temporal and spatial variability of groundwater recharge. This is especially true for the Mediterranean region, where a substantial fraction of long-term groundwater recharge is expected to occur during high magnitude precipitation events of above-average wet winters. To elucidate process understanding of infiltration processes during these extreme events, a monitoring network of precipitation gauges, meteorological stations, and soil moisture plots was installed in an area with a steep climatic gradient in the Jordan Valley region. In three soil moisture plots, Hydrus-1D was used to simulate water movement in the unsaturated soil zone with soil hydraulic parameters estimated by the Shuffled Complex Evolution Metropolis algorithm. To generalize our results, we modified soil depth and rainfall input to simulate the effect of the pronounced climatic gradient and soil depth variability on percolation fluxes and applied the calibrated model to a time series with 62 years of meteorological data. Soil moisture measurements showed a pronounced seasonality and suggested rapid infiltration during heavy rainstorms. Hydrus-1D successfully simulated short and long-term soil moisture patterns, with the majority of simulated deep percolation occurring during a few intensive rainfall events. Temperature drops in a nearby groundwater well were observed synchronously with simulated percolation pulses, indicating rapid groundwater recharge mechanisms. The 62 year model run yielded annual percolation fluxes of up to 66% of precipitation depths during wet years and of 0% during dry years. Furthermore, a dependence of recharge on the temporal rainfall distribution could be shown. Strong correlations between depth of recharge and soil depth were also observed.

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