Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

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Evaluation of Fengyun-3C Soil Moisture Products Using In-Situ Data from the Chinese Automatic Soil Moisture Observation Stations: A Case Study in Henan Province, China

Water ◽

10.3390/w11020248 ◽

2019 ◽

Vol 11 (2) ◽

pp. 248 ◽

Cited By ~ 3

Author(s):

Yongchao Zhu ◽

Xuan Li ◽

Simon Pearson ◽

Dongli Wu ◽

Ruijing Sun ◽

...

Keyword(s):

Soil Moisture ◽

Large Scale ◽

Spatial Scales ◽

Ground Vegetation ◽

Poor Correlation ◽

Vegetation Density ◽

Statistical Parameters ◽

In Situ Data ◽

Vegetation Water

Soil moisture (SM) products derived from passive satellite missions are playing an increasingly important role in agricultural applications, especially crop monitoring and disaster warning. Evaluating the dependability of satellite-derived soil moisture products on a large scale is crucial. In this study, we assessed the level 2 (L2) SM product from the Chinese Fengyun-3C (FY-3C) radiometer against in-situ measurements collected from the Chinese Automatic Soil Moisture Observation Stations (CASMOS) during a one-year period from 1 January 2016 to 31 December 2016 across Henan in China. In contrast, we also investigated the skill of the Advanced Microwave Scanning Radiometer 2 (AMSR2) and Soil Moisture Active/Passive (SMAP) SM products simultaneously. Four statistical parameters were used to evaluate these products’ reliability: mean difference, root-mean-square error (RMSE), unbiased RMSE (ubRMSE), and the correlation coefficient. Our assessment results revealed that the FY-3C L2 SM product generally showed a poor correlation with the in-situ SM data from CASMOS on both temporal and spatial scales. The AMSR2 L3 SM product of JAXA (Japan Aerospace Exploration Agency) algorithm had a similar level of skill as FY-3C in the study area. The SMAP L3 SM product outperformed the FY-3C temporally but showed lower performance in capturing the SM spatial variation. A time-series analysis indicated that the correlations and estimated error varied systematically through the growing periods of the key crops in our study area. FY-3C L2 SM data tended to overestimate soil moisture during May, August, and September when the crops reached maximum vegetation density and tended to underestimate the soil moisture content during the rest of the year. The comparison between the statistical parameters and the ground vegetation water content (VWC) further showed that the FY-3C SM product performed much better under a low VWC condition (<0.3 kg/m2) than a high VWC condition (>0.3 kg/m2), and the performance generally decreased with increased VWC. To improve the accuracy of the FY-3C SM product, an improved algorithm that can better characterize the variations of the ground VWC should be applied in the future.

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VALIDATION OF SMOS L2 AND L3 SOIL MOISTURE PRODUCTS OVER THE DUERO BASIN AT DIFFERENT SPATIAL SCALES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w3-1183-2015 ◽

2015 ◽

Vol XL-7/W3 ◽

pp. 1183-1188 ◽

Cited By ~ 9

Author(s):

A. González-Zamora ◽

N. Sánchez ◽

A. Gumuzzio ◽

M. Piles ◽

E. Olmedo ◽

...

Keyword(s):

Soil Moisture ◽

Large Scale ◽

Spatial Scales ◽

Single Point ◽

Small Scale ◽

Moisture Measurement ◽

Duero Basin ◽

Temporal Validation ◽

Soil Moisture Measurement

An increasing number of permanent soil moisture measurement networks are nowadays providing the means for validating new remotely sensed soil moisture estimates such as those provided by the ESA’s Soil Moisture and Ocean Salinity (SMOS) mission. Two types of in situ measurement networks can be found: small-scale (100–10000 km2), which provide multiple ground measurements within a single satellite footprint, and large-scale (>10000 km2), which contain a single point observation per satellite footprint. This work presents the results of a comprehensive spatial and temporal validation of a long-term (January, 2010 to June, 2014) dataset of SMOS-derived soil moisture estimates using two in situ networks within the Duero basin (Spain). The first one is the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS), which has been extensively applied for validation of soil moisture remote sensing observations, including SMOS. REMEDHUS can be considered within the small-scale network group (1300 km2). The other network started from an existing meteorological network from the Castilla y León region, where soil moisture probes were incorporated in 2012. This network can be considered within the large-scale group (65000 km2). Results from comparison to in situ show that the new reprocessed L2 product (v5.51) improves the accuracy of former soil moisture retrievals, making them suitable for developing new L3 products. Validation based on comparisons between dense/sparse networks showed that temporal patterns on soil moisture are well reproduced, whereas spatial patterns are difficult to depict given the different spatial representativeness of ground and satellite observations.

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Global soil moisture data derived through machine learning trained with in-situ measurements

Scientific Data ◽

10.1038/s41597-021-00964-1 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Sungmin O. ◽

Rene Orth

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Large Scale ◽

Short Term Memory ◽

Temporal Dynamics ◽

Soil Moisture Data ◽

Wide Range ◽

Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

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Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-filling of Monthly ESA CCI Soil Moisture in China

Remote Sensing ◽

10.3390/rs13142848 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2848

Author(s):

Hao Sun ◽

Qian Xu

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Water Resource Management ◽

Large Scale ◽

Gap Filling ◽

Spatial Continuity ◽

Study Results ◽

Data Gaps

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: 1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. 2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. 3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. 4) Over in situ SM networks, RF achieved better performance than the OK method. 5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China.

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Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

Hydrology and Earth System Sciences ◽

10.5194/hess-19-3845-2015 ◽

2015 ◽

Vol 19 (9) ◽

pp. 3845-3856 ◽

Cited By ~ 9

Author(s):

F. Todisco ◽

L. Brocca ◽

L. F. Termite ◽

W. Wagner

Keyword(s):

Soil Moisture ◽

Soil Loss ◽

Large Scale ◽

Central Italy ◽

Remote Sensing Data ◽

Loss Estimation ◽

Balance Model ◽

Estimation Accuracy ◽

Soil Losses

Abstract. The potential of coupling soil moisture and a Universal Soil Loss Equation-based (USLE-based) model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e., the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for the period 2008–2013. The results showed that including soil moisture observations in the event rainfall–runoff erosivity factor of the USLE enhances the capability of the model to account for variations in event soil losses, the soil moisture being an effective alternative to the estimated runoff, in the prediction of the event soil loss at Masse. The agreement between observed and estimated soil losses (through SM4E) is fairly satisfactory with a determination coefficient (log-scale) equal to ~ 0.35 and a root mean square error (RMSE) of ~ 2.8 Mg ha−1. These results are particularly significant for the operational estimation of soil losses. Indeed, currently, soil moisture is a relatively simple measurement at the field scale and remote sensing data are also widely available on a global scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the soil erosion process.

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Prospective White-light Imaging and In Situ Measurements of Quiescent Large-scale Solar-wind Streams from the Parker Solar Probe and Solar Orbiter

The Astrophysical Journal ◽

10.3847/1538-4357/aae978 ◽

2018 ◽

Vol 868 (2) ◽

pp. 137 ◽

Cited By ~ 3

Author(s):

Ming Xiong ◽

Jackie A. Davies ◽

Xueshang Feng ◽

Bo Li ◽

Liping Yang ◽

...

Keyword(s):

Solar Wind ◽

White Light ◽

Large Scale ◽

In Situ Measurements ◽

White Light Imaging

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Global downscaling of remotely sensed soil moisture using neural networks

Hydrology and Earth System Sciences ◽

10.5194/hess-22-5341-2018 ◽

2018 ◽

Vol 22 (10) ◽

pp. 5341-5356 ◽

Cited By ~ 10

Author(s):

Seyed Hamed Alemohammad ◽

Jana Kolassa ◽

Catherine Prigent ◽

Filipe Aires ◽

Pierre Gentine

Keyword(s):

Soil Moisture ◽

Spatial Resolution ◽

Land Surface ◽

Vegetation Index ◽

Spatial Scales ◽

Ancillary Data ◽

Repeat Time ◽

Spatiotemporal Scales ◽

Better Than

Abstract. Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1 km) is necessary in order to quantify its role in regional feedbacks between the land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2–3-day repeat time); however, their finest spatial resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has estimated soil moisture at two different spatial scales of 36 and 9 km since April 2015. In this study, we develop a neural-network-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25 km spatial resolution. Our approach uses the mean monthly Normalized Differenced Vegetation Index (NDVI) as ancillary data to quantify the subpixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9 km soil moisture estimates.

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Spatial variability and its scale dependency of observed and modeled soil moisture over different climate regions

Hydrology and Earth System Sciences ◽

10.5194/hess-17-1177-2013 ◽

2013 ◽

Vol 17 (3) ◽

pp. 1177-1188 ◽

Cited By ~ 45

Author(s):

B. Li ◽

M. Rodell

Keyword(s):

Soil Moisture ◽

Spatial Variability ◽

Land Surface ◽

Large Scale ◽

Warm Season ◽

Scale Dependency ◽

Convex Shape ◽

Spatial Moments ◽

Environmental Controls

Abstract. Past studies on soil moisture spatial variability have been mainly conducted at catchment scales where soil moisture is often sampled over a short time period; as a result, the observed soil moisture often exhibited smaller dynamic ranges, which prevented the complete revelation of soil moisture spatial variability as a function of mean soil moisture. In this study, spatial statistics (mean, spatial variability and skewness) of in situ soil moisture, modeled and satellite-retrieved soil moisture obtained in a warm season (198 days) were examined over three large climate regions in the US. The study found that spatial moments of in situ measurements strongly depend on climates, with distinct mean, spatial variability and skewness observed in each climate zone. In addition, an upward convex shape, which was revealed in several smaller scale studies, was observed for the relationship between spatial variability of in situ soil moisture and its spatial mean when statistics from dry, intermediate, and wet climates were combined. This upward convex shape was vaguely or partially observable in modeled and satellite-retrieved soil moisture estimates due to their smaller dynamic ranges. Despite different environmental controls on large-scale soil moisture spatial variability, the correlation between spatial variability and mean soil moisture remained similar to that observed at small scales, which is attributed to the boundedness of soil moisture. From the smaller support (effective area or volume represented by a measurement or estimate) to larger ones, soil moisture spatial variability decreased in each climate region. The scale dependency of spatial variability all followed the power law, but data with large supports showed stronger scale dependency than those with smaller supports. The scale dependency of soil moisture variability also varied with climates, which may be linked to the scale dependency of precipitation spatial variability. Influences of environmental controls on soil moisture spatial variability at large scales are discussed. The results of this study should be useful for diagnosing large scale soil moisture estimates and for improving the estimation of land surface processes.

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Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1900278116 ◽

2019 ◽

pp. 201900278 ◽

Cited By ~ 21

Author(s):

Troy S. Magney ◽

David R. Bowling ◽

Barry A. Logan ◽

Katja Grossmann ◽

Jochen Stutz ◽

...

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Spatial Scales ◽

Gross Primary Production ◽

Canopy Structure ◽

Operating Efficiency ◽

Conifer Forest ◽

Spatiotemporal Resolution ◽

Evergreen Forests ◽

Spectrometer System

Northern hemisphere evergreen forests assimilate a significant fraction of global atmospheric CO2 but monitoring large-scale changes in gross primary production (GPP) in these systems is challenging. Recent advances in remote sensing allow the detection of solar-induced chlorophyll fluorescence (SIF) emission from vegetation, which has been empirically linked to GPP at large spatial scales. This is particularly important in evergreen forests, where traditional remote-sensing techniques and terrestrial biosphere models fail to reproduce the seasonality of GPP. Here, we examined the mechanistic relationship between SIF retrieved from a canopy spectrometer system and GPP at a winter-dormant conifer forest, which has little seasonal variation in canopy structure, needle chlorophyll content, and absorbed light. Both SIF and GPP track each other in a consistent, dynamic fashion in response to environmental conditions. SIF and GPP are well correlated (R2 = 0.62–0.92) with an invariant slope over hourly to weekly timescales. Large seasonal variations in SIF yield capture changes in photoprotective pigments and photosystem II operating efficiency associated with winter acclimation, highlighting its unique ability to precisely track the seasonality of photosynthesis. Our results underscore the potential of new satellite-based SIF products (TROPOMI, OCO-2) as proxies for the timing and magnitude of GPP in evergreen forests at an unprecedented spatiotemporal resolution.

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Spatial scales of temperature and salinity variability estimated from Argo observations

Ocean Science Discussions ◽

10.5194/osd-12-1793-2015 ◽

2015 ◽

Vol 12 (4) ◽

pp. 1793-1814

Author(s):

F. Ninove ◽

P. Y. Le Traon ◽

E. Remy ◽

S. Guinehut

Keyword(s):

Large Scale ◽

Spatial Scales ◽

Deep Ocean ◽

General Tendency ◽

Zero Crossing ◽

Tropical Regions ◽

Covariance Functions ◽

Salinity Variability ◽

First Time

Abstract. Argo observations from 2005 to 2013 are used to characterize spatial scales temperature and salinity variations from the surface down to 1500 m. Simulations are first performed to analyze the sensitivity of results to Argo sampling; they show that several years of Argo observations are required to estimate the spatial scales of ocean variability over 20° × 20° boxes. Spatial scales are then computed over several large scale areas. Zonal and meridional spatial scales (Lx and Ly which are also zero crossing of covariance functions) vary as expected with latitudes. Scales are of about 100 km at high latitudes and more of 700 km in the Indian and Pacific equatorial/tropical regions. Zonal and meridional scales are similar: except in these tropical/equatorial regions where zonal scales are much larger (by a factor of 2 to 3) than meridional scales. Spatial scales are the largest close to the surface and have a general tendency for temperature to increase in deeper layers. There are significant differences between temperature and salinity scales, in particular, in the deep ocean. Results are consistent with previous studies based on sparse in-situ observations or satellite altimetry. They provide, however, for the first time a global description of temperature and salinity scales of variability and a characterization of their variations according to depths.

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