Climate Change Initiative Soil Moisture Research Articles

Spatial Downscaling of ESA CCI Soil Moisture Data Based on Deep Learning with an Attention Mechanism

Soil moisture (SM) is a critical variable affecting ecosystem carbon and water cycles and their feedback to climate change. In this study, we proposed a convolutional neural network (CNN) model embedded with a residual block and attention module, named SMNet, to spatially downscale the European Space Agency (ESA) Climate Change Initiative (CCI) SM product. In the SMNet model, a lightweight Convolutional Block Attention Module (CBAM) dual-attention mechanism was integrated to comprehensively extract the spatial and channel information from the high-resolution input remote sensing products, the reanalysis meteorological dataset, and the topographic data. The model was employed to downscale the ESA CCI SM from its original spatial resolution of 25 km to 1 km in California, USA, in the annual growing season (1 May to 30 September) from 2003 to 2021. The original ESA CCI SM data and in situ SM measurements (0–5 cm depth) from the International Soil Moisture Network were used to validate the model’s performance. The results show that compared with the original ESA CCI SM data, the downscaled SM data have comparable accuracy with a mean correlation (R) and root mean square error (RMSE) of 0.82 and 0.052 m3/m3, respectively. Moreover, the model generates reasonable spatiotemporal SM patterns with higher accuracy in the western region and relatively lower accuracy in the eastern Nevada mountainous area. In situ site validation results in the SCAN, the SNOTEL network, and the USCRN reveal that the R and RMSE are 0.62, 0.63, and 0.77, and 0.077 m3/m3, 0.093 m3/m3, and 0.078 m3/m3, respectively. The results are slightly lower than the validation results from the original ESA CCI SM data. Overall, the validation results suggest that the SMNet downscaling model proposed in this study has satisfactory performance in handling the task of soil moisture downscaling. The downscaled SM model not only preserves a high level of spatial consistency with the original ESA CCI SM model but also offers more intricate spatial variations in SM depending on the spatial resolution of model input data.

Evaluating the NASA MERRA-2 climate reanalysis and ESA CCI satellite remote sensing soil moisture over the contiguous United States

ABSTRACT Accurate large-scale soil moisture (SM) retrievals using the daily NASA MERRA-2’ climate reanalysis and ESA’ Climate Change Initiative (CCI) remote sensing datasets are compromised by temporal and spatial ambiguities. To address this deficiency, we assess the accuracy of the above-mentioned datasets against the Soil Climate Analysis Network (SCAN) in-situ measurements at nine sites across the contiguous United States (CONUS) during 2014–2016. The sites are selected to represent different climate regions over the CONUS. SM dynamics from NASA MERRA-2 and ESA CCI are compared with those of SCAN, and an SM dataset is developed based on a spatiotemporal analysis. Our results show that the NASA MERRA-2 and ESA CCI SM datasets have different accuracies at the nine SCAN sites in different seasons. The MERRA-2 and CCI SM datasets have the highest accuracy at sites with the lowest number of extreme events, indicating that both datasets cannot robustly capture extremum soil moisture values. The highest (lowest) agreement between the MERRA-2/CCI and SCAN SM data is observed in April (February) with the nine-site average unbiased root-mean-square-difference (ubRMSD) of 0.0638 cm3/cm3 (0.0914 cm3/cm3). In all the nine sites, the CCI SM data are more accurate than those of MERRA-2 in April–October. The CCI SM data shows greater accuracy in the sites with lower SM values and/or higher SM variability. MERRA-2 and CCI SM datasets show higher and lower accuracy at the sites with pasture and agricultural vegetation, respectively. Finally, a new SM dataset is created by using the more accurate SM data from MERRA-2 and CCI in each site and season.

Satellite-based soil moisture enhances the reliability of agro-hydrological modeling in large transboundary river basins

Satellite-based observations of soil moisture, leaf area index, precipitation, and evapotranspiration facilitate agro-hydrological modeling thanks to the spatially distributed information. In this study, the Climate Change Initiative Soil Moisture dataset (CCI SM, a product of the European Space Agency (ESA)) adjusted based on Soil Water Index (SWI) was used as an additional (in relation to discharge) observed dataset in agro-hydrological modeling over a large-scale transboundary river basin (Odra River Basin) in the Baltic Sea region. This basin is located in Central Europe within Poland, Czech Republic, and Germany and drains into the Baltic Sea. The Soil and Water Assessment Tool+ (SWAT+) model was selected for agro-hydrological modeling, and measured data from 26 river discharge stations and soil moisture from CCI SM (for topsoil and entire soil profile) over 1476 sub-basins were used in model calibration for the period 1997–2019. Kling–Gupta efficiency (KGE) and SPAtial EFficiency (SPAEF) indices were chosen as objective functions for runoff and soil moisture calibration, respectively. Two calibration strategies were compared: one involving only river discharge data (single-objective - SO), and the second one involving river discharge and satellite-based soil moisture (multi-objective – MO). In the SO approach, the average KGE for discharge was above 0.60, whereas in the MO approach, it increased to 0.67. The SPAEF values showed that SWAT+ has acceptable accuracy in soil moisture simulations. Moreover, crop yield assessments showed that MO calibration also increases the crop yield simulation accuracy. The results show that in this transboundary river basin, adding satellite-based soil moisture into the calibration process could improve the accuracy and consistency of agro-hydrological modeling.

Reconstructing long-term global satellite-based soil moisture data using deep learning method

Soil moisture is an essential component for the planetary balance between land surface water and energy. Obtaining long-term global soil moisture data is important for understanding the water cycle changes in the warming climate. To date several satellite soil moisture products are being developed with varying retrieval algorithms, however with considerable missing values. To resolve the data gaps, here we have constructed two global satellite soil moisture products, i.e., the CCI (Climate Change Initiative soil moisture, 1989–2021; CCIori hereafter) and the CM (Correlation Merging soil moisture, 2006–2019; CMori hereafter) products separately using a Convolutional Neural Network (CNN) with autoencoding approach, which considers soil moisture variability in both time and space. The reconstructed datasets, namely CCIrec and CMrec, are cross-evaluated with artificial missing values, and further againt in-situ observations from 12 networks including 485 stations globally, with multiple error metrics of correlation coefficients (R), bias, root mean square errors (RMSE) and unbiased root mean square error (ubRMSE) respectively. The cross-validation results show that the reconstructed missing values have high R (0.987 and 0.974, respectively) and low RMSE (0.015 and 0.032 m3/m3, respectively) with the original ones. The in-situ validation shows that the global mean R between CCIrec (CCIori) and in-situ observations is 0.590 (0.581), RMSE is 0.093 (0.093) m3/m3, ubRMSE is 0.059 (0.058) m3/m3, bias is 0.032 (0.037) m3/m3 respectively; CMrec (CMori) shows quite similar results. The added value of this study is to provide long-term gap-free satellite soil moisture products globally, which helps studies in the fields of hydrology, meteorology, ecology and climate sciences.

Spatial-Temporal Variation Characteristics and Influencing Factors of Soil Moisture in the Yellow River Basin Using ESA CCI SM Products

Soil moisture (SM) plays an important role in regulating terrestrial–atmospheric water circulation and energy balance. Most of the existing studies have explored the dynamic patterns of SM based on experimental methods. However, the analysis of large-scale regions and long-term SM sequences was limited. Alternatively, satellite remote sensing data is a potential source for SM analysis for large-scale basins. Therefore, the SM data from the European Space Agency (ESA) Climate Change Initiative (CCI) from 2000 to 2015 is used in this paper to analyze the SM spatial-temporal changes in the Yellow River Basin (YRB). Further, the Normalized Difference Vegetation Index (NDVI) and meteorological data are used to explore the relationships between SM and NDVI, precipitation, air temperature, and wind speed, respectively. The results showed that the overall trend of SM in the YRB was decreasing from southeast to northwest during the past 16 years. The upper reaches of the YRB had shown a humid trend, with a value of 0.00047 m3·m−3·year−1, mainly due to the increase in precipitation; there was an obvious drought trend in the middle reaches of the YRB, especially in Shanxi Province and Henan Province, with a value of −0.00030 m3·m−3·year−1, which may be owed to vegetation greening increasing the soil evaporation. Overall, it is determined that the main factors influencing SM changes were NDVI and precipitation, followed by air temperature and wind speed. This study can provide a scientific basis for the spatial-temporal distribution characteristics and attributions of SM in the YRB over a long time series.

Spatiotemporal Analysis of Soil Moisture Variation in the Jiangsu Water Supply Area of the South-to-North Water Diversion Using ESA CCI Data

The South-to-North Water Transfer Jiangsu Water Supply Area (JWSA) is a mega inter-basin water transfer area (water source) that provides water resources from JiangHuai, combines drainage and flooding management, and regulates nearby rivers and lakes. Analyzing the spatiotemporal soil moisture dynamics in the area will be informative regarding agricultural drought along with flood disaster assessment and will provide early warning studies. Therefore, we evaluated the quality of European Space Agency Climate Change Initiative Soil Moisture (ESA CCI_SM) data in the South-North Water Transfer JWSA. Furthermore, we utilized ensemble empirical modal decomposition, Mann-Kendall tests, and regression analysis to study the spatiotemporal variation in soil moisture for the past 29 years. The CCI _SM data displayed a high correlation with local soil measurements at nine sites. We next analyzed the CCI_SM data from three pumping stations (the Gaogang, Hongze, and Liushan stations) in the South-North Water Transfer JWSA. These stations had similar periodic characteristics of soil moisture, with significant periodic fluctuations around 3.1 d. The overall soil moisture at the three typical pumping stations demonstrated an increasing trend. We further investigated whether abrupt soil moisture changes existed at each station or not. The spatial distribution of soil moisture in the South-North Water Transfer JWSA was characterized as “dry north and wet south”, with higher soil moisture in winter, followed by autumn, and low soil moisture in spring and summer. Although the linear trend of soil moisture in the South-North Water Transfer JWSA varied in significance, the overall soil moisture in the JWSA has increased over the past 29 years. The areas with significantly enhanced soil moisture are mostly distributed in the Yangzhou and Huai’an areas in the southeastern part of the study area. The areas with significantly decreased soil moisture are small in size and mostly located in northern Xuzhou.

Downscaling satellite soil moisture for landscape applications: A case study in Delaware, USA

Study regionDelaware, USA and its surrounding watersheds. Study focusAn ensemble using multiple Kernel K-nearest neighbors (KKNN) models was trained to predict daily grids of SSM at 100-meter resolution based on SSM estimates from the European Space Agency’s Climate Change Initiative Soil Moisture Product, terrain data, soil maps, and local meteorological network data. Estimated SSM was evaluated against independent in situ SSM observations and were investigated for relationships with land cover class and vegetation phenology (i.e., NDVI). New hydrological insights for the regionDownscaled daily mean SSM estimates had lower error in space (27%) and greater predictive performance over time compared to the raw, coarse resolution remotely sensed SSM dataset when calibrated to field observed values. Downscaled SSM identified stronger and more widespread temporal relationships with NDVI than other estimation methods. However, both coarse and fine resolution datasets greatly underestimated SSM in wetland areas. The findings highlight the need for enhanced in situ SSM monitoring across diverse settings to improve landscape-level downscaled SSM. The downscaling methodology developed in this study was able to produce daily SSM estimates, providing a framework that can support future SSM modeling efforts, hydroecological investigations, and agricultural studies in this and other regions around the world when used in conjunction with ground-based monitoring networks.

Seamless downscaling of the ESA CCI soil moisture data at the daily scale with MODIS land products

Spatial downscaling has recently become a crucial process in the regional application of coarse-resolution passive microwave surface soil moisture (SSM) products. Extensive gaps in auxiliary optical/thermal infrared observation data (mainly caused by cloud cover) and gaps in coarse-resolution passive microwave SSM data lead to spatiotemporal discontinuity in downscaled SSM maps, thereby limiting their applications. An improved downscaling method for the 25-km European Space Agency (ESA) Climate Change Initiative (CCI) SSM product was proposed to obtain daily seamless downscaled SSM series at a 1-km scale. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra daily land surface temperature (LST) and normalized difference vegetation index (NDVI) products were used as auxiliary data for the downscaling process. Prior to the spatial downscaling, an annual temperature cycle model was applied to the 1-km daily daytime LST data to fill the data gaps caused by cloud cover and to derive the spatial-seamless LST (gap-filled). Subsequently, the gap-filled ESA CCI SSM was generated at the original resolution based on the relationships among the SSM, LST, and NDVI. Finally, these were utilized to obtain a seamless downscaled series SSM at 1-km spatial resolution with a value-consistent downscaling method. The proposed method was applied to data obtained for the Iberian Peninsula from January 1, 2016 to December 31, 2018. Based on the comparison with the precipitation dataset, the downscaled SSM exhibited strong temporal correlation with rainfall events. Evaluation using the in situ SSM from the REMEDHUS network highlighted the good performance of the downscaled SSM at network level with a correlation coefficient (R) of 0.820. The root-mean-square-error, unbiased root-mean-square error (ubRMSE), and bias were 0.091, 0.033, and 0.085 m3/m3, respectively. A comparison with an alternative downscaled SSM product produced by the Barcelona Expert Center, one of the soil moisture and ocean salinity mission (SMOS)–downscaled SSM datasets, also indicated that the downscaled SSM has better spatiotemporal coverage and performance in terms of R and ubRMSE with reference to the REMEDHUS network. These results confirmed that the proposed method is an efficient and convenient downscaling process that can be used to generate high-resolution SSM data without spatiotemporal gaps. The downscaled SSM data improves the accuracy of the original passive microwave SSM product in describing regional SSM variations and shows good potential for related applications at regional scale.

Regionalizing Root‐Zone Soil Moisture Estimates From ESA CCI Soil Water Index Using Machine Learning and Information on Soil, Vegetation, and Climate

AbstractThe European Space Agency (ESA), through the Climate Change Initiative (CCI), is currently providing nearly 4 decades of global satellite‐observed, fully homogenized soil moisture data for the uppermost 2–5 cm of the soil layer. These data are valuable as they comprise one of the most complete remotely sensed soil moisture data sets available in time and space. One main limitation of the ESA CCI soil moisture data set is the limited soil depth at which the moisture content is represented. In order to address this critical gap, we (a) estimate and calibrate the Soil Water Index using ESA CCI soil moisture against in situ observations from the International Soil Moisture Network and then (b) leverage machine learning techniques and physical soil, climate, and vegetation descriptors at a global scale to regionalize the calibration. We use this calibration to assess the root‐zone soil moisture for the period 2001–2018. The results are compared against the European Centre for Medium‐Range Weather Forecasts, ERA5 Land, and the Famine Early Warning Systems Network Land Data Assimilation System reanalyses soil moisture data sets, showing a good agreement, mainly over mid latitudes. This work contributes to the exploitation of ESA CCI soil moisture data, while the produced data can support large‐scale soil moisture‐related studies.

Comparison of gap-filling techniques applied to the CCI soil moisture database in Southern Europe

Soil moisture (SM) is a key variable that plays an important role in land-atmosphere interactions. Monitoring SM is crucial for many applications and can help to determine the impact of climate change. Therefore, it is essential to have continuous and long-term databases for this variable. Satellite missions have contributed to this; however, the continuity of the series is compromised due to the data gaps derived by different factors, including revisit time, presence of seasonal ice or Radio Frequency Interference (RFI) contamination. In this work, the applicability of different gap-filling techniques is evaluated on the ESA Climate Change Initiative (CCI) SM combined product, which is the longest available satellite-based SM data record. The methods used were linear, cubic and autoregressive interpolation and support vector machines (SVMs). This study focused on Southern Europe and spanned the years 2003–2015. The different methods were applied in the temporal and spatial domains and evaluated using the holdout cross-validation technique. A set of variables was introduced in the SVM model to estimate SM, namely, land surface temperature, precipitation, normalized difference vegetation index (NDVI), potential evaporation, soil texture and geographical coordinates. For the SVMs, several combinations of these variables were considered, including a principal component analysis (PCA) containing all of them. Although the different methods show a generally good performance, the SVM method outperforms the rest. Using the SM of the precedent day (SMt-1) is key to obtain good estimates. The median value of the correlation coefficient (R) obtained with the SVM and the SMt-1 series in the temporal analysis was 0.83, and the RMSE was 0.025 m3m−3. Similar results were obtained in the spatial analysis, with the best performance (R = 0.88; RMSE = 0.024 m3m−3) obtained by the SVM using the SMt-1 series and the static variables. The application of PCA to input variables was not beneficial, and the interpolation methods failed when dealing with large spatial or temporal gaps. A validation of the CCI SM series with in situ SM data from four networks located in Spain, France, Germany and Italy was also performed and no substantial differences were observed between results obtained with the original and with the reconstructed series. In addition, best inputs obtained with SVM were used to evaluate the random forest (RF) method in the temporal and spatial domain. This method showed a good ability to estimate soil moisture values in the temporal domain but to a lesser extent than SVM while for the spatial domain it did not seem to be as accurate. Our results confirm that we can efficiently deal with spatio-temporal gaps on observational SM databases using the SVM method and the past time series and soil texture as supporting information.

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.

Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

Prolonged duration and increased severity of agricultural droughts during 1978 to 2016 detected by ESA CCI SM in the humid Yunnan Province, Southwest China

Droughts in humid regions may have more severe consequences owing to a lack of drought-resistant measures and awareness. The Yunnan Province, in the humid southwestern part of China, experienced an extreme drought from 2009 to 2010. However, few studies have systematically explored agricultural droughts in this region. Based on revised global multi-source microwave-based SM products, the European Space Agency's Climate Change Initiative Soil Moisture (ESA CCI SM) data, the 10-day-scale soil moisture (SM) series was constructed and applied to detect droughts and analyze changes in drought characteristics, including duration, area, frequency, intensity, and severity. The results suggest that the intensity of agricultural drought in the Yunnan Province decreased, while the drought frequency increased. Drought duration increased by 20 days, collectively leading to a significant increase in drought severity in the last decade. Qujing in the east was most sensitive to agricultural drought, followed by Kunming and Chuxiong in the central area. The southwest region was prone to long drought and maintained an upward trend in terms of drought area. Regional SM with complicated temporal-spatial change patterns cannot be attributed to a single trend of “dry to dryer, wet to wetter”. This study may provide insights into drought analysis in other humid regions.

Reconciling Flagging Strategies for Multi-Sensor Satellite Soil Moisture Climate Data Records

Reliable soil moisture retrievals from passive microwave satellite sensors are limited during certain conditions, e.g., snow coverage, radio-frequency interference, and dense vegetation. In these cases, the retrievals can be masked using flagging algorithms. Currently available single- and multi-sensor soil moisture products utilize different flagging approaches. However, a clear overview and comparison of these approaches and their impact on soil moisture data are still lacking. For long-term climate records such as the soil moisture products of the European Space Agency (ESA) Climate Change Initiative (CCI), the effect of any flagging inconsistency resulting from combining multiple sensor datasets is not yet understood. Therefore, the first objective of this study is to review the data flagging system that is used within multi-sensor ESA CCI soil moisture products as well as the flagging systems of two other soil moisture datasets from sensors that are also used for the ESA CCI soil moisture products: The level 3 Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active/Passive (SMAP). The SMOS and SMAP soil moisture flagging systems differ substantially in number and type of conditions considered, critical flags, and data source dependencies. The impact on the data availability of the different flagging systems were compared for the SMOS and SMAP soil moisture datasets. Major differences in data availability were observed globally, especially for northern high latitudes, mountainous regions, and equatorial latitudes (up to 37%, 33%, and 32% respectively) with large seasonal variability. These results highlight the importance of a consistent and well-performing approach that is applicable to all individual products used in long-term soil moisture data records. Consequently, the second objective of the present study is to design a consistent and model-independent flagging strategy to improve soil moisture climate records such as the ESA CCI products. As snow cover, ice, and frozen conditions were demonstrated to have the biggest impact on data availability, a uniform satellite driven flagging strategy was designed for these conditions and evaluated against two ground observation networks. The new flagging strategy demonstrated to be a robust flagging alternative when compared to the individual flagging strategies adopted by the SMOS and SMAP soil moisture datasets with a similar performance, but with the applicability to the entire ESA CCI time record without the use of modelled approximations.

Estimating Crop and Grass Productivity over the United States Using Satellite Solar-Induced Chlorophyll Fluorescence, Precipitation and Soil Moisture Data

This study investigates how gross primary production (GPP) estimates can be improved with the use of solar-induced chlorophyll fluorescence (SIF) based on the interdependence between SIF, precipitation, soil moisture and GPP itself. We have used multi-year datasets from Global Ozone Monitoring Experiment-2 (GOME-2), Tropical Rainfall Measuring Mission (TRMM), European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM), and FLUXNET observations from ten stations in the continental United States. We have employed a GPP quantification framework that makes use of two factors whose influence on the SIF–GPP relationship was not evaluated previously—namely, differential plant sensitivity to water supply at different stages of its lifecycle and spatial variability patterns in SIF that are in contrast to those of GPP, precipitation, and soil moisture. It was found that over the Great Plains and Texas, fluorescence emission levels lag behind precipitation events from about two weeks for grasses to four weeks for crops. The spatial variability of SIF and GPP is shown to be characterized by different patterns: SIF demonstrates less variation over the same spatial extent as compared to GPP, precipitation and soil moisture. Thus, using newly introduced SIF–precipitation lead–lag relationships, we estimate GPP using SIF, precipitation and soil moisture data for grasses and crops over the US by applying the multiple linear regression technique. Our GPP estimates capture the drought impact over the US better than those from Moderate Resolution Imaging Spectroradiometer (MODIS). During the drought year of 2011 over Texas, our GPP values show a decrease by 50–75 gC/m2/month, as opposed to the normal yielding year of 2007. In 2012, a drought year over the Great Plains, we observe a significant reduction in GPP, as compared to 2007. Hence, estimating GPP using specific SIF–GPP relationships, and information on different plant functional types (PFTs) and their interactions with precipitation and soil moisture over the Great Plains and Texas regions can help produce more reasonable GPP estimates.

Modelling desert locust presences using 32-year soil moisture data on a large-scale

Abstract The desert locust is the world's most dangerous migratory pest according to the Food and Agriculture Organization of the United Nations (FAO), and its recession area extends over more than 30 countries. While promising assertions have been made to relate desert locust presences with remotely sensed soil moisture (SM), they have not yet been tested robustly in large-scale studies. The aim of this work was to evaluate the potential of soil moisture data to detect desert locust presences (solitarious nymphs) using the European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product from 1985 to 2017. Firstly, 5 machine learning algorithms were fitted using various pre-processing options and variable creation scenarios. Subsequently, the best performing models were fine-tuned using the k-fold cross validation technique and validate the results with an independent dataset taking random dates and locations. The best results were obtained by the weighted k-nearest neighbours (kknn) and random forest (rf) models. The kknn performance was ROC-AUC = 0.79, KAPPA = 0.61 and accuracy = 0.83; and the rf obtained a ROC-AUC = 0.91, KAPPA = 0.56 and accuracy = 0.81. In general, both models agreed that SM values above 0.11 m3/m3 led to increase the possibility to observe nymph of desert locust with a time delay between 35 and 79 days depending on the model. Furthermore, it was found that model performances increased when the time interval of the variables was smaller, so that we suggest the use of mean SM values over 4 days period to link presences and SM values. These results prove the validity of our methodology to identify favourable breeding areas by means of ESA CCI SM dataset using machine learning approaches over the entire recession area of desert locust, and it could be used in any of the affected countries for this pest. Future improvements in ESA CCI SM product (e.g. higher spatial resolution) may lead to improve model accuracies with monitoring and preventive purposes.

Long-term changes in surface soil moisture based on CCI SM in Yunnan Province, Southwestern China

Soil moisture (SM) plays an important role in regional runoff variations, energy dynamics, and vegetation productivity. It is also widely used to detecting agricultural drought. Recently, a severe drought occurred in Yunnan Province in southwestern China, so long-term changes in surface SM were of concern. However, there were only 18 SM stations in Yunnan Province, and the longest observation was less than five years; thus, long-term changes in Climate Change Initiative (CCI) SM were investigated. CCI SM and in situ SM had similar trends of temporal variations and a systematic difference. The revised CCI SM was built by a linear regression model based on the relationship between in situ SM at each station and the corresponding grid of CCI SM. The revised CCI SM showed an abnormally low value during the severe drought in 2009, indicating that the monthly revised CCI SM could detect drought in Yunnan Province. SM drought easily occurred during the dry seasons, and the frequency of drought seems to decrease, but the severity of drought increased in the 21st century. The seasonal SM struggled to reflect drought correctly and accurately. CCI SM needs validation before use due to its variable quality. In summary, CCI SM provides a better tool than in situ SM to investigate long-term changes in SM and detect drought, and can be used for climate modeling.

Evaluation of ESA Active, Passive and Combined Soil Moisture Products Using Upscaled Ground Measurements.

The European Space Agency (ESA) Climate Change Initiative (CCI) project combines multi-sensors at different microwave frequencies to derive three harmonized soil moisture products using active, passive and combined approaches. These long-term soil moisture products assist in understanding the global water and carbon cycles. However, extensive validations are a prerequisite before applying the retrieved soil moisture into climatic or hydrological models. To fulfill this objective, we assess the performances of three CCI soil moisture products (active, passive and combined) with respect to in-situ soil moisture networks located in China, Spain and Canada. In order to compensate the scale differences between ground stations and the CCI product’s coarse resolution, we adopted two upscaling approaches of Inverse Distance Weighting (IDW) interpolation and simple Arithmetic Mean (AM). The temporal agreements between the satellite retrieved and ground-measured soil moisture were quantified using the unbiased root mean square error (ubRMSE), RMSE, correlation coefficients (R) and bias. Furthermore, the temporal variability of the CCI soil moisture is interpreted and verified with respect to the Tropical Rainfall Measuring Mission (TRMM) precipitation observations. The results show that the temporal variations of CCI soil moisture agreed with the in-situ ground measurements and the precipitation observations over the China and Spain test sites. In contrast, a significant overestimation was observed over the Canada test sites, which may be due to the strong heterogeneity in soil and vegetation characteristics in accordance with the reported poor performance of soil moisture retrieval there. However, despite a retrieval bias, the relatively temporal variation of the CCI soil moisture also followed the ground measurements. For all the three test sites, the soil moisture retrieved from the combined approach outperformed the active-only and passive-only methods, with ubRMSE of 0.034, 0.050, and 0.050–0.054 m3/m3 over the test sites in China, Spain and Canada, respectively. Thus, the CCI combined soil moisture product is suggested to drive the climatic and hydrological studies.

A Study on the Assessment of Multi-Source Satellite Soil Moisture Products and Reanalysis Data for the Tibetan Plateau

Soil moisture is a key variable in the process of land–atmosphere energy and water exchange. Currently, there are a large number of operational satellite-derived soil moisture products and reanalysis soil moisture products available. However, due to the lack of in situ soil moisture measurements over the Tibetan Plateau (TP), their accuracy and applicability are unclear. Based on the in situ measurements of the soil moisture observing networks established at Maqu, Naqu, Ali, and Shiquanhe (Sq) by the Institute of Tibetan Plateau Research, the Chinese Academy of Sciences, the Northwest Institute of Eco-Environmental Resources, the Chinese Academy of Sciences and the University of Twente over the TP, the accuracy and reliability of the European Space Agency Climate Change Initiative Soil Moisture version 4.4 (ESA CCI SM v4.4) soil moisture products and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) soil moisture product were evaluated. The spatiotemporal distributions and interannual variations of the soil moisture were analyzed. Further, the climatological soil moisture changing trends across the TP were explored. The results show that with regard to the whole plateau, the combined product performs the best (unbiased root-mean-square error (ubRMSE) = 0.043 m3/m3, R = 0.66), followed by the active product (ubRMSE = 0.048 m3/m3, R = 0.62), the passive product (ubRMSE = 0.06 m3/m3, R = 0.61), and the ERA5 soil moisture product (ubRMSE = 0.067 m3/m3, R = 0.52). Considering the good spatiotemporal data continuity of the ERA5 soil moisture product, the ERA5 soil moisture data from 1979 to 2018 were used to analyze the climatological soil moisture changing trend for the entire TP surface. It was found that there was an increasing trend of soil moisture across the TP, which was consistent with the overall trends of increasing precipitation and decreasing evaporation. Moreover, the shrinkage of the cryosphere in conjunction with the background TP warming presumably contribute to soil moisture change.

Global Surface Soil Moisture Dynamics in 1979–2016 Observed from ESA CCI SM Dataset

Soil moisture (SM) is an important variable for the terrestrial surface system, as its changes greatly affect the global water and energy cycle. The description and understanding of spatiotemporal changes in global soil moisture require long time-series observation. Taking advantage of the European Space Agency (ESA) Climate Change Initiative (CCI) combined SM dataset, this study aims at identifying the non-linear trends of global SM dynamics and their variations at multiple time scales. The distribution of global surface SM changes in 1979–2016 was identified by a non-linear methodology based on a stepwise regression at the annual and seasonal scales. On the annual scale, significant changes have taken place in about one third of the lands, in which nonlinear trends account for 48.13%. At the seasonal scale, the phenomenon that “wet season get wetter, and dry season get dryer” is found this study via hemispherical SM trend analysis at seasonal scale. And, the changes in seasonal SM are more pronounced (change rate at seasonal scales is about 5 times higher than that at annual scale) and the areas seeing significant changes cover a larger surface. Seasonal SM fluctuations distributed in southwestern China, central North America and southern Africa, are concealed at the annual scale. Overall, non-linear trend analysis at multiple time scale has revealed more complex dynamics for these long time series of SM.