Agency Climate Change Initiative Soil Research Articles

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.

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.

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.

Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration

Although the complexity of physically-based models continues to increase, they still need to be calibrated. In recent years, there has been an increasing interest in using new satellite technologies and products with high resolution in model evaluations and decision-making. The aim of this study is to investigate the value of different remote sensing products and groundwater level measurements in the temporal calibration of a well-known hydrologic model i.e., Hydrologiska Bryåns Vattenbalansavdelning (HBV). This has rarely been done for conceptual models, as satellite data are often used in the spatial calibration of the distributed models. Three different soil moisture products from the European Space Agency Climate Change Initiative Soil Measure (ESA CCI SM v04.4), The Advanced Microwave Scanning Radiometer on the Earth Observing System (EOS) Aqua satellite (AMSR-E), soil moisture active passive (SMAP), and total water storage anomalies from Gravity Recovery and Climate Experiment (GRACE) are collected and spatially averaged over the Moselle River Basin in Germany and France. Different combinations of objective functions and search algorithms, all targeting a good fit between observed and simulated streamflow, groundwater and soil moisture, are used to analyze the contribution of each individual source of information.

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.

Evaluation of Remotely Sensed Soil Moisture for Landslide Hazard Assessment

Soil moisture is important in the triggering of many types of landslides. However, in situ soil moisture data are rarely available in hazardous zones. The advanced remote sensing technology could provide useful soil moisture information. In this study, an assessment has been carried out between the latest version of the European Space Agency Climate Change Initiative soil moisture product and the landslide events in a northern Italian region in the 14-year period 2002–2015. A clear correlation has been found between the satellite soil moisture and the landslide events, as over four-fifths of events had soil wetness conditions above the 50% regional soil moisture line. Attempts have also been made to explore the soil moisture thresholds for landslide occurrences under different environmental conditions (land cover, soil type, and slope). The results showed slope distribution could provide a rather distinct separation of the soil moisture thresholds, with thresholds becoming smaller for steeper areas, indicating dryer soil condition could trigger landslides at hilly areas than in plain areas. The thresholds validation procedure is then carried out. Forty five rainfall events between 2014 and 2015 are used as test cases. Contingency tables, statistical indicators, and receiver operating characteristic analysis for thresholds under different exceedance probabilities (1%–50%) are explored. The results have shown that the thresholds using 30% exceedance probability provide the best performance with the hitting rate at 0.92 and the false alarm at 0.50. We expect this study can provide useful information for adopting the remotely sensed soil moisture in the landslide early warnings.

The Added Value of Assimilating Remotely Sensed Soil Moisture for Estimating Summertime Soil Moisture‐Air Temperature Coupling Strength

AbstractTo date, the direct use of remote‐sensing soil moisture data sets for examining surface/atmosphere coupling strengths has been hampered by the presence of significant random errors and data gaps in these products. This study investigates the potential for obtaining an improved observation‐based lower bound of summertime soil moisture‐air temperature coupling strength via the assimilation of long‐term, remote‐sensing soil moisture data sets into a simple, prognostic model driven by observed rainfall. In particular, we utilize simultaneous scatterometer‐ and radiometer‐based soil moisture products obtained from the European Space Agency Climate Change Initiative soil moisture product and a triple collocation analysis approach to estimate the variance of modeling and observation errors (required as input by a data assimilation system). Results show that assimilating remotely sensed soil moisture leads to larger coupling strength estimates as measured by the absolute anomaly correlation with independent temperature observation data than those obtained from modeled or remotely sensed soil moisture alone. According to an analyses of soil moisture error impacts on coupling strength estimates, this increase in coupling strength is likely attributable to an improvement in the precision of the soil moisture product used to estimate it. Based on this, we conclude that data assimilation provides an improved lower bound on the magnitude of true coupling strength between soil moisture and screen‐level air temperature.

Evaluation of the European Space Agency Climate Change Initiative Soil Moisture Product over China Using Variance Reduction Factor

AbstractIn this study, we evaluated the European Space Agency Climate Change Initiative soil moisture product v02.1 (ESA CCI SM v02.1) using in situ observations collected at 547 stations in China from 1991 to 2013. A conventional validation was first conducted, and the triple collocation errors of ESA CCI SM and the European Centre for Medium Range Weather Forecasting reanalysis data were approximately 0.053 and 0.050 m3/m3, respectively. To obtain more reliable validation results, the average soil moisture of the in situ observations per ESA CCI SM pixel was also used as the validation sites. Variance reduction factor (VRF) was adopted to quantify the accuracy of the soil moisture validation sites, and the average VRF was estimated at 4.88%. The validation results were enhanced by excluding validation sites with VRF errors greater than 5% from the statistical analysis. Although the ESA CCI SM underestimated the in situ observations with a Bias of 0.05 m3/m3, a moderately high correlation coefficient of 0.44 and a relatively small unbiased root‐mean‐square difference of 0.05 m3/m3 were observed. This study provides information on the utilization of ESA CCI SM for ecological protection, climate change, and hydrological forecasting. It also suggests the adoption of VRF for future error corrections of satellite‐based products.