Soil moisture is considered to be a key variable to assess crop and drought conditions. However, readily available soil moisture datasets developed for monitoring agricultural drought conditions are uncommon. The aim of this work is to examine two global soil moisture datasets and a set of soil moisture web-based processing tools developed to demonstrate the value of the soil moisture data for drought monitoring and crop forecasting using the Google Earth Engine (GEE). The two global soil moisture datasets discussed in the paper are generated by integrating the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions’ satellite-derived observations into a modified two-layer Palmer model using a one-dimensional (1D) ensemble Kalman filter (EnKF) data assimilation approach. The web-based tools are designed to explore soil moisture variability as a function of land cover change and to easily estimate drought characteristics such as drought duration and intensity using soil moisture anomalies and to intercompare them against alternative drought indicators. To demonstrate the utility of these tools for agricultural drought monitoring, the soil moisture products and vegetation- and precipitation-based products were assessed over drought-prone regions in South Africa and Ethiopia. Overall, the 3-month scale Standardized Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI) showed higher agreement with the root zone soil moisture anomalies. Soil moisture anomalies exhibited lower drought duration, but higher intensity compared with SPIs. Inclusion of the global soil moisture data into the GEE data catalog and the development of the web-based tools described in the paper enable a vast diversity of users to quickly and easily assess the impact of drought and improve planning related to drought risk assessment and early warning. The GEE also improves the accessibility and usability of the earth observation data and related tools by making them available to a wide range of researchers and the public. In particular, the cloud-based nature of the GEE is useful for providing access to the soil moisture data and scripts to users in developing countries that lack adequate observational soil moisture data or the necessary computational resources required to develop them.

Results of numerous evaluation studies indicated that satellite-rainfall products are contaminated with significant systematic and random errors. Therefore, such products may require refinement and correction before being used for hydrologic applications. In the present study, we explore a rainfall-runoff modeling application using the Climate Prediction Center-MORPHing (CMORPH) satellite rainfall product. The study area is the Gilgel Abbay catchment situated at the source basin of the Upper Blue Nile basin in Ethiopia, Eastern Africa. Rain gauge networks in such area are typically sparse. We examine different bias correction schemes applied locally to the CMORPH product. These schemes vary in the degree to which spatial and temporal variability in the CMORPH bias fields are accounted for. Three schemes are tested: space and time-invariant, time-variant and spatially invariant, and space and time variant. Bias-corrected CMORPH products were used to calibrate and drive the Hydrologiska Byråns Vattenbalansavdelning (HBV) rainfall-runoff model. Applying the space and time-fixed bias correction scheme resulted in slight improvement of the CMORPH-driven runoff simulations, but in some instances caused deterioration. Accounting for temporal variation in the bias reduced the rainfall bias by up to 50%. Additional improvements were observed when both the spatial and temporal variability in the bias was accounted for. The rainfall bias was found to have a pronounced effect on model calibration. The calibrated model parameters changed significantly when using rainfall input from gauges alone, uncorrected, and bias-corrected CMORPH estimates. Changes of up to 81% were obtained for model parameters controlling the stream flow volume.

Over the past two decades, remote sensing has made possible the routine global monitoring of surface soil moisture. Regional agricultural drought monitoring is one of the most logical application areas for such monitoring. However, remote sensing alone provides soil moisture information for only the top few centimeters of the soil profile, while agricultural drought monitoring requires knowledge of the amount of water present in the entire root zone. The assimilation of remotely sensed soil moisture products into continuous soil water balance models provides a way of addressing this shortcoming. Here, we describe the assimilation of NASA's soil moisture active passive (SMAP) surface soil moisture data into the United States Department of Agriculture Foreign Agricultural Service (USDA FAS) Palmer model and assess the impact of SMAP on USDA FAS drought monitoring capabilities. The assimilation of SMAP is specifically designed to enhance the model skill and the USDA FAS drought capabilities by correcting for random errors inherent in its rainfall forcing data. The performance of this SMAP-based assimilation system is evaluated using two approaches. At global scale, the accuracy of the system is assessed by examining the lagged correlation agreement between soil moisture and the normalized difference vegetation index (NDVI). Additional regional-scale evaluation using in situ-based soil moisture estimates is carried out at seven of the SMAP core Cal/Val sites located in the USA. Both types of analysis demonstrate the value of assimilating SMAP into the USDA FAS Palmer model and its potential to enhance operational USDA FAS root-zone soil moisture information.

The El Niño–Southern Oscillation (ENSO) is one of the strongest drivers of climate variability that directly influences agricultural production. The aim of this study is to assess the impact of ENSO on agriculture in Southern and Eastern Africa by (1) exploring the association between ENSO, vegetation condition and soil moisture, and (2) analyzing the difference in soil moisture and vegetation conditions for two extreme ENSO phases (El Niño and La Niña). Our results indicate that vegetation conditions are strongly associated with ENSO and show a clear dipole pattern that is reversed between El Niño and La Niña. Lagged correlation analysis confirms the ability of soil moisture and ENSO to predict vegetation conditions with 1–3 months of lead-time. The temporal and spatial evolution of soil moisture and vegetation responses showed the expected dipole pattern during the El Niño and subsequent La Niña events. Results indicate that ENSO impact on crop yield varies with geographical location, crop types, and ENSO phases. For example, yields in La Niña years have been higher in Southern Africa but lower in Eastern Africa. Maize yield decreases associated with El Niño events were usually larger than corresponding yield increases during La Niña events over Southern Africa. Our findings highlight the impact of ENSO on agricultural production, which has significant potential to enhance the early warning system for agriculture and food security.

Wildfires are a major concern around the globe because of the immediate impact they have on people's lives, local ecosystems, and the environment. Soil moisture is one of the most important factors that influence wildfire occurrences and spread. However, it is also one of the most challenging hydrological variables to measure routinely and accurately. Therefore, soil moisture is significantly underutilized in operational wildfire risk applications. Thus, the aim here is to use a well-established operational soil moisture product to isolate the soil moisture-fire relationship and assess the utility of using soil moisture as a leading indicator of potential fire risk. We evaluated the value of remotely-sensed soil moisture observations from the soil moisture active passive sensor for monitoring and predicting fire risk in Australia and California. We quantified the relationship between observed fire activity and soil moisture conditions and analyzed the soil moisture conditions for two extreme fire events. Our findings show that fire activity is strongly associated with soil moisture anomalies. Lagged correlation analysis demonstrated that a remote-sensing based soil moisture product could predict fire activity with a 1–2 month lead-time. Soil moisture anomalies consistently decreased in the months preceding fire occurrence, often from normal to drier conditions, according to a spatiotemporal analysis of soil moisture in two extreme fire events. Overall, our findings indicate that soil moisture conditions prior to large wildfires can aid in their prediction and operational satellite-based soil moisture products such as the one used here have real value for supporting wildfire susceptibility and impacts.

An understanding of streamflow variability and its response to changes in climate conditions is essential for water resource planning and management practices that will help to mitigate the impacts of extreme events such as floods and droughts on agriculture and other human activities. This study investigated the relationship between precipitation, soil moisture, and streamflow over a wide range of watersheds across the United States using Google Earth Engine (GEE). The correlation analyses disclosed a strong association between precipitation, soil moisture, and streamflow, however, soil moisture was found to have a higher correlation with the streamflow relative to precipitation. Results indicated different strength of the association depends on the watershed classes and lag times assessments. The perennial watersheds showed higher coherence compared to intermittent watersheds. Previous month precipitation and soil moisture have a stronger influence on the current month streamflow, particularly in the snow-dominated watersheds. Monthly streamflow forecasting models were developed using an autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The results showed that the SVM model generally performed better than the ARIMA model. Overall streamflow forecasting model performance varied considerably among watershed classes, and perennial watersheds tend to exhibit better predictably compared to intermittent watersheds due to lower streamflow variability. The SVM models with precipitation and streamflow inputs performed better than those with streamflow input only. Results indicated that the inclusion of antecedent root-zone soil moisture improved the streamflow forecasting in most of the watersheds, and the largest improvements occurred in the intermittent watersheds. In conclusion, this work demonstrated that knowing the relationship between precipitation, soil moisture, and streamflow in different watershed classes will enhance the understanding of the hydrologic process and can be effectively utilized in improving streamflow forecasting for better satellite-based water resource management strategies.