Floods are one of the most common, devastating natural hazards around the world considering its scale and impact [1
]. It is considered the number one natural hazard in the United States (US) [5
]. The situation is aggravated by a dramatic increase in flood frequency and intensity due to the recent climate change [6
]. The agriculture sector is one of the most vulnerable sectors to flooding mainly for two reasons: croplands are outside of the coverage of conventional hazard management systems, and the vast spatial scale of croplands [9
]. Almost every year flooding causes significant crop damage over large agriculture area in the US [10
]. Recent examples are Hurricane Harvey-induced flood, and Hurricane Irma-induced flood in 2017, which accounted for a million-dollar crop loss in the south-eastern parts of the US [13
]; the Mid-Atlantic river flood in 2012 caused a multimillion-dollar crop loss in the east coast [15
]; the Mississippi River flood in 2011 accounted for more than sixty million dollars in the catchment of this river [16
]. Therefore, rapid flood progress monitoring is crucial for rapid crop loss assessment, crop condition monitoring, crop insurance, and policy making.
It is challenging to monitor floods over vast agriculture fields using stream gauges. Thus, spaceborne remote sensing has widely been used in flood inundation mapping and monitoring [17
]. Moderate to coarse spatial resolution optical remote sensing systems (e.g., MODIS, VIIRS, Landsat, Sentinel-2) provide data with fine temporal resolution anywhere from daily to every two weeks. However, optical remote sensing is unable to see through clouds and tree canopies. Therefore, it is difficult to monitor flood progress, especially in the rainy season using optical data due to the possible presence of clouds [18
]. Moreover, it is difficult to detect storm-induced floods because of the presence of clouds during the low-pressure oceanic condition. Hence, most of the optical remote sensing-based flood monitoring systems are unable to provide flood inundation information during these cloudy conditions. On the other hand, microwave remote sensing brings the opportunity for flood inundation mapping in cloudy condition since microwave systems can penetrate through clouds, aerosol, haze, and tree canopy [18
]. Although microwave remote sensing in flood mapping is becoming popular, data from most of the microwave remote sensing systems especially, Synthetic Aperture Radar (SAR), are complicated for processing and are not available free of charge from most of the sources [20
]. Free of charge SAR data have recently become available from Sentinel-1 which is a satellite mission of the European Space Agency (ESA). Although the temporal resolution of Sentinel-1 is ideally six days [20
], it is very common that SAR data for a particular location may not be available as frequent as Sentinel-1’s revisit capability from the official portal of ESA for data download. For instance, Sentinel-1 data are not available to download from the ESA portal for two flood cases (The Texas 2016 Flood and The Mississippi 2016 Flood); however, both flood cases have the duration more than two weeks. Therefore, flood monitoring with SAR data over large agriculture area is not cost effective in many cases. Another challenge is the temporal resolution of these SAR systems, which is more than ten days in most of the cases. Flood lasting less than a week can potentially damage crop depending on the phenology stage of crop. However, flood monitoring with SAR data may fail to detect these short-lived floods. Flood progress monitoring with higher temporal resolution is crucial for many application such as remote sensing based flood crop loss assessment (RF-CLASS) to assess the crop loss from short-lived floods [21
]. Soil Moisture Active Passive (SMAP), a NASA’s satellite mission, launched on January 2015, consisting of L-band microwaves Radar and Radiometer systems. It aims to provide global maps of soil moisture and freeze/thaw state every 2–3 days with high accuracy [24
]. One of the key science application of SMAP is to develop improved flood prediction and drought monitoring capabilities [25
]. Soil moisture is one of the key components in water-related natural hazards such as a flood. Soil moisture with high temporal resolution can lead to improved flood monitoring and forecasting for medium to vast watersheds where flood frequency and damage is high [25
]. Therefore, soil moisture wetness and saturation information from SMAP in combination with ancillary floodplain information can be used to monitor flood [26
Crop condition and growth primarily depend on the balance of two primary resources: soil, water, heat, and nutrients. The soil is the composition of organic matter, minerals, water, and air [28
]. Any extreme condition such as water shortage or extra water in the soil is detrimental to the crop growth and yield. Plant water stress condition, agriculture drought, takes place when soil moisture goes below the wilting point because there is no water for plant uptake. Similarly, soil moisture at saturation level can significantly damage the crop, since crop roots are unable to adequately respire due to the insufficient oxygen in the soil pores [27
]. The soil saturation and standing water hamper root growth, leaf area expansion, and photosynthesis. Therefore, this extreme condition, soil saturation, can be called agriculture flood which may lead to damage and crop yield loss. Soil saturation is the condition when all pores between soil particles are filled with water [31
]. Fine-textured soil (e.g., clay) usually more porous compared to coarse-textured soils (e.g., sand) [32
]. Soil moisture content in the volumetric measure is the volumetric water content in soil [33
]. The volume of water in soil can vary between zero (dry soil) and the volume of voids between soil particles, which is expressed as the degree of saturation. Volumetric moisture content in a soil equivalent to soil porosity is the indication of fully saturated soil [34
]. However, some soil pore space may contain entrapped air even when the soil is considered fully saturated. The percentage of entrapped air is usually between 3% to 7% of void space depending on soil type [33
]. The total porosity of a soil accounts for both the space available to be filled with water and the entrapped air. Therefore, effective soil porosity for water content can be estimated by 95% (assuming on an average 5% entrapped air) of total soil porosity. Thus, soil moisture content greater than effective soil porosity can be mapped as saturated soil for the indication of agriculture flood.
This study aims to use SMAP surface soil moisture information for the rapid monitoring of flood progress through soil saturation and floodplain information. The usefulness of rapid flood progress monitoring will be evaluated through some case studies on the recent floods in the US. Findings of the study will be helpful for the near real-time flood progress monitoring in cropland to support crop loss estimation, condition monitoring, and immediate policymaking.
The agriculture sector is one of the most affected sectors by flooding, but the conventional hazard management system pays very limited attention to this sector. Currently, optical remote sensing is widely used for the inundation mapping over a large area. While optical remote sensing systems offer considerable advantages by providing data with remarkably fine spatial resolution and temporal resolution, these systems are incapable for providing data in cloudy conditions. An alternative system is to use the SAR remote sensing because of the cloud penetration capability of microwave. However, coarse temporal resolution and complex data type of SAR systems put limits on more frequent data gathering and processing. Thus, the application of SAR data in flood monitoring for large agriculture regions has not gained much popularity.
This paper evaluates the potential of the use of soil moisture data to overcome the limitations mentioned above. Soil moisture above the effective soil porosity is an indication of soil saturation; and soil saturation in crop field over a longer period of time can be considered as an indicator of cropland inundation. SMAP L4 soil moisture products, which are derived from microwave remote sensing observation (SMAP L1), are available at 3-h intervals, thus it provides much finer temporal resolution than the SAR systems. Therefore, L4 data can provide a useful way to map cropland inundation overcoming the previous limitations. The results in this study provide evidence that inundated areas extracted from SMAP are largely similar to the FEMA’s declared counties. Besides, these inundation areas are found to have a similar spatial location with available reference flood maps.
The primary advantage of using the current technique of inundation mapping is that this technique can be used even when the optical or SAR remote sensing data are not available. The relatively small omission error, as reported in this study, indicates that the inundation maps derived from SMAP data were able to map most of the flooded areas in reference maps. Therefore, although the inundation maps are found to overestimate some flooded areas, it is still be useful in generating a rough estimation of inundation area using SMAP soil moisture data.
There are several limitations of using soil moisture data for flood mapping. First, in most of the cases, inundation extents from SMAP are larger than inundation areas derived from the other sources because of the coarse spatial resolution of SMAP. Second, soil moisture data is not available for the paved areas. Therefore, if most of the areas within a pixel contain impervious surface, the resulting soil moisture of the pixel will be low; as a result, it is hard to detect flood for these areas based on the soil moisture. Future works may focus on SMAP level-1 data which is the direct observation provided by the sensor, instead of the model drivel Level-4 surface soil moisture. Despite the limitations, this methodology can be used for cropland inundation mapping even is the absence of fine temporal resolution SAR data. In summary, the inundation map extracted from SMAP soil moisture can be helpful for rapid flood progress monitoring in croplands. The inundation information is also useful for the assessment of crop loss and prediction of future yield; the crop loss estimation and yield estimation can be eventually helpful for policy formulation and decision-making.