Using cygnss data to map flood inundation during the 2021 extreme precipitation in henan province, China

remote sensing Article Using CYGNSS Data to Map Flood Inundation during the 2021 Extreme Precipitation in Henan Province, China Shuangcheng Zhang 1,2, Zhongmin Ma 1,*, Zhenhong Li 1,3,4 , Pengfei Zhang 5, Qi Liu 1,6, Yang Nan 7, Jingjiang Zhang 8, Shengwei Hu 1, Yuxuan Feng 1and Hebin Zhao 1   Citation: Zhang, S.; Ma, Z.; Li, Z.; Zhang, P.; Liu, Q.; Nan, Y.; Zhang, J.; Hu, S.; Feng, Y.; Zhao, H. Using CYGNSS Data to Map Flood Inundation during the 2021 Extreme Precipitation in Henan Province, China. Remote Sens. 2021,13, 5181. https://doi.org/10.3390/rs13245181 Academic Editors: Kegen Yu, Joon Wayn Cheong and Sajad Tabibi Received: 28 November 2021 Accepted: 16 December 2021 Published: 20 December 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1College of Geology Engineering, Chang’an University, Xi’an 710054, China; [email protected] (S.Z.); [email protected] (Z.L.); [email protected] (Q.L.); [email protected] (S.H.); [email protected] (Y.F.); [email protected] (H.Z.) 2State Key Laboratory of Geo-Information Engineering, Xi’an 710054, China 3Big Data Center for Geosciences and Satellites, Chang’an University, Xi’an 710054, China 4Key Laboratory of Western China’s Mineral Resources and Geological Engineering, Ministry of Education, Xi’an 710054, China 5National Time Service Center, Chinese Academy of Sciences, Xi’an 710600, China; [email protected] 6Earth Observation Research Group, Institute of Space Sciences (ICE, CSIC), 08290 Barcelona, Spain 7GNSS Research Center, Wuhan University, Wuhan 430079, China; [email protected] 8 Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China; [email protected] *Correspondence: [email protected] Abstract: On 20 July 2021, parts of China’s Henan Province received the highest precipitation levels ever recorded in the region. Floods caused by heavy rainfall resulted in hundreds of casualties and tens of billions of dollars’ worth of property loss. Due to the highly dynamic nature of flood disasters, rapid and timely spatial monitoring is conducive for early disaster prevention, mid-term disaster relief, and post-disaster reconstruction. However, existing remote sensing satellites cannot provide high-resolution flood monitoring results. Seeing as spaceborne global navigation satellite systemreflectometry (GNSS-R) can observe the Earth’s surface with high temporal and spatial resolutions, it is expected to provide a new solution to the problem of flood hazards. Here, using the Cyclone Global Navigation Satellite System (CYGNSS) L1 data, we first counted various signal-to-noise ratios and the corresponding reflectivity to surface features in Henan Province. Subsequently, we analyzed changes in the delay-Doppler map of CYGNSS when the observed area was submerged and not submerged. Finally, we determined the submerged area affected by extreme precipitation using the threshold detection method. The results demonstrated that the flood range retrieved by CYGNSS agreed with that retrieved by the Soil Moisture Active Passive (SMAP) mission and the precipitation data retrieved and measured by the Global Precipitation Measurement mission and meteorological stations. Compared with the SMAP results, those obtained by CYGNSS have a higher spatial resolution and can monitor changes in the areas affected by the floods over a shorter period. Keywords: Cyclone Global Navigation Satellite System; flood inundation; extreme precipitation; global navigation satellite system-reflectometry; Soil Moisture Active Passive 1. Introduction Henan Province, a provincial-level administrative region of China, is located in central China (31 ◦ 23 0 –36 ◦ 22 0 N, 110 ◦ 21 0 –116 ◦ 39 0 E). The terrain is high in the west; low in the east; and composed of plains, basins, mountains, and hills. It crosses four river basins, namely the Haihe River, Yellow River, Huaihe River, and Yangtze River, and average annual rainfall in the region is 500–900 mm [ 1 ]. In mid-to-late July 2021, the central and northern parts of Henan Province were hit by extreme precipitation. Floods and other secondary disasters caused by heavy rainfall have greatly endangered the property and safety of local residents [2,3]. Remote Sens. 2021,13, 5181. https://doi.org/10.3390/rs13245181 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021,13, 5181 2 of 15 Timely and accurate mapping of the scope of the submerged area affected by a flood can provide rescue personnel critical information for decision making. Additionally, this mapping can improve the understanding of the temporal and spatial characteristics of floods over time to support flood forecasting and early-warning methods [ 4 – 6 ]. Satellite remote sensing is considered an effective method for monitoring flood disasters [ 7 ]. However, the ground surface is usually covered by thick clouds during floods; therefore, optical remote sensing is limited [ 8 ]. Microwave remote sensing is not typically affected by clouds or vegetation. Many researchers report that this sensing method is the best means of monitoring flood disasters [ 9 , 10 ]. However, the spatial or temporal resolution of the data provided by existing microwave remote sensing satellites is limited [11]. Global Navigation Satellite System-Reflectometry (GNSS-R) is an emerging remote sensing technology based on the reflected signals of navigation satellites. As early as 1988, Hall and Cordey proposed the idea of using Global Positioning System (GPS) scattered signals to sense the roughness of the sea surface [ 12 ]. Moreover, in 1993, Martin-Neira of the European Space Agency proposed the Passive Reflection and Interferometry System, which uses passive reflection and interferometry technology for remote ocean sensing [ 13 ]. Subsequently, Zavorotny and Voronovich suggested a scientific model based on the Kirchhoff approximation and geometric optics limit (Z-V model) in 2000, which explained GPS signals scattered by the ocean and laid the theoretical foundation of this new technology [ 14 ]. Since then, theoretical studies and experiments worldwide have been carried out on GNSS reflected signals, including the development of new GNSS-R receivers; altimeter and scatterometer tests based on different platforms such as shore-based, tower-based, airplane, etc.; and research on signal reception, principle verification, and the feasibility of using GNSS-R to estimate ocean parameters (sea surface height, sea surface wind, sea surface roughness, etc.). GNSS-R remote sensing apparatuses have gradually shifted from ground and airplane platforms to satellite platforms, and the research objectives have extended beyond oceans to land, atmosphere, and polar regions [ 15 – 20 ]. In recent years, the successful launches of many GNSS-R low-orbit satellites have opened a new era for spaceborne GNSS-R. The first satellite-borne on-orbit verification research was carried out by the American Jet Propulsion Laboratory using the satellite-borne imaging radar-C mounted on the space shuttle [ 21 ], which promoted the development of satellite-based GNSS-R. Subsequently, many satellites, including Britain’s Disaster Monitoring System-1 [ 22 ] and Britain’s Techdemosat-1 [23], have implemented many GNSS-R on-board studies. The Cyclone Global Navigation Satellite System (CYGNSS) is a GNSS-R LEO satellite formation consisting of eight small satellites launched by NASA at the end of 2016, with an orbital height of approximately 524 km. Each small satellite is equipped with a special four-channel GNSS-R payload for transmitting reflected signals in the L1 band of the GPS, and the observation range is approximately 38 south latitude to 38 north latitude [ 24 – 26 ]. Although the application of CYGNSS in monitoring sea surface wind has been used for some time [ 26 – 28 ], its data have been proven to invert many land parameters, such as soil moisture [ 29 – 32 ], biomass [ 33 ], wetland monitoring [ 34 , 35 ], inland water bodies [ 36 – 38 ], and target detection [ 39 ] with increasing research. The feasibility of using CYGNSS data to monitor floods was first confirmed by Chew et al. [ 40 ] through the successful use of CYGNSS data to map the flood inundation in the southeastern United States and the Caribbean during the 2017 Atlantic hurricane season. The results show that compared to SMAP brightness temperature data and Landsat optical images, the results obtained by CYGNSS have higher temporal and spatial resolutions. Wei Wan et al. [ 41 ] utilized CYGNSS data to map the distribution of inundated areas affected by heavy rainfall during the 2017 Chinese typhoon season. Studies have shown that the results of the flooding area distribution obtained by CYGNSS are in good agreement with rainfall data, SMAP, and SMOS brightness temperature data. Rajabi et al. [ 42 ] studied the feasibility of using CYGNSS data to detect and map flood distributions during heavy rains in Sistan and Baluchistan in 2020. The results show that the CYGNSS signal-to-noise ratio observation can be used to detect and map the distribution of a flood disaster, and the results are in good Remote Sens. 2021,13, 5181 3 of 15 agreement with the flood disaster distribution obtained from MODIS optical images. Chew et al. [ 43 ] proposed a forward model describing changes in the surface reflectance measured by CYGNSS due to floods of different surface types. Both the model and observations indicate that the relationship between surface reflectivity and surface water extent strongly depend on the micro-scale surface roughness of the land and water. Al-Khaldi et al. [ 44 , 45 ] proposed a coherent signal detection method based on the CYGNSS raw count delayDoppler map (DDM), which marks the coherence by calculating its “diffusion” of the power, and applied the method to inland water-body mapping. Based on previous studies, this study processed CYGNSS L1 data and used the different sensitivities of surface reflectivity (SR) to different ground features to obtain the range of flooding in Henan Province during one extreme precipitation event. The results showed that using CYGNSS data, one could obtain high spatial resolution flood monitoring results (3, 6 km), and the inversion results revealed the movement of floods between cities over a short period. This further demonstrated the suitability of using satellite-based GNSS-R technology to monitor changes in inland water bodies. 2. Materials and Methods 2.1. Study Area In mid-July 2021, Typhoon In-fa began to affect eastern and central China. From the Digital Elevation Model (DEM) shown in Figure 1a (DEM data are detailed in Section 2.3), the Taihang Mountains and Funiu Mountains in the northwestern and western part of Henan Province blocked the westward movement of water vapor caused by the typhoon, resulting in abundant water vapor in the central and northern parts of Henan Province, leading to this heavy rainfall. The stable and inactive weather system caused long-term rainfall. Figure 1b shows that among long-term rainfall events, the extremely heavy rainstorm in Zhengzhou on 20 July was the most detrimental, with the maximum rainfall exceeding 600 mm in some areas. However, the rainfall in Zhengzhou rapidly lessened (but was still more than 50 mm) from 21 July onward, and the heavy rainfall shifted northward, affecting the cities of Xinxiang and Hebi in the north of Zhengzhou. Figure 1c,d demonstrate the precipitation distribution on 20 July and 21 July, respectively, in Henan Province derived from the Global Precipitation Measurement (GPM) mission (GPM data are detailed in Section 2.3). Similar to the measured data, the heavy rainfall obviously moved northward on 21 July. Considering the spatial coverage of CYGNSS data and the characteristics of this heavy rainfall disaster, this study divided the research time into three parts, namely, the stage before the occurrence of heavy rain (1–30 June), the first stage before the heavy rain moved northward (16–20 July), and the second stage after the heavy rain moved northward (21–25 July ), to study the distribution characteristics of floods before and after the extreme precipitation moved northward, respectively. As this flood disaster mainly influenced the northern and central parts of Henan Province, Zhengzhou, Xinxiang, Hebi, and Luoyang, which are located in this area, were selected as the main research objects. Remote Sens. 2021,13, 5181 4 of 15 Figure 1. ( a ) Digital Elevation Model (DEM) in Henan Province; ( b ) precipitation data measured by some meteorological stations; ( c ) precipitation in Henan Province on 20 July (Global Precipitation Measurement (GPM)-derived); (d) precipitation in Henan Province on 21 July (GPM-derived). 2.2. CYGNSS Data The CYGNSS data used in this article is Level 1 Version 3.0, which can be downloaded from https://podaac.jpl.nasa.gov/ (Accessed date: 12 November 2021). In October 2020, the CYGNSS scientific team released Level 1 Version 3.0 data. Compared with the data included in the previous version (v2.1), this version reevaluates the effective isolated radiated power (EIRP) by using the direct signal power and antenna gain measured by a CYGNSS delay-Doppler measuring instrument, which reduces the uncertainty caused by the antenna pattern [ 46 , 47 ]. In addition, raw intermediate frequency data containing phase information, full DDM data, and software receivers for processing intermediate frequency data have also been released https://podaac-tools.jpl.nasa.gov/drive/files/ allData/cygnss/L1, accessed on 12 December 2021. Owing to the relative movement of GPS satellites, receivers, the earth, and reflections from different positions around the specular reflection point, a basic observation of the CYGNSS DDM was produced. The CYGNSS Level 1 DDM was composed of 17 delays (0.25 µ s) × 11 Doppler (500 Hz). Each CYGNSS Level 1 DDM is formed by 1 ms coherent integration and 1000 ms incoherent accumulation [ 14 , 29 , 46 ]. Combining the eight small satellites of the CYGNSS mission and the four-channel receiver on each small satellite, CYGNSS can simultaneously provide 32 DDM observations per second. Starting in July 2019, the incoherent integration time of CYGNSS was reduced to 0.5 s. The theoretical terrestrial spatial resolution has also changed from 7 × 0.5 km to 3.5 × 0.5 km. The improvement in spatial resolution is conducive to developing land applications [ 44 ]. To ensure the quality of the results, data were filtered according to the following criteria: (1) DDM signal-to-noise ratio (SNR) greater than 1.5 dB; (2) receiver antenna gain greater than 0 dB; and (3) specular reflection point elevation angle greater than 30◦. Remote Sens. 2021,13, 5181 5 of 15 2.3. Ancillary Data Due to the lack of real comparison data on spatial and temporal scales, we used a method similar to that reported in a previous study to compare the flooded area derived from CYGNSS [ 40 , 41 ]. The Soil Moisture Active Passive (SMAP) L3 soil moisture product (9 × 9 km) also works in the L-band to calculate the inundated area. It is a microwave remote sensing satellite that is used for global soil moisture monitoring by NASA and can provide two global soil moisture products, 36 × 36 km and 9 × 9 km (9 × 9 km products are obtained using the Backus–Gilbert interpolation method to process 36 ×36 km products) [ 48 ]. Studies have shown that for unsubmerged land, the upper limit of soil volumetric water content is about 0.4 cm 3 cm −3 , and the average water content of submerged land is 0.4–0.45 cm 3 cm −3 [ 49 ]. SMAP data can be downloaded from the NASA National Snow and Ice Data Center website. GPM level 3 10 × 10 km global rainfall measurement data are obtained from the joint use of a multi-satellite radiometer and an infrared satellite that has been calibrated by microwaves (GPM data can be downloaded from https://pmm.nasa.gov/) (Accessed date: 3 October 2021). This study used GPM data to study temporal and spatial changes in precipitation during floods. Concurrently, to evaluate the impact of different land types and land use on the results, remote sensing monitoring data of land use in Henan Province in 2020 was used with a spatial resolution of 1 km. These data were obtained from the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences. The Shuttle Radar Topography Mission 90 m DEM data (v4.1) of the U.S. Space Shuttle Endeavor are used to study the influence of different terrains on flood distribution. 2.4. CYGNSS SR The working principle of GNSS-R is similar to that of bistatic radar, which involves inverting geophysical parameters by processing L-band signals reflected from the Earth’s surface. However, unlike traditional bistatic radar, GNSS-R technology measures the forward scattering signals of navigation satellites and therefore is less susceptible to surface roughness. GNSS-R technology uses a large number of free navigation satellites as signal sources and therefore this technology costs less than other microwave remote sensing technologies. The basic measurement value used in this study is the SR, which is calculated from the DDM observations in the CYGNSS L1 data. Studies have shown that reflectivity largely depends on the physical parameters of the surface, such as soil moisture, surface water, roughness, and vegetation coverage. Generally speaking, for the reflection signals of the L-band navigation satellites used by CYGNSS, a wetter surface will produce a stronger reflectivity, which is the basis of this study. For a low-orbit satellite in a bistatic configuration, the total power received by the receiver is composed of coherent and incoherent scattering, and its proportion depends on the dielectric constant and roughness of the surface [ 31 ]. Therefore, the total bistatic radar scattering power Prl can be determined as follows: Prl =Pc rl +Pi rl (1) Which can be further evaluated as: Pc rl =Pt rGt 4π(Rts +Rsr)2 Grλ2 4πτrl (2) Pi rl =Pt rGtGrλ2 (4π)3R2 tsR2 sr σrl (3) where Pc rl and Pi rl are the coherent reflection power and the incoherent scattering power, respectively; Pt r is the transmit power of the GPS satellite antenna; Gt is the GPS satellite antenna gain; Gr is the CYGNSS antenna gain; Rts and Rsr are the distances from the Remote Sens. 2021,13, 5181 6 of 15 GPS satellite to the specular point and from the specular point to the CYGNSS receiver, respectively; τrl represents the SR; and σrl is the bistatic radar cross-section (m2). When using CYGNSS for mapping inland water bodies, we assume that the reflected signal is dominated by coherent reflections [34,37,40] and convert Equation (2) into dB: 10 log Pc rl =10 log Pt r+10 log Gt+10 log Gr+20log λ+10 log τrl −20 log(Rts +Rsr)−20 log(4π)(4) Solving for surface reflectivity, we obtain: SR =10 log τrl =10 log Pc rl −10 log Pt r−10 log Gt−10 log Gr−20log λ+20 log(Rts +Rsr)+20 log(4π)(5) Because DDM is also affected by the incident angle, we added the correction of the incident angle [43]: SR =SR −10 log cosnθ(6) where θis the incident angle, n is the correction coefficient, and the value is generally 1. Notably, the SR calculated using Equations (5) and (6) is negative; in previous studies, a fixed constant was added to the SR to make it positive, which was not done in this study. To further obtain the distribution of flood inundation from the SR, we used the threshold detection method commonly used in CYGNSS inversion of inland surface waters [ 34 , 40 , 41 ]. The SRs of the permanent water bodies in the studied area were collected, and their average value was taken as the threshold. If a certain SR exceeds this threshold, it can be expected that the SR from inundated areas and non-inundated areas will be lower than the threshold. The detailed process for selecting the threshold is presented in the next section. For a certain research area, CYGNSS data must be integrated into space and time. Although CYGNSS measurements have a set resolution (over land, the smallest spatial resolution is approximately 3.5 km along-track and 0.5 km across-track, or 5.5 km 2 [ 50 ]), owing to the pseudo-random distribution of its observations, for analysis, we need to grid CYGNSS SR to ensure a consistent spatial reference for comparison over time [ 34 ]. Considering the three different research stages selected in this study, we used two different grids: 3 × 3 km and 6 × 6 km. The value of each grid depends on the average of all SR measurements falling into the grid. 3. Results 3.1. SR Threshold To determine the extent of inundation expansion caused by precipitation, we followed the threshold detection method where other monostatic radar data are used to retrieve the inundated area [ 11 , 51 ]. In other words, when the CYGNSS SR exceeds a certain preset threshold, it is considered that the area has been flooded, and vice versa. This method has also been widely used in previous studies using CYGNSS data to retrieve the submerged state of the surface. However, owing to the different parameters, such as the topography, roughness, and vegetation of the studied area, this threshold is not certain [40–42]. For instance, the threshold used by [ 40 ] was 12 dB for the medium-vegetation density and typical roughness. However, in another study case with cropland with high soil moisture, high-to-moderate vegetation, and moderate-to-low land cover heterogeneity, the threshold was 17 dB [ 41 ]. Therefore, in order to make the results more accurate and reliable, the threshold value should be carefully selected according to the actual situation of the study area. Figure 2shows the land cover and land use in Henan Province and the distribution of CYGNSS SR in Henan Province from 1 June to 5 June (scattered form). Different land types such as upland land, paddy fields, forests, and grasslands were widely distributed in Henan Province and the SR was larger when passing through extensive water bodies (paddy fields in the south). The SRs of the different surface features exhibited different characteristics. First, we evaluated the impact of different land types on the CYGNSS SR. Remote Sens. 2021,13, 5181 7 of 15 Figure 2. ( a ) Land use and cover in Henan Province. Note: There are two main rice fields in Henan Province, one in the south (red box) and the other in the north (black box). The planting cycle of the southern rice-producing area is from early May to early September, and the planting cycle of the northern rice-producing area is from mid-July to mid-October. Therefore, during this research period, the rice-producing area in the north was actually planted as upland crops, which can be considered as upland. ( b ) Cyclone Global Navigation Satellite System (CYGNSS) surface reflectivity (SR) (scattered form) from 1 June to 5 June. To study differences in SRs corresponding to different land types in Henan Province, we processed all the CYGNSS L1 data (188,815 DDMs) from Henan Province between 1 April and 30 June. The land use and land cover in Henan Province were divided into six types: upland fields, paddy fields, woodlands, grasslands, water, and residential and factory areas. Finally, the spatial analysis method was used to count the number of CYGNSS specular points in different land classes and to compare their corresponding SR and SNR values. Table 1shows the results of the statistical analyses. Table 1. Statistical results of surface reflectivity (SR) and signal-to-noise ratio (SNR) of different land types in Henan Province (1 April 2021–30 June 2021). Land Use and Cover SR Number Proportion SNR (dB) SR (dB) Upland field 118,990 63.02% 5.22 −19.13 Paddy field 13,586 7.20% 8.33 −14.58 Woodland 13,589 7.20% 4.53 −21.57 Grassland 7043 3.73% 3.98 −22.49 Water 5388 2.85% 9.16 −13.21 Residential and factory areas 30,219 16.00% 5.56 −18.49 As shown in Table 1, the SR and SNR values corresponding to different land types vary significantly. Although the method used did not consider the influence of terrain and surface roughness, statistical results still indicated differential sensitivity of CYGNSS data to different features. For example, water and paddy fields had the highest SR values of approximately − 13 dB and − 15 dB, respectively; woodland and grassland had the lowest SR values of approximately − 22 dB; and upland field and residential and factory areas had similar SR values of approximately − 19 dB. The statistical results of the SNR values were similar to those of the SR values, with the highest values found for water and paddy fields, approximately 9 dB and 8 dB, respectively; followed by upland field and residential and factory areas at approximately 5 dB; and the lowest values found for woodland and grassland areas, at approximately 4 dB. Further in-depth research requires higher-resolution land use and land cover data, and simultaneously, a more refined classification of ground features is required. For example, forest land can be further classified into forests and low shrubs. Finally, the influence of the surface roughness around the specular reflection point Remote Sens. 2021,13, 5181 8 of 15 must also be considered. Based on the statistical results in Table 1, we used − 13 dB as the SR threshold for this study. 3.2. DDM Changes before and after Flooding The basic observation information provided by CYGNSS is the DDM. The DDM reflected by the land mainly depends on the dielectric constant, roughness, and vegetation coverage near the specular point (which, for smooth reflecting surfaces, is approximately the size of the first Fresnel zone). Generally, in the case of medium-to-low vegetation coverage, the flatter the reflecting surface, the stronger the energy of the reflected signal and the more concentrated the reflected power received in the DDM. For a rough surface, scattering near the specular point will make the generated DDM appear as typical or atypical horseshoe shapes [ 45 ]. Therefore, after the flood, the corresponding DDM will change with variations in the reflective surface properties of flooded areas. Figure 3shows changes in the DDM before and after floods in the same area. On 5 June, before the flood occurred, the DDM showed a clear horseshoe shape, and the power distribution was relatively scattered. On 23 July, the area was submerged by water, the DDM showed obvious changes, the reflected signal energy increased, and the power was mainly concentrated in a few pixels. On 13 August, the flood subsided, and the power distribution in the DDM reappeared in the shape of a horseshoe, similar to before the flood. Table 2shows the detailed information of these three DDMs. Figure 3. Delay-Doppler map (DDM) changes before and after floods in the same area. (a) 5 June (before the flood); (b) 23 July (flooding); (c) 13 August (after the flood). Table 2. Changes in delay-Doppler map (DDM) parameters before and after floods in the same area. Effective isolated radiated power (EIRP), surface reflectivity (SR), and signal-to-noise ratio (SNR). Date Satellite Number SP Position Track Number EIRP (Watt) SNR (dB) SR (dB) 5 June Cy02 114 ◦ 19 0 E 34◦420N 251 528.57 8.03 −17.96 23 July Cy03 114 ◦ 15 0 E 34◦480N 466 724.74 16.93 −6.15 13 August Cy02 114 ◦ 16 0 E 34◦420N 969 595.33 7.67 −18.25 Table 2provides more detailed statistics on the parameter information of the three DDMs in Figure 3. After the flood occurred, the SNR and SR in the same area increased significantly by 9 dB and 12 dB, respectively. When the flood subsided, the SNR and SR dropped to pre-flood levels. Changes in the DDM parameters in Table 2confirm the effectiveness of using the SR threshold method to monitor flood distribution. 3.3. SMAP Flood-Monitoring Results For comparison with the CYGNSS results, we also used SMAP data to invert the submerged area because the working frequency bands of SMAP and CYGNSS are both in the L-band, and their sensitivity to surface physical parameters is similar. As mentioned Remote Sens. 2021,13, 5181 9 of 15 in the Materials and Methods section, we believe that when the soil moisture is greater than 0.4 cm 3 cm −3 , the area is covered by water, and vice versa. Figure 4shows the change in SMAP soil moisture before and after the flood as well as the flood distribution range obtained by the threshold method. Figure 4. Soil Moisture Active Passive (SMAP) soil moisture changes before and after floods in Henan Province and the results of SMAP inversion of flood areas. ( a ) SMAP soil moisture on 19 June; ( b ) SMAP soil moisture on 26 June; ( c ) SMAP soil moisture on 20 July; ( d ) SMAP soil moisture on 23 July; ( e ) SMAP inversion results of floods in the first stage of heavy rainfall (16–20 July); ( f ) SMAP inversion results of floods in the second stage of heavy rainfall (21–25 July). Figure 4highlights that before the floods (Figure 4a,b), most areas of Henan Province, except for the rice-producing areas in the south, had soil moisture between 0.05–0.3 cm3cm−3 . After the heavy rain triggered floods in mid-July, the soil moisture values in the northern and central parts of Henan Province increased rapidly (Figure 4c,d) and exceeded 0.45 cm 3 cm −3 in some areas. Figure 4e shows that during the first stage of heavy rainfall, large areas of central and northern Henan were flooded. However, due to the limited resolution of the SMAP satellite (36 × 36 km), the results of the inversion revealed a large-flooded-area effect, which was clearly not in line with observed events. Figure 4f demonstrates the results of the SMAP satellite during the second stage of heavy rainfall. Flood disasters moved northward, but the results revealed that Zhengzhou was not