Passive Microwave Remote Sensing Soil Moisture Data in Agricultural Drought Monitoring: Application in Northeastern China

Drought is the costliest disaster around the world and in China as well. Northeastern China is one of China’s most important major grain producing areas. Frequent droughts have harmed the agriculture of this region and further threatened national food security. Therefore, the timely and effective monitoring of drought is extremely important. In this study, the passive microwave remote sensing soil moisture data, i.e., the SMOS soil moisture (SMOS-SM) product, was compared to several in situ meteorological indices through Pearson correlation analysis to assess the performance of SMOS-SM in monitoring drought in northeastern China. Then, maps based on SMOS-SM and in situ indices were created for July from 2010 to 2015 to identify the spatial pattern of drought distributions. Our results showed that the SMOS-SM product had relatively high correlation with in situ indices, especially SPI and SPEI values of a nine-month scale for the growing season. The drought patterns shown on maps generated from SPI-9, SPEI-9 and sc-PDSI were also successfully captured using the SMOS-SM product. We found that the SMOS-SM product effectively monitored drought patterns in northeastern China, and this capacity would be enhanced when field capacity information became available.

River Runoff Modelling and Hydrological Drought Assessment Based on High-Resolution Brightness Temperatures in Mainland China

Under the background of global climate change, drought is causing devastating impacts on the balance of the regional water resources system. Hydrological drought assessment is critical for drought prevention and water resources management. However, in China to assess hydrological drought at national scale is still challenging basically because of the difficulty of obtaining runoff data. In this study, we used the state-of-the-art passive microwave remote sensing techniques in river runoff modelling and thus assessed hydrological drought in Mainland China in 1996–2016. Specifically, 79 typical hydrological stations in 9 major basins were selected to simulate river runoff using the M/C signal method based on a high-resolution passive microwave bright temperature dataset. The standardized runoff index (SRI) was calculated for the spatial and temporal patterns of hydrological drought. Results show that passive microwave remote sensing can provide an effective way for runoff modelling as 92.4% and 59.5% of the selected 79 stations had the Pearson correlation coefficient (R) and the Nash-Sutcliffe efficiency coefficient (NS) scores greater than 0.5. Especially in areas located on Qinghai-Tibet Plateau in the Inland and the Southwest River Basin, the performance of the M/C signal method is quite outstanding. Further analysis indicates that stations with small rivers in the plateau areas with sparse vegetation tend to have better simulated results, which are usually located in drought-prone regions. Hydrological drought assessment shows that 30 out of the 79 stations present significant increasing trends in SRI-3 and 18 indicate significant decreasing trends. The duration and severity of droughts in the non-permanent dry areas of the Hai River Basin, the middle reaches of the Yangtze River Basin and the Southwest of China were found out to be more frequent and severe than other regions. This work can provide guidance for extending the applications of remote sensing data in drought assessment and other hydrological research.

River Flood Detection Using Passive Microwave Remote Sensing in a Data-Scarce Environment: A Case Study for Two River Basins in Malawi

Detecting and forecasting riverine floods is of paramount importance for adequate disaster risk management and humanitarian response. However, this is challenging in data-scarce and ungauged river basins in developing countries. Satellite remote sensing data offers a cost-effective, low-maintenance alternative to the limited in-situ data when training, parametrizing and operating flood models. Utilizing the signal difference between a measurement (M) and a dry calibration (C) location in Passive Microwave Remote Sensing (PMRS), the resulting rcm index simulates river discharge in the measurement pixel. Whilst this has been demonstrated for several river basins, it is as of yet unknown at what ratio of the spatial scales of the river width vs. the PMRS pixel resolution it remains effective in East-Africa. This study investigates whether PMRS imagery at 37 GHz can be effectively used for flood preparedness in two small-scale basins in Malawi, the Shire and North Rukuru river basins. Two indices were studied: The m index (rcm expressed as a magnitude relative to the average flow) and a new index that uses an additional wet calibration cell: rcmc. Furthermore, the results of both indices were benchmarked against discharge estimates from the Global Flood Awareness System (GloFAS). The results show that the indices have a similar seasonality as the observed discharge. For the Shire River, rcmc had a stronger correlation with discharge (ρ = 0.548) than m (ρ = 0.476), and the former predicts discharge more accurately (R2 = 0.369) than the latter (R2 = 0.245). In Karonga, the indices performed similarly. The indices do not perform well in detecting individual flood events when comparing the signal to a flood impact database. However, these results are sensitive to the threshold used and the impact database quality. The method presented simulated Shire River discharge and detected floods more accurately than GloFAS. It therefore shows potential for river monitoring in data-scarce areas, especially for rivers of a similar or larger spatial scale than the Shire River. Upstream pixels could not directly be used to forecast floods occurring downstream in these specific basins, as the time lag between discharge peaks did not provide sufficient warning time.

Variation in Ice Phenology of Large Lakes Over the Northern Hemisphere Based on Passive Microwave Remote Sensing Data

Ice phenology data of 22 large lakes of the Northern Hemisphere for 40 years (1979−2018) have been retrieved from passive microwave remote sensing brightness temperature (Tb). The results were compared with site-observation data and visual interpretation from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectivity products images (MOD09GA). The mean absolute errors of four lake ice phenology parameters, including freeze-up start date (FUS), freeze-up end date (FUE), break-up start date (BUS), and break-up end date (BUE) against MODIS-derived ice phenology were 2.50, 2.33, 1.98, and 3.27 days, respectively. The long-term variation in lake ice phenology indicates that FUS and FUE are delayed; BUS and BUE are earlier; ice duration (ID) and complete ice duration (CID) have a general decreasing trend. The average change rates of FUS, FUE, BUS, BUE, ID, and CID of lakes in this study from 1979 to 2018 were 0.23, 0.23, −0.17, −0.33, −0.67, and −0.48 days/year, respectively. Air temperature and latitude are two dominant driving factors of lake ice phenology. Lake ice phenology for the period 2021−2100 was predicted by the relationship between ice phenology and air temperature for each lake. Compared with lake ice phenology changes from 1990 to 2010, FUS is projected to be delayed by 3.1 days and 11.8 days under Representative Concentration Pathways (RCPs) 2.6 and 8.5 scenarios, respectively; BUS is projected to be earlier by 3.3 days and 10.7 days, respectively; and ice duration from 2080 to 2100 will decrease by 6.5 days and 21.9 days, respectively.

Flood monitoring using passive microwave remote sensing in the Senegal River, Western Mali

<div>Water supports life, however it does come with hazards. Floods area amongst the most impactful environmental disasters. Accurate flood forecasting and early warning are critical for disaster risk management. Detecting and forecasting floods at an early stage is certainly relevant for Mali, hence crucial in order to prevent a hazard from turning into a disaster. Remotely sensed river monitoring can be an effective, systematic and time-efficient technique to detect and forecast extreme floods. Conventional flood forecasting systems require extensive data inputs and software to model floods. Moreover, most models rely on discharge data, which is not always available and is less accurate in a overbank flow situations. There is a need for an alternative method which detects riverine inundation, while making use of the available state-of-the-art.</div><div>This research investigates the use of passive microwave remote sensing with different spatial resolutions for the detection and forecasting of flooding. Brightness temperatures from two different downscaled spatial resolutions  (1 x 1 km and 10 x 10 km) are extracted from passive microwave remote sensing sensors and are converted into discharge estimators: a dry CM ratio and a wet CMc ratio. Surface water has a low emission, thus let the CM ratio increase as the surface water percentage in the pixel increases. Sharp increases are observed for over-bank flow conditions.<br><p>Overall, we compared the passive microwave remote sensing model results of the different spatial resolutions to the results of a conventional global runoff model GloFAS. The passive microwave remote sensing model does not require extensive input data when used as an Early Warning System (EWS),<span> as many smaller-scale EWS do, we suggest that when improved, the passive microwave remote sensing method is implemented as part of an integrative EWS solution, including a passive microwave remote sensing model and various other models. This would allow for early warnings in data-scarce regions and at a variety of lead times. In order for this to be effective, we suggest that more research be done on correctly setting the trigger threshold, and into the potential spatial interpretation of CMc.</span></p> </div>

Reconstruction of Snow Depth Data at Moderate Spatial Resolution (1 km) from Remotely Sensed Snow Data and Multiple Optimized Environmental Factors: A Case Study over the Qinghai-Tibetan Plateau

Snow depth distribution in the Qinghai-Tibetan plateau is important for atmospheric circulation and surface water resources. In-situ observations at meteorological stations and remote observation by passive microwave remote sensing technique are two main approaches for monitoring snow depth at regional or global levels. However, the meteorological stations are often scarce and unevenly distributed in mountainous regions because of inaccessibility, so are the in-situ snow depth measurements. Passive microwave remote sensing data can alleviate the unevenness issue, but accuracy and spatial (e.g., 25 km) and temporal resolutions are low; spatial heterogeneity in snow depth is thus hard to capture. On the other hand, optical sensors such as moderate resolution imaging spectroradiometer (MODIS) onboard Terra and Aqua satellites can monitor snow at moderate spatial resolution (1 km) and high temporal resolution (daily) but only snow area extent, not snow depth. Fusing passive microwave snow depth data with optical snow area extent data provides an unprecedented opportunity for generating snow depth data at moderate spatial resolution and high temporal resolution. In this article, a linear multivariate snow depth reconstruction (LMSDR) model was developed by fusing multisource snow depth data, optical snow area extent data, and environmental factors (e.g., spatial distribution, terrain features, and snow cover characteristics), to reconstruct daily snow depth data at moderate resolution (1 km) for 16 consecutive hydrological years, taking Qinghai-Tibetan Plateau (QTP) as a case study. We found that snow cover day (SCD) and environmental factors such as longitude, latitude, slope, surface roughness, and surface fluctuation have a significant impact on the variations of snow depth over the QTP. Relatively high accuracy (root mean square error (RMSE) = 2.26 cm) was observed in the reconstructed snow depth when compared with in-situ data. Compared with the passive microwave remote sensing snow depth product, constructing a nonlinear snow depletion curve product with an empirical formula and fusion snow depth product, the LMSDR model (RMSE = 2.28 cm, R2 = 0.63) demonstrated a significant improvement in accuracy of snow depth reconstruction. The overall spatial accuracy of the reconstructed snow depth was 92%. Compared with in-situ observations, the LMSDR product performed well regarding different snow depth intervals, land use, elevation intervals, slope intervals, and SCD and performed best, especially when the snow depth was less than 3 cm. At the same time, a long-time snow depth series reconstructed based on the LMSDR model reflected interannual variations of snow depth well over the QTP.