An agricultural drought index for assessing droughts using a water balance method: a case study in Jilin Province, Northeast China

Drought, which causes the economic, social, and environmental losses, also threatens food security worldwide. In this study, we developed a vegetation-soil water deficit (VSWD) method to better assess agricultural droughts. The VSWD method considers precipitation, potential evapotranspiration (PET) and soil moisture. The soil moisture from different soil layers was compared with the in situ drought indices to select the appropriate depths for calculating soil moisture during growing seasons. The VSWD method and other indices for assessing the agricultural droughts, i.e., Scaled Drought Condition Index (SDCI), Vegetation Health Index (VHI) and Temperature Vegetation Dryness Index (TVDI), were compared with the in situ and multi-scales of Standardized Precipitation Evapotranspiration Index (SPEIs). The results show that the VSWD method has better performance than SDCI, VHI, and TVDI. Based on the drought events collected from field sampling, it is found that the VSWD method can better distinguish the severities of agricultural droughts than other indices mentioned here. Moreover, the performances of VSWD, SPEIs, SDCI and VHI in the major historical drought events recorded in the study area show that VSWD has generated the most sensible results than others. However, the limitation of the VSWD method is also discussed

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones

Monitoring pre-monsoon drought in Bangladesh using remote sensing technique

Drought has been a prevalent concern in Bangladesh over the past few decades, and the findings of several studies have indicated that Bangladesh has a high risk of drought, in association with a significant increasing trend of temperature. However, little attention has so far been given in Bangladesh to the mitigation and monitoring of drought, although few studies have been conducted for drought assessment based on either rainfall and temperature or a drought index based on rainfall, such as the Standardized Precipitation Index (SPI). The objective of this study is to assess drought conditions in Bangladesh using long-term satellite data from January to May (2001-2014). Temperature-Vegetation Dryness Index (TVDI) is a drought index based on remote sensing data that exploits the relationship between Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) for estimating soil moisture condition has been used. A systematic approach was adopted in the methodology for considerations to; i) identify the spatial and temporal variation of drought using TVDI, ii) examine the relationship between TVDI and other climatology as well as environmental variables (such as soil moisture, LST, NDVI, rainfall, and Land Use Land Cover (LULC)), and iii) compare TVDI result with field investigation. Results indicate that drought is a concerning problem in Bangladesh and drought conditions varies spatially and temporally. It is clearly observed from the TVDI results that the problem of drought was not prominent in January and February (2001-2014) due to low temperatures. But the effect of drought was considerably high for the rest of the three months, of March, April and May (2001-2014) due to high temperature. However, there were still severe drought conditions have been observed in several small parts of the study areas where no additional water supply were available during that time, except rainfall. Nevertheless, a large part of the study area was still unaffected by the drought even during very hot weather condition due to massive irrigation which has been ascertained during the field investigations. Among the investigated parameters, very closed agreements were found between TVDI and LST as well as NDVI and LULC, although relationships between TVDI and rest of the parameters were not well-defined. This study also found that the TVDI result is in a good agreement with the field investigation. Most importantly, the correlation between TVDI and field investigation clearly indicates the important of this TVDI for the investigation of drought in a complex environment in Bangladesh, where drought estimation using only meteorological data (rainfall and temperature) is ineffective due to anthropogenic and environmental factors which modified the soil moisture condition across the ground very severely. Finally, this study highlights the potential of drought monitoring using remote sensing technique especially on the use of TVDI in Bangladesh due to lack of meteorological data

Surface Soil Moisture Retrievals from Remote Sensing:Current Status, Products & Future Trends

Advances in Earth Observation (EO) technology, particularly over the last two decades, have shown that soil moisture content (SMC) can be measured to some degree or other by all regions of the electromagnetic spectrum, and a variety of techniques have been proposed to facilitate this purpose. In this review we provide a synthesis of the efforts made during the last 20 years or so towards the estimation of surface SMC exploiting EO imagery, with a particular emphasis on retrievals from microwave sensors. Rather than replicating previous overview works, we provide a comprehensive and critical exploration of all the major approaches employed for retrieving SMC in a range of different global ecosystems. In this framework, we consider the newest techniques developed within optical and thermal infrared remote sensing, active and passive microwave domains, as well as assimilation or synergistic approaches. Future trends and prospects of EO for the accurate determination of SMC from space are subject to key challenges, some of which are identified and discussed within. It is evident from this review that there is potential for more accurate estimation of SMC exploiting EO technology, particularly so, by exploring the use of synergistic approaches between a variety of EO instruments. Given the importance of SMC in Earth’s land surface interactions and to a large range of applications, one can appreciate that its accurate estimation is critical in addressing key scientific and practical challenges in today’s world such as food security, sustainable planning and management of water resources. The launch of new, more sophisticated satellites strengthens the development of innovative research approaches and scientific inventions that will result in a range of pioneering and ground-breaking advancements in the retrievals of soil moisture from space

Response of Agricultural Drought to Meteorological Drought - A case study of the Winter Wheat Above the Bengbu Sluice in the Huaihe River Basin, China

This study investigated the responses of winter wheat to drought for the above part of the Bengbu Sluice in the Huaihe River based on the daily scale dataset of 60 meteorological stations from 1961–2015. Crop water deficit index (CWDI) and relative moisture index (M) were used to examine the winter wheat drought and meteorological drought, respectively. We then analyzed the spatial-temporal evolution characteristics of these two kinds of drought to calculate the time lag of winter wheat drought to meteorological drought, and finally discuss the relationship between the time lag of winter wheat drought to meteorological drought and the underlying surface geographical factors, and drew the following conclusions. (1) In terms of time scale, for CWDI, except for the filling and mature period, the CWDI at other growth periods showed a slight downward trend; for M, there was no significant change in the interannual trend of each growth period. In terms of spatial scale, the proportion of above moderate drought level in each station of CWDI and M presented a decreasing feature from north to south. (2) The time lag of winter wheat drought to meteorological drought was the shortest (3.21 days) in the greening and heading period and the longest in the over-wintering period (84.35 days). (3) The correlation between the geographical factors and the time lag of winter wheat drought in each growth period was better than 0.5. The high-value points of the relation between the underlying surface geographical factors and the time lag of winter wheat drought were mostly distributed in the mountainous areas with poor soil field capacity and at a greater depth of shallow groundwater, high elevation and steep slope in the areas with aspects to the east and northeast, and the northern areas with less precipitation and lower temperature

Mapping Spatio-Temporal Cropland Changes Due To Water Stress In Krishna River Basin Using Temporal Satellite Data

Natural hazards namely droughts, floods, cyclones, hailstorms, volcanic eruptions, earth quakes, landslides, forest fire, locust outbreak etc, are common on the earth’s surface. Most of them are of climatic origin. Incidence of these hazards causes loss of human life, failure of crops and destruction of ecosystems. Consequently, the social as well as economic conditions of any region is disoriented. Natural hazards cannot be prevented but the loss can be minimized to some extent by taking appropriate disaster mitigation strategies. These strategies can be achieved by developing early warning systems and developing effective communication systems to take immediate action during the incidence of disasters, improving medical services and training to the people individually; how to react when disaster warning announced in a region, on their own without waiting for the help. Thus, disaster management includes warning, prevention, planning, preparedness, monitoring, and assessment and relief activity

Developing a Remote Sensing-Based Combined Drought Indicator Approach for Agricultural Drought Monitoring over Marathwada, India

The increasing drought severities and consequent devastating impacts on society over the Indian semi-arid regions demand better drought monitoring and early warning systems. Operational agricultural drought assessment methods in India mainly depend on a single input parameter such as precipitation and are based on a sparsely located in-situ measurements, which limits monitoring precision. The overarching objective of this study is to address this need through the development of an integrated agro-climatological drought monitoring approach, i.e., combined drought indicator for Marathwada (CDI_M), situated in the central part of Maharashtra, India. In this study, satellite and model-based input parameters (i.e., standardized precipitation index (SPI-3), land surface temperature (LST), soil moisture (SM), and normalized difference vegetation index (NDVI)) were analyzed at a monthly scale from 2001 to 2018. Two quantitative methods were tested to combine the input parameters for developing the CDI_M. These methods included an expert judgment-based weight of each parameter (Method-I) and principle component analysis (PCA)-based weighting approach (Method-II). Secondary data for major types of crop yields in Marathwada were utilized to assess the CDI_M results for the study period. CDI_M maps depict moderate to extreme drought cases in the historic drought years of 2002, 2009, and 2015–2016. This study found a significant increase in drought intensities (p ≤ 0.05) and drought frequency over the years 2001–2018, especially in the Latur, Jalna, and Parbhani districts. In comparison to Method-I (r ≥ 0.4), PCA-based (Method-II) CDI_M showed a higher correlation (r ≥ 0.60) with crop yields in both harvesting seasons (Kharif and Rabi). In particular, crop yields during the drier years showed a greater association (r \u3e 6.5) with CDI_M over Marathwada. Hence, the present study illustrated the effectiveness of CDI_M to monitor agricultural drought in India and provide improved information to support agricultural drought management practices

Evaluation of soil moisture downscaling using a simple thermal-based proxy - the REMEDHUS network (Spain) example

Soil moisture retrieved from satellite microwave remote sensing normally has spatial resolution on the order of tens of kilometers, which are too coarse for many regional hydrological applications such as agriculture monitoring and drought prediction. Therefore, various downscaling methods have been proposed to enhance the spatial resolution of satellite soil moisture products. The aim of this study is to investigate the validity and robustness of the simple vegetation temperature condition index (VTCI) downscaling scheme over a dense soil moisture observational network (REMEDHUS) in Spain. First, the optimized VTCI was determined through sensitivity analyses of VTCI to surface temperature, vegetation index, cloud, topography, and land cover heterogeneity, using data from Moderate Resolution Imaging Spectroradiometer∼(MODIS) and MSG SEVIRI (METEOSAT Second Generation-Spinning Enhanced Visible and Infrared Imager). Then the downscaling scheme was applied to improve the spatial resolution of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA CCI) soil moisture, which is a merged product based on both active and passive microwave observations. The results from direct validation against soil moisture observations, spatial pattern comparison, as well as seasonal and land use analyses show that the downscaling method can significantly improve the spatial details of CCI soil moisture while maintaining the accuracy of CCI soil moisture. The accuracy level is comparable to other downscaling methods that were also validated against the REMEDHUS network. Furthermore, slightly better performance of MSG SEVIRI over MODIS was observed, which suggests the high potential of applying a geostationary satellite for downscaling soil moisture in the future. Overall, considering the simplicity, limited data requirements and comparable accuracy level to other complex methods, the VTCI downscaling method can facilitate relevant hydrological applications that require high spatial and temporal resolution soil moisture. © 2015 Author(s)