Assimilating Remote Sensing based Soil Moisture in an Ecosystem Model (BEPS) for Agricultural Drought Assessment

Author(s):  
Lin Zhu ◽  
Jing M Chen ◽  
Qiming Qin ◽  
Mei Huang ◽  
Lianxi Wang ◽  
...  
Author(s):  
S. Saxena ◽  
K. Choudhary ◽  
R. Saxena ◽  
A. Rabha ◽  
P. Tahlani ◽  
...  

<p><strong>Abstract.</strong> Agricultural drought is concerned with the soil moisture deficiency in relation to meteorological droughts and climatic factors and their impacts on agricultural production and economic profitability. Present study is based on two years <i>kharif</i> seasons i.e. 2018 and 2017, comparison of drought assessment using remote sensing, soil moisture indices, rainfall and crop sown area as per the New Drought Manual, December, 2016. The drought assessment was carried out at district and sub-district level under National Agricultural Drought Assessment and Monitoring System (NADAMS) project. Drought trigger-1 is checked with rainfall deviation and dry spell. During 2017, the final drought categories were defined on the basis of Rainfall, Moisture and Vegetation Condition Index. During 2018, the final district level drought categories are defined using 3 indicators, where sown area upto end of August was also considered. Based on the approach defined in the New Drought Manual, analysis was carried out at district level for 17 major agricultural drought prone states of the country. State wise Rainfall deviation, dry spell, NDVI/NDWI situation was compared for both the years. Remote sensing based vegetation and water indices are important impact indicator out of 4 because it gives an idea of crop profile and surface wetness condition respectively. Thus the present study is an attempt to compare the drought situation in <i>kharif</i> season of years 2017 and 2018 on the basis of different impact indicators.</p>


Author(s):  
S. K. Padhee ◽  
B. R. Nikam ◽  
S. P. Aggarwal ◽  
V. Garg

Drought is an extreme condition due to moisture deficiency and has adverse effect on society. Agricultural drought occurs when restraining soil moisture produces serious crop stress and affects the crop productivity. The soil moisture regime of rain-fed agriculture and irrigated agriculture behaves differently on both temporal and spatial scale, which means the impact of meteorologically and/or hydrological induced agriculture drought will be different in rain-fed and irrigated areas. However, there is a lack of agricultural drought assessment system in Indian conditions, which considers irrigated and rain-fed agriculture spheres as separate entities. On the other hand recent advancements in the field of earth observation through different satellite based remote sensing have provided researchers a continuous monitoring of soil moisture, land surface temperature and vegetation indices at global scale, which can aid in agricultural drought assessment/monitoring. Keeping this in mind, the present study has been envisaged with the objective to develop agricultural drought assessment and prediction technique by spatially and temporally assimilating effective drought index (EDI) with remote sensing derived parameters. The proposed technique takes in to account the difference in response of rain-fed and irrigated agricultural system towards agricultural drought in the Bundelkhand region (The study area). <br><br> The key idea was to achieve the goal by utilizing the integrated scenarios from meteorological observations and soil moisture distribution. EDI condition maps were prepared from daily precipitation data recorded by Indian Meteorological Department (IMD), distributed within the study area. With the aid of frequent MODIS products viz. vegetation indices (VIs), and land surface temperature (LST), the coarse resolution soil moisture product from European Space Agency (ESA) were downscaled using linking model based on Triangle method to a finer resolution soil moisture product. EDI and spatially downscaled soil moisture products were later used with MODIS 16 days NDVI product as key elements to assess and predict agricultural drought in irrigated and rain-fed agricultural systems in Bundelkhand region of India. Meteorological drought, soil moisture deficiency and NDVI degradation were inhabited for each and every pixel of the image in GIS environment, for agricultural impact assessment at a 16 day temporal scale for Rabi seasons (October&ndash;April) between years 2000 to 2009. Based on the statistical analysis, good correlations were found among the parameters EDI and soil moisture anomaly; NDVI anomaly and soil moisture anomaly lagged to 16 days and these results were exploited for the development of a linear prediction model. The predictive capability of the developed model was validated on the basis of spatial distribution of predicted NDVI which was compared with MODIS NDVI product in the beginning of preceding Rabi season (Oct&ndash;Dec of 2010).The predictions of the model were based on future meteorological data (year 2010) and were found to be yielding good results. The developed model have good predictive capability based on future meteorological data (rainfall data) availability, which enhances its utility in analyzing future Agricultural conditions if meteorological data is available.


2021 ◽  
Author(s):  
Martin Hirschi ◽  
Bas Crezee ◽  
Sonia I. Seneviratne

&lt;p&gt;Drought events cause multiple impacts on the environment, the society and the economy. Here, we analyse recent major drought events with different metrics using a common framework. The analysis is based on current reanalysis (ERA5, ERA5-Land, MERRA-2) and merged remote-sensing products (ESA-CCI soil moisture, gridded satellite soil moisture from the Copernicus Climate Data Store), focusing on soil moisture (or agricultural) drought. The events are characterised by their severity, magnitude, duration and spatial extent, which are calculated from standardised daily anomalies of surface and root-zone soil moisture. We investigate the ability of the different products to represent the droughts and set the different events in context to each other. The considered products also offer opportunities for drought monitoring since they are available in near-real time.&lt;/p&gt;&lt;p&gt;All investigated products are able to represent the investigated drought events. Overall, ERA5 and ERA5-Land often show the strongest, and the remote-sensing products often weaker responses based on surface soil moisture. The weaker severities of the events in the remote-sensing products are both related to shorter event durations as well as less pronounced average negative standardised soil moisture anomalies, while the magnitudes (i.e., the minimum of the standardised anomalies over time) are comparable to the reanalysis products. Differing global distributions of long-term trends may explain some differences in the drought responses of the products. Also, the lower penetration depth of microwave remote sensing compared to the top layer of the involved land surface models could explain the partly weaker negative standardized soil moisture anomalies in the remote-sensing products during the investigated events. In the root zone (based on the reanalysis products), the drought events often show prolonged durations, but weaker magnitudes and smaller spatial extents.&lt;/p&gt;


Author(s):  
Anne M. Smith

Remote sensing can provide timely and economical monitoring of large areas. It provides the ability to generate information on a variety of spatial and temporal scales. Generally, remote sensing is divided into passive and active depending on the sensor system. The majority of remote-sensing studies concerned with drought monitoring have involved visible–infrared sensor systems, which are passive and depend on the sun’s illumination. Radar (radio detection and ranging) is an active sensor system that transmits energy in the microwave region of the electromagnetic spectrum and measures the energy reflected back from the landscape target. The energy reflected back is called backscatter. The attraction of radar over visible– infrared remote sensing (chapters 5 and 6) is its independence from the sun, enabling day/night operations, as well as its ability to penetrate cloud and obtain data under most weather conditions. Thus, unlike visible–infrared sensors, radar offers the opportunity to acquire uninterrupted information relevant to drought such as soil moisture and vegetation stress. Drought conditions manifest in multiple and complex ways. Accordingly, a large number of drought indices have been defined to signal abnormally dry conditions and their effects on crop growth, river flow, groundwater, and so on (Tate and Gustard, 2000). In the field of radar remote sensing, much work has been devoted to developing algorithms to retrieve geophysical parameters such as soil moisture, crop biomass, and vegetation water content. In principle, these parameters would be highly relevant for monitoring agricultural drought. However, despite the existence of a number of radar satellite systems, progress in the use of radar in environmental monitoring, particularly in respect to agriculture, has been slower than anticipated. This may be attributed to the complex nature of radar interactions with agricultural targets and the suboptimal configuration of the satellite sensors available in the 1990s (Ulaby, 1998; Bouman et al., 1999). Because most attention is still devoted to the problem of deriving high-quality soil moisture and vegetation products, there have been few investigations on how to combine such radar products with other data and models to obtain value-added agricultural drought products.


Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2777
Author(s):  
Tao Cheng ◽  
Siyang Hong ◽  
Bensheng Huang ◽  
Jing Qiu ◽  
Bikui Zhao ◽  
...  

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.


2020 ◽  
Author(s):  
Maria Jose Escorihuela ◽  
Pere Quintana Quintana-Seguí ◽  
Vivien Stefan ◽  
Jaime Gaona

&lt;p&gt;Drought is a major climatic risk resulting from complex interactions between the atmosphere, the continental surface and water resources management. Droughts have large socioeconomic impacts and recent studies show that drought is increasing in frequency and severity due to the changing climate.&lt;/p&gt;&lt;p&gt;Drought is a complex phenomenon and there is not a common understanding about drought definition. In fact, there is a range of definitions for drought. In increasing order of severity, we can talk about: meteorological drought is associated to a lack of precipitation, agricultural drought, hydrological drought and socio-economic drought is when some supply of some goods and services such as energy, food and drinking water are reduced or threatened by changes in meteorological and hydrological conditions. &amp;#8232;&lt;/p&gt;&lt;p&gt;A number of different indices have been developed to quantify drought, each with its own strengths and weaknesses. The most commonly used are based on precipitation such as the precipitation standardized precipitation index (SPI; McKee et al., 1993, 1995), on precipitation and temperature like the Palmer drought severity index (PDSI; Palmer 1965), others rely on vegetation status like the crop moisture index (CMI; Palmer, 1968) or the vegetation condition index (VCI; Liu and Kogan, 1996). Drought indices can also be derived from climate prediction models outputs. Drought indices base on remote sensing based have traditionally been limited to vegetation indices, notably due to the difficulty in accurately quantifying precipitation from remote sensing data. The main drawback in assessing drought through vegetation indices is that the drought is monitored when effects are already causing vegetation damage. In order to address drought in their early stages, we need to monitor it from the moment the lack of precipitation occurs.&lt;/p&gt;&lt;p&gt;Thanks to recent technological advances, L-band (21 cm, 1.4 GHz) radiometers are providing soil moisture fields among other key variables such as sea surface salinity or thin sea ice thickness. Three missions have been launched: the ESA&amp;#8217;s SMOS was the first in 2009 followed by Aquarius in 2011 and SMAP in 2015.&lt;/p&gt;&lt;p&gt;A wealth of applications and science topics have emerged from those missions, many being of operational value (Kerr et al. 2016, Mu&amp;#241;oz-Sabater et al. 2016, Mecklenburg et al. 2016). Those applications have been shown to be key to monitor the water and carbon cycles. Over land, soil moisture measurements have enabled to get access to root zone soil moisture, yield forecasts, fire and flood risks, drought monitoring, improvement of rainfall estimates, etc.&lt;/p&gt;&lt;p&gt;The advent of soil moisture dedicated missions (SMOS, SMAP) paves the way for drought monitoring based on soil moisture data. Initial assessment of a drought index based on SMOS soil moisture data has shown to be able to precede drought indices based on vegetation by 1 month (Albitar et al. 2013).&lt;/p&gt;&lt;p&gt;In this presentation we will be analysing different drought episodes in the Ebro basin using both soil moisture and vegetation based indices to compare their different performances and test the hypothesis that soil moisture based indices are earlier indicators of drought than vegetation ones.&lt;/p&gt;


2019 ◽  
Vol 167 ◽  
pp. 43-55 ◽  
Author(s):  
Jongjin Baik ◽  
Muhammad Zohaib ◽  
Ungtae Kim ◽  
Muhammad Aadil ◽  
Minha Choi

2020 ◽  
Author(s):  
Depeng Zuo ◽  
Siyang Cai ◽  
Zongxue Xu ◽  
Hong Yang

&lt;p&gt;Most research on drought assessment adopted historical in-situ observations, however, there has been increased data availability from remote sensing during the recent years. This study utilizes the two sources of data in drought assessment. Using the historical in-situ observations, the spatiotemporal variations of meteorological drought were firstly investigated by calculating the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) at 1, 3, 6-month time scales in Northeast China. Using remote sensing data, the combined deficit index (CDI) for agricultural drought assessment was computed based on tri-monthly sum of deficit in antecedent rainfall and deficit in monthly NDVI at land cover type and sub-type levels in the same region. In the end, the agricultural drought calculated by the CDI was evaluated against the deficit in crop yield, as well as deficit in Land Surface Temperature (LST) and Evapotranspiration (ET), in order to verify the applicability of the CDI for agricultural drought assessment in the study region. The results showed that the CDI has better correlations with the SPEI (R&lt;sup&gt;2&lt;/sup&gt;=0.48) than the SPI (R&lt;sup&gt;2&lt;/sup&gt;=0.05) at 3-month scales with weight factor a=0.5 in dry farming areas. The spatial pattern of the CDI showed that the area of agricultural drought increased from July to October. In addition, a significant linear correlation was found between the CDI and anomaly in annual agricultural yield (R&lt;sup&gt;2&lt;/sup&gt;=0.55), and anomaly in monthly land surface temperature (R&lt;sup&gt;2&lt;/sup&gt;=0.42). The results prove that the CDI calculated by remote sensing data is not only a reliable indicator for agricultural drought assessment in Northeast China, but also provides useful information for agricultural drought disaster prevention and mitigation, and water management improvement.&lt;/p&gt;


2020 ◽  
Vol 22 (4) ◽  
pp. 937-956
Author(s):  
Odai Al Balasmeh ◽  
Richa Babbar ◽  
Tapas Karmaker

Abstract Wadi Shueib catchment in Jordan is a water stress area and climate change is creating a further deficiency in precipitation, streamflow, and soil moisture; which are a deterrent to agriculture production in the area. In order to analyze the drought-like situation in the area, a hybrid drought index (HDI) has been developed considering the combined effect of these three variables. Fuzzy analytical hierarchy process (F-AHP) and entropy weight methods were carried out to develop a hybrid drought index (HDI) which combines meteorological, hydrological, and agricultural drought indices based on precipitation, streamflow, and soil moisture data in the area. The wavelet transform (WT) with cross wavelet (XCT) and wavelet coherence (WTC) were applied to investigate the interaction and the relations between the HDI index, drought indices, and large-scale sunspot activity Niño3.4 index. The results show that HDI can easily capture the trend of the drought-like conditions in the area based on the available data. The trend analysis of HDI revealed an increasing trend in the drought incidences in the near future. The study can be used as an early alarm for drought in the area, which can be helpful in the decision-making process towards water resources planning and management in the future.


2021 ◽  
Vol 252 ◽  
pp. 112124 ◽  
Author(s):  
Alzira Gabrielle Soares Saraiva Souza ◽  
Alfredo Ribeiro Neto ◽  
Laio Lucas de Souza

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