A novel agricultural drought monitoring framework using remote sensing products

2021 ◽  
Author(s):  
Trupti Satapathy ◽  
Meenu Ramadas ◽  
Jörg Dietrich
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Water Stress ◽  
Land Surface ◽  
Condition Index ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Crop Water ◽  
Vegetation Health ◽  
Crop Water Stress

<p>Among natural hazards, droughts are known to be very complex and disastrous owing to their creeping nature and widespread impacts. Specifically, the occurrence of agricultural droughts poses a threat to the productivity and socio-economic development of countries such as India. In this study, we propose a novel framework for agricultural drought monitoring integrating the different indicators of vegetation health, crop water stress and soil moisture, that are derived from remote sensing satellite data. The drought monitoring is performed over Odisha, India, for the period 2000-2019. Soil moisture and land surface temperature datasets from GLDAS Noah Land Surface Model and surface reflectance data from MODIS (MOD09GA) are used in this study. We compared the utility of popular indices: (i) soil moisture condition index, soil moisture deficit index and soil wetness deficit index to represent the soil moisture level; (ii) temperature condition index, vegetation condition index and normalised difference water index to indicate vegetation health; (iii) short wave infrared water stress to represent crop water stress condition. Correlation analyses between these indices and the seasonal crop yields are performed, and suitable indicators are chosen. The popular entropy weight method is then used to integrate the indices and develop the proposed composite drought index. The index is then used for monitoring the agricultural drought condition over the study area in drought periods. The proposed framework for week- to month-scale monitoring have potential applications in identification of agricultural drought hotspots, analysis of trends in drought severity, and drought early warning for agricultural water management.</p>

scholarly journals DROUGHT MONITORING OVER PADDY FIELD AREA IN INDRAMAYU DISTRICT, WEST JAVA USING REMOTELY SENSED INDICES

2010 ◽  
Vol 5 (1) ◽  
Author(s):  
Parwati ◽  
Miao Jungang ◽  
Orbita Roswintiarti
Keyword(s):  
Soil Moisture ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Condition Index ◽  
Dry Season ◽  
Precipitation Index ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
West Java ◽  
Vegetation Health

In this research, several meteorological and agricultural drought indices based on remote sensing data are built for drought monitoring over paddy area in Indramayu District, West Java, Indonesia. The meteorological drought index of Standardized Precipitation Index (SPI) is developed from monthly Outgoing Long Wave Radiation (OLR) data from 1980 to 2005. The SPI represents the deficient of precipitation. Meanwhile, the agricultural drought of Vegetation Health Index (VHI) was developed from daily Moderate-resolution ImagingSpectroradiometer (MODIS) data during dry season (May-August) 2003-2006. The VHI was designed to monitoring vegetation health, soil moisture, and thermal conditions. The result shows that the agricultural drought occurate in Indramayu District, especially in the northern and southern part during the dry season in 2003 and 2004. It is found that there is a strong correlation between VHI and soil moisture measured in the field (r=0.84). Key words:Agricultural drought, Meteorological drought, Standardized Precipitation Index, Temperature Condition Index, Vegetation Condition Index.

scholarly journals Comparison of Multi-Year Reanalysis, Models, and Satellite Remote Sensing Products for Agricultural Drought Monitoring over South Asian Countries

2021 ◽  
Vol 13 (16) ◽  
pp. 3294
Author(s):  
Muhammad Shahzaman ◽  
Weijun Zhu ◽  
Irfan Ullah ◽  
Farhan Mustafa ◽  
Muhammad Bilal ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
South Asia ◽  
Satellite Remote Sensing ◽  
Crop Production ◽  
Condition Index ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Vegetation Condition ◽  
Vegetation Condition Index

The substantial reliance of South Asia (SA) to rain-based agriculture makes the region susceptible to food scarcity due to droughts. Previously, most research on SA has emphasized the meteorological aspects with little consideration of agrarian drought impressions. The insufficient amount of in situ precipitation data across SA has also hindered thorough investigation in the agriculture sector. In recent times, models, satellite remote sensing, and reanalysis products have increased the amount of data. Hence, soil moisture, precipitation, terrestrial water storage (TWS), and vegetation condition index (VCI) products have been employed to illustrate SA droughts from 1982 to 2019 using a standardized index/anomaly approach. Besides, the relationships of these products towards crop production are evaluated using the annual national production of barley, maize, rice, and wheat by computing the yield anomaly index (YAI). Our findings indicate that MERRA-2, CPC, FLDAS (soil moisture), GPCC, and CHIRPS (precipitation) are alike and constant over the entire four regions of South Asia (northwest, southwest, northeast, and southeast). On the other hand, GLDAS and ERA5 remain poor when compared to other soil moisture products and identified drought conditions in regions one (northwest) and three (northeast). Likewise, TWS products such as MERRA-2 TWS and GRACE TWS (2002–2014) followed the patterns of ERA5 and GLDAS and presented divergent and inconsistent drought patterns. Furthermore, the vegetation condition index (VCI) remained less responsive in regions three (northeast) and four (southeast) only. Based on annual crop production data, MERRA-2, CPC, FLDAS, GPCC, and CHIRPS performed fairly well and indicated stronger and more significant associations (0.80 to 0.96) when compared to others. Thus, the current outcomes are imperative for gauging the deficient amount of data in the SA region, as they provide substitutes for agricultural drought monitoring.

scholarly journals A Review of Crop Water Stress Assessment Using Remote Sensing

2021 ◽  
Vol 13 (20) ◽  
pp. 4155
Author(s):  
Uzair Ahmad ◽  
Arturo Alvino ◽  
Stefano Marino
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Land Surface ◽  
Crop Production ◽  
Crop Water ◽  
Ecological Zones ◽  
Sensing Systems ◽  
Equivalent Water Thickness ◽  
Remote Sensing Systems ◽  
Crop Water Stress

Currently, the world is facing high competition and market risks in improving yield, crop illness, and crop water stress. This could potentially be addressed by technological advancements in the form of precision systems, improvements in production, and through ensuring the sustainability of development. In this context, remote-sensing systems are fully equipped to address the complex and technical assessment of crop production, security, and crop water stress in an easy and efficient way. They provide simple and timely solutions for a diverse set of ecological zones. This critical review highlights novel methods for evaluating crop water stress and its correlation with certain measurable parameters, investigated using remote-sensing systems. Through an examination of previous literature, technologies, and data, we review the application of remote-sensing systems in the analysis of crop water stress. Initially, the study presents the relationship of relative water content (RWC) with equivalent water thickness (EWT) and soil moisture crop water stress. Evapotranspiration and sun-induced chlorophyll fluorescence are then analyzed in relation to crop water stress using remote sensing. Finally, the study presents various remote-sensing technologies used to detect crop water stress, including optical sensing systems, thermometric sensing systems, land-surface temperature-sensing systems, multispectral (spaceborne and airborne) sensing systems, hyperspectral sensing systems, and the LiDAR sensing system. The study also presents the future prospects of remote-sensing systems in analyzing crop water stress and how they could be further improved.

scholarly journals Water Stress Regime of Irrigated Crops Based on Remote Sensing and Ground-Based Data

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1117
Author(s):  
Anatoly Mikhailovich Zeyliger ◽  
Olga Sergeevna Ermolaeva
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Crop Yield ◽  
Satellite Monitoring ◽  
Agricultural Crop ◽  
Crop Water ◽  
Stress Regime ◽  
Irrigation Water Use ◽  
Crop Water Stress ◽  
Crop Irrigation

In the past few decades, combinations of remote sensing technologies with ground-based methods have become available for use at the level of irrigated fields. These approaches allow an evaluation of crop water stress dynamics and irrigation water use efficiency. In this study, remotely sensed and ground-based data were used to develop a method of crop water stress assessment and analysis. Input datasets of this method were based on the results of ground-based and satellite monitoring in 2012. Required datasets were collected for 19 irrigated alfalfa crops in the second year of growth at three study sites located in Saratovskoe Zavolzhie (Saratov Oblast, Russia). Collected datasets were applied to calculate the dynamics of daily crop water stress coefficients for all studied crops, thereby characterizing the efficiency of crop irrigation. Accordingly, data on the crop yield of three harvests were used. An analysis of the results revealed a linear relationship between the crop yield of three cuts and the average value of the water stress coefficient. Further application of this method may be directed toward analyzing the effectiveness of irrigation practices and the operational management of agricultural crop irrigation.

scholarly journals On the utility of land surface models for agricultural drought monitoring

2012 ◽  
Vol 16 (9) ◽  
pp. 3451-3460 ◽  
Author(s):  
W. T. Crow ◽  
S. V. Kumar ◽  
J. D. Bolten
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Cross Correlation ◽  
Root Zone ◽  
Vegetation Indices ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Global Evaluation ◽  
Land Surface Models ◽  
Surface Models

Abstract. The lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely sensed vegetation indices (VI) is examined between January 2000 and December 2010 to quantify the skill of various soil moisture models for agricultural drought monitoring. Examined modeling strategies range from a simple antecedent precipitation index to the application of modern land surface models (LSMs) based on complex water and energy balance formulations. A quasi-global evaluation of lagged VI/soil moisture cross-correlation suggests, when globally averaged across the entire annual cycle, soil moisture estimates obtained from complex LSMs provide little added skill (< 5% in relative terms) in anticipating variations in vegetation condition relative to a simplified water accounting procedure based solely on observed precipitation. However, larger amounts of added skill (5–15% in relative terms) can be identified when focusing exclusively on the extra-tropical growing season and/or utilizing soil moisture values acquired by averaging across a multi-model ensemble.

Characterising recent drought events using current reanalysis and remote-sensing soil moisture products within a common framework

2021 ◽  
Author(s):  
Martin Hirschi ◽  
Bas Crezee ◽  
Sonia I. Seneviratne
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Root Zone ◽  
Microwave Remote Sensing ◽  
Agricultural Drought ◽  
Climate Data ◽  
Data Store ◽  
Common Framework ◽  
Long Term Trends

&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;

scholarly journals Multispectral Image-Based Estimation of Drought Patterns and Intensity around Lake Chad, Africa

2019 ◽  
Vol 11 (21) ◽  
pp. 2534 ◽  
Author(s):  
Willibroad Gabila Buma ◽  
Sang-Il Lee
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Condition Index ◽  
Drought Monitoring ◽  
World Population ◽  
Lake Chad ◽  
In Situ Data

As the world population keeps increasing and cultivating more land, the extraction of vegetation conditions using remote sensing is important for monitoring land changes in areas with limited ground observations. Water supply in wetlands directly affects plant growth and biodiversity, which makes monitoring drought an important aspect in such areas. Vegetation Temperature Condition Index (VTCI) which depends on thermal stress and vegetation state, is widely used as an indicator for drought monitoring using satellite data. In this study, using clear-sky Landsat multispectral images, VTCI was derived from Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI). Derived VTCI was used to observe the drought patterns of the wetlands in Lake Chad between 1999 and 2018. The proportion of vegetation from WorldView-3 images was later introduced to evaluate the methods used. With an overall accuracy exceeding 90% and a kappa coefficient greater than 0.8, these methods accurately acquired vegetation training samples and adaptive thresholds, allowing for accurate estimations of the spatially distributed VTCI. The results obtained present a coherent spatial distribution of VTCI values estimated using LST and NDVI. Most areas during the study period experienced mild drought conditions, though severe cases were often seen around the northern part of the lake. With limited in-situ data in this area, this study presents how VTCI estimations can be developed for drought monitoring using satellite observations. This further shows the usefulness of remote sensing to improve the information about areas that are difficult to access or with poor availability of conventional meteorological data.

scholarly journals Estimating Soil Moisture Under Low Frequency Surface Irrigation Using Crop Water Stress Index

2003 ◽  
Vol 129 (1) ◽  
pp. 27-35 ◽  
Author(s):  
Paul D. Colaizzi ◽  
Edward M. Barnes ◽  
Thomas R. Clarke ◽  
Christopher Y. Choi ◽  
Peter M. Waller
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Low Frequency ◽  
Stress Index ◽  
Crop Water ◽  
Surface Irrigation ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Crop Water Stress

Crop Water Stress Detection Using Remote Sensing

2005 ◽  
Author(s):  
M. Susan Moran ◽  
Pablo J. Zarco-Tejada ◽  
Thomas R. Clarke
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Stress Detection ◽  
Crop Water ◽  
Crop Water Stress

scholarly journals Comparing soil moisture anomalies from multiple independent sources over different regions across the globe

2017 ◽  
Author(s):  
Carmelo Cammalleri ◽  
Jürgen V. Vogt ◽  
Bernard Bisselink ◽  
Ad de Roo
Keyword(s):  
Soil Moisture ◽  
North America ◽  
Southern Africa ◽  
Land Surface ◽  
Root Zone ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Distributed Hydrological Model ◽  
Ground Station ◽  
Insight Into

Abstract. Agricultural drought events can affect large regions across the World, implying the urge for a suitable global tool for an accurate monitoring of this phenomenon. Soil moisture anomalies are considered a good metric to capture the occurrence of agricultural drought events, and they have become an important component of several operational drought monitoring systems. In the framework of the JRC Global Drought Observatory (GDO, http://edo.jrc.ec.europa.eu/gdo/) the suitability of modelled and/or satellite-derived proxy of soil moisture anomalies was investigated. In this study, three datasets have been evaluated as possible proxies of root zone soil moisture anomalies: (1) soil moisture from the Lisflood distributed hydrological model (LIS), (2) remotely sensed land surface temperature data from the MODIS satellite (LST), and (3) the combined passive/active microwave skin soil moisture dataset developed by ESA (CCI). Due to the independency of these three datasets, the Triple Collocation (TC) technique has been applied, aiming at quantifying the likely error associated to each dataset in comparison to the unknown true status of the system. TC analysis was performed on five macro-regions (namely North America, Europe, India, Southern Africa and Australia) detected as suitable for the experiment, providing insight into the mutual relationship between these datasets as well as assessment of the accuracy of each method. A clear outcome of the TC analysis is the good performance of remote sensing datasets, especially CCI, over dry regions such as Australia and Southern Africa, whereas the outputs of LIS seem to be more reliable over areas that are well monitored through meteorological ground station networks, such as North America and Europe. In a global drought monitoring system, these results can be used to design an ensemble system that exploits the advantages of each dataset.

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