A New Soil Moisture Agricultural Drought Index (SMADI) Integrating MODIS and SMOS Products: A Case of Study over the Iberian Peninsula

Effective mapping and monitoring of soil moisture content (SMC) in space and time is an expected application of remote sensing for agricultural development and drought mitigation, particularly in the context of global climate change impact, given that agricultural drought is occurring more frequently and severely worldwide. This study aims to develop a regional algorithm for estimating SMC by using Landsat 8 (L8) imagery, based on analyses of the response of soil reflectance, by corresponding L8 bands with the change of SMC from dry to saturated states, in all 103 soil samples taken in the central region of Vietnam. The L8 spectral band ratio of the near-infrared band (NIR: 850–880 nm, band 5) versus the short-wave infrared 2 band (SWIR2: 2110 to 2290 nm, band 7) shows the strongest correlation to SMC by a logarithm function (R2 = 0.73 and the root mean square error, RMSE ~ 12%) demonstrating the high applicability of this band ratio for estimating SMC. The resultant maps of SMC estimated from the L8 images were acquired over the northern part of the Central Highlands of Vietnam in March 2015 and March 2016 showed an agreement with the pattern of severe droughts that occurred in the region. Further discussions on the relationship between the estimated SMC and the satellite-based retrieved drought index, the Normal Different Drought Index, from the L8 image acquired in March 2016, showed a strong correlation between these two variables within an area with less than 20% dense vegetation (R2 = 0.78 to 0.95), and co-confirms the bad effect of drought on almost all areas of the northern part of the Central Highlands of Vietnam. Directly estimating SMC from L8 imagery provides more information for irrigation management and better drought mitigation than by using the remotely sensed drought index. Further investigations on various soil types and optical sensors (i.e., Sentinel 2A, 2B) need to be carried out, to extend and promote the applicability of the prosed algorithm, towards better serving agricultural management and drought mitigation.

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Development of Integrated Crop Drought Index by Combining Rainfall, Land Surface Temperature, Evapotranspiration, Soil Moisture, and Vegetation Index for Agricultural Drought Monitoring

Remote Sensing ◽

10.3390/rs13091778 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1778

Author(s):

Soo-Jin Lee ◽

Nari Kim ◽

Yangwon Lee

Keyword(s):

Soil Moisture ◽

Surface Temperature ◽

Crop Yield ◽

Land Surface Temperature ◽

Land Surface ◽

Drought Index ◽

Vegetation Index ◽

The State ◽

Agricultural Drought ◽

Drought Monitoring

Various drought indices have been used for agricultural drought monitoring, such as Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Soil Water Deficit Index (SWDI), Normalized Difference Vegetation Index (NDVI), Vegetation Health Index (VHI), Vegetation Drought Response Index (VegDRI), and Scaled Drought Condition Index (SDCI). They incorporate such factors as rainfall, land surface temperature (LST), potential evapotranspiration (PET), soil moisture content (SM), and vegetation index to express the meteorological and agricultural aspects of drought. However, these five factors should be combined more comprehensively and reasonably to explain better the dryness/wetness of land surface and the association with crop yield. This study aims to develop the Integrated Crop Drought Index (ICDI) by combining the weather factors (rainfall and LST), hydrological factors (PET and SM), and a vegetation factor (enhanced vegetation index (EVI)) to better express the wet/dry state of land surface and healthy/unhealthy state of vegetation together. The study area was the State of Illinois, a key region of the U.S. Corn Belt, and the quantification and analysis of the droughts were conducted on a county scale for 2004–2019. The performance of the ICDI was evaluated through the comparisons with SDCI and VegDRI, which are the representative drought index in terms of the composite of the dryness and vegetation elements. The ICDI properly expressed both the dry and wet trend of the land surface and described the state of the agricultural drought accompanied by yield damage. The ICDI had higher positive correlations with the corn yields than SDCI and VegDRI during the crucial growth period from June to August for 2004–2019, which means that the ICDI could reflect the agricultural drought well in terms of the dryness/wetness of land surface and the association with crop yield. Future work should examine the other factors for ICDI, such as locality, crop type, and the anthropogenic impacts, on drought. It is expected that the ICDI can be a viable option for agricultural drought monitoring and yield management.

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A new agricultural drought index considering the irrigation water demand and water supply availability

Natural Hazards ◽

10.1007/s11069-020-04278-0 ◽

2020 ◽

Vol 104 (3) ◽

pp. 2409-2429

Author(s):

Zikang Xing ◽

Miaomiao Ma ◽

Yongqiang Wei ◽

Xuejun Zhang ◽

Zhongbo Yu ◽

...

Keyword(s):

Soil Moisture ◽

Irrigation Water ◽

Water Demand ◽

Drought Index ◽

Agricultural Drought ◽

Drought Event ◽

Moisture Condition ◽

Irrigation Water Demand ◽

Moisture Variation ◽

The Impact

Abstract Agricultural drought has a tremendous impact on crop yields and economic development under the context of global climate change. As an essential component of water balance in irrigated areas, artificial irrigation, which is not widely incorporated into agricultural drought indices in previous studies. Therefore, an irrigation water deficit index (IWDI) based on the estimation of irrigation water demand and supply is proposed. The performance of the new index was compared with the Soil Moisture Anomaly Percentage Index (SMAPI) over the upstream of the Zi River basin (UZRB). The results indicated the IWDI is highly correlated with precipitation, runoff, and potential evapotranspiration, combined with a more comprehensive moisture condition than the previous agricultural drought index. Due to the consideration of crop growth process and farmland spatial distribution, the proposed index showed a significant advantage in stressing drought conditions of agricultural concentration area and eliminating the impact of invalid soil moisture drought of non-growing seasons. Furthermore, the drought condition identified by the new index presented a good agreement with the historical drought event that occurred in 2013.7–8, which accurately reproduced the soil moisture variation and vegetation growth dynamics.

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Development and Evaluation of an Agricultural Drought Index by Harnessing Soil Moisture and Weather Data

Water ◽

10.3390/w11071375 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1375 ◽

Cited By ~ 5

Author(s):

Ali Ajaz ◽

Saleh Taghvaeian ◽

Kul Khand ◽

Prasanna H. Gowda ◽

Jerry E. Moorhead

Keyword(s):

Soil Moisture ◽

Crop Production ◽

Drought Index ◽

Root Zone ◽

Correlation Coefficients ◽

Meteorological Drought ◽

Agricultural Drought ◽

Weather Data ◽

Drought Indices ◽

Growth Stages

A new agricultural drought index was developed for monitoring drought impacts on agriculture in Oklahoma. This new index, called the Soil Moisture Evapotranspiration Index (SMEI), estimates the departure of aggregated root zone moisture from reference evapotranspiration. The SMEI was estimated at five locations across Oklahoma representing different climates. The results showed good agreement with existing soil moisture-based (SM) and meteorological drought indices. In addition, the SMEI had improved performance compared to other indices in capturing the effects of temporal and spatial variations in drought. The relationship with crop production is a key characteristic of any agricultural drought index. The correlations between winter wheat production and studied drought indices estimated during the growing period were investigated. The correlation coefficients were largest for SMEI (r > 0.9) during the critical crop growth stages when compared to other drought indices, and r decreased by moving from semi-arid to more humid regions across Oklahoma. Overall, the results suggest that the SMEI can be used effectively for monitoring the effects of drought on agriculture in Oklahoma.

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A hybrid drought index for drought assessment in Wadi Shueib catchment area in Jordan

Journal of Hydroinformatics ◽

10.2166/hydro.2020.038 ◽

2020 ◽

Vol 22 (4) ◽

pp. 937-956

Author(s):

Odai Al Balasmeh ◽

Richa Babbar ◽

Tapas Karmaker

Keyword(s):

Soil Moisture ◽

Large Scale ◽

Drought Index ◽

Agricultural Drought ◽

Wavelet Coherence ◽

Sunspot Activity ◽

Drought Indices ◽

Entropy Weight ◽

Drought Assessment ◽

Near Future

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.

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Crop and Location Specific Agricultural Drought Quantification: Part III. Forecasting Water Stress and Yield Trends

Transactions of the ASABE ◽

10.13031/trans.11651 ◽

2017 ◽

Vol 60 (3) ◽

pp. 741-752 ◽

Cited By ~ 2

Author(s):

Rachel L. McDaniel ◽

Clyde Munster ◽

John Nielsen-Gammon

Keyword(s):

Water Management ◽

Soil Moisture ◽

Water Stress ◽

Water Conservation ◽

Drought Index ◽

Swat Model ◽

Agricultural Drought ◽

Weather Data ◽

Drought Prediction ◽

Yield Trends

Abstract. Agriculture is the largest water consumer, with 70% of global water withdrawals being used for irrigation. Water scarcity issues are being exacerbated by drought and population increases, making efficient water resource management in agricultural production increasingly important. The objective of this article is to evaluate the use of short-term weather forecasts for agricultural drought prediction. A crop-specific, linear regression drought analysis technique was used in this study. This study takes place in the upper Colorado River basin (UCRB) in west Texas. Five variables associated with agricultural drought (precipitation, temperature, biomass production, soil moisture depletion, and transpiration) were scaled and used to estimate cotton yields. The yield percentiles were used as a drought index. Precipitation and temperature were forecasted with a two-week lead time using probable scenarios based on historical data. The other three variables were estimated using the SWAT model. Forecasts were generated for each week of the growing season from 2010 through 2013. Four statistics were used to evaluate model performance, including the Nash-Sutcliffe coefficient of efficiency (NSE), the coefficient of determination (R2), and two error indices, the percent bias (PBIAS) and the RMSE-observations standard deviation ratio (RSR). Comparing the variables using the forecasted weather data to those using the observed weather data revealed that four of the five performed satisfactorily. Temperature performed the best statistically, with an NSE of 0.85 and PBIAS of 9.4%. Precipitation (NSE = 0.51, PBIAS = -34%), cumulative biomass (NSE = 0.69, PBIAS = -38%), and transpiration (NSE = 0.53, PBIAS = 11%) also performed well. However, the soil moisture depletion forecasts (NSE = 0.28, PBIAS = 11%) were unsatisfactory. The forecasted cotton yield trends (NSE = 0.72, PBIAS = -12%) and drought index (NSE = 0.76, PBIAS = -13%) both performed satisfactorily, indicating that this forecasting method may be used for decision making related to agricultural water management, including irrigation timing. Keywords: Crop modeling, Drought, Drought index, Forecasting, Hydrologic modeling, SWAT, Water conservation, Water management, Water stress.

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Integrating effective drought index (EDI) and remote sensing derived parameters for agricultural drought assessment and prediction in Bundelkhand region of India

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-8-89-2014 ◽

2014 ◽

Vol XL-8 ◽

pp. 89-100 ◽

Cited By ~ 3

Author(s):

S. K. Padhee ◽

B. R. Nikam ◽

S. P. Aggarwal ◽

V. Garg

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Land Surface ◽

Drought Index ◽

Meteorological Data ◽

Vegetation Indices ◽

Agricultural Drought ◽

Predictive Capability ◽

Drought Assessment ◽

Soil Moisture Anomaly

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–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–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.

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