Agricultural Drought and Soil Moisture Analysis Using Satellite Image Based Indices

Sreekesh, S.; Kaur, Navneet; Naik, S. R. Sreerama

doi:10.5194/isprs-archives-xlii-3-w6-507-2019

Cited by 12 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Agricultural drought traits that occur after meteorological droughts were evaluated, and the impact of meteorological droughts on agriculture and soil moisture for the Malaprabha sub-basin is observed (Sreekesh et al 2019) based on Standard Precipitation Index (SPI), NDVI, TCI, VCI, Vegetation Health Index (VHI), and Temperature-Vegetation Dryness Index (TVDI). VHI was found to be more suitable for agricultural drought assessment.…”

Section: Related Workmentioning

confidence: 99%

A Bio-inspired and Deep Learning Based Hybrid Model for Agricultural Drought Assessment

Chaudhari,

Anchalia,

Kakati

et al. 2024

JWMM

View full text Add to dashboard Cite

Agricultural droughts can cause many serious hazards. Drought monitoring indices, namely Normalized Difference Vegetation Index (NDVI), Atmospherically Resistant Vegetation Index (ARVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI) have been used for an agricultural drought assessment. Satellite images from the Kolar region of Karnataka are used to calculate these indices. This paper proposes an integration model based on Convolutional Neural Networks (CNN) and a bio-inspired algorithm (Sparrow Search Algorithm (SSA) and Barnacles Mating Optimizer (BMO)) considering the indices as population. Performance is compared with the standalone CNN model in terms of efficiency. For the CNN, the accuracy, time taken for Epoch1, and time taken for Epoch2 is 91%, 16s (3s/step), and 2s (2s/step), respectively. For the CNN integrated with SSA, it is 94%, 3s (3s/step) and 0s (43ms/step), respectively. For the CNN integrated with BMO, it is 94%, 3s (2s/step) and 0s (46ms/step) respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

A Bio-inspired and Deep Learning Based Hybrid Model for Agricultural Drought Assessment

Chaudhari,

Anchalia,

Kakati

et al. 2024

JWMM

View full text Add to dashboard Cite

show abstract

“…Agricultural drought is usually de ned as the de cit of soil moisture, which has a direct impact on crop growth. Soil moisture plays an important part involving climate, vegetation signs of agricultural drought, and its severity (Sreekesh et al, 2019). Due to the complexity and severity of drought, it is very di cult to identify and assess the characteristics of drought (X.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of meteorological drought based on the SPI and SPEI Indices in Benin

BOUKO,

Gao,

Mutelo

et al. 2024

Preprint

View full text Add to dashboard Cite

In Benin, drought refers to a meteorological disaster that causes insufficient soil moisture and has adverse consequences on agriculture, the environment, and food security. However, to better understand the evolution of drought, the Mann–Kendall trend test, and comparative statistical evaluation of drought indices were applied in this paper. To achieve this objective, firstly, this article used daily precipitation, temperature, and relative humidity data from the National Meteorological Agency of Benin (NMAB) to calculate the standardized precipitation index (SPI), the standardized evapotranspiration of precipitation (SPEI), and used the Mann-Kendall trend test to quantify the importance of characteristic drought trends in the three (03) zones of Benin at different time scales. Then, we performed a statistical and comparative evaluation of SPI and SPEI based on Pearson's correlation coefficient, and cross-wavelet transform, to identify the strengths of SPI and SPEI. The results showed that (1) as the time scale increased, the temporal variations of the SPI and SPEI were increasingly consistent across all 3 areas of Benin; but there remains a difference between SPI and SPEI; (2) the drought trends identified by the SPI and the SPEI in certain areas of Benin were quite different. In Zone 2 (Central Benin), SPI showed an insignificant decreasing trend (P > 0.05), while SPEI showed a significant decreasing trend (P < 0.05) on all time scales. In Zone 3 (northern Benin), SPI showed a significantly increasing trend (P < 0.05) except for SPI-1, while SPEI showed a significantly decreasing trend except for SPEI-12. (3) the two indices (SPI/SPEI) show a strong correlation with each other; (4) Compared to the drought index (SPI/SPEI) and the standard relative humidity indices (SRHI), the SRHI-3 and SPEI-3 gave the largest correlation coefficient (r = 0.5), The SPEI could be more appropriate than SPI for monitoring drought in Benin. It should be noted that the adaptability of SPI and SPEI may be different across periods and regions.

show abstract

“…To comprehensively assess the impacts of drought, the integration of remote sensing data from sources such as satellites, drones, and ground-based sensors is imperative (Jiao et al, 2015). Satellite imagery plays a crucial role in assessing vegetation health and condition (Sreekesh et al, 2019). Various indices, such as Normalized Difference Vegetation Index (NDVI) (Forkel et al, 2013), Vegetation Health Index (VHI) (Liang et al, 2021), Standardized Precipitation Index (SPI) (Ren et al, 2008) and Gravity Recovery and Climate Experiment -Drought Severity Index (GRACE-DSI) (Satish Kumar et al, 2021), are employed to gauge the severity of drought by analyzing factors like vegetation status, precipitation, and soil moisture data (Sreekesh et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Satellite-based drought assessment in the endorheic basin of Lake Balkhash

Yegizbayeva,

Koshim,

Bekmuhamedov

et al. 2024

Front. Environ. Sci.

View full text Add to dashboard Cite

Introduction: This study investigates into the drought-prone region of the Lake Balkhash basin, conducting a thorough analysis spanning 2 decades, emphasizing its significant impact on agriculture and water challenges in Central Asia. Beyond the specific focus on this region, the research aims to contribute valuable insights that extend our understanding of the broader implications of drought in the area.Methods: Utilizing MODIS satellite imagery, the study employs the Vegetation Health Index a comprehensive indicator combining the Vegetation Condition Index and Temperature Condition Index ranging from 0 (extreme aridity) to 100 (optimal moisture). The assessment of drought conditions from 2000 to 2020 includes probabilistic mapping, trend analysis, and Pearson’s correlation coefficients. Connections with hydrological factors, such as river water levels and the Balkhash Lake area, are explored, along with in-depth analyses of land use data and the GRACE dataset on water equivalent thickness, enhancing the study’s depth and reliability.Results: Drought affected approximately 44% of the Balkhash Lake Basin during the study period, impacting both Kazakhstan and China. Severe drought episodes occurred in 2000, 2008, 2014, and 2015, highlighting the region’s vulnerability. Analysis of drought trends revealed diverse patterns: 23% exhibited an increase, 17% showed a decrease, and 60% remained stable. Correlations between drought and hydrological parameters varied among stations, with positive correlations at Kapshagay and Shelek Stations, a weak correlation at Ayagoz Station, and a significant positive correlation at Lepsy despite the elevation.Discussion: This research underscores the intricate link between drought and hydrological factors in the Balkhash Lake Basin, emphasizing the need for precise water resource management and climate adaptation. Crucial strategies include proactive monitoring, tailored interventions, and the application of probabilistic drought mapping to enhance water supply management, contributing actionable insights for sustainable practices in the region.Conclusion: This study significantly advances our understanding of drought dynamics in the Balkhash Lake Basin, recommending adaptive strategies, site-specific interventions, and sustainable water management. The findings provide a crucial foundation for informed water resource decisions in Central Asia, emphasizing the importance of region-specific approaches to address diverse challenges posed by drought.

show abstract

Agricultural Drought and Soil Moisture Analysis Using Satellite Image Based Indices

Cited by 12 publications

References 23 publications

A Bio-inspired and Deep Learning Based Hybrid Model for Agricultural Drought Assessment

A Bio-inspired and Deep Learning Based Hybrid Model for Agricultural Drought Assessment

Comparative analysis of meteorological drought based on the SPI and SPEI Indices in Benin

Satellite-based drought assessment in the endorheic basin of Lake Balkhash

Contact Info

Product

Resources

About