A review of agricultural drought assessment with remote sensing data: methods, issues, challenges and opportunities

Mullapudi, Archana; Vibhute, Amol D.; Mali, S. M.; Patil, C. H.

doi:10.1007/s12518-022-00484-6

Cited by 22 publications

(4 citation statements)

References 63 publications

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“…In the present study, Sentinel-2 satellite images were collected at 10m spatial resolutions throughout the growing season and successfully identified and classified multiple crops. The supervised (random forest, SAM, and MLC) [35] and unsupervised (NDVI, k-means, and ISODATA) approaches were successfully implemented on preprocessed data. The results were tested using a confusion matrix to determine the methods' performance.…”

Section: B Discussion and Findingsmentioning

confidence: 99%

Mapping Crop Types At a 10 m Scale Using Sentinel-2 Data and Machine Learning Methods

Khan,

H. Patil,

D. Vibhute

et al. 2024

IJCDS

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Crop classification plays a vital role in crop status monitoring, crop area estimation, and food production. Remote sensing data is widely accepted for crop classification at remote locations. However, crop classification is challenging due to spectral and spatial similarities, complex land structures, temporal inconsistencies, and environmental parameters. Machine learning models must be robust, particularly when dealing with a variety of crop types and changing environmental factors. This study examines the extent to which various algorithms generalize, emphasizing the importance of adaptability to various farming systems and geographical conditions. It explores transfer learning and ensemble approaches as possible ways to improve the resilience and flexibility of the model. In the present study, an effort has been made to identify, classify, and map multiple crops from the complex environment using the Sentinel-2 dataset and advanced machine learning methods such as random forest, Spectral Angle Mapper (SAM), Maximum Likelihood Classifier (MLC), K-means clustering and Iterative Self-Organizing Data Analysis Technique (ISODATA). The crop spectral features were identified using the Normalized Difference Vegetation Index (NDVI). The NDVI outcomes ranged between -0.91 and 0.54, which were then used to identify crop areas. Ground reference data, Google map, and Google Earth data were used to determine the crop classes, train the data, and validate the results. The five major crops viz. Cotton, Paddy, Orchard, Yellow split Pigeon peas, Chickpeas, and Other crops were identified and classified efficiently. According to the experimental results, the random forest approach had the best overall accuracy, 87.71%, and a kappa value of 0.86 than other methods. Alternatively, the ISODATA method provided an overall accuracy of 85.01% with a kappa value of 0.82. The agricultural decision-makers can use the results of this study for decision-making and management.

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Section: B Discussion and Findingsmentioning

confidence: 99%

Mapping Crop Types At a 10 m Scale Using Sentinel-2 Data and Machine Learning Methods

Khan,

H. Patil,

D. Vibhute

et al. 2024

IJCDS

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“…The utilization of spatial datasets derived from satellite RS and GIS technology provides valuable insights for evaluating and modeling agricultural drought-risk patterns, monitoring drought conditions, and generating maps depicting drought vulnerability (Mullapudi et al, 2023;Raihan et al, 2023k). Hoque et al (2021) involved the integration of geospatial methodologies and fuzzy logic in order to create a complete spatial drought risk inventory model that can be utilized for effective operational drought management.…”

Section: Evaluation Of Drought Conditionsmentioning

confidence: 99%

A Systematic Review of Geographic Information Systems (GIS) in Agriculture for Evidence-Based Decision Making and Sustainability

Raihan

2024

GSSR

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The aim of this study was to consolidate current information on the utilization of Geographic Information Systems (GIS) and Remote Sensing (RS) in the agricultural sector, with a focus on their role in promoting evidence-based policies and practices to enhance agricultural sustainability. Additionally, this review sought to identify the challenges hindering the widespread adoption of GIS and RS applications, particularly in low- and middle-income nations. This study employed the methodology of systematic literature review. The findings indicate that the utilization of GIS technology in the agricultural sector has experienced a notable increase over the past few years. The primary areas of use for GIS that have been identified encompass crop yield estimation, assessment of soil fertility, monitoring of cropping patterns, evaluation of drought conditions, detection and management of pests and crop diseases, implementation of precision agriculture techniques, and management of fertilizer and weed control. GIS technology possesses the capacity to augment the sustainability of agriculture by incorporating the spatial aspect of agricultural practices into agricultural policies. Furthermore, the potential of GIS in facilitating evidence-based decision making is expanding. Given the escalating peril of climate change on agriculture and food security, there exists a heightened imperative to include GIS into policy formulation and decision-making processes to enhance the sustainability of agricultural practices. The findings of this study might be beneficial in informing the development of policies that effectively integrate sustainable and climate-smart practices in agriculture.

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“…Additionally, certain research delves into historical drought occurrences and the underlying causes [22][23][24], whereas others project future drought scenarios through methodologies such as the statistical downscaling of Global Climate Models (GCMs) [25][26][27], machine learning, AI techniques [28][29][30], etc. Furthermore, the impact assessments of drought extend across agricultural [31], ecological [32], and socioeconomic domains [7], reflecting the intricate interplay between water scarcity and diverse societal sectors.…”

Section: Introductionmentioning

confidence: 99%

A CMIP6 Multi-Model Analysis of the Impact of Climate Change on Severe Meteorological Droughts through Multiple Drought Indices—Case Study of Iran’s Metropolises

Afsari,

Nazari-Sharabian,

Hosseini

et al. 2024

Water

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This study extensively explores the impact of climate change on meteorological droughts within metropolises in Iran. Focused on Tehran, Mashhad, Isfahan, Karaj, Shiraz, and Tabriz, this research employed CMIP6 climate models under varying climate change scenarios (SSPs) to forecast severe meteorological droughts spanning the period from 2025 to 2100. The investigation utilized a diverse set of drought indices (SPI, DI, PN, CZI, MCZI, RAI, and ZSI) to assess the drought severity in each city. This study is crucial as it addresses the pressing concerns of rapidly decreasing water levels in Iran’s dams, serious declines in underground aquifers, and the compounding issues of land subsidence and soil erosion due to excessive groundwater withdrawal in the face of severe droughts. This study culminated in the generation of box plots and heatmaps based on the results. These visual representations elucidated the distribution of the drought values under different indices and scenarios and provided a depiction of the probability of severe drought occurrences until the end of the century for each city. The resulting findings serve as invaluable tools, furnishing policymakers with informed insights to proactively manage and fortify metropolitan resilience against the evolving challenges posed by a changing climate.

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A review of agricultural drought assessment with remote sensing data: methods, issues, challenges and opportunities

Cited by 22 publications

References 63 publications

Mapping Crop Types At a 10 m Scale Using Sentinel-2 Data and Machine Learning Methods

Mapping Crop Types At a 10 m Scale Using Sentinel-2 Data and Machine Learning Methods

A Systematic Review of Geographic Information Systems (GIS) in Agriculture for Evidence-Based Decision Making and Sustainability

A CMIP6 Multi-Model Analysis of the Impact of Climate Change on Severe Meteorological Droughts through Multiple Drought Indices—Case Study of Iran’s Metropolises

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