Modeling and mapping the spatiotemporal variation in agricultural drought based on a satellite-derived vegetation health index across the highlands of Ethiopia

Chere, Zerihun; Abegaz, Assefa; Tamene, Lulseged; Abera, Wuletawu

doi:10.1007/s40808-022-01439-x

Cited by 14 publications

(3 citation statements)

References 59 publications

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“…The VHI has been considered one of the substantial drought indicators in previous studies; however, they were generally related to drought-based monitoring, detection, modeling, mapping, and risk assessment (Aksoy et al 2019;Aitekeyeva et al 2020;Kocaaslan et al 2021;Rojas 2021;Chere et al 2022;Fathi-Taperasht et al 2023). There is a lack of research on using VHI to forecast future drought conditions using machine learning techniques and time series data.…”

Section: Discussionmentioning

confidence: 99%

“…They found that the TCI and VHI had a strong correlation with soil moisture and crop yield anomalies, indicating they have the ability to detect agricultural and vegetation-based drought. Similarly, Chere et al ( 2022 ) found that the VHI can identify moderate to severe agricultural droughts, with 26.3% of the total crop-growing areas showing a decreasing VHI trend. Additionally, the correlation between the VHI and crop yields was found to be good in most of the northern, central, and southeastern regions of Ethiopia.…”

Section: Introductionmentioning

confidence: 86%

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Next-level vegetation health index forecasting: A ConvLSTM study using MODIS Time Series

KARTAL,

IBAN,

SEKERTEKIN

2024

Environ Sci Pollut Res

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The Vegetation Health Index (VHI) is a metric used to assess the health and condition of vegetation, based on satellite-derived data. It offers a comprehensive indicator of stress or vigor, commonly used in agriculture, ecology, and environmental monitoring for forecasting changes in vegetation health. Despite its advantages, there are few studies on forecasting VHI as a future projection, particularly using up-to-date and effective machine learning methods. Hence, the primary objective of this study is to forecast VHI values by utilizing remotely sensed images. To achieve this objective, the study proposes employing a combined Convolutional Neural Network (CNN) and a specific type of Recurrent Neural Network (RNN) called Long Short-Term Memory (LSTM), known as ConvLSTM. The VHI time series images are calculated based on the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. In addition to the traditional image-based calculation, the study suggests using global minimum and global maximum values (global scale) of NDVI and LST time series for calculating the VHI. The results of the study showed that the ConvLSTM with a 1-layer structure generally provided better forecasts than 2-layer and 3-layer structures. The average Root Mean Square Error (RMSE) values for the 1-step, 2-step, and 3-step ahead VHI forecasts were 0.025, 0.026, and 0.026, respectively, with each step representing an 8-day forecast horizon. Moreover, the proposed global scale model using the applied ConvLSTM structures outperformed the traditional VHI calculation method.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 86%

Next-level vegetation health index forecasting: A ConvLSTM study using MODIS Time Series

KARTAL,

IBAN,

SEKERTEKIN

2024

Environ Sci Pollut Res

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“…This method is not only time-consuming and labourious, but also inevitably brings errors in information transfer, resulting in inconvenient information updates and poor timeliness [9]. With the continuous development of agricultural remote sensing technology, remote sensing has been gradually applied to crop monitoring [10], yield estimation [11], and disaster early warning [12], which has greatly improved the convenience and timeliness of monitoring agricultural information [13,14]. In particular, the emergence of high-resolution remote sensing images in recent years has substantially enhanced our ability to monitor agricultural information.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Cropland Abandonment Based on Long Time Series Remote Sensing Data: A Case Study of Fujian Province, China

Jin

Zhu

et al. 2023

Agronomy

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Farmland is the basis for human survival and development. The phenomenon of cropland abandonment has seriously affected national agricultural production and food security. In this study, remote sensing monitoring of abandoned cropland is carried out based on multisource time series remote sensing data using the Google Earth Engine (GEE) cloud platform. Landsat and Sentinel-2 time series data from 2010–2021 were used to obtain monthly synthetic cloud-free image sets in combination with cropland plot data. The time series farmland probability dataset was generated using the random forest classification method. The LandTrendr algorithm was used to extract and analyse the time series cropland probability dataset. Finally, this study also explored the drivers of change in abandoned cropland in Fujian Province. The results show that (1) the LandTrendr algorithm can effectively extract abandoned farmland and avoid the impact of pseudovariation resulting from non-farmland categories. A total of 87.02% of the abandoned farmland was extracted in 2018; 87.50% of the abandoned farmland was extracted in 2020. (2) The abandoned area in Fujian Province fluctuated after a significant increase in 2012, with the abandoned area exceeding 30 thousand hectares. Since 2017, the abandoned area has decreased to slightly below 30 thousand hectares. (3) The regression results of the factors affecting abandoned cropland in Fujian Province show that the increase in the number of agricultural workers and the improvement in soil organic matter content will significantly reduce the area of abandoned cropland in Fujian Province, while the increase in the rate of urbanization, poor road accessibility, and insufficient irrigation conditions will increase the area of abandoned cropland. The results of this study are useful for conducting surveys of cropland abandonment and obtaining timely and accurate data on cropland abandonment. The results of this study are of great significance for the development of effective measures to stop the abandonment of cropland, and ensure the implementation of food security strategies.

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Vegetation drought risk assessment based on the multi-weight methods in Northwest China

Chen,

Wang,

Bento

et al. 2023

Environ Monit Assess

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Modeling and mapping the spatiotemporal variation in agricultural drought based on a satellite-derived vegetation health index across the highlands of Ethiopia

Cited by 14 publications

References 59 publications

Next-level vegetation health index forecasting: A ConvLSTM study using MODIS Time Series

Next-level vegetation health index forecasting: A ConvLSTM study using MODIS Time Series

Monitoring of Cropland Abandonment Based on Long Time Series Remote Sensing Data: A Case Study of Fujian Province, China

Vegetation drought risk assessment based on the multi-weight methods in Northwest China

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