Assessing Agricultural Vulnerability to Drought in a Heterogeneous Environment: A Remote Sensing-Based Approach

Abstract

Agriculture is one of the fundamental economic activities in most countries; however, this sector suffers from various natural hazards including flood and drought. The determination of drought-prone areas is essential to select drought-tolerant crops in climate sensitive vulnerable areas. This study aims to enhance the detection of agricultural areas with vulnerability to drought conditions in a heterogeneous environment, taking Bangladesh as a case study. The normalized difference vegetation index (NDVI) and land cover products from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images have been incorporated to compute the vegetation index. In this study, a modified vegetation condition index (mVCI) is proposed to enhance the estimation of agricultural drought. The NDVI values ranging between 0.44 to 0.66 for croplands are utilized for the mVCI. The outcomes of the mVCI are compared with the traditional vegetation condition index (VCI). Precipitation and crop yield data are used for the evaluation. The mVCI maps from multiple years (2006–2018) have been produced to compute the drought hazard index (DHI) using a weighted sum overlay method. The results show that the proposed mVCI enhances the detection of agricultural drought compared to the traditional VCI in a heterogeneous environment. The “Aus” rice-growing season (sown in mid-March to mid-April and harvested in mid-July to early August) receives the highest average precipitation (>400 mm), and thereby this season is less vulnerable to drought. A comparison of crop yields reveals the lowest productivity in the drought year (2006) compared to the non-drought year (2018), and the DHI map presents that the north-west region of Bangladesh is highly vulnerable to agricultural drought. This study has undertaken a large-scale analysis that is important to prioritize agricultural zones and initiate development projects based on the associated level of vulnerability.

Analysis of Spatial Distribution of the Drought Hazard Index (DHI) by Integration AHP-GIS-Remote Sensing in Gorontalo Regency

“…According to (Malik et al, 2019) the relationship between LST and NDVI has shown a strong negative correlation in summer, rainy, and winter seasons (R 2 > 0.9). The use of the NDVI threshold and the consideration of separating croplands from other land cover types reduces the inclusion of misclassified drought areas, thereby improving agricultural drought estimation (Faridatul & Ahmed, 2020). The combination of the NDVI and LST provides extremely useful information for agricultural drought monitoring and early warning systems for farmers with a high negative correlation (Sruthi & Aslam, 2015).…”

“…Additionally, due to the sparse nature of infrastructure networks in major countries', continuous rainfall records are few or impossible to gather promptly. In addition, meteorological drought indices, e.g., SPI, RAI, and SPEI, have been commonly used and limited by the distribution of weather stations with the provision of only point data (Faridatul & Ahmed, 2020). Therefore, they failed to visualize the spatial detail with the inability to determine drought susceptibility across spatial units, thereby decreasing the reliability of the drought index.…”

Đánh giá sự phân bố và diễn biến hạn hán trên lưu vực sông Hồng sử dụng thông số độ ẩm đất mô phỏng trên mô hình thủy văn VIC

“…Trong bối cảnh tác động của biến đổi khí hậu và tình trạng hạn hán tới các hoạt động kinh tế, xã hội có xu hướng gia tăng và ngày càng trở nên trầm trọng, công tác nghiên cứu, đánh giá và dự báo nhằm đưa ra các cảnh báo sớm và các biện pháp phòng chống hạn hán là việc làm cấp thiết, đem lại lợi ích trực tiếp đến đời sống xã hội. Việc ứng dụng các công cụ tiên tiến như mô hình hoá, vệ tinh viễn thám, hệ thống thông tin địa lý (Geographic Information System -GIS) có thể đem lại nhiều lợi ích và khắc phục những hạn chế về phạm vi không gian và thời gian của các biện pháp quan trắc truyền thống trong công tác đánh giá và dự báo hạn hán nói riêng và quản lý tài nguyên nước nói chung [3][4][5][6][7][8].…”

Assessment of Soil Moisture Anomaly Sensitivity to Detect Drought Spatio-Temporal Variability in Romania

“…NDVI is a well-known vegetation index [54]. It is frequently used in studies aimed at analyzing the impact of drought on vegetation, the results being satisfactory [55,56]. The negative values of the NDVI anomaly (less than −1) represent browning vegetation, when they are between −1 and 1 they represent the vegetation in normal vegetation conditions, and when they are over 1 they represent greening vegetation.…”