An Integrated Drought Index (Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll Fluorescence Dryness Index, VMFDI) Based on Multisource Data and Its Applications in Agricultural Drought Management
To more precisely monitor drought, a new remote sensing-based drought index, the Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll fluorescence Dryness Index (VMFDI), with a spatial resolution of 1 km based on vapor pressure deficit (VPD), soil moisture (SM), and sun-induced chlorophyll f...
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MDPI AG
2024-12-01
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| author | Caiyun Deng Li Zhang Tianhe Xu Siqi Yang Jian Guo Lulu Si Ran Kang Hermann Josef Kaufmann |
| author_facet | Caiyun Deng Li Zhang Tianhe Xu Siqi Yang Jian Guo Lulu Si Ran Kang Hermann Josef Kaufmann |
| author_sort | Caiyun Deng |
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| description | To more precisely monitor drought, a new remote sensing-based drought index, the Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll fluorescence Dryness Index (VMFDI), with a spatial resolution of 1 km based on vapor pressure deficit (VPD), soil moisture (SM), and sun-induced chlorophyll fluorescence (SIF) data was constructed via a three-dimensional spatial distance model, and it was used to monitor dryness in the Yellow River Basin during 2003–2020. The spatiotemporal variations in and main factors of the VMFDI and agroecosystem responses were analyzed via the Theil–Sen median and Mann–Kendall tests and Liang–Kleeman information flow. The results revealed the following: (1) The VMFDI effectively monitors regional drought and is more sensitive than other indices like the standardized precipitation evapotranspiration index (SPEI) and GRACE drought severity index and single variables. (2) VMFDI values fluctuated seasonally in the Yellow River Basin, peaking in August and reaching their lowest in March. The basin becomes drier in winter but wetter in spring, summer, and autumn, with the middle and lower reaches, particularly Shaanxi and Gansu, being drought-prone. The VMFDI values in the agroecosystem were lower. (3) SM and VPD dominated drought at the watershed and agroecosystem scales, respectively. Key agroecosystem indicators, including greenness (NDVI), gross primary productivity (GPP), water use efficiency (WUE), and leaf area index (LAI), were negatively correlated with drought (<i>p</i> < 0.05). When VPD exceeded a threshold range of 7.11–7.17 ha, the relationships between these indicators and VPD shifted from positive to negative. The specific VPD thresholds in maize and wheat systems were 8.03–8.57 ha and 7.15 ha, respectively. Suggestions for drought risk management were also provided. This study provides a new method and high-resolution data for accurately monitoring drought, which can aid in mitigating agricultural drought risks and promoting high-quality agricultural development. |
| format | Article |
| id | doaj-art-967a0a7d48a8464899fc77072b4a48ab |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
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| series | Remote Sensing |
| spelling | doaj-art-967a0a7d48a8464899fc77072b4a48ab2025-08-20T02:01:21ZengMDPI AGRemote Sensing2072-42922024-12-011624466610.3390/rs16244666An Integrated Drought Index (Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll Fluorescence Dryness Index, VMFDI) Based on Multisource Data and Its Applications in Agricultural Drought ManagementCaiyun Deng0Li Zhang1Tianhe Xu2Siqi Yang3Jian Guo4Lulu Si5Ran Kang6Hermann Josef Kaufmann7School of Space Science and Technology, Shandong University, Weihai 264209, ChinaSchool of Space Science and Technology, Shandong University, Weihai 264209, ChinaSchool of Space Science and Technology, Shandong University, Weihai 264209, ChinaSchool of Space Science and Technology, Shandong University, Weihai 264209, ChinaSchool of Space Science and Technology, Shandong University, Weihai 264209, ChinaInstitute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València, 46022 Valencia, SpainSchool of Space Science and Technology, Shandong University, Weihai 264209, ChinaInstitute of Space Sciences, Shandong University, Weihai 264209, ChinaTo more precisely monitor drought, a new remote sensing-based drought index, the Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll fluorescence Dryness Index (VMFDI), with a spatial resolution of 1 km based on vapor pressure deficit (VPD), soil moisture (SM), and sun-induced chlorophyll fluorescence (SIF) data was constructed via a three-dimensional spatial distance model, and it was used to monitor dryness in the Yellow River Basin during 2003–2020. The spatiotemporal variations in and main factors of the VMFDI and agroecosystem responses were analyzed via the Theil–Sen median and Mann–Kendall tests and Liang–Kleeman information flow. The results revealed the following: (1) The VMFDI effectively monitors regional drought and is more sensitive than other indices like the standardized precipitation evapotranspiration index (SPEI) and GRACE drought severity index and single variables. (2) VMFDI values fluctuated seasonally in the Yellow River Basin, peaking in August and reaching their lowest in March. The basin becomes drier in winter but wetter in spring, summer, and autumn, with the middle and lower reaches, particularly Shaanxi and Gansu, being drought-prone. The VMFDI values in the agroecosystem were lower. (3) SM and VPD dominated drought at the watershed and agroecosystem scales, respectively. Key agroecosystem indicators, including greenness (NDVI), gross primary productivity (GPP), water use efficiency (WUE), and leaf area index (LAI), were negatively correlated with drought (<i>p</i> < 0.05). When VPD exceeded a threshold range of 7.11–7.17 ha, the relationships between these indicators and VPD shifted from positive to negative. The specific VPD thresholds in maize and wheat systems were 8.03–8.57 ha and 7.15 ha, respectively. Suggestions for drought risk management were also provided. This study provides a new method and high-resolution data for accurately monitoring drought, which can aid in mitigating agricultural drought risks and promoting high-quality agricultural development.https://www.mdpi.com/2072-4292/16/24/4666drought monitoringthree-dimensional spatial distance modelspatiotemporal variationLiang–Kleeman information flowagroecosystem responsesmultisource data |
| spellingShingle | Caiyun Deng Li Zhang Tianhe Xu Siqi Yang Jian Guo Lulu Si Ran Kang Hermann Josef Kaufmann An Integrated Drought Index (Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll Fluorescence Dryness Index, VMFDI) Based on Multisource Data and Its Applications in Agricultural Drought Management Remote Sensing drought monitoring three-dimensional spatial distance model spatiotemporal variation Liang–Kleeman information flow agroecosystem responses multisource data |
| title | An Integrated Drought Index (Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll Fluorescence Dryness Index, VMFDI) Based on Multisource Data and Its Applications in Agricultural Drought Management |
| title_full | An Integrated Drought Index (Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll Fluorescence Dryness Index, VMFDI) Based on Multisource Data and Its Applications in Agricultural Drought Management |
| title_fullStr | An Integrated Drought Index (Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll Fluorescence Dryness Index, VMFDI) Based on Multisource Data and Its Applications in Agricultural Drought Management |
| title_full_unstemmed | An Integrated Drought Index (Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll Fluorescence Dryness Index, VMFDI) Based on Multisource Data and Its Applications in Agricultural Drought Management |
| title_short | An Integrated Drought Index (Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll Fluorescence Dryness Index, VMFDI) Based on Multisource Data and Its Applications in Agricultural Drought Management |
| title_sort | integrated drought index vapor pressure deficit soil moisture sun induced chlorophyll fluorescence dryness index vmfdi based on multisource data and its applications in agricultural drought management |
| topic | drought monitoring three-dimensional spatial distance model spatiotemporal variation Liang–Kleeman information flow agroecosystem responses multisource data |
| url | https://www.mdpi.com/2072-4292/16/24/4666 |
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