Soybean Water Monitoring and Water Demand Prediction in Arid Region Based on UAV Multispectral Data
Soil moisture content is a key factor influencing plant growth and agricultural productivity, directly impacting water uptake, nutrient absorption, and stress resistance. This study proposes a rapid, low-cost, non-destructive method for dynamically monitoring soil moisture at depths of 0–200 cm thro...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/15/1/88 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832589391465283584 |
|---|---|
| author | Shujie Jia Mingyi Cui Lei Chen Shangyuan Guo Hui Zhang Zheyu Bai Yaoyu Li Linqiang Deng Fuzhong Li Wuping Zhang |
| author_facet | Shujie Jia Mingyi Cui Lei Chen Shangyuan Guo Hui Zhang Zheyu Bai Yaoyu Li Linqiang Deng Fuzhong Li Wuping Zhang |
| author_sort | Shujie Jia |
| collection | DOAJ |
| description | Soil moisture content is a key factor influencing plant growth and agricultural productivity, directly impacting water uptake, nutrient absorption, and stress resistance. This study proposes a rapid, low-cost, non-destructive method for dynamically monitoring soil moisture at depths of 0–200 cm throughout the crop growth period under dryland conditions, with validation in soybean cultivation. During critical soybean growth stages, UAV multispectral data of the canopy were collected, and ground measurements were conducted for three GPS-referenced 50 cm × 50 cm plots to obtain canopy leaf water content, coverage, and soil volumetric moisture at 20 cm intervals. Ten vegetation indices were constructed from multispectral data to explore statistical relationships between vegetation indices, surface soil moisture, canopy leaf water content, and deeper soil moisture. Predictive models were developed and evaluated. Results showed that the NDVI-based nonlinear regression model achieved the best performance for leaf water content (R<sup>2</sup> = 0.725), and a significant correlation was found between canopy leaf water content and 0–20 cm soil moisture (R<sup>2</sup> = 0.705), enabling predictions of deeper soil moisture. Surface soil models accurately estimated 0–200 cm soil moisture distribution (R<sup>2</sup> = 0.9995). Daily water dynamics simulations provided robust support for precision irrigation management. This study demonstrates that UAV multispectral remote sensing combined with ground sampling is a valuable tool for soybean water management, supporting precision agriculture and sustainable water resource utilization. |
| format | Article |
| id | doaj-art-3c8551d682b34102987f2586de1de721 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-3c8551d682b34102987f2586de1de7212025-01-24T13:16:40ZengMDPI AGAgronomy2073-43952024-12-011518810.3390/agronomy15010088Soybean Water Monitoring and Water Demand Prediction in Arid Region Based on UAV Multispectral DataShujie Jia0Mingyi Cui1Lei Chen2Shangyuan Guo3Hui Zhang4Zheyu Bai5Yaoyu Li6Linqiang Deng7Fuzhong Li8Wuping Zhang9College of Software, Shanxi Agricultural University, Taigu 030801, ChinaCollege of Software, Shanxi Agricultural University, Taigu 030801, ChinaCollege of Software, Shanxi Agricultural University, Taigu 030801, ChinaCollege of Software, Shanxi Agricultural University, Taigu 030801, ChinaCollege of Software, Shanxi Agricultural University, Taigu 030801, ChinaSchool of Public Administration, Shanxi University of Finance and Economics, Taiyuan 030006, ChinaCollege of Agricultural Engineering, Shanxi Agricultural University, Taigu 030801, ChinaCollege of Software, Shanxi Agricultural University, Taigu 030801, ChinaCollege of Software, Shanxi Agricultural University, Taigu 030801, ChinaCollege of Software, Shanxi Agricultural University, Taigu 030801, ChinaSoil moisture content is a key factor influencing plant growth and agricultural productivity, directly impacting water uptake, nutrient absorption, and stress resistance. This study proposes a rapid, low-cost, non-destructive method for dynamically monitoring soil moisture at depths of 0–200 cm throughout the crop growth period under dryland conditions, with validation in soybean cultivation. During critical soybean growth stages, UAV multispectral data of the canopy were collected, and ground measurements were conducted for three GPS-referenced 50 cm × 50 cm plots to obtain canopy leaf water content, coverage, and soil volumetric moisture at 20 cm intervals. Ten vegetation indices were constructed from multispectral data to explore statistical relationships between vegetation indices, surface soil moisture, canopy leaf water content, and deeper soil moisture. Predictive models were developed and evaluated. Results showed that the NDVI-based nonlinear regression model achieved the best performance for leaf water content (R<sup>2</sup> = 0.725), and a significant correlation was found between canopy leaf water content and 0–20 cm soil moisture (R<sup>2</sup> = 0.705), enabling predictions of deeper soil moisture. Surface soil models accurately estimated 0–200 cm soil moisture distribution (R<sup>2</sup> = 0.9995). Daily water dynamics simulations provided robust support for precision irrigation management. This study demonstrates that UAV multispectral remote sensing combined with ground sampling is a valuable tool for soybean water management, supporting precision agriculture and sustainable water resource utilization.https://www.mdpi.com/2073-4395/15/1/88soil moisture contentleaf water contentvegetation indexPenman–Monteith formuladaily water requirement of soybean |
| spellingShingle | Shujie Jia Mingyi Cui Lei Chen Shangyuan Guo Hui Zhang Zheyu Bai Yaoyu Li Linqiang Deng Fuzhong Li Wuping Zhang Soybean Water Monitoring and Water Demand Prediction in Arid Region Based on UAV Multispectral Data Agronomy soil moisture content leaf water content vegetation index Penman–Monteith formula daily water requirement of soybean |
| title | Soybean Water Monitoring and Water Demand Prediction in Arid Region Based on UAV Multispectral Data |
| title_full | Soybean Water Monitoring and Water Demand Prediction in Arid Region Based on UAV Multispectral Data |
| title_fullStr | Soybean Water Monitoring and Water Demand Prediction in Arid Region Based on UAV Multispectral Data |
| title_full_unstemmed | Soybean Water Monitoring and Water Demand Prediction in Arid Region Based on UAV Multispectral Data |
| title_short | Soybean Water Monitoring and Water Demand Prediction in Arid Region Based on UAV Multispectral Data |
| title_sort | soybean water monitoring and water demand prediction in arid region based on uav multispectral data |
| topic | soil moisture content leaf water content vegetation index Penman–Monteith formula daily water requirement of soybean |
| url | https://www.mdpi.com/2073-4395/15/1/88 |
| work_keys_str_mv | AT shujiejia soybeanwatermonitoringandwaterdemandpredictioninaridregionbasedonuavmultispectraldata AT mingyicui soybeanwatermonitoringandwaterdemandpredictioninaridregionbasedonuavmultispectraldata AT leichen soybeanwatermonitoringandwaterdemandpredictioninaridregionbasedonuavmultispectraldata AT shangyuanguo soybeanwatermonitoringandwaterdemandpredictioninaridregionbasedonuavmultispectraldata AT huizhang soybeanwatermonitoringandwaterdemandpredictioninaridregionbasedonuavmultispectraldata AT zheyubai soybeanwatermonitoringandwaterdemandpredictioninaridregionbasedonuavmultispectraldata AT yaoyuli soybeanwatermonitoringandwaterdemandpredictioninaridregionbasedonuavmultispectraldata AT linqiangdeng soybeanwatermonitoringandwaterdemandpredictioninaridregionbasedonuavmultispectraldata AT fuzhongli soybeanwatermonitoringandwaterdemandpredictioninaridregionbasedonuavmultispectraldata AT wupingzhang soybeanwatermonitoringandwaterdemandpredictioninaridregionbasedonuavmultispectraldata |