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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Water Management and Systems Research » Research » Publications at this Location » Publication #362169
Title: Crop coefficient estimation method of maize by UAV remote sensing and soil moisture monitoring

Author
item ZHANG, YU - Northwest A&f University
item ZHANG, LIYUAN - Northwest A&f University
item Zhang, Huihui
item SONG, CHAOYANG - Northwest A&f University
item LIN, GUANGHUA - Northwest A&f University
item HAN, WENTING - Northwest A&f University

Submitted to: Transactions of the Chinese Society of Agricultural Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/10/2018
Publication Date: 1/20/2019
Citation: Zhang, Y., Zhang, L., Zhang, H., Song, C., Lin, G., Han, W. 2019. Crop coefficient estimation method of maize by UAV remote sensing and soil moisture monitoring. Transactions of the Chinese Society of Agricultural Engineering. 35(1):83-89. https://doi: 10.11975/j.issn.1002-6819.2019.01.010.
DOI: https://doi.org/10.11975/j.issn.1002-6819.2019.01.010

Interpretive Summary: This study tested the feasibility of estimating crop coefficient (Kc) by unmanned aerial vehicle (UAV)-based multispectral imagery for maize under different water stresses. The experiment was conducted in Zhaojun town Experimental Station in Dalate Qi, Inner Mongolia. Full irrigation (TR1) was designed as 50% of field water holding capacity based on previous research results and local situation. Deficit treatments, TR2-TR4, were applied with different amounts of irrigation water during late vegetative, reproductive and maturation stages. The maize was planted on May 20, 2017 and irrigated with a sprinkler irrigation system. The experimental area was partitioned into 4 regions for the different treatments. In each region, a squared area with the side length of 12 m was chosen for plant height and leaf area index measurements. The measurements were carried out every 2-5 days. Soil moisture at 30-cm depth was determined in each area as the surface soil moisture (SM). Climatic parameters were collected from a local meteorological station. Crop coefficient was calculated by dual crop coefficient method given by FAO56 based on meteorological parameters and plant height. A multispectral UAV monitoring system was to used to acquire multispectral images from maize under different water stress conditions. The results showed that the water stress reduced LAI significantly, and SR and SM decreased in the late growth stage. The correlation analysis showed that SM had the highest correlation with Kc for TR1, TR2 and TR4 but LAI was highly correlated with Kc in the TR3. By stepwise regression analysis, the 3-variable model (LAI, SM and SR) had the highest accuracy to estimate crop coefficient.

Technical Abstract: The rapid and accurate acquisition of evapotranspiration (ET) in field crops is an urgent issue to be solved in crop ET researches. In this study, the feasibility of estimating crop coefficient (Kc) by unmanned aerial vehicle (UAV)-based remote sensing in maize under different water stresses was tested. The experiment was conducted in Zhaojun town Experimental Station in Dalate Qi, Inner Mongolia. Full irrigation (TR1) was designed as 50% of field water holding capacity based on previous research results and local situation, which was considered as the baseline. The water stress condition (80% of the base soil moisture) was applied in the fast growth stage for TR2-TR4. The 82% and 43% of the base soil moisture were applied for the late growth stage of TR2 and TR3, respectively. In the middle growth stage, the water stress with 65% of the base soil moisture was applied for TR4. The maize was planted on May 20, 2017 and irrigated with a sprinkler irrigation system. The experimental area was partitioned into 4 regions for the different treatments. In each region, a squared area with the side length of 12 m was chosen for plant height and leaf area index measurements. The measurements were carried out every 2-5 days. Soil moisture at 30-cm depth was determined in each area as the surface soil moisture. Climatic parameters were collected from a local meteorological station. Crop coefficient was calculated by dual crop coefficient method given by FAO56 based on meteorological parameters and plant height. A multispectral UAV monitoring system was to used to acquire multispectral images from maize under different water stress conditions. The dynamic change of leaf area index (LAI), soil moisture, simple ratio index (SR), and Kc was analyzed. The results showed that the water stress reduced LAI significantly, SR and surface soil moisture decreased in the late growth stage, and Kc was low in the late growth stage. The correlation analysis between Kc, SR, LAI and surface soil moisture showed that surface soil moisture had the highest correlation with Kc for tTR1, TR2 and TR4 but LAI was highly correlated with Kc in the TR3. By stepwise regression analysis, the 3-variable model had the highest accuracy with r2 of 0.63 and RMSE of 0.21 The validation showed that r2 was 0.60 and RMSE was 0.21. It indicated that the 3-variable model was well to estimate crop coefficient.