Abstract
This study proposes a method for determining the optimal image date to improve the evaluation of cultivated land quality (CLQ). Five vegetation indices: leaf area index (LAI), difference vegetation index (DVI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI) are first retrieved using the PROSAIL model and Gaofen-1 (GF-1) images. The indices are then introduced into four regression models at different growth stages for assessing CLQ. The optimal image date of CLQ evaluation is finally determined according to the root mean square error (RMSE). This method is tested and validated in a rice growth area of Southern China based on 115 sample plots and five GF-1 images acquired at the tillering, jointing, booting, heading to flowering, and milk ripe and maturity stage of rice in 2015, respectively. The results show that the RMSEs between the measured and estimated CLQ from four vegetation index-based regression models at the heading to flowering stage are smaller than those at the other growth stages, indicating that the image date corresponding with the heading to flowering stage is optimal for CLQ evaluation. Compared with other vegetation index-based models, the LAI-based logarithm model provides the most accurate estimates of CLQ. The optimal model is also driven using the GF-1 image at the heading to flowering stage to map CLQ of the study area, leading to a relative RMSE of 14.09% at the regional scale. This further implies that the heading to flowering stage is the optimal image time for evaluating CLQ. This study is the first effort to provide an applicable method of selecting the optimal image date to improve the estimation of CLQ and thus advanced the literature in this field.
Highlights
Cultivated land provides food and is an important resource and material basis for human survival and development [1,2]
(45 samples from each of five growth stages) of rice are compared by calculating the root mean square error (RMSE) and relative
It has been well known that evaluating and mapping cultivated land quality (CLQ) using remote sensing data is a quick and effective method but very challenging due to different image dates being selected with different spectral responses, which will affect the accuracy of CLQ evaluation
Summary
Cultivated land provides food and is an important resource and material basis for human survival and development [1,2]. The evaluation of cultivated land quality (CLQ) plays an important role in improving and protecting cultivated land and is related to land productive potential [3,4]. Sensors 2019, 19, 4937 traditional measurement methods, including field and laboratory measurements, provide an accurate evaluation of CLQ, they are time-consuming and costly [5,6,7], and do not meet the need of modern cultivated land management. Remote sensing technology offers a unique means for rapid evaluation of CLQ at a regional scale with a low cost [4].
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