Correlation of satellite-based LAI and actual crop yield

• Видавець: Scientific Centre for Aerospace Research of the Earth Institute of Geological Science National Academy of Sciences of Ukraine, Kyiv, Ukraine
• Дата: 2023
• Автори: Kryvoshein, Оleksandr, Kryvobok, Оleksii, Kozhushko, Olena
• Формат: Стаття
• Мова: English
• Опубліковано: Scientific Centre for Aerospace Research of the Earth Institute of Geological Science National Academy of Sciences of Ukraine, Kyiv, Ukraine 2023
• Теми: crop; actual yield; correlation; Satellite-based LAI; індекс площі листкової поверхні LAI; фактична врожайність сільськогосподарських культур; кореляція; супутникові дані
• Онлайн доступ: https://ujrs.org.ua/ujrs/article/view/238

The main objective of this article was to investigate the correlation between actual crop yield and Sentinel-2 Leaf Area Index (LAI) for the further possibility of predict model creating. To do so, the following steps have been done. Step 1 – the dataset of actual crop yield was collected for 2364 fields in Ukraine represented with maize, soy, sunflower, winter wheat, winter rapeseed and winter barley. Step 2 – the dataset of Sentinel-2 LAI was collected for 2016-2018 period according to the actual crop yield available. Step 3 – LAI preprocessing (spatial averaging, temporal interpolation/extrapolation to fill the time series gaps, smoothing time series dynamics, temporal averaging). In order to accomplish the process of filling the gaps for the LAI time series, the regular time series dynamics of LAI with a 1-day interval were created using 4 methods: linear interpolation, spline interpolation, LOCF (Last Observation Carried Forward) and ARIMA (AutoRegressive Integrated Moving Average). The time series smoothing process have been accomplish using the local polynomial regression (LOESS) function with different degrees of smoothing. The LAI dynamics preprocessing step did not strongly affect the improvement of the correlation coefficients. Thus, the smoothing process for the time series LAI dynamics at the 0.1 degree of smoothing according to the LOCF and ARIMA gap-filling methods of improved correlation coefficients by 0.01 on average. Step 4 – actual yield values were related to preprocessed satellite-based LAI (correlation of actual yields and LAI). A strong relationship was not indicated (with averaged by vegetation periods correlation coefficient of 0.4 for maize, 0.52 – soy, 0.39 – sunflower, 0.86 – winter barley, 0.54 – winter rapeseed and 0.5 – winter wheat). Since the reliability of obtained correlation coefficients also depends on how many observed data points were in the sample, the hypothesis test of the "significance of the correlation coefficient" has been performed and shows the significance level of p < 0.05 for all crops except winter barley (there is insufficient evidence to conclude that high correlation coefficient of 0.86 for this crop is significant). The average correlation coefficient for all crops is about 0.5 (p < 0.05) which is considered low/moderate. Thus, an attempt to create a linear crop yield prediction model using only Leaf Area Index (LAI) derived from Sentinel-2 will not be effective (based on the cases considered).