Why Scikit and statsmodel provide different Coefficient of determination?
You need to add an intercept to statsmodels manually, while it is added automatically in sklearn.
import altair as alt
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
import statsmodels.api as sm

np.random.seed(0)
data = pd.DataFrame({
'Date': pd.date_range('1990-01-01', freq='D', periods=50),
'NDVI': np.random.uniform(low=-1, high=1, size=(50)),
'RVI': np.random.uniform(low=0, high=1.4, size=(50))
})

X = data[['NDVI']].values
X2 = data[['NDVI']].columns
Y = data['RVI'].values

# Sklearn (note syntax order X,Y in fit)
regressor = LinearRegression()  
model = regressor.fit(X, Y)
print(&quot;Coef:&quot;, model.coef_)
print(&quot;Constant:&quot;, model.intercept_)
print(&quot;R2:&quot;, model.score(X,Y))

# Statsmodels (note syntax order Y,X in fit)
X = sm.add_constant(X) # manually add a constant here
model = sm.OLS(Y, X).fit() 
print(model.summary())


Results:
Sklearn:
Coef: [-0.06561888]
Constant: 0.5756540424787774
R2: 0.0077907160447101545

Statsmodels:
                            OLS Regression Results
==============================================================================
Dep. Variable:                      y   R-squared:                       0.008
Model:                            OLS   Adj. R-squared:                 -0.013
Method:                 Least Squares   F-statistic:                    0.3769
Date:                Tue, 19 May 2020   Prob (F-statistic):              0.542
Time:                        11:18:42   Log-Likelihood:                -25.536
No. Observations:                  50   AIC:                             55.07
Df Residuals:                      48   BIC:                             58.90
Df Model:                           1
Covariance Type:            nonrobust
==============================================================================
                 coef    std err          t      P&gt;|t|      [0.025      0.975]
------------------------------------------------------------------------------
const          0.5757      0.059      9.796      0.000       0.457       0.694
x1            -0.0656      0.107     -0.614      0.542      -0.281       0.149
==============================================================================
Omnibus:                        5.497   Durbin-Watson:                   2.448
Prob(Omnibus):                  0.064   Jarque-Bera (JB):                3.625
Skew:                           0.492   Prob(JB):                        0.163
Kurtosis:                       2.122   Cond. No.                         1.85
==============================================================================