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Satellite-based estimation of soil organic carbon in Portuguese grasslands
Publication . Morais, Tiago G.; Jongen, Marjan; Tufik, Camila; Rodrigues, Nuno R.; Gama, Ivo; Serrano, João; Gonçalves, Maria C.; Mano, Raquel; Domingos, Tiago; Teixeira, Ricardo F. M.
Soil organic carbon (SOC) sequestration is one of the main ecosystem services provided by well-managed grasslands. In the Mediterranean region, sown biodiverse pastures (SBP) rich in legumes are a nature-based, innovative, and economically competitive livestock production system. As a co-benefit of increased yield, they also contribute to carbon sequestration through SOC accumulation. However, SOC monitoring in SBP require time-consuming and costly field work. Methods: In this study, we propose an expedited and cost-effective indirect method to estimate SOC content. In this study, we developed models for estimating SOC concentration by combining remote sensing (RS) and machine learning (ML) approaches. We used field-measured data collected from nine different farms during four production years (between 2017 and 2021). We utilized RS data from both Sentinel-1 and Sentinel-2, including reflectance bands and vegetation indices. We also used other covariates such as climatic, soil, and terrain variables, for a total of 49 inputs. To reduce multicollinearity problems between the different variables, we performed feature selection using the sequential feature selection approach. We then estimated SOC content using both the complete dataset and the selected features. Multiple ML methods were tested and compared, including multiple linear regression (MLR), random forests (RF), extreme gradient boosting (XGB), and artificial neural networks (ANN). We used a random cross-validation approach (with 10 folds). To find the hyperparameters that led to the best performance, we used a Bayesian optimization approach. Results: Results showed that the XGB method led to higher estimation accuracy than the other methods, and the estimation performance was not significantly influenced by the feature selection approach. For XGB, the average root mean square error (RMSE), measured on the test set among all folds, was 2.78 g kg−1 (r2 equal to 0.68) without feature selection, and 2.77 g kg−1 (r2 equal to 0.68) with feature selection (average SOC content is 13 g kg−1). The models were applied to obtain SOC content maps for all farms. Discussion: This work demonstrated that combining RS and ML can help obtain quick estimations of SOC content to assist with SBP management
Characterization of portuguese sown rainfed grasslands using remote sensing and machine learning
Publication . Morais, Tiago G.; Jongen, Marjan; Tufik, Camila; Rodrigues, Nuno R.; Gama, Ivo; Fangueiro, David; Serrano, João; Vieira, Susana; Domingos, Tiago; Teixeira, Ricardo F.M.
Grasslands are crucial ecosystems that support and provide a diverse number of ecosystem
services. Sown biodiverse pastures rich in legumes (SBP) were developed with the main
goal of increasing grassland production while minimizing fertilizers inputs. In this paper,
the main properties of SBP in Portugal were estimated using remote sensing and machine
learning in six different farms and two production years (spring 2018 and 2019). Four
pasture characteristics were considered: aboveground standing biomass, fraction of le-
gumes, plant nitrogen (N) content and plant phosphorus (P) content. Remote sensing data
were obtained from Sentinel-2. The spectral bands combined with 5 vegetation indices
and 9 covariates were used. Multiple linear regression, LASSO, Ridge, random forests,
XGBoost and LightGBM regression models were used. Two cross-validation approaches
were used: (1) a random approach with random selection of the folds (RN-CV), and (2)
a structured approach where each fold is a unique combination of farm and year, which
is subsequently used to assess the performance of the model obtained with the 8 other
folds (LLYO-CV). Results showed that the random forest method had the best estima-
tion accuracy for all pasture characteristics. Regarding cross-validation approaches, the
algorithms with RN-CV have higher estimation accuracy for all pasture characteristics (on
average about 10% lower RMSE and an R2 85% higher), as compared to the algorithms
with LLYO-CV. However, LLYO-CV should avoid overfitting and improve generalization
of the models because in each fold the model is tested in a farm and year that was not
used for training. The RMSE for all variables were significantly low, especially in RN-CV.
Plant P is the variable where the choice of CV approach has the least influence (RMSE
of test set with RN-CV: 0.71 g P kg− 1; LLYO-CV: 0.72 g P kg− 1). Standing biomass is
the variable with the highest difference between CV approaches (RN-CV: 722 kg ha− 1;
LLYO-CV: 825 kg ha− 1). The RMSE, of legumes and plant N were moderately affected
by the CV approach (legume RN-CV: 0.11; LLYO-CV: 0.12 – plant N RN-CV: 3.96 g N
kg− 1; LLYO-CV: 3.99 g N kg− 1). The algorithms developed here were applied for entire
parcels in the two farms with the most different climate conditions as demonstration of
their potential future use for precision farming
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Fundação para a Ciência e a Tecnologia
Programa de financiamento
CEEC IND 2018
Número da atribuição
CEECIND/00365/2018/CP1572/CT0012