Forest aboveground biomass (AGB) is a prime forest parameter that requires global level estimates to study the global carbon cycle. Light detection and ranging (LiDAR) is the state-of-the-art technology for AGB prediction but it is expensive, and its coverage is restricted to small areas. On the contrary, spaceborne Earth observation data are effective and economical information sources to estimate and monitor AGB at a large scale. In this paper, we present a study on the use of different spaceborne multispectral remote sensing data for the prediction of forest AGB. The objective is to evaluate the effects of temporal, spectral, and spatial capacities of multispectral satellite data for AGB prediction. The study was performed on multispectral data acquired by Sentinel-2, RapidEye, and Dove satellites which are characterized by different spatial resolutions, temporal availability, and number of spectral bands. A systematic process of least absolute shrinkage and selection operator (lasso) variable selection generalized linear modeling, leave-one-out cross-validation, and analysis was accomplished on each satellite dataset for AGB prediction. Results point out that the multitemporal data based AGB models were more effective in prediction than the single-time models. In addition, red-edge and short wave infrared (SWIR) channel dependent variables showed significant improvement in the modeling results and contributed to more than 50% of the selected variables. Results also suggest that high spatial resolution plays a smaller role than spectral and temporal information in the prediction of AGB. The overall analysis emphasizes a good potential of spaceborne multispectral data for developing sophisticated methods for AGB prediction especially with specific spectral channels and temporal information.

Modelling and large-scale mapping of forest aboveground biomass (AGB) is a complicated, challenging and expensive task. There are considerable variations in forest characteristics that creates functional disparity for different models and needs comprehensive evaluation. Moreover, the human-bias involved in the process of modelling and evaluation affects the generalization of models at larger scales. In this paper, we present an automated machine learning (AutoML) framework for modelling, evaluation and stacking of multiple base models for AGB prediction. We incorporate a hyperparameter optimization procedure for automatic extraction of targeted features from multitemporal Sentinel-2 data that minimizes human-bias in the proposed modelling pipeline. We integrate the two independent frameworks for automatic feature extraction and automatic model ensembling and evaluation. The results suggest that the extracted target-oriented features have excessive contribution of red-edge and short-wave infrared spectrum. The feature importance scale indicates a dominant role of summer based features as compared to other seasons. The automated ensembling and evaluation framework produced a stacked ensemble of base models that outperformed individual base models in accurately predicting forest AGB. The stacked ensemble model delivered the best scores of $\rm{R^{2}_{cv}=0.71}$ and RMSE = 74.44 Mgha ^-1 . The other base models delivered $\rm{R^{2}_{cv}}$ and RMSE ranging between 0.38–0.66 and 81.27–109.44 Mg ha ^-1 respectively. The model evaluation metrics indicated that the stacked ensemble model was more resistant to outliers and achieved a better generalization. Thus, the proposed study demonstrated an effective automated modelling pipeline for predicting AGB by minimizing human-bias and deployable over large and diverse forest areas.

This research work aims to study the effect of training parameter concept and sample size in the process of classification by using a fuzzy Possibilistic c-Means (PCM) approach for Pigeon Pea specific crop mapping. For specific class extraction, the “mean” of the training data is considered as a training parameter of the classification algorithm. In this study, we proposed an “Individual Sample as Mean” (ISM) approach where the individual training sample is accounted as a mean parameter for the fuzzy PCM classifier. In order to avoid the spectral overlap of target Pigeon pea crop with other crops in the study area, a temporal indices database was generated from Sentinel 2A/2B satellite images acquired during the 2019–2020 Pigeon Pea crop cycle. The spectral dimensionality of temporal data was reduced to extract the required bands to achieve maximum enhancement of the target crop class in the temporal data. Further, the training sample size was increased to study the heterogeneity within the class in the classified output. The proposed ISM approach delivered a higher mean membership difference (MMD) between the Pigeon Pea crop and the co-cultivated Cotton crop as compared to the conventional mean method. This indicated that a better separation was achieved between the target crop and the spectrally similar crop grown, that were cultivated in the same study area. When the sample size was gradually increased from 5 to 60, the MMD values within the Pigeon Pea test fields remained in the range 0.013–0.02, thereby implying that the proposed algorithm works better even with a small number of training samples. The heterogeneity was better handled using the proposed ISM approach since the variance obtained within Pigeon Pea field was only 0.008, as compared to that of 0.02 achieved using the conventional mean approach.

Aboveground biomass (AGB) is an important forest attribute directly linked to the forest carbon pool. The use of satellite remote sensing (RS) data has increased for AGB prediction due to their large footprint and low-cost availability. However, they have been limited due to saturation effect that leads to low prediction precision. In this article, we propose an innovative and dynamic architecture based on generative neural network that extracts target oriented generative features for predicting forest AGB using satellite RS data. These features are more resilient to mixed forest types and geographical conditions as compared to the traditional features and models. The effectiveness of the proposed features was assessed by experiments performed using multispectral, synthetic aperture radar, and combined dual-source datasets. The proposed model achieved best performance in terms of precision, model agreement, and overfitting as compared to the other conventional models for all analyzed datasets. The t-distributed stochastic neighbor embedding scatterplots of the generative features clearly show one dimension of the feature space associated with the target AGB. Feature importance analysis indicated that the produced generative features were more significant than the conventional analytical features. Also, the model provided a robust framework for homogeneous fusion of multisensor features from satellite RS data for predicting AGB.