the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Jun 2026
A Weakly Supervised Deep Learning Framework for Estimating Above-Ground Biomass for Non-Forest Landscapes From Optical Images
Abstract. Above-ground biomass (AGB) maps are essential for carbon accounting and sustainable land management, yet AGB for non-forest landscapes remains poorly accounted for in global datasets. Here, we make use of deep learning and high-resolution PlanetScope imagery to introduce the concept of AGB contribution maps, which are high-resolution AGB predictions that capture local patterns. These maps can be predicted at any resolution from 1 to 100 m, providing insights into the spatial features included in the coarser resolution AGB maps, being essential for mapping trees outside forests. Our method employs a weakly supervised hybrid framework that transfers information from an existing 100 m global AGB map to high‑resolution optical satellite imagery, enabling the interpretation of detailed spatial patterns. We demonstrate that our map achieves detailed and spatially consistent patterns of woody vegetation in African savanna landscapes comparable to UAV-based LiDAR. Aggregated AGB values are well aligned with independent in-situ measurements (r2 = 0.71, bias 1 %), which is contrary to the original coarse AGB map used for training (r2 = 0.17, bias 48 %), indicating the capability of our approach to refine the existing map towards a higher accuracy for estimating tree biomass outside forests. This suggests that our model has learned tree-level information that is not present in the original AGB training data, providing a framework to refine existing coarse-resolution AGB maps. The granular and multi-resolution results provide no contribution to global efforts in sustainable land management of non-forest landscapes at any preferred scale and resolution.
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Status: open (until 01 Oct 2026)
- RC1: 'Comment on egusphere-2026-2924', Anonymous Referee #1, 11 Aug 2026 reply
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- 1
General comments:
This paper presents a weakly supervised deep learning model for Above Ground Biomass (AGB) estimation for non-forest areas. The model uses Planet images at 3m resolution as the input and AGB reference maps at 100m to predict 10, 30, 50, or 100m resolution AGB products. The authors highlight that most researchers focus on forest areas, ignoring small non-forest open-canopy areas. Studying this areas is important in the current context of climate change. The authors insist that their main contribution do not consist in the production of high resolution maps, but rather in the focus on mapping those non-forest areas. Moreover, the authors state that the used spatial pooling method improves the 100m resolution AGB predictions thanks to the extraction of the fine-grained spatial features from Planet imagery.
Overall, the paper addresses an interesting and relevant research problem, particularly because AGB mapping is known to be challenging due to the lack of reliable ground-truth data. Hence, it is understandable that training and evaluating an AGB prediction model can be challenging. The authors provide a quantitative evaluation of their method against one independent UAV-based reference dataset, with generally encouraging results. However, this evaluation alone is not sufficient to assess the robustness of the method. The article lacks some standard evaluation protocols and metrics commonly used for model training and evaluation. In addition, some of the conclusions and discussions are not fully supported by the presented results, and some aspects of the results are not sufficiently discussed.
Specific comments:
General:
1.
Line 310. You say: “However, the key output is not the high-resolution of the AGB contribution map. [...] The major achievement is the fact that by aggregating the high-resolution contribution map to the scale of a desired operational AGB map (typically > 30 × 30 m resolution), we show a highly improved relationship with UAV data as compared to previous maps, including the map that has been used for training/supervision (Xu et al., 2021).”
Moreover, the abstract states : “The granular and multi-resolution results provide no contribution to global efforts in sustainable land management of non-forest landscapes at any preferred scale and resolution.”
At the same time, on the line 83 of the introduction you state: “Therefore, it offers a scalable solution for mapping high-resolution AGB in landscapes where such information has historically been unavailable or incomplete.”
which is contradictory to the information in the abstract and discussion.
The paper is presented as using a weakly supervised learning approach. If the main contribution is producing AGB maps at 100 m resolution, it is unclear what the role of the weakly supervised approach is in this contribution, since the model ultimately produces the same type of output as the target data. The authors should clarify the main contribution of the paper and explain more clearly what is achieved by the proposed weakly supervised approach.
Dataset:
2.
Did you divide your dataset into train/test/validation? How many patches were used? The more detailed information should be given in the dataset section.
Overall, there is no clear information about all the products used for evaluation.
Suggestion: It could be easier for readers, if you make an overview table that describes all the data that was used with months/year, resolution and surface.
Moreover, sometimes it is not clear if during the comparison you used the products from the same year.
Finally, you do not indicate which month the Planet image used for model training was takes. How do you deal with seasonality while making predictions?
Model
3.
The reproducibility of your method is not possible with the description given in the article. Hence, the model should be better explained: 1/Figure 1 needs to be detailed (the encoder-decoder part, bilinear upscaling, ….); 2/The experimental setup should be presented: the number of layers and other hyperparameters, as well as training time.
4.
Convolution margin effect. The paper does not appear to account for the convolution margin effect when applying the model to generate the AGB maps. Since predictions near patch boundaries can be affected by the limited spatial context and padding, this could introduce spatial artefacts in the resulting maps. Please clarify how this effect is handled and, if it is not explicitly addressed, discuss its potential impact on the results.
Validation of the method
5.
Quantitative evaluation. You present a quantitative evaluation on an independent dataset; however, you do not report any evaluation of the model on the test set of the original dataset. This should be reported as part of the standard evaluation protocol. Without this information, it is difficult to assess the model performance and interpret the subsequent evaluations.
For example, at line 260 you state: “The original map used for training has a weak relationship (R² = 0.17) with the UAV data and a high underestimation bias of 48%. When aggregated to 100 m, our map has a considerably higher (R² = 0.71) and a lower bias −1% at the 100 m plot level.” This raises the question of how well the model performs with respect to the original reference data. Please report the results on the test set of the original dataset, including the relevant evaluation metrics, so that the model's performance can be properly assessed.
Moreover, RMSE and MAPE should be reported in all experiments to assess the magnitude of the errors, as R² alone does not provide information about the actual prediction error, especially on the extreme values.
Line 254. You state: “The strong relationship is evident over the entire value range, almost following the 1:1 regression line, see Fig. 3.” However, most of the values appear to be concentrated in the lower range, and the points show substantial scatter around the 1:1 line, particularly relative to the magnitude of the values. Therefore, the strength of the relationship over the entire value range is difficult to assess from the figure. Reporting RMSE and MAPE would help quantify the magnitude of these errors and provide a more complete assessment of the model performance. Moreover, a density scatterplot (e.g., using point density or hexagonal binning) would be more appropriate for visualizing the distribution of points in this case.
It is understandable that lower AGB values can be difficult to predict, and your algorithm shows the overall improvement over the existing maps, but the accuracy of the prediction of your method should be assessed and discussed for different value ranges.
Line 307: “Considering that the error is below 10 Mg/ha, multitemporal maps may provide indications on the carbon accumulation of plantations already after a few years of growth.” – giving the previous comment, I am not sure this is fully correct.
6.
Line 255 “Interestingly, when aggregating our map to 100 m and comparing both the original 100 m AGB map used for training and our map aggregated to 100 m with the UAV derived AGB, our map shows an improved relationship with higher correlation values.”
How did you compare the original AGB map with the UAV-derived AGB if they do not have the same resolution? You state that the UAV-derived biomass is computed at the 20 m plot level by delineating individual tree crowns. What about small vegetation that does not have a clearly identifiable crown? Is it considered as zero biomass?
How did you perform the resampling of the UAV-based map from 20m to 100m?
7.
High resolution maps. It is known that the presented method (Weak supervision via spatial pooling) tend to produce homogeneous (or only weakly varying) high-resolution predictions within each low-resolution pixel. From the presented results, it is not clear whether your method overcomes this limitation.
Figure 4 presents the standard deviation of the pixels within a hectare. However, there are several issues with this figure. First, the figure does not include a colorbar, making it impossible to assess the magnitude of the reported spatial variability. If the colorbar from subfigure (d) is intended to apply to the subfigure (e), this should be made explicit and the color bar should be centered appropriately.
Second, it is difficult to visually compare your high resolution results to the satellite imagery without a proper zoom level on the high resolution results: I suggest selecting a couple of 2x2 or 3x3 pixels low resolution patches for of heterogenous areas and present the corresponding high resolution results with sufficient zoom level. For biomass maps, the 0 value should be set to white to better see textures.
8.
The paper does not provide any quantitative evaluation of the spatial consistency of the super-resolved biomass maps, although, you claim that “The high spatial resolution of our map gives confidence and clarity in what features are included in the AGB calculations.”
In Section 3.2, the evaluation is performed only on 20 × 20 pixel plots containing vegetation. However, this does not show whether the model correctly predicts zero biomass in non-vegetated areas.
This could be evaluated quite easily. For example, the predicted biomass maps could be converted into vegetation/non-vegetation masks and compared with masks derived from the LiDAR reference data. The spatial consistency could then be reported using metrics such as the F1 score or mIoU.
9.
National-scale mapping. I do not understand which year the national-scale predictions correspond to. Were the predictions generated for the same year and spatial extent as the data used for training? If so, the higher correlation with the Xu et al. map compared with the other maps is not surprising, since the Xu et al. map was used as the training target. This comparison therefore does not provide an independent assessment of the model.
Moreover, the reported MAPE of 366% at 100 m resolution appears extremely high and raises questions about the quality of your model predictions. Please, discuss this result.
Finally, the maps used for comparison appear to correspond to different years. This makes a direct comparison difficult, as differences between the maps may partly reflect temporal variability rather than differences in mapping performance. Ideally, the maps should be compared for the same year, or the potential impact of the temporal mismatch should be explicitly discussed.
Figure 6. Why your model prediction values are all concentrated around 18000 Mg per 1km2? How it is then possible that your model has higher total AGB in table 2 if other models predict higher values? Please, such homogenious prediction values of your model.
10.
Overall, the authors should provide at least one additional quantitative comparison with independent reference data. Otherwise, the generalizability of the method cannot be properly assessed.
Figures
11.
Figure S2. Optical images (especially 1st row) can not be analysed properly due to the chosen color normalization. Please, normalize properly or use false color scheme for the images, so the vegetation is better seen.
On the second row, it can be seen that two images do not correspond to the same year (the absence of the vegetation on the right). On the first row, the predicted vegetation pattern clearly do not match with Google Earth image: the forest gap in the middle of the image is not represented in the prediction. Please, explain. Does the Google Earth image match the Planet image?
Technical corrections:
Figure 5 - “(a) Land-cover map showing the non-forest classes preserved in the analysis.” This is not correct, you show land cover map for the first row and satellite images for other rows. Please, change.
The text in sections 1 and 2 is not divided into paragraphs. Moreover, most Figures do not have a colormap legend and/or a scale; the chosen colormaps make it complicated to visually analyse the results. Some results need to be presented in tables for clarity.
For colormaps – use white for “no biomass” zero values and colors for biomass. It is difficult to see vegetation patterns in high resolution predictions with magenta colorscheme.
Please, add scale to all maps.
The results from Section 3.2 should be put in a table for the clarity.
You use different author’s names for the method used for the reference AGB map - Saatchi and XU – please, use the same name, it is can be confusing for readers.