Fine-scale forest monitoring is essential for understanding canopy structure and its dynamics, which are key indicators of carbon stocks, biodiversity, and forest health. Deep learning is particularly effective for this task, as it integrates spectral, temporal, and spatial signals that jointly reflect the canopy structure. To address this need, we introduce THREASURE-Net, a novel end-to-end framework for Tree Height REgression And SUper-REsolution. The model is trained on Sentinel-2 time series using reference height metrics derived from LiDAR HD data at multiple spatial resolutions over Metropolitan France to produce annual height maps. We evaluate three model variants, producing tree-height predictions at 10 m, 5 m, and 2.5 m resolution. THREASURE-Net does not rely on any pretrained model, nor on reference very high resolution optical imagery to train its super-resolution module; instead, it learns solely from LiDAR-derived height information. Our approach outperforms existing state-of-the-art methods based on Sentinel data and is competitive with methods based on very high resolution imagery. It can be deployed to generate high-precision annual canopy-height maps, achieving mean absolute errors of 2.63 m, 2.70 m, and 2.89 m at 10 m, 5 m, and 2.5 m resolution, respectively. These results highlight the potential of THREASURE-Net for scalable and cost-effective structural monitoring of temperate forests using only freely available satellite data. The source code for THREASURE-Net is available at: https://github.com/Global-Earth-Observation/threasure-net.

This study investigates the synergistic use of dual-polarization Sentinel-1 time series for forest loss monitoring across mixed land-cover types. While VH polarization generally provides higher contrast between intact vegetation and deforested areas, VV polarization is assumed to be more effective over areas characterized by vegetation remnants left after clearing. These insights motivate the development of fpol-BOCD, an unsupervised Bayesian polarimetric change detection method with Near Real-Time (NRT) capabilities. The algorithm is designed to leverage the complementary scattering properties of both polarizations, improving detection sensitivity across diverse land-cover conditions. Notably, fpol-BOCD jointly processes VH and VV Sentinel-1 data with comparable computational complexity relative to single-polarization approaches. Evaluation is performed using two datasets of forest loss events, extracted from the MapBiomas Alerta reference dataset for 2020 in the Cerrado and Amazon biomes in Brazil. One dataset focuses on small-scale clearings (0.1-2 hectares), while the other includes a randomly selected subset of larger deforested patches. The method improves detection accuracy by approximately 10% compared to single-polarization BOCD methods in areas likely containing residual vegetation after clearing, e.g., small disturbances in the Cerrado and large ones in the Amazon, while producing very few false alarms relative to the simple merging of single-polarization alerts. Additionally, fpol-BOCD outperforms the operational optical-based GLAD-L alerts in the Cerrado, achieving a 47% increase in true detections In the Amazon, a comparison with the operational SAR-based RADD alerts shows a 34% increase in true positives for small clearings. The detection speed of fpol-BOCD matches single-polarization BOCD methods. Temporal analysis shows a peak in forest loss during the dry season, with unexpected early wet-season increases likely linked to a lengthening of the dry period. Overall, results highlight the value of polarimetric data for efficient, more accurate forest loss monitoring in complex tropical environments.

Deter-RT é um novo sistema para detecção automática de desmatamento na Amazônia, com alta capacidade de personalização conforme as necessidades do usuário. Este estudo comparou nove configurações do sistema, combinando três valores do fator que regula a otimização espacial entre ecorregiões e três cenários de aplicação de filtros morfológicos. Os resultados foram avaliados pelos índices omissão, comissão, número de intersecções e formato. Os testes (2022–2023) mostraram que maiores valores do fator aumentaram os erros de omissão e reduziram os de comissão, enquanto o filtro melhorou os índices de geometria dos polígonos. A configuração preferida adota valores do fator empiricamente estimados e o novo filtro morfológico proposto (T8). Outros dois cenários, um com diferenças no filtro (T5) e um com fatores mais conservadores (T7), apresentaram resultados de qualidade semelhante.

Over the past four decades, forests have experienced major disturbances, highlighting the need for Near Real-Time (NRT) monitoring. Traditional optical-based detection is cloud-sensitive, whereas Synthetic Aperture Radar (SAR)-based frameworks enable all-weather observation. Yet, SAR monitoring has mainly focused on humid tropical forests, with reduced performance in regions showing strong seasonal backscatter variation, such as tropical savannas. Detecting small-scale forest loss also remains difficult due to the spatial resolution loss from speckle filtering. This paper presents an unsupervised SAR-based disturbance detection method with NRT capabilities, using Bayesian inference. Building on an existing methodology, the approach processes single-polarization Sentinel-1 SAR time series through Bayesian conjugate analysis. Forest disturbance is framed as a changepoint detection problem, where each new observation updates the probability of forest loss using prior information and a data model. The algorithm uses a hidden Markov chain to adapt recursively to seasonal variation and bypasses spatial filtering, preserving native data resolution and enhancing small-scale forest loss detection. Additionally, a methodology accounts for proximity to past disturbances. The method is tested on two 2020 reference datasets from the Brazilian Amazon and Cerrado savanna. The first covers small validation polygons (0.1-1 ha, excluding selective logging), totaling 2,650 ha in the Amazon and 450 ha in the Cerrado. The second includes larger clearings totaling 11,200 ha in the Amazon, and 12,700 ha in the Cerrado. A further comparison is conducted with operational NRT forest loss monitoring approaches. Results show substantial gains in detecting small-scale disturbances with reduced false alarms. In the Amazon, the method achieves an F1-score of 97.3% versus 93.1% for the current leading NRT approach. In the Cerrado, it reaches an F1-score of 97.4%, far exceeding the 33.3% of the optical-based method. For larger clearings, performance matches existing SAR approaches in the Amazon. While combined optical-SAR monitoring increases true positives, it also raises false alarm rates. In the Cerrado, the proposed method clearly outperforms optical monitoring, and in both regions it improves timeliness relative to individual operational approaches.

Aboveground biomass (AGB) is an essential component of the Earth's carbon cycle. Yet, large uncertainties remain in its spatial distribution and temporal evolution. Satellite remote sensing can help improve the accuracy of AGB estimates. In particular, the L-band (1.41 GHz) vegetation optical depth (VOD) derived from the SMOS (Soil Moisture and Ocean Salinity) mission is a good AGB proxy. Averaging the SMOS L-VOD over a year and linking it to an existing AGB map constitute a well-established method to derive a spatial relationship between the two quantities. Then, a temporal extrapolation of this spatial relation derives global and harmonized AGB time series from the L-VOD. This study refines this protocol by analyzing the impact of three factors on the AGB–VOD calibration. First, an analysis shows that ascending and descending VOD can be properly merged to estimate the AGB. Second, the use of a single global spatial relationship is preferred over several regional ones. Third, this new AGB dataset is compared with other published AGB datasets to assess the validity of the temporal extrapolation. The produced dataset provides vegetation biomass values up to 300 Mg ha−1 from 2011 onward. It shows more interannual variability than the other available time series and presents globally lower AGB estimates. In general, the resulting AGB is consistent with the AGB maps of the Climate Change Initiative (CCI) Biomass version 5 (average Pearson's correlation coefficient 0.87) and can be used in AGB studies. The AGB dataset has been produced from the Level 2 SMOS products with one global VOD–AGB relationship, mixing ascending and descending orbits. The AGB dataset, including the spatial bias, is open-access and the NetCDF files are available at https://doi.org/10.12770/95f76ff0-5d89-430d-80db-95fbdd77f543 (Boitard et al., 2024).

To improve our understanding of the carbon cycle, precise estimates of forest biomass are needed. High values of dense tropical forest biomass are particularly important, as they determine uncertainties in carbon stock assessment and carbon loss due to deforestation and forest degradation. However, estimating Above Ground Biomass ( AGB) of tropical forests based on remote sensing systems remains challenging, most existing satellite systems are not sensitive to AGB in the high range. In this paper, we assess the use of P-band SAR tomography technique to provide AGB with reduced uncertainties in the range of 200-400 Mg.ha (-1). We present the expected contribution of the BIOMASS mission in estimating the carbon loss from deforestation and from forest degradation, and in providing the Digital Elevation Model under dense forests.

The Amazon biome, undergoing significant deforestation, requires robust monitoring systems for effective management and conservation. This study introduces DETER-RT, a novel deforestation detection system that combines the Synthetic Aperture Radar (SAR)-based DETER-R and the TropiSCO systems to enhance detection capabilities using Sentinel-1 satellite data. DETER-RT utilizes a double-threshold technique to optimize the detection of deforestation by balancing detection accuracy and minimizing false positives across different forest types and conditions in the Amazon. The new system modulates detection thresholds based on the proximity of new disturbances to previously detected deforestation, incorporating a dynamic, regionalized threshold adjustment to cater to the variable characteristics of the Amazon’s diverse forest cover. Initial results indicate that DETER-RT provides more timely and accurate warnings compared to existing methods, especially during the fire season, where its performance is less impacted by smoke and haze that typically hinder optical sensors. This approach exemplifies the integration of advanced remote sensing technologies and analytical techniques in environmental monitoring.

Remote sensing satellites allow large-scale and fast detections of forest loss. Operational forest loss detection systems have been mainly developed over tropical forests; however, it is increasingly important to have access to accurate and up-to-date information on temperate forests. In this article, we adapted a Sentinel-1-based near real-time tropical forest loss detection method, based on the radar change ratio, to detect French temperate forests clear-cuts. Using ancillary data, annual and submonthly clear-cuts were assessed for broadleaf and conifer forests, for various tree species, over public and private forests. Using 967 validation plots, the maps exhibited recall and precision of 80.9% and 99.4%, respectively. The clear-cuts area shows remarkable stability over time from 2020. We found seven times more clear-cuts in private forests than in public forests, although the surface area of private forests is only three times that of public forests. It was also demonstrated that only 1.6% out of 4530 dieback reference plots, and 6.2% of maps of forest bark beetle attacks, were confused with clear-cuts before clear-cuts actually occurred, which makes our maps complementary with forest dieback maps. Collectively, the findings of this study could have significant implications for the implementation of a radar-satellite-based system designed for the real-time detection of large-scale clear-cuts in European temperate forests.

In this paper, we propose an unsupervised statistical approach for near real-time monitoring of forest loss, leveraging Bayesian inference. We address the identification of forest loss as a change-point detection problem within non-filtered Sentinel-1 single polarization time series data. Each new observation contributes to the probability of deforestation occurrence, utilizing prior knowledge and a data model. Our method offers the advantage of detecting small-scale deforestation without resorting to spatial filtering techniques, thus preserving the native spatial resolution of the Sentinel-1 measurements. To assess its effectiveness, we conducted comparative evaluations against existing operational deforestation monitoring systems. The validation campaign revealed that our method exhibits enhanced detection performance with low false alarm rates with respect to existing systems across diverse landscapes, including dense forest regions such as the Brazilian Amazon, as well as seasonality-dependent areas like the Cerrado, which is strongly under-monitored by existing technology. This robustness stems from the sequential adaptive process inherent in our approach, which enables effective monitoring even in the presence of backscatter variations.

The world’s forests are undergoing significant changes due to loss and degradation, emphasizing the need for Near Real-Time (NRT) monitoring to prevent further damage. Traditional monitoring methods using optical imagery are hindered by cloud coverage, while newer Synthetic Aperture Radar (SAR) systems, although operational in all weather conditions, face challenges such as sensitivity to soil moisture and the need for spatial filtering to reduce speckle effects. These limitations affect the detection of small-scale forest loss, especially in seasonally variable regions like dry forests and savannas. This paper presents a SAR-based forest disturbance detection method using Bayesian inference. Unlike traditional methods, this approach maintains the native resolution of the data by avoiding spatial filtering. Forest disturbance is modelled as a change-point detection problem within a non-filtered Sentinel-1 time series, where each new observation updates the probability of forest loss by leveraging prior information and a data model. This sequential adaptation ensures robustness against variations and trends, making it effective in monitoring disturbances across diverse forest types, including areas affected by seasonality. The proposed method was tested against other NRT monitoring systems for the year 2020, using small validation polygons (under 1 hectare) in the Brazilian Amazon and Cerrado savanna. Results demonstrate significant improvements in detecting small-scale disturbances and drastically reduced false alarm rates in both biomes. Notably, in the seasonality-sensitive Cerrado, our solution completely outperforms the leading and only existing optical technology.

Passive microwave observations at different frequencies suffer extinction effects of the different vegetation components (branches, leaves, trunk) across the canopy of the soil’s microwave emission. These effects are often represented as a frequency-dependent variable called the Vegetation Optical Depth (VOD), which has been used (recently) to estimate Above-Ground Biomass (AGB). Low frequency observations, more particularly at L-band (1.4 GHz), have been shown to be sensitive to the woody components of plants (and thus to AGB), hence the growing interest in their use to monitor carbon stocks evolution.In this study, and thanks to the multi-angle capabilities of the SMOS mission, a new approach to estimate AGB maps directly from multi-angular passive L-band Brightness temperatures (TBs) is proposed, thus surpassing the dependence on intermediate variables like the VOD. Biomass estimates are produced from Artificial Neural Networks (ANN), using as reference the three AGB maps of the Climate Change Initiative (CCI) for the years 2010, 2017 and 2018; the SMOS multi-angle TBs for the same years were selected as inputs. The best set of predictors for ANNs and the optimal learning data-set configuration to estimate AGB are proposed based on a sensitivity analysis; the use of TBs in both Vertical and Horizontal polarization, plus a polarization ratio provided the closest biomass estimates to the reference AGB maps.ANNs trained from a purely data-driven approach explained 76% of AGB variability globally (incidence angles >35º showed high synergies with AGB); a hybrid approach (coupling ANN with variables derived from physically based models) slightly increased this value (+3%). However, when the trained models are applied to datasets from years different than those used during the training stage, a decrease in retrieval’s quality was observed; a new training scheme based on multi-year training sets is presented, results showed more stability from this kind of training schemes for temporal analyses.Finally, ANN- and VOD-based estimates were compared with respect to different AGB reference maps, the former outperformed the latter in all evaluation metrics. VOD-based inversions tend to underestimate AGB due to their quick saturation (around 200 Mg/ha) on densely forested regions. Additionally, a strong simplification of the spatial variations of AGB was observed; maps produced from this methodology present abrupt transitions between densely and sparsely vegetated areas, a characteristic that was not observed in the reference maps. When using VOD-derived maps these limitations should be considered, especially when employing them to study the temporal evolution of carbon stocks. The ANN methodology here proposed proves to be a promising technique for the estimation of global AGB maps, with robust results both in the spatial representation and in the temporal reproduction of AGB maps.

There is growing interest in using passive microwave observations and vegetation optical depth (VOD) to study the above-ground biomass (AGB) and carbon stocks evolution. L-band observations, in particular, have been shown to be very sensitive to AGB. Here, thanks to the multiangle capabilities of the soil moisture and ocean salinity mission, a new approach to estimate AGB directly from multiangular L-band brightness temperatures (TBs) is proposed, thus surpassing the use of intermediate variables such as VOD. The European Space Agency (ESA) Climate Change Initiative (CCI) Biomass maps for the years 2010, 2017, and 2018 are used as the AGB reference. AGB estimates from artificial neural networks (ANN) using a purely data-driven approach explained up to 88% of AGB variability globally; even so, a decrease in retrieval performance was observed when models are applied to data from years different than the year used for their training. A new training methodology based on multiyear training sets is presented, leading to results showing more stability for temporal analyses. The best set of predictors and an optimal learning dataset configuration are proposed based on an assessment of the accuracy of the estimates. The ANN methodology using TBs is a promising alternative with respect to the common method of using a parametric function to estimate AGB from VOD. ANNs AGB estimates showed a higher correlation with CCI AGB maps ($R$2 $\sim$0.87 instead of $\sim$0.84) and presented a stronger agreement with their spatial structure and less differences in residual maps.

ABSTRACT More than half a decade after the launch of the Sentinel-1A C-band SAR satellite, several near real-time forest disturbances detection systems based on backscattering time series analysis have been developed and made operational. Every system has its own particular approach to change detection. Here, we have compared the performance of the main SAR-based near real-time operational forest disturbance detection systems produced by research agencies (INPE, in Brazil, CESBIO, in France, JAXA, in Japan, and Wageningen University, in the Netherlands), and compared them to the state-of-the-art optical algorithm, University of Maryland’s GLAD-S2. We implemented an innovative validation protocol, specially conceived to encompass all the analysed systems, which measured every system’s accuracy and detection speed in four different areas of the Amazon basin. The results indicated that, when parametrized equally, all the Sentinel-1 SAR methods outperformed the reference optical method in terms of sample-count F1-Score, having comparable results among them. The GLAD-S2 optical method showed superior results in terms of user’s accuracy (UA), issuing no false detections, but had a lower producer accuracy (PA, 84.88%) when compared to the Sentinel-1 SAR-based systems (PA 90%). Wageningen University’s system, RADD, proved to be relatively faster, especially in heavily clouded regions, where RADD warnings were issued 41 days before optical ones, and the one that better performs on small disturbed patches ( 0.25 ha) with a UA of 70.11%. Of all the high-resolution SAR methods, CESBIO’s had the best results regarding UA (99.0%). Finally, we tested the potential of three hypothetical combined optical-SAR systems. The results show that these combined systems would have excellent detection capabilities, exceeding largely the producer’s accuracy of all the tested methods at the cost of a slightly diminished user’s accuracy, and constitute a promising and feasible approach for the forthcoming forest monitoring systems. POLICY HIGHLIGHTS Recently developed automated SAR-based tropical forest disturbance detection systems showed excellent detection accuracies, even in small, difficult-to-spot deforested patches. SAR detections can be as precise and as fast as optical ones, being more precise and faster in very cloudy areas or in areas subjected to illegal mining. The combination of recently developed SAR and optical warnings systems can yield optimized results, in terms of overall accuracy and producer's accuracy.

Mapping forest resources and carbon is important for improving forest management and meeting the objectives of storing carbon and preserving the environment. Spaceborne remote sensing approaches have considerable potential to support forest height monitoring by providing repeated observations at high spatial resolution over large areas. This study uses a machine learning approach that was previously developed to produce local maps of forest parameters (basal area, height, diameter, etc.). The aim of this paper is to present the extension of the approach to much larger scales such as the French national coverage. We used the GEDI Lidar mission as reference height data, and the satellite images from Sentinel-1, Sentinel-2 and ALOS-2 PALSA-2 to estimate forest height and produce a map of France for the year 2020. The height map is then derived into volume and aboveground biomass (AGB) using allometric equations. The validation of the height map with local maps from ALS data shows an accuracy close to the state of the art, with a mean absolute error (MAE) of 4.3 m. Validation on inventory plots representative of French forests shows an MAE of 3.7 m for the height. Estimates are slightly better for coniferous than for broadleaved forests. Volume and AGB maps derived from height shows MAEs of 75 tons/ha and 93 m${}^3$/ha respectively. The results aggregated by sylvo-ecoregion and forest types (owner and species) are further improved, with MAEs of 23 tons/ha and 30 m${}^3$/ha. The precision of these maps allows to monitor forests locally, as well as helping to analyze forest resources and carbon on a territorial scale or on specific types of forests by combining the maps with geolocated information (administrative area, species, type of owner, protected areas, environmental conditions, etc.). Height, volume and AGB maps produced in this study are made freely available.

Anthropogenic climate change is now considered to be one of the main factors causing an increase in both the frequency and severity of wildfires. These fires are prone to release substantial quantities of CO2 into the atmosphere and to endanger natural ecosystems and biodiversity. Depending on the ecosystem and climate regime, fires have distinct triggering factors and impacts. To better analyse this phenomenon, we investigated post-fire vegetation anomalies over different biomes, from 2012 to 2020. The study was performed using several remotely sensed quantities ranging from visible–infrared vegetation indices (the enhanced vegetation index (EVI)) to vegetation opacities obtained at several passive-microwave wavelengths (X-band, C-band, and L-band vegetation optical depth (X-VOD, C-VOD, and L-VOD)), ranging from 2 to 20 cm. It was found that C- and X-VOD are mostly sensitive to fire impact on low-vegetation areas (grass and shrublands) or on tree leaves, while L-VOD depicts the fire impact on tree trunks and branches better. As a consequence, L-VOD is probably a better way of assessing fire impact on biomass. The study shows that L-VOD can be used to monitor fire-affected areas as well as post-fire recovery, especially over densely vegetated areas.

Above ground biomass (AGB) maps were estimated directly from microwave brightness temperatures (TB) using a machine learning approach. The accuracy of AGB retrievals from Artificial Neural Networks (ANN) is explored using both a multi-angular (using TBs products from the SMOS mission) and multi-frequency approach (using multi frequency measurements from the AMSR-E mission), an additional ANN inversion including optical indexes (MODIS-NDVI) is also discussed. Higher incidence angles have been shown to provide more information during the inversion process for AGB estimates than lower angles. Retrievals from the multi-angular lower-frequency inversion (SMOS - 1.4GHz) performed better than any individual higher-frequency retrievals. The addition of multi-frequency TBs (AMSR-E) to lower-frequency multi-angular TBs improves the performance of ANN models (from $\mathrm{R}^{2}\approx 0.94$ to 0.96). Adding MODIS-NDVI to the inversion process improves the performance in an additional ~0.05%.

Forests are one of the key elements in ecological transition policies in Europe. Sustainable forest management is needed in order to optimise wood harvesting, while preserving carbon storage, biodiversity and other ecological functions. Forest managers and public bodies need improved and cost-effective forest monitoring tools. Research studies have been carried out to assess the use of optical and radar images for producing forest height or biomass maps. The main limitations are the quantity, quality and representativeness of the reference data for model training. The Global Ecosystem Dynamics Investigation (GEDI) mission (full waveform LiDAR on board the International Space Station) has provided an unprecedented number of forest canopy height samples from 2019. These samples could be used to improve reference datasets. This paper aims to present and validate a method for estimating forest dominant height from open access optical and radar satellite images (Sentinel-1, Sentinel-2 and ALOS-2 PALSAR-2), and then to assess the use of GEDI samples to replace field height measurements in model calibration. Our approach combines satellite image features and dominant height measurements, or GEDI metrics, in a Support Vector Machine regression algorithm, with a feature selection process. The method is tested on mixed uneven-aged broadleaved and coniferous forests in France. Using dominant height measurements for model training, the cross-validation shows 7.3 to 11.6% relative Root Mean Square Error (RMSE) depending on the forest class. When using GEDI height metrics instead of field measurements for model training, errors increase to 12.8–16.7% relative RMSE. This level of error remains satisfactory; the use of GEDI could allow the production of dominant height maps on large areas with better sample representativeness. Future work will focus on confirming these results on new study sites, improving the filtering and processing of GEDI data, and producing height maps at regional or national scale. The resulting maps will help forest managers and public bodies to optimise forest resource inventories, as well as allow scientists to integrate these cartographic data into climate models.

Most land surface models can, depending on the simulation experiment, calculate the vegetation distribution and dynamics internally by making use of biogeographical principles or use vegetation maps to prescribe spatial and temporal changes in vegetation distribution. Irrespective of whether vegetation dynamics are simulated or prescribed, it is not practical to represent vegetation across the globe at the species level because of its daunting diversity. This issue can be circumvented by making use of 5 to 20 plant functional types (PFTs) by assuming that all species within a single functional type show identical land–atmosphere interactions irrespective of their geographical location. In this study, we hypothesize that remote-sensing-based assessments of aboveground biomass can be used to constrain the process in which real-world vegetation is discretized in PFT maps. Remotely sensed biomass estimates for Africa were used in a Bayesian framework to estimate the probability density distributions of woody, herbaceous and bare soil fractions for the 15 land cover classes, according to the United Nations Land Cover Classification System (UN-LCCS) typology, present in Africa. Subsequently, the 2.5th and 97.5th percentiles of the probability density distributions were used to create 2.5 % and 97.5 % credible interval PFT maps. Finally, the original and constrained PFT maps were used to drive biomass and albedo simulations with the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) model. This study demonstrates that remotely sensed biomass data can be used to better constrain the share of dense forest PFTs but that additional information on bare soil fraction is required to constrain the share of herbaceous PFTs. Even though considerable uncertainties remain, using remotely sensed biomass data enhances the objectivity and reproducibility of the process by reducing the dependency on expert knowledge and allows assessing and reporting the credible interval of the PFT maps which could be used to benchmark future developments.

Over the past 30 years, strong agricultural growth has changed the socio-economic status of Viet Nam: improving food security, boosting agricultural exports, and creating livelihoods for people. However, the agricultural sector has already been impacted by climate change, and projections for the next few decades indicate that the climate warming trends and anthropogenic pressures are likely to be accelerated. In this chapter, we examine evolution in crop yields in the past decades, and its predicted evolution in the future. The results vary widely between crops, agro-ecological zones and climate scenarios, but most findings concur on the decline of crop yields in the 2030–2050 horizon. On the other hand, the habitat suitability for rice and other major crops will undergo drastic changes. We find that without adaptation, the risks of increasing saline intrusion, and that of permanent inundation due to sea level rise, will significantly reduce (up to 50% by 2050) the land suitable for rice cultivation in the Mekong delta . However, these two main threats to rice cultivation are accentuated by anthropogenic pressures (ground water pumping and sand mining), which require specific policies to be mitigated. Among the adaptation practices, we highlight practices that mitigate the greenhouse gas emissions from agriculture. In particular, the Alternate Wetting and Drying irrigation of rice fields is a single mitigation practice that can reduce the methane emissions from rice fields in Viet Nam by 40%. However, to derive adaptation and mitigation measures for the agriculture sector over the coming decades will require assessments against a background of wider environmental, economic and social evolutions.