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Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes; and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit area, suggesting that CH4 emissions from paddy rice in Monsoon Asia have likely not contributed to the renewed growth of atmospheric CH4 in recent years.

The terrestrial component of the Earth system has witnessed considerable changes in the past decades due to anthropogenic action. Throughout this period, the NASA Terra mission has been constantly monitoring the surface with the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. When combined with the MODIS instrument on-board of the Aqua platform, we obtain a hyper-frequent revisit capability providing sub-daily observations globally at a moderate resolution of ~250 m, but with a strong multi-angular variation of the observation footprint. Here we propose to exploit this particular configuration provided by the combined Terra + Aqua constellation to infer spatial homogeneity from the temporal stability of the observation time series. We thus designed a Temporal Coherence Index (TCI) based on the concept of information entropy. We test it on both synthetic and real landscapes showing its capacity to isolate which time series correspond to homogeneous targets based only on the MODIS measurements themselves. The index is calculated annually for the entirety of emerged surfaces for the period 2003–2020 by leveraging on the scaling capacity of the cloud computing Google Earth Engine (GEE). We foresee multiple practical applications for this index as a novel information layer that should foster the integration of surface data with satellite records, a practice that is increasingly necessary in Earth System Science.

Leaf area index (LAI) and leaf chlorophyll content (Chll) represent key biophysical and biochemical controls on water, energy and carbon exchange processes in the terrestrial biosphere. In combination, LAI and Chll provide critical information on vegetation density, vitality and photosynthetic potentials. However, simultaneous retrieval of LAI and Chll from space observations is extremely challenging. Regularization strategies are required to increase the robustness and accuracy of retrieved properties and enable more reliable separation of soil, leaf and canopy parameters. To address these challenges, the REGularized canopy reFLECtance model (REGFLEC) inversion system was refined to incorporate enhanced techniques for exploiting ancillary LAI and temporal information derived from multiple satellite scenes. In this current analysis, REGFLEC is applied to a time-series of Landsat data. A novel aspect of the REGFLEC approach is the fact that no site-specific data are required to calibrate the model, which may be run in a largely automated fashion using information extracted entirely from image-based and other widely available datasets. Validation results, based upon in-situ LAI and Chll observations collected over maize and soybean fields in central Nebraska for the period 2001–2005, demonstrate Chll retrieval with a relative root-mean-square-deviation (RMSD) on the order of 19% (RMSD = 8.42 μg cm− 2). While Chll retrievals were clearly influenced by the version of the leaf optical properties model used (PROSPECT), the application of spatio-temporal regularization constraints was shown to be critical for estimating Chll with sufficient accuracy. REGFLEC also reproduced the dynamics of in-situ measured LAI well (r2 = 0.85), but estimates were biased low, particularly over maize (LAI was underestimated by ~ 36 %). This disparity may be attributed to differences between effective and true LAI caused by significant foliage clumping not being properly accounted for in the canopy reflectance model (SAIL). Additional advances in the retrieval of canopy biophysical and leaf biochemical constituents will require innovative use of existing remote sensing data within physically realistic canopy reflectance models along with the ability to exploit the enhanced spectral and spatial capabilities of upcoming satellite systems.

The concept of canopy spectral invariants expresses the observation that simple algebraic combinations of leaf and canopy spectral transmittance and reflectance become wavelength independent and determine a small set of canopy structure specific variables. This set includes the canopy interceptance, the recollision and the escape probabilities. These variables specify an accurate relationship between the spectral response of a vegetation canopy to the incident solar radiation at the leaf and the canopy scale and allow for a simple and accurate parameterization for the partitioning of the incoming radiation into canopy transmission, reflection and absorption at any wavelength in the solar spectrum. This paper presents a solid theoretical basis for spectral invariant relationships reported in literature with an emphasis on their accuracies in describing the shortwave radiative properties of the three-dimensional vegetation canopies. The analysis of data on leaf and canopy spectral transmittance and reflectance collected during the international field campaign in Flakaliden, Sweden, June 25¿July 4, 2002 supports the proposed theory. The results presented here are essential to both modeling and remote sensing communities because they allow the separation of the structural and radiometric components of the measured/modeled signal. The canopy spectral invariants offer a simple and accurate parameterization for the shortwave radiation block in many global models of climate, hydrology, biogeochemistry, and ecology. In remote sensing applications, the information content of hyperspectral data can be fully exploited if the wavelength-independent variables can be retrieved, for they can be more directly related to structural characteristics of the three-dimensional vegetation canopy.

International audience; MODIS has been providing daily imagery for retrieving land surface properties with a spatial resolution 250m since the year 2000. In many places, this pixel size is closer to that of individual landscape elements, such as managed forest stands or crop fields, than those of time series more typically used in vegetation analyses (with pixel sizes ranging from about 1km to 8km). With such spatial resolution, combined with its increasingly long archive, MODIS data offers great potential for vegetation monitoring applications in general, and crop growth monitoring in particular. However, due to its whiskbroom design, the observation geometry of the MODIS instrument, combined with the spatial uncertainty in the registration of the images, can result in different (albeit neighbouring) physical areas being mapped onto the same pixel depending on the view zenith angle (which varies from day to the next). Rather than considering this as an inconvenience, a method is here proposed to exploit this peculiarity to identify pixels corresponding to a homogeneous plant cover, in order to retrieve surface specific time series of satellite products. This method is based on quantifying the temporal signal-to-noise ratio (SNR, hereafter) of the daily MODIS NDVI time series, defined as the variance of smoothed temporal signal over the variance of the residues. If consecutive observations of the same pixel (which have thus sampled the spatial vicinity of that pixel) provide similar NDVI values, the resulting temporal signal is relatively stable and the SNR is high. Such stability can indicate that the signal comes from a spatially homogeneous surface, such as a single large field covered by the same crop with similar agro-management practices. On the contrary, a noisy time series indicates instead a transition zone between different land uses or between fields with different management practices. SNR maps therefore serve as a proxy for sub-pixel homogeneity from which time series originating from a specific land cover or land use can be retrieved. This approach is demonstrated over 12 contrasting agricultural landscapes across the globe from which clearly distinctive crop specific signals are extracted. Exploiting the full MODIS archive to derive surface specific information in this way should open new avenues for regional to global agricultural monitoring applications. Expanding this method to derive satellite products for specific land cover classes could also be useful for many other applications linked to dynamics of land cover and land use change.