Inconsistencies of interannual variability and trends in long-term satellite leaf area index products

Understanding the long-term performance of global satellite leaf area index (LAI) products is important for global change research. However, few effort has been devoted to evaluating the long-term time-series consistencies of LAI products. This study compared four long-term LAI products (GLASS, GLOBMAP, LAI3g, and TCDR) in terms of trends, interannual variabilities, and uncertainty variations from 1982 through 2011. This study also used four ancillary LAI products (GEOV1, MERIS, MODIS C5, and MODIS C6) from 2003 through 2011 to help clarify the performances of the four long-term LAI products. In general, there were marked discrepancies between the four long-term LAI products. During the pre-MODIS period (1982-1999), both linear trends and interannual variabilities of global mean LAI followed the order GLASS>LAI3g>TCDR>GLOBMAP. The GLASS linear trend and interannual variability were almost 4.5 times those of GLOBMAP. During the overlap period (2003-2011), GLASS and GLOBMAP exhibited a decreasing trend, TCDR no trend, and LAI3g an increasing trend. GEOV1, MERIS, and MODIS C6 also exhibited an increasing trend, but to a much smaller extent than that from LAI3g. During both periods, the R2 of detrended anomalies between the four long-term LAI products was smaller than 0.4 for most regions. Interannual variabilities of the four long-term LAI products were considerably different over the two periods, and the differences followed the order GLASS>LAI3g>TCDR>GLOBMAP. Uncertainty variations quantified by a collocation error model followed the same order. Our results indicate that the four long-term LAI products were neither intraconsistent over time nor interconsistent with each other. These inconsistencies may be due to NOAA satellite orbit changes and MODIS sensor degradation. Caution should be used in the interpretation of global changes derived from the four long-term LAI products

Nitrogen addition delays the emergence of an aridity-induced threshold for plant biomass

Crossing certain aridity thresholds in global drylands can lead to abrupt decays of ecosystem attributes such as plant productivity, potentially causing land degradation and desertification. It is largely unknown, however, whether these thresholds can be altered by other key global change drivers known to affect the water-use efficiency and productivity of vegetation, such as elevated CO2 and nitrogen (N). Using >5000 empirical measurements of plant biomass, we showed that crossing an aridity (1–precipitation/potential evapotranspiration) threshold of ∼0.50, which marks the transition from dry sub-humid to semi-arid climates, led to abrupt declines in aboveground biomass (AGB) and progressive increases in root:shoot ratios, thus importantly affecting carbon stocks and their distribution. N addition significantly increased AGB and delayed the emergence of its aridity threshold from 0.49 to 0.55 (P < 0.05). By coupling remote sensing estimates of leaf area index with simulations from multiple models, we found that CO2 enrichment did not alter the observed aridity threshold. By 2100, and under the RCP 8.5 scenario, we forecast a 0.3% net increase in the global land area exceeding the aridity threshold detected under a scenario that includes N deposition, in comparison to a 2.9% net increase if the N effect is not considered. Our study thus indicates that N addition could mitigate to a great extent the negative impact of increasing aridity on plant biomass in drylands. These findings are critical for improving forecasts of abrupt vegetation changes in response to ongoing global environmental change.This research was supported by the Second Tibetan Plateau Scientific Expedition and Research (2019QZKK0305), the Fundamental Research Funds for the Central Universities (lzujbky-2022-ct01), "111" Project (BP0719040) and "Innovation Star" project of Gansu Province's outstanding graduate students in 2023 (2023CXZX-132). FTM is supported by Generalitat Valenciana (CIDEGENT/2018/041) and the Spanish Ministry of Science and Innovation (EUR2022-134048). ZZ is supported by the National Natural Science Foundation of China (41901122) and the Shenzhen Fundamental Research Program (GXWD20201231165807007- 20200814213435001). JP is supported by the Spanish Government grant TED2021-132627B-I00 funded by MCIN, AEI/10.13039/501100011033 and the European Union NextGenerationEU/PRTR, the Fundación Ramón Areces grant CIVP20A6621, and the Catalan Government grant SGR2021-1333

Recent Changes in Global Photosynthesis and Terrestrial Ecosystem Respiration Constrained From Multiple Observations

To assess global carbon cycle variability, we decompose the net land carbon sink into the sum of gross primary productivity (GPP), terrestrial ecosystem respiration (TER), and fire emissions and apply a Bayesian framework to constrain these fluxes between 1980 and 2014. The constrained GPP and TER fluxes show an increasing trend of only half of the prior trend simulated by models. From the optimization, we infer that TER increased in parallel with GPP from 1980 to 1990, but then stalled during the cooler periods, in 1990-1994 coincident with the Pinatubo eruption, and during the recent warming hiatus period. After each of these TER stalling periods, TER is found to increase faster than GPP, explaining a relative reduction of the net land sink. These results shed light on decadal variations of GPP and TER and suggest that they exhibit different responses to temperature anomalies over the last 35 years

Contrasting effects of CO₂ fertilization, land-use change and warming on seasonal amplitude of Northern Hemisphere CO₂ exchange

Continuous atmospheric CO₂ monitoring data indicate an increase in the amplitude of seasonal CO₂-cycle exchange (SCA_(NBP)) in northern high latitudes. The major drivers of enhanced SCA_(NBP) remain unclear and intensely debated, with land-use change, CO₂ fertilization and warming being identified as likely contributors. We integrated CO₂-flux data from two atmospheric inversions (consistent with atmospheric records) and from 11 state-of-the-art land-surface models (LSMs) to evaluate the relative importance of individual contributors to trends and drivers of the SCA_(NBP) of CO₂ fluxes for 1980–2015. The LSMs generally reproduce the latitudinal increase in SCA_(NBP) trends within the inversions range. Inversions and LSMs attribute SCA_(NBP) increase to boreal Asia and Europe due to enhanced vegetation productivity (in LSMs) and point to contrasting effects of CO₂ fertilization (positive) and warming (negative) on SCA_(NBP). Our results do not support land-use change as a key contributor to the increase in SCA_(NBP). The sensitivity of simulated microbial respiration to temperature in LSMs explained biases in SCA_(NBP) trends, which suggests that SCA_(NBP) could help to constrain model turnover times

Temperature increase reduces global yields of major crops in four independent estimates

Imbalance-P paper contact with: josep peñuelas: [email protected], rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population

Lower land-use emissions responsible for increased net land carbon sink during the slow warming period

The terrestrial carbon sink accelerated during 1998–2012, concurrently with the slow warming period, but the mechanisms behind this acceleration are unclear. Here we analyse recent changes in the net land carbon sink (NLS) and its driving factors, using atmospheric inversions and terrestrial carbon models. We show that the linear trend of NLS during 1998–2012 is about 0.17 ± 0.05 Pg C yr−2 , which is three times larger than during 1980–1998 (0.05 ± 0.05 Pg C yr−2). According to terrestrial carbon model simulations, the intensification of the NLS cannot be explained by CO2 fertilization or climate change alone. We therefore use a bookkeeping model to explore the contribution of changes in land-use emissions and find that decreasing land-use emissions are the dominant cause of the intensification of the NLS during the slow warming period. This reduction of land-use emissions is due to both decreased tropical forest area loss and increased afforestation in northern temperate regions. The estimate based on atmospheric inversions shows consistently reduced land-use emissions, whereas another bookkeeping model did not reproduce such changes, probably owing to missing the signal of reduced tropical deforestation. These results highlight the importance of better constraining emissions from land-use change to understand recent trends in land carbon sinks

Exploring the Best-Matching Plant Traits and Environmental Factors for Vegetation Indices in Estimates of Global Gross Primary Productivity

As the largest source of uncertainty in carbon cycle studies, accurate quantification of gross primary productivity (GPP) is critical for the global carbon budget in the context of global climate change. Numerous vegetation indices (VIs) based on satellite data have participated in the construction of GPP models. However, the relative performance of various VIs in predicting GPP and what additional factors should be combined with them to reveal the photosynthetic capacity of vegetation mechanistically better are still poorly understood. We constructed two types of models (universal and plant functional type [PFT]-specific) for solar-induced chlorophyll fluorescence (SIF), near-infrared reflectance of vegetation (NIRv), and Leaf Area Index (LAI) based on two widely used machine learning algorithms, i.e., the random forest (RF) and back propagation neural network (BPNN) algorithms. A total of thirty plant traits and environmental factors with legacy effects are considered in the model. We then systematically investigated the ancillary variables that best match each vegetation index in estimating global GPP. Four types of models (universal and PFT-specific, RF and BPNN) consistently show that SIF performs best when modeled using a single vegetation index (R2 = 0.67, RMSE = 2.24 g C·m−2·d−1); however, NIRv combined with CO2, plant traits, and climatic factors can achieve the highest prediction accuracy (R2 = 0.87, RMSE = 1.40 g C·m−2·d−1). Plant traits effectively enhance all prediction models’ accuracy, and climatic variables are essential factors in improving the accuracy of NIRv- or LAI-based GPP models, but not the accuracy of SIF-based models. Our findings provide valuable information for the configuration of the data-driven models to improve the accuracy of predicting GPP and provide insights into the physiological and ecological mechanisms underpinning GPP prediction

Object-Oriented Unsupervised Change Detection Based on Neighborhood Correlation Images and k-Means Clustering for the Multispectral and High Spatial Resolution Images

An unsupervised change-detection problem is formulated as a binary classification problem corresponding to the change and no change areas. This paper proposes a novel unsupervised object-oriented change detection method based on neighborhood correlation images (NCIs) and k-means clustering for high-resolution remote sensing images. We tested our proposed method in two study areas of Beijing with RapidEye images and compared it with three other popular change detection methods based on different images: change vector analysis (CVA), principal component analysis (PCA), and multivariate alteration detection (MAD). The results indicate that our method has the highest overall accuracy (90.80% in Shunyi District, Beijing and 90.40% in Daxing District, Beijing) and Kappa coefficient (0.7922 in Shunyi District, Beijing and 0.7796 in Daxing District, Beijing). In addition, the McNemar test indicates that our method is robust and stable across different study areas. We concluded that the object-oriented NCIs method outperforms traditional difference images (CVA, PCA, and MAD) in unsupervised change detection. The experimental results demonstrate the effectiveness of the proposed approach in solving the problem of unsupervised change detection for high-resolution images