Rapid and large changes in coastal wetland structure in China's four major river deltas.

Coastal wetlands provide essential ecosystem goods and services but are extremely vulnerable to sea-level rise, extreme climate, and human activities, especially the coastal wetlands in large river deltas, which are regarded as "natural recorders" of changes in estuarine environments. In addition to the area (loss or gain) and quality (degradation or improvement) of coastal wetlands, the information on coastal wetland structure (e.g., patch size and number) are also major metrics for coastal restoration and biodiversity protection, but remain very limited in China's four major river deltas. In this study, we quantified the spatial-temporal dynamics of total area (TA) and patch number (PN) of coastal wetlands with different sizes in the four deltas and the protected areas (PAs) and assessed the effects of major driving factors during 1984-2020. We also investigated the effectiveness of PAs through the comparison of TA and PN of coastal wetlands before and after the years in which PAs were listed as Ramsar Sites. We found both TA and PN experienced substantial losses in the Liaohe River Delta and Yellow River Delta but recent recoveries in the Yangtze River Delta. The coastal wetlands had a relatively stable and variable trend in TA but had a continually increasing trend in PN in the Pearl River Delta. Furthermore, reduced coastal reclamation, ecological restoration projects, and rapid expansion of invasive plants had great impacts on the coastal wetland structure in various ways. We also found that PAs were effective in halting the decreasing trends in coastal wetland areas and slowing the expansion of reclamation, but the success of PAs is being counteracted by soaring exotic plant invasions. Our findings provide vital information for the government and the public to address increasing challenges of coastal restoration, management, and sustainability in large river deltas.

Anthropogenic activities dominated tropical forest carbon balance in two contrary ways over the Greater Mekong Subregion in the 21st century.

The tropical forest carbon (C) balance threatened by extensive socio-economic development in the Greater Mekong Subregion (GMS) in Asia is a notable data gap and remains contentious. Here we generated a long-term spatially quantified assessment of changes in forests and C stocks from 1999 to 2019 at a spatial resolution of 30 m, based on multiple streams of state-of-the-art high-resolution satellite imagery and in situ observations. Our results show that (i) about 0.54 million square kilometers (21.0% of the region) experienced forest cover transitions with a net increase in forest cover by 4.3% (0.11 million square kilometers, equivalent to 0.31 petagram of C [Pg C] stocks); (ii) forest losses mainly in Cambodia, Thailand, and in the south of Vietnam, were also counteracted by forest gains in China due mainly to afforestation; and (iii) at the national level during the study period an increase in both C stocks and C sequestration (net C gain of 0.087 Pg C) in China from new plantation, offset anthropogenetic emissions (net C loss of 0.074 Pg C) mainly in Cambodia and Thailand from deforestation. Political, social, and economic factors significantly influenced forest cover change and C sequestration in the GMS, positively in China while negatively in other countries, especially in Cambodia and Thailand. These findings have implications on national strategies for climate change mitigation and adaptation in other hotspots of tropical forests.

Spatiotemporal variability and key factors of evergreen forest encroachment in the southern Great Plains.

Woody plant encroachment has been long observed in the southern Great Plains (SGP) of the United States. However, our understanding of its spatiotemporal variability, which is the basis for informed and targeted management strategy, is still poor. This study investigates the encroachment of evergreen forest, which is the most important encroachment component in the SGP. A validated evergreen forest map of the SGP (30 m resolution, for the time period 2015 to 2017) from our previous study was utilized (referred to as evergreen_base). Sample plots of evergreen forest (as of 2017) were collected across the study area, based on which a threshold of winter season (January and February) mean normalized difference vegetation index (NDVIwinter) was derived for each of the 5 sub-regions, using Landsat 7 surface reflectance data from 2015 to 2017. Then a NDVIwinter layer was created for each year within the four time periods of 1985-1989, 1995-1999, 2005-2009, and 2015-2017, with winter season surface reflectance data from Landsat 4, 5, and 7. By applying the sub-region specific NDVIwinter thresholds to the annual NDVIwinter layers and the evergreen_base, a SGP evergreen forest map was generated for each of those years. The annual evergreen forest maps within each time period were composited into one. According to the resulting four composite evergreen forest maps, mean annual encroachment rate (km2/year) was calculated at sub-region and ecoregion scales, over each of the three temporal stages 1990-1999, 2000-2009, and 2010-2017, respectively. To understand the spatiotemporal variability of the encroachment, the encroachment rate at each temporal stage was related to the corresponding initial evergreen forest area, mean annual precipitation (MAP), and mean annual burned area (MABA) through linear regression and pairwise comparison. Results suggest that most of the ecoregions have seen a slowing trend of evergreen forest encroachment since 1990. The temporal trend of encroachment rate tends to be consistent with that of MAP, but opposite to that of MABA. The spatial variability of the encroachment rate among ecoregions can be largely (>68%) explained by initial evergreen forest area but shows no significant relationship with MAP or MABA. These findings provide pertinent guidance for the combat of woody plant encroachment in the SGP under the context of climate change.

TROPOMI reveals dry-season increase of solar-induced chlorophyll fluorescence in the Amazon forest.

Photosynthesis of the Amazon rainforest plays an important role in the regional and global carbon cycles, but, despite considerable in situ and space-based observations, it has been intensely debated whether there is a dry-season increase in greenness and photosynthesis of the moist tropical Amazonian forests. Solar-induced chlorophyll fluorescence (SIF), which is emitted by chlorophyll, has a strong positive linear relationship with photosynthesis at the canopy scale. Recent advancements have allowed us to observe SIF globally with Earth observation satellites. Here we show that forest SIF did not decrease in the early dry season and increased substantially in the late dry season and early part of wet season, using SIF data from the Tropospheric Monitoring Instrument (TROPOMI), which has unprecedented spatial resolution and near-daily global coverage. Using in situ CO2 eddy flux data, we also show that cloud cover rarely affects photosynthesis at TROPOMI's midday overpass, a time when the forest canopy is most often light-saturated. The observed dry-season increases of forest SIF are not strongly affected by sun-sensor geometry, which was attributed as creating a pseudo dry-season green-up in the surface reflectance data. Our results provide strong evidence that greenness, SIF, and photosynthesis of the tropical Amazonian forest increase during the dry season.

Divergent trends of open-surface water body area in the contiguous United States from 1984 to 2016.

The contiguous United States (CONUS), especially the West, faces challenges of increasing water stress and uncertain impacts of climate change. The historical information of surface water body distribution, variation, and multidecadal trends documented in remote-sensing images can aid in water-resource planning and management, yet is not well explored. Here, we detected open-surface water bodies in all Landsat 5, 7, and 8 images (∼370,000 images, >200 TB) of the CONUS and generated 30-meter annual water body frequency maps for 1984-2016. We analyzed the interannual variations and trends of year-long water body area, examined the impacts of climatic and anthropogenic drivers on water body area dynamics, and explored the relationships between water body area and land water storage (LWS). Generally, the western half of the United States is prone to water stress, with small water body area and large interannual variability. During 1984-2016, water-poor regions of the Southwest and Northwest had decreasing trends in water body area, while water-rich regions of the Southeast and far north Great Plains had increasing trends. These divergent trends, mainly driven by climate, enlarged water-resource gaps and are likely to continue according to climate projections. Water body area change is a good indicator of LWS dynamics in 58% of the CONUS. Following the 2012 prolonged drought, LWS in California and the southern Great Plains had a larger decrease than surface water body area, likely caused by massive groundwater withdrawals. Our findings provide valuable information for surface water-resource planning and management across the CONUS.

Estimation of the relative contributions of forest areal expansion and growth to China's forest stand biomass carbon sequestration from 1977 to 2018.

As a prominent part of global and regional terrestrial carbon (C) pools, increases in forest biomass C sinks can be attributed to either forest areal expansion (FAE) or increased biomass C density (IBCD). Accurate estimates of the relative contributions of FAE and IBCD to forest C sequestration can improve our understanding of forest C cycling processes and will help to formulate rational afforestation policies to cope with global warming. In this study, the Continuous Biomass Expansion Factor (CBEF) model and Forest Identity concept were used to map the spatiotemporal variation of the relative contribution of FAE and IBCD to the C sequestration of forest (natural and planted forests) in China and seven regions during the past 40 years. Our results suggest that: (1) total forest biomass C density and stocks of forest increased from 35.41 Mg C ha-1 and 4128.50 Tg C to 43.95 Mg C ha-1 and 7906.23 Tg C in China from 1977 to 2018, respectively; (2) for all forests, the IBCD has been a smaller contributor to C sinks than FAE in China from 1977 to 2018 (33.27 vs. 66.73%); (3) the contribution of FAE to C sinks is greater than that of IBCD in planted forests (63.99 vs. 36.01%), while in natural forests, IBCD has a larger contribution than FAE (57.82 vs. 42.18%) from 1977 to 2018 and the relative contribution of FAE has exceeded IBCD in the last decade; and (4) these patterns varied at the regional level such that the relative contribution of FAE increased for planted forests in most regions but for natural forests, IBCD gradually reached saturation and C stocks declined in northern regions in the last decade. The results from this study suggest that total biomass C sinks will keep increasing because of the increased forest area contributed by afforestation and the relatively young trees in planted forests. This study facilitates a more comprehensive assessment of forest C budgets and improves our understanding of ecological mechanisms of forest biomass carbon stock and dynamics.

Quantifying expansion and removal of Spartina alterniflora on Chongming island, China, using time series Landsat images during 1995-2018.

The rampant encroachment of Spartina alterniflora into coastal wetlands of China over the past decades has adversely affected both coastal ecosystems and socio-economic systems. However, there are no annual or multi-year epoch maps of Spartina saltmarsh in China, which hinders our understanding and management of Spartina invasion. In this study, we selected Chongming island, China, where Spartina saltmarsh had expanded rapidly since its introduction in the 1990s. We investigated phenology of Spartina, Phragmites and Scirpus saltmarshes, and the time series vegetation indices derived from Landsat images showed that Spartina saltmarsh did not green-up in April-May and stayed green in December-January, which differed from the phenology of Phragmites and Scirpus saltmarshes. We developed a pixel- and phenology-based algorithm that used time series Landsat data to identify and map Spartina saltmarsh, and we applied it to quantify the temporal dynamics (expansion and removal) of Spartina saltmarsh on Chongming island during 1995-2018. The resultant maps showed that Spartina saltmarsh area on Chongming island increased from ~4 ha in 1995 to ~2,067 ha in 2012 but dropped substantially to ~729 ha in 2016 after a large-scale ecological engineering project (US$ 186 million) was started to remove Spartina during 2013-2016. Chongming island still had ~1,315 ha Spartina saltmarsh in 2018, and majority of it was distributed outside the Chongming Dongtan National Nature Reserve, which could serve as the sources for reinvasion in the near future. This study demonstrates the feasibility of using time series Landsat images, pixel- and phenology-based algorithm, and GEE platform to identify and map Spartina saltmarsh over years in the region, which is useful to the management of invasive plants in coastal wetlands.

Tracking annual changes of coastal tidal flats in China during 1986-2016 through analyses of Landsat images with Google Earth Engine.

Tidal flats (non-vegetated area), along with coastal vegetation area, constitute the coastal wetlands (intertidal zone) between high and low water lines, and play an important role in wildlife, biodiversity and biogeochemical cycles. However, accurate annual maps of coastal tidal flats over the last few decades are unavailable and their spatio-temporal changes in China are unknown. In this study, we analyzed all the available Landsat TM/ETM+/OLI imagery (~ 44,528 images) using the Google Earth Engine (GEE) cloud computing platform and a robust decision tree algorithm to generate annual frequency maps of open surface water body and vegetation to produce annual maps of coastal tidal flats in eastern China from 1986 to 2016 at 30-m spatial resolution. The resulting map of coastal tidal flats in 2016 was evaluated using very high-resolution images available in Google Earth. The total area of coastal tidal flats in China in 2016 was about 731,170 ha, mostly distributed in the provinces around Yellow River Delta and Pearl River Delta. The interannual dynamics of coastal tidal flats area in China over the last three decades can be divided into three periods: a stable period during 1986-1992, an increasing period during 1993-2001 and a decreasing period during 2002-2016. The resulting annual coastal tidal flats maps could be used to support sustainable coastal zone management policies that preserve coastal ecosystem services and biodiversity in China.

Mapping coastal wetlands of China using time series Landsat images in 2018 and Google Earth Engine.

Coastal wetlands, composed of coastal vegetation and non-vegetated tidal flats, play critical roles in biodiversity conservation, food production, and the global economy. Coastal wetlands in China are changing quickly due to land reclamation from sea, aquaculture, industrialization, and urbanization. However, accurate and updated maps of coastal wetlands (including vegetation and tidal flats) in China are unavailable, and the detailed spatial distribution of coastal wetlands are unknown. Here, we developed a new pixel- and phenology-based algorithm to identify and map coastal wetlands in China for 2018 using time series Landsat imagery (2,798 ETM+/OLI images) and the Google Earth Engine (GEE). The resultant map had a very high overall accuracy (98%). There were 7,474.6 km2 of coastal wetlands in China in 2018, which included 5,379.8 km2 of tidal flats, 1,856.4 km2 of deciduous wetlands, and 238.3 km2 of evergreen wetlands. Jiangsu Province had the largest area of coastal wetlands in China, followed by Shandong, Fujian, and Zhejiang Provinces. Our study demonstrates the high potential of time series Landsat images, pixel- and phenology-based algorithm, and GEE for mapping coastal wetlands at large scales. The resultant coastal wetland maps at 30-m spatial resolution serve as the most current dataset for sustainable management, ecological assessments, and conservation of coastal wetlands in China.

The predictive value of a preoperative systemic immune-inflammation index and prognostic nutritional index in patients with esophageal squamous cell carcinoma.

Growing evidence indicates that systemic inflammation response and malnutrition status are correlated with survival in certain types of solid tumors. The aim of this study is to evaluate the association between the systemic immune-inflammation index (SII) and prognostic nutritional index (PNI) and overall survival (OS) in patients with esophageal squamous cell carcinoma (ESCC) after esophagectomy. A consecutive series of 655 patients with resected ESCC who underwent esophagectomy were enrolled in the retrospective study. The preoperative SII was defined as platelet × neutrophil/lymphocyte counts. The PNI was calculated as albumin concentration (g/L) + 5 × total lymphocyte count (109 /L). The optimal cut-off values of SII, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and PNI were determined by receiver operating characteristic analysis. Survival analysis was performed using the Kaplan-Meier method with a log-rank test, followed by a multivariate Cox proportional hazards model. A high SII was significantly related to tumor size, histological type, invasion depth, and TNM stage (p < 0.05). A low PNI was significantly associated with age, tumor size, invasion depth, lymph node metastasis, and TNM stage (p < 0.05). Univariate analysis revealed that age, smoking history, tumor size, invasion depth, lymph node metastasis, SII, NLR, PLR, and PNI were predictors of OS (p < 0.05). Multivariate analysis identified age (p = 0.041), tumor size (p = 0.016), invasion depth (p < 0.001), lymph node metastasis (p < 0.001), SII (p = 0.033), and PNI (p = 0.022) as independent prognostic factors correlated with OS. There was a significant inverse relationship between the SII and PNI (r = 0.309; p < 0.001). The predictive value increased when the SII and PNI were considered in combination. Our results demonstrate that the preoperative high SII and low PNI are powerful indicators of aggressive biology and poor prognosis for patients with ESCC. The combination of SII and PNI can enhance the accuracy of prognosis.

The impact of surveillance and control on highly pathogenic avian influenza outbreaks in poultry in Dhaka division, Bangladesh.

In Bangladesh, the poultry industry is an economically and socially important sector, but it is persistently threatened by the effects of H5N1 highly pathogenic avian influenza. Thus, identifying the optimal control policy in response to an emerging disease outbreak is a key challenge for policy-makers. To inform this aim, a common approach is to carry out simulation studies comparing plausible strategies, while accounting for known capacity restrictions. In this study we perform simulations of a previously developed H5N1 influenza transmission model framework, fitted to two separate historical outbreaks, to assess specific control objectives related to the burden or duration of H5N1 outbreaks among poultry farms in the Dhaka division of Bangladesh. In particular, we explore the optimal implementation of ring culling, ring vaccination and active surveillance measures when presuming disease transmission predominately occurs from premises-to-premises, versus a setting requiring the inclusion of external factors. Additionally, we determine the sensitivity of the management actions under consideration to differing levels of capacity constraints and outbreaks with disparate transmission dynamics. While we find that reactive culling and vaccination policies should pay close attention to these factors to ensure intervention targeting is optimised, across multiple settings the top performing control action amongst those under consideration were targeted proactive surveillance schemes. Our findings may advise the type of control measure, plus its intensity, that could potentially be applied in the event of a developing outbreak of H5N1 amongst originally H5N1 virus-free commercially-reared poultry in the Dhaka division of Bangladesh.

Changes in area and number of nature reserves in China.

Nature reserves (NR) are the cornerstone of biodiversity conservation. Over the past 60 years, the rapid expansion of NRs in China, one of the world's megadiverse countries, has played a critical role in slowing biodiversity loss. We examined the changes in the number and area of China's NRs from 1956 to 2014 and analyzed the effect of economic development on the expansion of China's NRs from 2005 to 2014 with linear models. Despite a continuing increase in the number of NRs, the total area of China's NRs decreased by 3% from 2007 to 2014. This loss resulted from downsizing and degazettement of existing NRs and a slowdown in the establishment of new ones. Nature reserves in regions with rapid economic development exhibited a greater decrease in area, suggesting that downsizing and degazettement of NRs are closely related to the intensifying competition between economic growth and conservation. For example, boundary adjustments to national NRs, the most strictly protected NRs, along the coast of China's Yellow Sea, a global biodiversity hotspot with a fast-growing economy, resulted in the loss of one-third of the total area. One of the most important ecosystems in these NRs, tidal wetlands, decreased by 27.8% because of boundary adjustments and by 25.2% because of land reclamation. Our results suggest conservation achievement, in terms of both area and quality, are declining at least in some regions in the Chinese NR estate. Although the designation of protected areas that are primarily managed for sustainable use has increased rapidly in recent years in China, we propose that NRs with biodiversity conservation as their main function should not be replaced or weakened.

Cambios en la Superficie y el Número de Reservas Naturales en China Resumen Las reservas naturales (RN) son la piedra angular de la conservación de la biodiversidad. Durante los últimos 60 años, la rápida expansión de las RN en China, uno de los países megadiversos, ha jugado un papel crítico en la reducción de la pérdida de biodiversidad. Examinamos los cambios en el número y superficie de las RN en China de 1956 a 2014 y analizamos el efecto del desarrollo económico en la expansión de las RN en China de 2005 a 2014 mediante modelos lineales. A pesar del incremento continuo en el número de RN, la superficie total de RN en China decreció en 3% de 2007 a 2014. Esta pérdida resultó de la reducción y cambio de registro de RN existentes y una desaceleración en el establecimiento de RN nuevas. Las reservas naturales en regiones con desarrollo económico rápido presentaron una mayor disminución en la superficie, lo que sugiere que la reducción y cambio de registro de RN están relacionados cercanamente con la intensificación de la competencia entre crecimiento económico y conservación. Por ejemplo, ajustes en los límites de RN nacionales, las RN más estrictamente protegidas, a lo largo de la costa del Mar Amarillo, un sitio de importancia para la biodiversidad global con una economía en rápido crecimiento, resultó en la pérdida de un tercio de la superficie total. Uno de los ecosistemas más importantes en estas RN, humedales mareales, decreció en 27.8% debido a ajustes en los límites y en 25.2% debido a la reclamación de tierras. Nuestros resultados sugieren que los logros de conservación, en términos tanto de área como de calidad, están declinando en las RN de China. Aunque la designación de áreas protegidas administradas primariamente para un uso sustentable ha incrementado rápidamente en años recientes en China, proponemos que las RN cuya principal función es la conservación de la biodiversidad no deben ser reemplazadas o debilitadas.

Changing Geographic Patterns and Risk Factors for Avian Influenza A(H7N9) Infections in Humans, China.

The fifth epidemic wave of avian influenza A(H7N9) virus in China during 2016-2017 demonstrated a geographic range expansion and caused more human cases than any previous wave. The factors that may explain the recent range expansion and surge in incidence remain unknown. We investigated the effect of anthropogenic, poultry, and wetland variables on all epidemic waves. Poultry predictor variables became much more important in the last 2 epidemic waves than they were previously, supporting the assumption of much wider H7N9 transmission in the chicken reservoir. We show that the future range expansion of H7N9 to northern China may increase the risk of H7N9 epidemic peaks coinciding in time and space with those of seasonal influenza, leading to a higher risk of reassortments than before, although the risk is still low so far.

Enhanced gross primary production and evapotranspiration in juniper-encroached grasslands.

Woody plant encroachment (WPE) into grasslands has been occurring globally and may be accelerated by climate change in the future. This land cover change is expected to alter the carbon and water cycles, but it remains uncertain how and to what extent the carbon and water cycles may change with WPE into grasslands under current climate. In this study, we examined the difference of vegetation indices (VIs), evapotranspiration (ET), gross primary production (GPP), and solar-induced chlorophyll fluorescence (SIF) during 2000-2010 between grasslands and juniper-encroached grasslands. We also quantitatively assessed the changes of GPP and ET for grasslands with different proportions of juniper encroachment (JWPE). Our results suggested that JWPE increased the GPP, ET, greenness-related VIs, and SIF of grasslands. Mean annual GPP and ET were, respectively, ~55% and ~45% higher when grasslands were completely converted into juniper forests under contemporary climate during 2000-2010. The enhancement of annual GPP and ET for grasslands with JWPE varied over years ranging from about +20% GPP (~+30% for ET) in the wettest year (2007) to about twice as much GPP (~+55% for ET) in the severe drought year (2006) relative to grasslands without encroachment. Additionally, the differences in GPP and ET showed significant seasonal dynamics. During the peak growing season (May-August), GPP and ET for grasslands with JWPE were ~30% and ~40% higher on average. This analysis provided insights into how and to what degree carbon and water cycles were impacted by JWPE, which is vital to understanding how JWPE and ecological succession will affect the regional and global carbon and water budgets in the future.

Exacerbated grassland degradation and desertification in Central Asia during 2000-2014.

Grassland degradation and desertification is a complex process, including both state conversion (e.g., grasslands to deserts) and gradual within-state change (e.g., greenness dynamics). Existing studies hardly separated the two components and analyzed it as a whole based on time series vegetation index data, which cannot provide a clear and comprehensive picture for grassland degradation and desertification. Here we propose an integrated assessment strategy, by considering both state conversion and within-state change of grasslands, to investigate grassland degradation and desertification process in Central Asia. First, annual maps of grasslands and sparsely vegetated land were generated to track the state conversions between them. The results showed increasing grasslands were converted to sparsely vegetated lands from 2000 to 2014, with the desertification region concentrating in the latitude range of 43-48° N. A frequency analysis of grassland vs. sparsely vegetated land classification in the last 15 yr allowed a recognition of persistent desert zone (PDZ), persistent grassland zone (PGZ), and transitional zone (TZ). The TZ was identified in southern Kazakhstan as one hotspot that was unstable and vulnerable to desertification. Furthermore, the trend analysis of Enhanced Vegetation Index during thermal growing season (EVITGS ) was investigated in individual zones using linear regression and Mann-Kendall approaches. An overall degradation across the area was found; moreover, the second desertification hotspot was identified in northern Kazakhstan with significant decreasing in EVITGS , which was located in PGZ. Finally, attribution analyses of grassland degradation and desertification were conducted by considering precipitation, temperature, and three different drought indices. We found persistent droughts were the main factor for grassland degradation and desertification in Central Asia. Considering both state conversion and gradual within-state change processes, this study provided reference information for identification of desertification hotspots to support further grassland degradation and desertification treatment, and the method could be useful to be extended to other regions.

Avian influenza H5N1 viral and bird migration networks in Asia.

The spatial spread of the highly pathogenic avian influenza virus H5N1 and its long-term persistence in Asia have resulted in avian influenza panzootics and enormous economic losses in the poultry sector. However, an understanding of the regional long-distance transmission and seasonal patterns of the virus is still lacking. In this study, we present a phylogeographic approach to reconstruct the viral migration network. We show that within each wild fowl migratory flyway, the timing of H5N1 outbreaks and viral migrations are closely associated, but little viral transmission was observed between the flyways. The bird migration network is shown to better reflect the observed viral gene sequence data than other networks and contributes to seasonal H5N1 epidemics in local regions and its large-scale transmission along flyways. These findings have potentially far-reaching consequences, improving our understanding of how bird migration drives the periodic reemergence of H5N1 in Asia.

FluoSpec 2-An Automated Field Spectroscopy System to Monitor Canopy Solar-Induced Fluorescence.

Accurate estimation of terrestrial photosynthesis has broad scientific and societal impacts. Measurements of photosynthesis can be used to assess plant health, quantify crop yield, and determine the largest CO2 flux in the carbon cycle. Long-term and continuous monitoring of vegetation optical properties can provide valuable information about plant physiology. Recent developments of the remote sensing of solar-induced chlorophyll fluorescence (SIF) and vegetation spectroscopy have shown promising results in using this information to quantify plant photosynthetic activities and stresses at the ecosystem scale. However, there are few automated systems that allow for unattended observations over months to years. Here we present FluoSpec 2, an automated system for collecting irradiance and canopy radiance that has been deployed in various ecosystems in the past years. The instrument design, calibration, and tests are recorded in detail. We discuss the future directions of this field spectroscopy system. A network of SIF sensors, FluoNet, is established to measure the diurnal and seasonal variations of SIF in several ecosystems. Automated systems such as FluoSpec 2 can provide unique information on ecosystem functioning and provide important support to the satellite remote sensing of canopy photosynthesis.

Assessing agricultural drought in summer over Oklahoma Mesonet sites using the water-related vegetation index from MODIS.

Agricultural drought, a common phenomenon in most parts of the world, is one of the most challenging natural hazards to monitor effectively. Land surface water index (LSWI), calculated as a normalized ratio between near infrared (NIR) and short-wave infrared (SWIR), is sensitive to vegetation and soil water content. This study examined the potential of a LSWI-based, drought-monitoring algorithm to assess summer drought over 113 Oklahoma Mesonet stations comprising various land cover and soil types in Oklahoma. Drought duration in a year was determined by the number of days with LSWI <0 (DNLSWI) during summer months (June-August). Summer rainfall anomalies and LSWI anomalies followed a similar seasonal dynamics and showed strong correlations (r 2 = 0.62-0.73) during drought years (2001, 2006, 2011, and 2012). The DNLSWI tracked the east-west gradient of summer rainfall in Oklahoma. Drought intensity increased with increasing duration of DNLSWI, and the intensity increased rapidly when DNLSWI was more than 48 days. The comparison between LSWI and the US Drought Monitor (USDM) showed a strong linear negative relationship; i.e., higher drought intensity tends to have lower LSWI values and vice versa. However, the agreement between LSWI-based algorithm and USDM indicators varied substantially from 32 % (D 2 class, moderate drought) to 77 % (0 and D 0 class, no drought) for different drought intensity classes and varied from ∼30 % (western Oklahoma) to >80 % (eastern Oklahoma) across regions. Our results illustrated that drought intensity thresholds can be established by counting DNLSWI (in days) and used as a simple complementary tool in several drought applications for semi-arid and semi-humid regions of Oklahoma. However, larger discrepancies between USDM and the LSWI-based algorithm in arid regions of western Oklahoma suggest the requirement of further adjustment in the algorithm for its application in arid regions.

Comparison of solar-induced chlorophyll fluorescence, light-use efficiency, and process-based GPP models in maize.

Accurately quantifying cropland gross primary production (GPP) is of great importance to monitor cropland status and carbon budgets. Satellite-based light-use efficiency (LUE) models and process-based terrestrial biosphere models (TBMs) have been widely used to quantify cropland GPP at different scales in past decades. However, model estimates of GPP are still subject to large uncertainties, especially for croplands. More recently, space-borne solar-induced chlorophyll fluorescence (SIF) has shown the ability to monitor photosynthesis from space, providing new insights into actual photosynthesis monitoring. In this study, we examined the potential of SIF data to describe maize phenology and evaluated three GPP modeling approaches (space-borne SIF retrievals, a LUE-based vegetation photosynthesis model [VPM], and a process-based soil canopy observation of photochemistry and energy flux [SCOPE] model constrained by SIF) at a maize (Zea mays L.) site in Mead, Nebraska, USA. The result shows that SIF captured the seasonal variations (particularly during the early and late growing season) of tower-derived GPP (GPP_EC) much better than did satellite-based vegetation indices (enhanced vegetation index [EVI] and land surface water index [LSWI]). Consequently, SIF was strongly correlated with GPP_EC than were EVI and LSWI. Evaluation of GPP estimates against GPP_EC during the growing season demonstrated that all three modeling approaches provided reasonable estimates of maize GPP, with Pearson's correlation coefficients (r) of 0.97, 0.94, and 0.93 for the SCOPE, VPM, and SIF models, respectively. The SCOPE model provided the best simulation of maize GPP when SIF observations were incorporated through optimizing the key parameter of maximum carboxylation capacity (Vcmax). Our results illustrate the potential of SIF data to offer an additional way to investigate the seasonality of photosynthetic activity, to constrain process-based models for improving GPP estimates, and to reasonably estimate GPP by integrating SIF and GPP_EC data without dependency on climate inputs and satellite-based vegetation indices.

Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine.

Area and spatial distribution information of paddy rice are important for understanding of food security, water use, greenhouse gas emission, and disease transmission. Due to climatic warming and increasing food demand, paddy rice has been expanding rapidly in high latitude areas in the last decade, particularly in northeastern (NE) Asia. Current knowledge about paddy rice fields in these cold regions is limited. The phenology- and pixel-based paddy rice mapping (PPPM) algorithm, which identifies the flooding signals in the rice transplanting phase, has been effectively applied in tropical areas, but has not been tested at large scale of cold regions yet. Despite the effects from more snow/ice, paddy rice mapping in high latitude areas is assumed to be more encouraging due to less clouds, lower cropping intensity, and more observations from Landsat sidelaps. Moreover, the enhanced temporal and geographic coverage from Landsat 8 provides an opportunity to acquire phenology information and map paddy rice. This study evaluated the potential of Landsat 8 images on annual paddy rice mapping in NE Asia which was dominated by single cropping system, including Japan, North Korea, South Korea, and NE China. The cloud computing approach was used to process all the available Landsat 8 imagery in 2014 (143 path/rows, ~3290 scenes) with the Google Earth Engine (GEE) platform. The results indicated that the Landsat 8, GEE, and improved PPPM algorithm can effectively support the yearly mapping of paddy rice in NE Asia. The resultant paddy rice map has a high accuracy with the producer (user) accuracy of 73% (92%), based on the validation using very high resolution images and intensive field photos. Geographic characteristics of paddy rice distribution were analyzed from aspects of country, elevation, latitude, and climate. The resultant 30-m paddy rice map is expected to provide unprecedented details about the area, spatial distribution, and landscape pattern of paddy rice fields in NE Asia, which will contribute to food security assessment, water resource management, estimation of greenhouse gas emissions, and disease control.