Dryness stress can limit vegetation growth and is often characterized by low soil moisture (SM) and high atmospheric water demand (vapor pressure deficit, VPD). However, the relative role of SM and VPD in limiting ecosystem production remains debated and is difficult to disentangle, as SM and VPD are coupled through land-atmosphere interactions, hindering the ability to predict ecosystem responses to dryness. Here, we combine satellite observations of solar-induced fluorescence with estimates of SM and VPD and show that SM is the dominant driver of dryness stress on ecosystem production across more than 70% of vegetated land areas with valid data. Moreover, after accounting for SM-VPD coupling, VPD effects on ecosystem production are much smaller across large areas. We also find that SM stress is strongest in semi-arid ecosystems. Our results clarify a longstanding question and open new avenues for improving models to allow a better management of drought risk.

We evaluated the response of the Earth land biomes to drought by correlating a drought index with three global indicators of vegetation activity and growth: vegetation indices from satellite imagery, tree-ring growth series, and Aboveground Net Primary Production (ANPP) records. Arid and humid biomes are both affected by drought, and we suggest that the persistence of the water deficit (i.e., the drought time-scale) could be playing a key role in determining the sensitivity of land biomes to drought. We found that arid biomes respond to drought at short time-scales; that is, there is a rapid vegetation reaction as soon as water deficits below normal conditions occur. This may be due to the fact that plant species of arid regions have mechanisms allowing them to rapidly adapt to changing water availability. Humid biomes also respond to drought at short time-scales, but in this case the physiological mechanisms likely differ from those operating in arid biomes, as plants usually have a poor adaptability to water shortage. On the contrary, semiarid and subhumid biomes respond to drought at long time-scales, probably because plants are able to withstand water deficits, but they lack the rapid response of arid biomes to drought. These results are consistent among three vegetation parameters analyzed and across different land biomes, showing that the response of vegetation to drought depends on characteristic drought time-scales for each biome. Understanding the dominant time-scales at which drought most influences vegetation might help assessing the resistance and resilience of vegetation and improving our knowledge of vegetation vulnerability to climate change.

Greenhouse gas emissions have largely changed the global climate, leading to an increase in the frequency and extent of droughts. Forests are essential natural resources, and they play an important role in maintaining ecological security. Effectively monitoring drought stress in forests can help promote sustainable forestry development. Solar-induced chlorophyll fluorescence is a spectral signal released by vegetation photosynthesis after light absorption. In this study, we used solar-induced chlorophyll fluorescence data (SIF), canopy fluorescence yield (SIFyield) data, vegetation indices (NDVI, EVI), leaf area index (LAI), and fraction of absorbed photosynthetically active radiation (fPAR) to study forest drought stress in the Yunnan, Fujian, Shaanxi, and Heilongjiang provinces in China, respectively. The temporal and spatial ranges of drought stress indicated by the Standardized Precipitation-Evapotranspiration Index (SPEI) values were used as a reference (SPEI ≤ −0.5 indicates the occurrence of drought). Firstly, the standardized anomalous values of SIF, SIFyield, NDVI, EVI, LAI, and fPAR were calculated. The temporal and spatial response abilities of each variable to drought stress were analyzed. Secondly, the correlation between each variable and the drought indicator SPEI was quantified. Finally, the validity and variability of SIF and other variables for drought monitoring were analyzed and verified with a random forest classification model. The results showed that on a temporal scale, SIFyield showed an earlier response to drought stress than other variables and the abnormal change of SIFyield was higher than other variables by 10% or more. Spatially, the range of drought areas indicated by SIFyield and SPEI had more coincident areas than other variables. The overall correlation between SIFyield and SPEI was also higher during the drought period, especially during late drought periods when other variables showed negative correlations. For SIFyield, the correlation coefficients of the Yunnan, Fujian, Shaanxi, and Heilongjiang provinces were 0.57, 0.43, 0.32, and 0.49, respectively. Additionally, the variable importance assessment using a random forest model also indicated that SIFyield is more sensitive to forest droughts. We concluded that SIFyield is an effective tool for monitoring forest drought stress in various regions of China and that it can provide a scientific basis for forest drought monitoring.

Solar-induced chlorophyll fluorescence (SIF) brings major advancements in measuring terrestrial photosynthesis. Several recent studies have evaluated the potential of SIF retrievals from the Orbiting Carbon Observatory-2 (OCO-2) in estimating gross primary productivity (GPP) based on GPP data from eddy covariance (EC) flux towers. However, the spatially and temporally sparse nature of OCO-2 data makes it challenging to use these data for many applications from the ecosystem to the global scale. Here, we developed a new global ‘OCO-2’ SIF data set (GOSIF) with high spatial and temporal resolutions (i.e., 0.05°, 8-day) over the period 2000–2017 based on a data-driven approach. The predictive SIF model was developed based on discrete OCO-2 SIF soundings, remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and meteorological reanalysis data. Our model performed well in estimating SIF (R2 = 0.79, root mean squared error (RMSE) = 0.07 W m−2 μm−1 sr−1). The model was then used to estimate SIF for each 0.05° × 0.05° grid cell and each 8-day interval for the study period. The resulting GOSIF product has reasonable seasonal cycles, and captures the similar seasonality as both the coarse-resolution OCO-2 SIF (1°), directly aggregated from the discrete OCO-2 soundings, and tower-based GPP. Our SIF estimates are highly correlated with GPP from 91 EC flux sites (R2 = 0.73, p < 0.001). They capture the expected spatial and temporal patterns and also have remarkable ability to highlight the crop areas with the highest daily productivity across the globe. Our product also allows us to examine the long-term trends in SIF globally. Compared with the coarse-resolution SIF that was directly aggregated from OCO-2 soundings, GOSIF has finer spatial resolution, globally continuous coverage, and a much longer record. Our GOSIF product is valuable for assessing terrestrial photosynthesis and ecosystem function, and benchmarking terrestrial biosphere and Earth system models.

研究植被的动态变化及其影响因素，不仅能够揭示植被覆盖动态变化特征与气候变化之间的响应机制，同时对区域的植被恢复以及生态可持续具有重要意义。本文基于MODIS遥感卫星数据，借助变异系数、趋势分析、相关分析与Hurst指数探究了2000—2020年西北干旱半干旱区植被覆盖逐季变化特征、影响因素及未来趋势。结果表明：（1） 归一化植被指数（Normalized Difference Vegetation Index, NDVI）空间变异程度在冬季偏高，且高波动主要分布在新疆与内蒙古大兴安岭的草地与未利用地区域。（2） NDVI随季节波动较大，在林地与耕地最为明显。（3） NDVI主要为改善趋势，其中，春季改善面积最大（84.63%），冬季最小（72.52%），且林地改善最为显著。（4） 各季度NDVI均受地表温度与降水量影响（Significance=0.05），且夏季地表温度与冬季降水量逐年递增对植被生长具有抑制作用。（5） 未来NDVI主要呈改善趋势。值得注意的是，退化区域零星分布于新疆塔里木盆地、准噶尔盆地等地区。本研究旨在为西北干旱半干旱区的生态修复与治理，以及局部气候暖湿化的应对提供理论参考。

黄河流域作为中国东部平原的生态屏障,研讨其植被覆盖的时空变化有助于生态环境治理。本文利用GEE平台,基于Landsat数据通过像元二分模型反演了1990—2020年黄河流域植被覆盖度（FVC）,并通过Theil-Sen Median趋势分析和 Mann-Kendall检验方法剖析FVC的时空变化趋势,挖掘出FVC趋势变化与海拔、坡度、坡向等地形因子之间的响应关系。结果表明：① 黄河流域FVC整体呈现西北低东南高的空间分布趋势,其中低等FVC占整个流域面积的45%,主要集中于西北部干旱半干旱地区;② 流域中部植被覆盖改善明显,占整个流域的57.07%,西北部和东南部退化程度相对较高;③ 植被覆盖受地形效应影响较为显著,在坡度大于40°及高程（-31~637 m）时高等级FVC占比较高,坡度8~18°及高程1852~2414 m范围内植被改善效果相对较好。结果可以为黄河流域生态环境保护及高质量发展提供科学支撑。

利用遥感云计算技术科学揭示农业干旱的时空特征对推进生态文明建设和农牧业可持续发展具有重要意义。以内蒙古自治区为研究对象，基于谷歌地球引擎（GEE）利用多源遥感、气象数据构建研究区1982—2021年多种遥感干旱指数年尺度时间序列，对比分析归一化植被供水指数（NVSWI）、作物缺水指数（CWSI）、温度植被旱情指数（TVDI）、植被状况指数（VCI）和温度状况指数（TCI）对农业干旱的表征能力，并基于优选指数分析1982—2021年内蒙古的干旱时空变化特征。结果表明：（1）CWSI对农业干旱的表征能力明显优于其他4种干旱指数。（2）1982—2021年的CWSI总体呈下降趋势，变化不显著区域面积占比77.5%；极显著下降区域面积占比9.4%，而显著上升区域面积占比9.4%。（3）干旱程度自西向东北逐步减轻，其中中旱区域面积比例最高，占比77.55%；其次为重旱和特旱区域，面积占比分别为26.38%和23.07%；轻旱和无旱区域面积比例最低，面积占比分别为18.35%和2.95%。不同气候类型中CWSI与年均标准化降水蒸散指数的相关性具有显著差异，其中冷性草原气候、热夏冬干冷温气候及温夏冬干冷温气候呈极显著负相关（P<0.01），而在其余气候类型区域中相关性不显著。研究结果可为内蒙古农牧业生产和干旱预警提供理论支撑和决策依据。

Precipitation data from 104 meteorological stations in Inner Mongolia from 1960 to 2018 were analyzed to examine the regionalization and characteristics of precipitation variations. Using rotated empirical orthogonal function (REOF) analysis and K-means clustering, Inner Mongolia was divided into six precipitation subregions: the northeastern Hulunbuir area (subregion I); most of Hinggan League, northern Xilin Gol League, and northwestern Tongliao City (subregion II); most of Tongliao City and Chifeng City and east–central and southern Xilin Gol League (subregion III); southern Xilin Gol League, north–central Ulan Chab City, northern Hohhot City, most of Baotou City and north–central Bayannur City (subregion IV); Ordos City, southern Bayannur, and southeastern Alxa League (subregion V); and west–central Alxa League and parts of western Bayannur City (subregion VI). Precipitation showed a spatial gradient with higher annual averages in the east (400.85 mm in subregion I) and lower averages in the west (90.65 mm in subregion VI). From 1960 to 2018, precipitation exhibited an overall increasing trend consistent across the subregions. However, most regions showed decreasing trends from 1990 to 2010. The rate of precipitation change varied significantly across the subregions, reflecting distinct spatial dynamics.

草地植被覆盖度（FVC）是反映草地生态状况最直接的指标之一。目前在大区域内构建准确的FVC估算模型，进行长时间序列的动态分析，仍是一个挑战。基于大量地面调查样本，使用2000-2020年MODIS遥感数据、气象数据，通过随机森林模型进行FVC分区建模与结果估算。利用Sen+Mann-Kendall趋势分析法、Hurst指数法等，分析FVC时空变化特征和未来变化情况。研究表明：1）内蒙古草地FVC随机森林模型精度表现为分区优于全区，有效地降低了空间异质性的影响。2）内蒙古草地FVC总体上呈东高西低的分布格局，空间差异明显。3）近21年，内蒙古草地FVC总体呈波动上升趋势，年增长率为0.2%·a^-1；增长区域面积占比（79.5%）大于减少区域面积占比（20.5%），并且极显著增长和显著增长占比（28.3%）远大于极显著减少和显著减少（1.6%）。4）未来内蒙古草地FVC总体为正持续性发展。预测增长区域（57.6%）多于减少区域（42.4%），其中极显著增长和显著增长占比较高（25.9%），未来草地FVC整体向好发展。