The ability to regionally monitor crop progress and condition through the growing season benefits both crop management and yield estimation. In the United States, these metrics are reported weekly at ...state or district (multiple counties) levels by the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) using field observations provided by trained local reporters. However, the ground data collection process supporting this effort is time consuming and subjective. Furthermore, operational crop management and yield estimation efforts require information with more granularity than at the state or district level. This paper evaluates remote sensing approaches for mapping crop phenology using vegetation index time-series generated by fusing Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) surface reflectance imagery to improve temporal sampling over that provided by Landsat alone. The case study focuses on an agricultural region in central Iowa from 2001 to 2014. Our objectives are 1) to assess Landsat-MODIS data fusion results over cropland; 2) to map crop phenology at 30m resolution using fused surface reflectance data; and 3) to identify the relationships between remotely sensed crop phenology metrics and the crop progress stages reported by NASS. The results show that detailed spatial and temporal variability in vegetation development across this landscape can be identified using the fused Landsat-MODIS data. The mean difference (bias) in Normalized Difference Vegetation Index (NDVI) between actual Landsat observations and the fused Landsat-MODIS data, generated for Landsat overpass dates, is in the range of −0.011 to 0.028 for every year. The derived phenological metrics show distinct features for different crops and natural vegetation at field scales. Strong correlations are observed between remotely sensed phenological stages, based on NDVI curve inflection points, and the observed crop physiological growth stages from the NASS Crop Progress (CP) reports. The green-up dates detected from remote sensing data typically occurred during crop vegetative stages when 2–4 leaves were developed for both corn and soybeans, or about 1–3weeks after the reported emergence dates when the plant were first visible to ground-based observers. Despite being a lagging indicator, remotely sensed green-up can be used effectively to backcast emergence, e.g. as input to spatially distributed crop models. The differences in green-up date between corn and soybean were 8–10days, consistent with the offset in emergence dates reported by NASS at district level. The reported harvest dates were typically about 2–3weeks after the dormancy stage was detected via remote sensing for corn and about 1–2weeks for soybeans. This suggests that probable harvest times for individual fields may be predicted 1–3weeks ahead using remote sensing data. The results suggest that crop phenology and certain growth stages at field scales (30m spatial resolution) can be linked and mapped by integrating imagery from multiple remote sensing platforms.
•Remote sensing approaches for mapping crop phenology at field scale are developed.•Spatial details and seasonal variability are captured from data fusion approach.•30-m crop phenological metrics are generated using the fused Landsat-MODIS data.•Crop phenology from remote sensing are correlated to crop physiological stages.
Freshwater resources are becoming increasingly limited in many parts of the world, and decision makers are demanding new tools for monitoring water availability and rates of consumption. Remotely ...sensed thermal-infrared imagery collected by Landsat provides estimates of land-surface temperature that allow mapping of evapotranspiration (ET) at the spatial scales at which water is being used. This paper explores the utility of moderate-resolution thermal satellite imagery in water resource management. General modeling techniques for using land-surface temperature in mapping the surface energy balance are described, including methods developed to safeguard ET estimates from expected errors in the remote sensing inputs. Examples are provided of how remotely sensed maps of ET derived from Landsat thermal imagery are being used operationally by water managers today: in monitoring water rights, negotiating interstate compacts, estimating water-use by invasive species, and in determining allocations for agriculture, urban use, and endangered species protection. Other applications include monitoring drought and food insecurity, and evaluation of large-scale land-surface and climate models. To better address user requirements for high-resolution, time-continuous ET data, novel techniques have been developed to improve the spatial resolution of Landsat thermal-band imagery and temporal resolution between Landsat overpasses by fusing information from other wavebands and satellites. Finally, a strategy for future modification to the Landsat program is suggested, improving our ability to track changes in water use due to changing climate and growing population. The long archive of global, moderate resolution TIR imagery collected by the Landsat series is unmatched by any other satellite program, and will continue to be an invaluable asset to better management of our earth's water resources.
► Thermal satellite data provide valuable information for water resource management. ► We summarize applications for moderate resolution thermal data from Landsat. ► Uses include monitoring water consumption, ecosystem health, and food security. ► Synergistic fusion with data from other satellite platforms improves utility. ► An optimal satellite system configuration for global water management is outlined.
FLASH DROUGHTS Otkin, Jason A.; Svoboda, Mark; Hunt, Eric D. ...
Bulletin of the American Meteorological Society,
05/2018, Letnik:
99, Številka:
5
Journal Article
Recenzirano
Odprti dostop
Given the increasing use of the term “flash drought” by the media and scientific community, it is prudent to develop a consistent definition that can be used to identify these events and to ...understand their salient characteristics. It is generally accepted that flash droughts occur more often during the summer owing to increased evaporative demand; however, two distinct approaches have been used to identify them. The first approach focuses on their rate of intensification, whereas the second approach implicitly focuses on their duration. These conflicting notions for what constitutes a flash drought (i.e., unusually fast intensification vs short duration) introduce ambiguity that affects our ability to detect their onset, monitor their development, and understand the mechanisms that control their evolution. Here, we propose that the definition for “flash drought” should explicitly focus on its rate of intensification rather than its duration, with droughts that develop much more rapidly than normal identified as flash droughts. There are two primary reasons for favoring the intensification approach over the duration approach. First, longevity and impact are fundamental characteristics of drought. Thus, short-term events lasting only a few days and having minimal impacts are inconsistent with the general understanding of drought and therefore should not be considered flash droughts. Second, by focusing on their rapid rate of intensification, the proposed “flash drought” definition highlights the unique challenges faced by vulnerable stakeholders who have less time to prepare for its adverse effects.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The reliability of standard meteorological drought indices based on measurements of precipitation is limited by the spatial distribution and quality of currently available rainfall data. Furthermore, ...they reflect only one component of the surface hydrologic cycle, and they cannot readily capture nonprecipitation-based moisture inputs to the land surface system (e.g., irrigation) that may temper drought impacts or variable rates of water consumption across a landscape. This study assesses the value of a new drought index based on remote sensing of evapotranspiration (ET). The evaporative stress index (ESI) quantifies anomalies in the ratio of actual to potential ET (PET), mapped using thermal band imagery from geostationary satellites. The study investigates the behavior and response time scales of the ESI through a retrospective comparison with the standardized precipitation indices and Palmer drought index suite, and with drought classifications recorded in the U.S. Drought Monitor for the 2000–09 growing seasons. Spatial and temporal correlation analyses suggest that the ESI performs similarly to short-term (up to 6 months) precipitation-based indices but can be produced at higher spatial resolution and without requiring any precipitation data. Unique behavior is observed in the ESI in regions where the evaporative flux is enhanced by moisture sources decoupled from local rainfall: for example, in areas of intense irrigation or shallow water table. Normalization by PET serves to isolate the ET signal component responding to soil moisture variability from variations due to the radiation load. This study suggests that the ESI is a useful complement to the current suite of drought indicators, with particular added value in parts of the world where rainfall data are sparse or unreliable.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Daily continuous evapotranspiration (ET) estimates of 1 km spatial resolution can benefit agricultural water resources management at regional scales. Thermal infrared remote sensing-derived land ...surface temperature (LST) is a critical variable for ET estimation using energy balance-based models. However, missing LST information under cloudy conditions remains a long-standing barrier for spatiotemporally continuous monitoring of daily ET at regional scales. In this study, LST data of 1 km spatial resolution at 11:00 local solar time under all-weather conditions across the North China Plain (NCP) were first generated using a data fusion approach developed previously. Second, combined with the generated LST data, MODIS products, and meteorological forcing from the China Land Data Assimilation System, the Two-Source Energy Balance model (TSEB) and a temporal upscaling method were jointly used to estimate daily ET at 1 km spatial resolution across the NCP for a decade from 2008 to 2017. In particular, to better incorporate the impact of crop phenology on ET and improve the ET estimation, the fraction of greenness in TSEB was determined in terms of a leaf area index threshold during the crop growth period. Compared with observed instantaneous latent heat flux (LE) corrected for energy balance closure, the estimated LE reasonably captures inter- and intra-annual variations in LE measured at the Huailai, Daxing, Weishan, and Guantao flux towers, with R2 of 0.63–0.79. Estimated daily ET against in situ ET measurements with energy balance closure at the Huailai, Daxing, and Guantao sites showed good performance in terms of R2 greater than 0.70 and RMSE below 0.91 mm/d. These accuracies are comparable with published results, with our ET data set validated by many more observations than previous studies and featuring spatiotemporal continuity and high spatial resolution across the entire NCP for a decade. Furthermore, seasonal ET variations reflected by our product outperform two widely used global products in capturing water consumption characteristics in the winter wheat-summer maize rotation system. In terms of temporal trends, annual ET estimates across the NCP show a decreasing and then increasing trend over the past decade, which is attributed to the increased cropping intensity over the recent years reflected by an increase in leaf area index.
•TSEB and LST fusion were jointly used to obtain daily ET with an improved fg characterization.•ET maps of high accuracy, spatiotemporal continuity and fine spatial resolution were generated.•Performance of the ET estimates at multiple temporal scales was systematically evaluated.•Trends of decadal ET estimates across the NCP over a decade were triggered by agricultural intensification.
•We examined the evolution of several drought metrics during the 2012 flash drought.•The Evaporative Stress Index (ESI) provided early warning of drought development.•NLDAS models sometimes depicted ...very different soil moisture anomalies.•Crop yield departures consistent with drought severity depicted by the ESI.
This study examines the evolution of several model-based and satellite-derived drought metrics sensitive to soil moisture and vegetation conditions during the extreme flash drought event that impacted major agricultural areas across the central U.S. during 2012. Standardized anomalies from the remote sensing based Evaporative Stress Index (ESI) and Vegetation Drought Response Index (VegDRI) and soil moisture anomalies from the North American Land Data Assimilation System (NLDAS) are compared to the United States Drought Monitor (USDM), surface meteorological conditions, and crop and soil moisture data compiled by the National Agricultural Statistics Service (NASS).
Overall, the results show that rapid decreases in the ESI and NLDAS anomalies often preceded drought intensification in the USDM by up to 6wk depending on the region. Decreases in the ESI tended to occur up to several weeks before deteriorations were observed in the crop condition datasets. The NLDAS soil moisture anomalies were similar to those depicted in the NASS soil moisture datasets; however, some differences were noted in how each model responded to the changing drought conditions. The VegDRI anomalies tracked the evolution of the USDM drought depiction in regions with slow drought development, but lagged the USDM and other drought indicators when conditions were changing rapidly. Comparison to the crop condition datasets revealed that soybean conditions were most similar to ESI anomalies computed over short time periods (2–4wk), whereas corn conditions were more closely related to longer-range (8–12wk) ESI anomalies. Crop yield departures were consistent with the drought severity depicted by the ESI and to a lesser extent by the NLDAS and VegDRI datasets.
Drought can have pervasive and wide-spread impacts to forest health, as evidenced in several severe events occurring over the recent decades. Extensive forest die-off due to drought can impair the ...ecological functioning of forests, impacting habitat, water yield and quality from forested lands, and altering forest fire dynamics and intensity. Satellite remote sensing provides an effective means for detecting and monitoring spatial patterns of forest mortality over large areas, exploiting free and open long-term image archives available at a range in spatial and temporal resolutions. While remotely sensed surface reflectances and vegetation indices have been widely used to study optical response of forest canopies to drought events, retrievals of evapotranspiration (ET) derived from thermal satellite imagery – particularly at resolutions approaching crown scale - can provide insights into cumulative tree stresses that can incite disease and trigger mortality. In this study, we applied a multi-sensor satellite data fusion approach to estimate daily 30-m resolution ET and an associated Evaporative Stress Index (ESI) to study drought-induced mortality in a temperate forest at the Missouri Ozark AmeriFlux (MOFLUX) site, located in the central United States. The study covered the period from 2010 to 2014, including an exceptional drought year of 2012. Modeled ET agreed well with eddy flux measurements from the MOFLUX tower, with average monthly relative errors of 15%. Plot-scale ESI, describing temporal anomalies in the ratio of actual-to-reference ET, was used as an index of relative forest health to investigate relationships between forest mortality and drought severity. ESI showed good agreement with observed predawn leaf water potential, especially during the drought year. Furthermore, plot-scale ESI was also correlated with the subsequent year's tree mortality, suggesting the importance of considering the forest health condition prior to drought when studying drought-induced forest impacts. This study demonstrates the utility of multi-year ET remote sensing data at the stand or plot scale as an indicator of forest health and as a predictor of future mortality due to drought.
•Drought-induced tree mortality is studied using ET retrieved from thermal imagery.•The daily 30-m ET captured variable drought impacts at near crown-scale.•Pre-drought forest condition plays an important role in understanding mortality.•ET complements other climate variables for predicting drought-induced mortality.
Land-surface temperature retrieved from thermal infrared (TIR) remote sensing has proven to be a valuable constraint in surface energy balance models for estimating evapotranspiration (ET). For ...optimal utility in agricultural water management applications, frequent thermal imaging (<4-day revisit) at sub-field (100 m or less) spatial resolution is desired. While, the current suite of Landsat satellites (7 and 8) provides the required spatial resolution, the 8-day combined revisit can be inadequate to capture rapid changes in surface moisture status or crop phenology, particularly in areas of persistent cloud cover. The new ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission, with an average 4-day revisit interval and nominal 70-m resolution, provides a valuable research platform for augmenting Landsat TIR sampling and for investigating TIR-based ET mapping mission requirements more broadly. This study investigates the interoperability of Landsat and ECOSTRESS imaging for developing ET image timeseries with high spatial (30-m) and temporal (daily) resolution. A data fusion algorithm is used to fuse Landsat and ECOSTRESS ET retrievals at 30 m with daily 500-m retrievals using TIR data from the Moderate Resolution Imaging Spectroradiometer (MODIS) over target agricultural sites spanning the United States.The added value of the combined multi-source dataset is quantified in comparison with daily flux tower observations collected within these target domains. In addition, we investigate ET model performance as a function of ECOSTRESS view angle, overpass time, and time separation between TIR and Landsat visible to shortwave infrared (VSWIR) data acquisitions used to generate land-surface temperature, leaf area index, and albedo inputs to the surface energy balance model. The results demonstrate the value of the higher temporal sampling provided by ECOSTRESS, especially in areas that are frequently impacted by cloud cover. Limiting usage to ECOSTRESS scenes collected between 9:00 a.m. to 5:00 p.m. and nadir viewing angles <20° yielded daily (24-h) ET retrievals of comparable quality to the well-tested Landsat baseline. We also discuss challenges in using land-surface temperature from a thermal free-flyer system for ET retrieval, which may have ramifications for future TIR water-use mapping missions.
•Landsat thermal infrared constrains field-scale evapotranspiration (ET) retrievals.•ECOSTRESS thermal imaging effectively augments Landsat sampling.•Extra sampling improves ET timeseries in areas of high cloud cover frequency.•Lack of shortwave bands on ECOSTRESS limits accuracy of ET retrievals.•These findings have ramifications for design of future water use mapping missions.
Large-scale crop monitoring and yield estimation are important for both scientific research and practical applications. Satellite remote sensing provides an effective means for regional and global ...cropland monitoring, particularly in data-sparse regions that lack reliable ground observations and reporting. The conventional approach of using visible and near-infrared based vegetation index (VI) observations has prevailed for decades since the onset of the global satellite era. However, other satellite data encompass diverse spectral ranges that may contain complementary information on crop growth and yield, but have been largely understudied and underused. Here we conducted one of the first attempts at synergizing multiple satellite data spanning a diverse spectral range, including visible, near-infrared, thermal and microwave, into one framework to estimate crop yield for the U.S. Corn Belt, one of the world's most important food baskets. Specifically, we included MODIS Enhanced VI (EVI), estimated Gross Primary Production based on GOME-2 solar-induced fluorescence (SIF-GPP), thermal-based ALEXI Evapotranspiration (ET), QuikSCAT Ku-band radar backscatter, and AMSR-E X-band passive microwave Vegetation Optical Depth (VOD) in this study, benchmarked on USDA county-level crop yield statistics. We used Partial Least Square Regression (PLSR), an effective statistical model for dimension reduction, to distinguish commonly shared and unique individual information from the various satellite data and other ancillary climate information for crop yield estimation. In the PLSR model that includes all of the satellite data and climate variables from 2007 to 2009, we assessed the first two major PLSR components and found that the first component (an integrated proxy of crop aboveground biomass) explained 82% variability of modelled crop yield, and the second component (dominated by environmental stresses) explained 15% variability of modelled crop yield. We found that most of the satellite derived metrics (e.g. SIF-GPP, radar backscatter, EVI, VOD, ALEXI-ET) share common information related to aboveground crop biomass (i.e. the first component). For this shared information, the SIF-GPP and backscatter data contain almost the same amount of information as EVI at the county scale. When removing the above shared component from all of the satellite data, we found that EVI and SIF-GPP do not provide much extra information; instead, Ku-band backscatter, thermal-based ALEXI-ET, and X-band VOD provide unique information on environmental stresses that improves overall crop yield predictive skill. In particular, Ku-band backscatter and associated differences between morning and afternoon overpasses contribute unique information on crop growth and environmental stress. Overall, using satellite data from various spectral bands significantly improves regional crop yield predictions. The additional use of ancillary climate data (e.g. precipitation and temperature) further improves model skill, in part because the crop reproductive stage related to harvest index is highly sensitive to environmental stresses but they are not fully captured by the satellite data used in our study. We conclude that using satellite data across various spectral ranges can improve monitoring of large-scale crop growth and yield beyond what can be achieved from individual sensors. These results also inform the synergistic use and development of current and next generation satellite missions, including NASA ECOSTRESS, SMAP, and OCO-2, for agricultural applications.
•A novel framework to estimate crop yield for US Corn Belt is provided.•Satellite optical, fluorescence, thermal and microwave data are synergized.•Share and unique information of different satellite data is identified using PLSR.•Climate information is necessary to be included to quantify harvest index.•A synergized use of various-spectral satellite data improves yield estimation.
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