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Freeze-thaw cycles strongly affect the transformation of soil phosphorus (P) and shape the composition of P pools. This study aimed to investigate the effects of successive freeze-thaw cycles on the transformation of soil labile P and enzymatically hydrolysable organic P (Po) fractions. Accordingly, five physico-chemically distinct soils were subjected to two, five, and ten freeze-thaw cycles, with each cycle including incubation at −10 °C (freeze) for 12 h and 5 °C (thaw) for 12 h. Control soils were maintained at 5 °C, and the bicarbonate-extractable P and hydrolysable Po fractions were analysed at the end of the incubation period (10 d). Freeze-thaw cycles increased the levels of bicarbonate-extractable inorganic P, bicarbonate-extractable Po, labile monoester P, and phytate-like P, but had no effect on the diester P and unknown Po contents. The interaction between soil type and freeze-thaw cycles significantly affected the NaHCO3-extractable Pi, and phytate-like P fractions, but did not affect the bicarbonate-extractable Po, and labile monoester P fractions. The extent of increase in NaHCO3-extractable Pi largely depended on the amount of organic matter in the soil. In most cases, bicarbonate-extractable P and hydrolysable Po fractions reached their maximum levels after two freeze-thaw cycles and declined or remained constant thereafter. Our results suggest that freeze-thaw cycles exacerbated the transformation of soil labile P fractions, including enzymatically hydrolysable Po species, especially in the earlier stages.

The launch of the National Oceanic and Atmospheric Administration (NOAA)/ National Aeronautics and Space Administration (NASA) Suomi National Polar-orbiting Partnership (S-NPP) and its follow-on NOAA Joint Polar Satellite Systems (JPSS) satellites marks the beginning of a new era of operational satellite observations of the Earth and atmosphere for environmental applications with high spatial resolution and sampling rate. The S-NPP and JPSS are equipped with five instruments, each with advanced design in Earth sampling, including the Advanced Technology Microwave Sounder (ATMS), the Cross-track Infrared Sounder (CrIS), the Ozone Mapping and Profiler Suite (OMPS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Clouds and the Earth’s Radiant Energy System (CERES). Among them, the ATMS is the new generation of microwave sounder measuring temperature profiles from the surface to the upper stratosphere and moisture profiles from the surface to the upper troposphere, while CrIS is the first of a series of advanced operational hyperspectral sounders providing more accurate atmospheric and moisture sounding observations with higher vertical resolution for weather and climate applications. The OMPS instrument measures solar backscattered ultraviolet to provide information on the concentrations of ozone in the Earth’s atmosphere, and VIIRS provides global observations of a variety of essential environmental variables over the land, atmosphere, cryosphere, and ocean with visible and infrared imagery. The CERES instrument measures the solar energy reflected by the Earth, the longwave radiative emission from the Earth, and the role of cloud processes in the Earth’s energy balance. Presently, observations from several instruments on S-NPP and JPSS-1 (re-named NOAA-20 after launch) provide near real-time monitoring of the environmental changes and improve weather forecasting by assimilation into numerical weather prediction models. Envisioning the need for consistencies in satellite retrievals, improving climate reanalyses, development of climate data records, and improving numerical weather forecasting, the NOAA/Center for Satellite Applications and Research (STAR) has been reprocessing the S-NPP observations for ATMS, CrIS, OMPS, and VIIRS through their life cycle. This article provides a summary of the instrument observing principles, data characteristics, reprocessing approaches, calibration algorithms, and validation results of the reprocessed sensor data records. The reprocessing generated consistent Level-1 sensor data records using unified and consistent calibration algorithms for each instrument that removed artificial jumps in data owing to operational changes, instrument anomalies, contaminations by anomaly views of the environment or spacecraft, and other causes. The reprocessed sensor data records were compared with and validated against other observations for a consistency check whenever such data were available. The reprocessed data will be archived in the NOAA data center with the same format as the operational data and technical support for data requests. Such a reprocessing is expected to improve the efficiency of the use of the S-NPP and JPSS satellite data and the accuracy of the observed essential environmental variables through either consistent satellite retrievals or use of the reprocessed data in numerical data assimilations.

The spatiotemporal mean rain rate (MR) can be characterized by the rain frequency (RF) and the conditional rain rate (CR). We computed these parameters for each season using the TMPA 3-hourly, 0.25° gridded data for the 1998–2017 period at a quasi-global scale, 50°N~50°S. For the global long-term average, MR, RF, and CR are 2.83 mm/d, 10.55%, and 25.05 mm/d, respectively. The seasonal time series of global mean RF and CR show significant decreasing and increasing trends, respectively, while MR depicts only a small but significant trend. The seasonal anomaly of RF decreased by 5.29% and CR increased 13.07 mm/d over the study period, while MR only slightly decreased by −0.029 mm/day. The spatiotemporal patterns in MR, RF, and CR suggest that although there is no prominent trend in the total precipitation amount, the frequency of rainfall events becomes smaller and the average intensity of a single event becomes stronger. Based on the co-variability of RF and CR, the paper optimally classifies the precipitation over land and ocean into four categories using K-means clustering. The terrestrial clusters are consistent with the dry and wet climatology, while categories over the ocean indicate high RF and medium CR in the Inter Tropical Convergence Zone (ITCZ) region; low RF with low CR in oceanic dry zones; and low RF and high CR in storm track areas. Empirical Orthogonal Function (EOF) analysis was then performed, and these results indicated that the major pattern of MR is characterized by an El Niño-Southern Oscillation (ENSO) signal while RF and CR variations are dominated by their trends.

Surface soil moisture (SSM), the average water content of surface soil (up to 5 cm depth), plays a key role in the energy exchange within the ecosystem. We estimated SSM in areas with vegetation cover (grassland) by combining microwave and optical satellite measurements in the central Tibetan Plateau (TP) in 2015. We exploited TERRA moderate resolution imaging spectroradiometer (MODIS) and Sentinel-1A synthetic aperture radar (SAR) observations to estimate SSM through a simplified water-cloud model (sWCM). This model considers the impact of vegetation water content (VWC) to SSM retrieval by integrating the vegetation index (VI), the normalized difference water index (NDWI), or the normalized difference infrared index (NDII). Sentinel-1 SAR C-band backscattering coefficients, incidence angle, and NDWI/NDII were assimilated in the sWCM to monitor SSM. The soil moisture and temperature monitoring network on the central TP (CTP-SMTMN) measures SSM within the study area, and ground measurements were applied to train and validate the model. Via the proposed methods, we estimated the SSM in vegetated area with an R2 of 0.43 and a ubRMSE of 0.06 m3/m3 when integrating the NDWI and with an R2 of 0.45 and a ubRMSE of 0.06 m3/m3 when integrating the NDII.

Coal-derived pyrite biooxidation using Acidithiobacillus ferrooxidans is a principal method for coal desulfurization. However, jarosite synthesized in the pyrite biooxidation system can get distributed on the pyrite surfaces and inhibit pyrite biooxidation. In this study, K-jarosite, NH4-jarosite, and Na-jarosite biosynthesis was studied in liquid systems. Then, quick dissolution of K-jarosite was investigated at pH 0.2–1.0, and pyrite biooxidation efficiency before and after jarosite removal from the biooxidized pyrite surface was examined. The results showed that pure K-jarosite was collected from K-jarosite biosynthesis system. However, the minerals harvested from NH4-jarosite and Na-jarosite biosynthesis systems were a mixture of jarosite and schwertmannite. The K-jarosite dissolution efficiency reached 46.0% and 78.4% at 48 h when the initial pH of dissolve system was 1.0 and 0.2, respectively. Moreover, jarosite removal from biooxidized pyrite surface by rapidly dissolving could enhance iron and sulfur dissolution from pyrite in the subsequent biooxidation process.

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite continually provides global observations used to retrieve over 20 VIIRS Environmental Data Record (EDR) products. Among them, the cloud mask product is essential for many other VIIRS EDR products such as aerosols, ocean color, and active fire. The reprocessed S-NPP VIIRS Sensor Data Record (SDR) data produced by NOAA/Center for Satellite Applications and Research (STAR) have shown improved stability and consistency. Recently, the VIIRS Enterprise Cloud Mask (ECM) has been reprocessed using the reprocessed VIIRS SDR data. This study assesses the reprocessed ECM product by comparing the reprocessed cloud mask types and cloud probability with those from the operational VIIRS ECM product. It found that the overall differences are small. Most of the discrepancies occur between neighboring types at the cloud edge. These findings help lay the foundation for the user community to understand the reprocessed ECM product. In addition, due to the better quality of the reprocessed VIIRS SDR data that are utilized to generate the reprocessed ECM product, it is expected that the reprocessed ECM product will have better stability and consistency compared to the operational ECM products. Therefore, the reprocessed ECM product is a useful benchmark for the user community.

Many regions of the world have experienced dramatic changes in occurrence of heat waves. Investigating changes in their spatial patterns is of great importance for understanding the causes of heat waves and their related processes. This paper, based on observation data collected from 611 meteorological stations, used the weighted mean center tool in ArcGIS to calculate China’s heat wave center of gravity and to trace its migration over the past five decades. The results show an association between its north-south movements and the change of Arctic Oscillation Index, especially for the period after the mid-1980s, during which substantial north-south displacements took place. This study also revealed a close relationship between its west-east movements and the change in annual number of heat wave days before the 1990s, after which it was replaced by a time-dependent relationship that showed increasing heat wave trend only.

The Horn of Africa ((HOA), including Djibouti, Eritrea, Ethiopia, and Somalia) has been slammed by extreme drought within the past years, and has become one of the most food-insecure regions in the world. Millions of people in the HOA are undernourished and are at risk of famine. Meanwhile, global climate change continues to cause more extreme weather and climate events, such as drought and heat waves, which have significant impacts on crop production and food security. This study aimed to investigate extreme drought in the Horn of Africa region, using satellite remote sensing data products from the Moderate Resolution Imaging Spectroradiometer (MODIS), a key instrument onboard the National Aeronautics and Space Administration (NASA) satellites Terra and Aqua, as well as Tropical Rainfall Measuring Mission (TRMM) precipitation data products. Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI) data from 2000 to 2017 were derived from the MODIS measurements and analyzed for assessments of the temporal trend of vegetation health and the impacts of extreme drought events. The results demonstrated the severity of vegetation stress and extreme drought during the past decades. From 1998 to 2017, monthly precipitation over major crop growth seasons decreased significantly. From 2001 to 2017, the mean VHI anomaly of HOA cropland decreased significantly, at a trend of −0.2364 ± 0.1446/year, and the mean TCI anomaly decreased at a trend of −0.2315 ± 0.2009/year. This indicated a deterioration of cropland due to drought conditions in the HOA. During most of the crop growth seasons in 2015 and 2016, the VHI values were below the 10-year (2001–2010) average: This was caused by extreme drought during the 2015–2016 El Niño event, one of the strongest El Niño events in recorded history. In addition, monthly VHI anomalies demonstrated a high correlation with monthly rainfall anomalies in July and August (the growth season of major crops in the HOA), and the trough points of the monthly rainfall and VHI anomaly time series of July and August were consistent with the timing of drought events and El Niño events.

How accurate can satellite measure the climate trends in atmospheric temperatures?, Observations from the satellite microwave sounders play a vital role in measuring the long-term temperature trends for climate change monitoring. Changes in diurnal sampling over time and calibration drift have been the main sources of uncertainties in the satellite-measured temperature trends. We examine observations from the first of a series of U.S. new generation of microwave sounder, the Advanced Technology Microwave Sounder (ATMS), which has been flying onboard the National Oceanic and Atmospheric Administration (NOAA)/NASA Suomi National Polar-orbiting Partnership (SNPP) environmental satellite since late 2011. The SNPP satellite has a stable afternoon orbit that has close to the same local observation time as NASA’s Aqua satellite that has been carrying the heritage microwave sounder, the Advanced Microwave Sounding Unit-A (AMSU-A), from 2002 until the present. The similar overpass timing naturally removes most of their diurnal differences. In addition, direct comparison of temperature anomalies between the two instruments shows little or no relative calibration drift for most channels. Our results suggest that both SNPP/ATMS and Aqua/AMSU-A instruments have achieved absolute stability in the measured atmospheric temperatures within 0.04 K per decade. This uncertainty is small enough to allow reliable detection of the temperature climate trends and help to resolve debate on relevant issues. We also analyze AMSU-A observations onboard the European MetOp-A satellite that has a stable morning orbit 8 hours apart from the SNPP overpass time. Their comparison reveals large asymmetric trends between day and night in the lower- and mid-tropospheric temperatures over land. This information could help to improve climate data records for temperature trend detection with improved accuracy. The SNPP satellite will be followed by four NOAA operational Joint Polar Satellite System (JPSS) satellites, providing accurate and stable measurement for decades to come. The primary mission of JPSS is for weather forecasting. Now, with the added feature of stable orbits, JPSS observations can also be used to monitor changes in climate with much lower uncertainty than the previous generation of NOAA operational satellites.

Global climate change has been resulting in more extreme weather and climate events, such as drought and heat wave. Food security has become a critical issue because of global population increase and extreme weather and climate events. The Horn of Africa (HOA, including Djibouti, Eritrea, Ethiopia, and Somalia) is one of the most food-insecure regions in the world. Millions of people in the HOA are undernourished and at the risk of famine. Food security is a very complicated and interdisciplinary problem across many sectors. To study food security, it is essential to investigate innovative solutions to integrate and analyze various related data and information, especially satellite remote sensing measurements from space, and weather and climate information from ground observations. Satellite Remote sensing measurements have advantages in spatial and temporal coverage and can be used for long-term monitoring of crop status and drought events from space. This study aims to investigate the impacts of extreme agriculture drought and food security in the HOA region using the remote sensing data products from the NASA MODIS Moderate-resolution Imaging Spectroradiometer (MODIS), a key instrument onboard the NASA satellites Terra and Aqua. The Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI) data from year 2000 to 2017 are derived from the MODIS land surface reflectance and land surface temperature products and analyzed for assessment of vegetation health and the impacts of extreme drought events. The spatial patterns and temporal trends of vegetation stress and extreme drought events are discovered, which demonstrates the critical regions and period with food security. The results also show that the drought and food insecurity have increasing trends. For a case study, the strongest El Nino event of 2015–2016 impacting on extreme drought events in the HOA is analyzed. This study provides an integrated approach to food security monitoring and assessment, which is very helpful for in decision making and strategic planning for agriculture management, disaster relief and food security.

Carbon dioxide (CO2) flux plays a key role in the energy balance of ecosystems and effective monitor CO2 flux from soil at both large scale and high temporal resolution remains a critical scientific problem of soil science. To address this scientific problem, this study tested a novel model to estimate CO2 flux from soil with highly dense vegetation cover. In contrast to previous approaches, the developed model monitored daily CO2 flux at both high spatial and temporal resolutions. This model is applicable for different forest types by fusing the daily TERRA Moderate Resolution Imaging Spectroradiometer (MODIS), Land Remote Sensing Satellite (Landsat) measurements, and monthly in-situ CO2 flux measurements collected by non-permanent chambers in the Great Dismal Swamp (GDS), the U.S. Then, daily vegetation index and surface soil temperature were estimated at high spatial resolution, and the CO2 flux was retrieved through a semi-empirical model. Two-year ground observations in the GDS were used to train and validate the model. Over the GDS area, the validation results of the retrieved CO2 flux had a coefficient of determination (R2) of 0.47, and a root mean square error (RMSE) of 1.25e + 05 μg CO2/m2/h. Further analysis showed that the performance of the proposed method varied slightly with forest types. The difference between R2 was less than 0.07 and the difference between RMSE was less than 0.08e + 05 μg CO2/m2/h. The GDS has three main forest types: maple gum (Acer rubrum and Nyssa sylvatica), Atlantic white cedar (Chamaecyparis thyoides), and pine pocosin (Pinus serotina). For the maple-gum, the R2 was 0.51, and the RMSE was 1.14e + 05 μg CO2/m2/h. For Atlantic white cedar, the R2 was 0.62, and the RMSE was 1.15e + 05 μg CO2/m2/h. For pine pocosin, the R2 was 0.44, and the RMSE was 1.07e + 05 μg CO2/m2/h. The proposed method enables estimation of CO2 flux within forested areas, and forest type affects the monitoring results.

In 2012, the USA Corn Belt, an intensive agricultural region of the USA, was hit by a widespread severe drought, affecting states such as Illinois, Iowa, Nebraska, and Indiana. In this study, time series 2000–2012 of Moderate Resolution Imaging Spectroradiometer MODIS measurements were investigated to assess the 2012 drought conditions during the corn-growing season. Seven MODIS indices generated based on eight day MODIS reflectance and land surface temperature LST products were examined with standardized precipitation index SPI and Palmer-Z across the Corn Belt to evaluate the relative performance of each MODIS index to detect agricultural drought. The normalized difference infrared index NDII6 anomaly shows the highest correlation coefficient r with SPI at three time scales and correlates best with Palmer-Z, which suggests good sensitivity of the NDII6 anomaly to precipitation and moisture deficiency in agricultural areas. The temporal and spatial features of drought provided by MODIS indices were compared with maps of the USA Drought Monitor USDM, the current advanced tool for drought monitoring. The rapid intensification of drought across the Corn Belt in 2012 summer captured by MODIS index anomalies agreed with the changes of USDM maps quite well, especially in August and September when extreme drought occurred. Through comparison with the USDM drought map, the NDII6 anomaly demonstrated an advantage in monitoring drought condition over irrigated land and showed the potential to advance fine-scale agricultural drought monitoring by providing more detailed spatial characterization.

Vegetation water content (VWC) is of vital significance to many applications in agriculture, such as crop yield estimation and precision irrigation. However, previous studies mainly estimate VWC at either high spatial resolution or temporal resolution due to the limitation of space-borne observation systems. In this paper, we target on monitoring daily plant VWC as well as canopy VWC at 30 m high spatial resolution with the fusion of optical measurements from the Land Remote Sensing Satellite (Landsat) Operational Land Imager (OLI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) in an agricultural area. The study area locates in the central of Iowa, the United States (USA), the study period ranges from August 1 to August 17, 2016. Landsat 8 OLI observations are with 16-day revisit cycle and 30-m spatial resolution, MODIS daily surface reflectance product used in this study has 500-m spatial resolution. Six Landsat OLI images and thirty MODIS images were used to estimate daily remotely sensed reflectance data at 30m first. Then, the fused satellite measurements were used to calculate Normalized Difference Water Index (NDWI). Ground-based plant VWC and canopy VWC measurements were collected during the Soil Moisture Active Passive Validation Experiment (SMAPVEX16) to calibrate and validate the model for plant VMC and canopy VWC estimation separately with the NDWI. The results of validation with in-situ measurements showed an R2 of 0.44 for the corn plant VWC, an R2 of 0.66 for the corn canopy VWC; and an R2 of 0.78 for the soybean plant VWC, an R2 of 0.85 for the soybean canopy VWC, respectively.

The detection and mapping of burned areas from wildland fires is one of the most important approaches for evaluating the impacts of fire events. In this study, a novel burned area detection algorithm for rapid response applications using Moderate Resolution Imaging Spectroradiometer MODIS 500 m surface reflectance data was developed. Spectra from bands 5 and 6, the composite indices of the Normalized Burn Ratio, and the Normalized Difference Vegetation Index were employed as indicators to discover burned pixels. Historical statistical data were used to provide pre-fire baseline information. Differences in the current post-fire and historical pre-fire data were input into a support vector machine classifier, and the fire-affected pixels were detected and mapped by the support vector machine classification process. Compared with the existing MODIS level 3 monthly burned area product MCD45, the new algorithm is able to generate burned area maps on a daily basis when new data become available, which is more applicable to rapid response scenarios when major fire incidents occur. The algorithm was tested in three mega-fire cases that occurred in the continental USA. The experimental results were validated against the fire perimeter database generated by the Geospatial Multi-Agency Coordination Group and were compared with the MCD45 product. The validation results indicated that the algorithm was effective in detecting burned areas caused by mega-fires.

Diurnal temperature range (DTR) is an important indicator for climate change. In this paper, diurnal air temperature range variations of the continental United States over the past one hundred years were investigated to discover the temporal trend and spatial patterns. While the annual mean DTR of the United States has steadily decreased during the past decades, it is found that the decreased amplitude has spatial and seasonal patterns. Seasonal and spatial variations of DTR were analyzed for the four regions, northeastern, northwestern, southeastern, and southwestern. Fall and summer witnessed a significant decrease in DTR in all regions. Spring and winter, on the other hand, have experienced much smaller decreases. Temporal trend and spatial patterns of daily maximum and minimum temperatures were also investigated to gain insight of DTR change.

Fuel moisture content (FMC) is an important variable for predicting the occurrence and spread of wildfire. Because FMC is calculated from the ratio of canopy water content to dry-matter content, we hypothesized that FMC may be estimated by remote sensing with a ratio of a vegetation water index to a vegetation dry-matter index. Four vegetation water indices, six dry-matter indices, and the resulting water/dry-matter index ratios were calculated using simulated leaf reflectances from the PROSPECT model. Two water indices, the Normalized Difference Infrared Index (NDII) and the Normalized Difference Water Index (NDWI), were more correlated with leaf water content than with FMC, and were not correlated with leaf dry-matter content. Two dry-matter indices, the Normalized Dry Matter Index (NDMI) and a recent index (unnamed) were correlated to leaf dry matter content, were inversely correlated with FMC, and were not correlated with water content. Ratios of these water indices and these dry-matter indices were highly and consistently correlated with FMC. Ratios of other water indices with other dry-matter indices were not consistently correlated with FMC. The ratio of NDII with NDMI was strongly related to FMC by a quadratic polynomial equation with an R 2 of 0.947. Spectral reflectance data were acquired for single leaves and leaf stacks of Quercus alba , Acer rubrum , and Zea mays ; the relationship between FMC and NDII/NDMI had an R 2 of 0.853 and was almost identical to the equation from the PROSPECT model simulations. For the SAIL model simulations, the relationship between NDII/NDMI and FMC at the canopy scale had an R 2 of 0.900, but the quadratic polynomial equation differed from the equations determined from the PROSPECT simulations and spectral reflectance data. NDMI requires narrow-band sensors to measure the effect of dry matter on reflectance at 1722 nm whereas NDII may be determined with many different sensors. Therefore, monitoring FMC with NDII/NDMI requires either a new sensor or a combination of two sensors, one with high temporal resolution for monitoring water content and one with high spectral resolution for estimating dry-matter content.

MODerate resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Thickness (AOT) and the Atmospheric Infrared Sounder (AIRS) atmospheric profiles (temperature and moisture) Level 2 products are studied to investigate dust aerosols impacts on hurricanes through spatial analysis. Spatial regions were selected based on two concentric circles (an annulus) and segmented by left and right regions of 180° each around the hurricanes center. Statistical relationships between MODIS AOT, AIRS temperature and percent relative humidity (RH) of the hurricanes Isabel (2003), Frances (2004), Katrina (2005) and Helene (2006) are presented. The variation of temperature and RH values represented in static-left, static-right, dynamic-left and dynamic-right for the 500 mb, 700 mb and 850 mb pressure levels are analyzed to determine the impacts of dust aerosols on temperature and RH. Average AOT was found highest in Helene (0.27) than Isabel (0.23), Frances (0.18) and Katrina (0.16). Negative impacts on the atmospheric moisture levels and positive on the temperature was found by dust aerosols. This study concludes that in 2006 dust dampened the force of hurricane Helene contributing towards extremely dryer atmosphere and inhibiting the conductive to intensify the hurricane.

Efficient and accurate detection of the temporal dynamics and spatial variations of leaf dry matter content would help monitor key properties and processes in vegetation and the wider ecosystem. However, leaf water content strongly absorbs at shortwave infrared wavelengths, reducing the signal from dry matter. The major objective of this study was to examine relationship between spectral reflectance of fresh leaves and the ratio of leaf dry mass to leaf area, across a wide range of species at the leaf scale. A narrow-band, normalized index combining two distinct wavebands centred at 1649 and 1722 nm achieved the highest overall performance and discriminatory power compared to either single band or first derivatives. The normalized index was evaluated using the PROSPECT leaf optical properties spectra simulated reflectance spectra and field measurements from the Leaf Optical Properties Experiment LOPEX data set. Both evaluations show that leaf dry matter contents were retrievable with R2 of 0.845 and 0.681 and regression slopes of 0.903 and 0.886. This study suggests that spectral reflectance measurements hold promise for the assessment of dry matter content for green leaves. Further investigation needs to be conducted to evaluate the effectiveness of this normalized index at canopy scales.

A new approach is developed for quick detection of sand and dust storms SDSs over arid and semi-arid regions of the northwestern part of China, where the bright-reflecting source areas of Asian dust outbreaks are located. The Asian dust particles, once with proper conditions, can even transport across the Pacific Ocean and reach the USA and Canada. Remote-sensing data products of mineral dust near its source are deficient because of the radiance contributions of the bright surface. In this article, based on Moderate Resolution Imaging Spectroradiometer MODIS measurements, consecutive separation of dust cloud from bright underlying surface and water/ice cloud is completed by utilizing a refined cloud mask algorithm and the normalized difference dust index NDDI. Thresholds are determined through statistical analysis of MODIS measurements over the Taklimakan and Gobi deserts. Validations with ground observations over the sites in Inner Mongolia and Xinjiang in China demonstrated good performance of the proposed method in separating SDS from bright surface and cloud.

At about 1720 nm wavelength, there is an absorption feature of leaf dry matter based on a C−H stretch overtone, which is difficult to detect in fresh green leaves because of the absorption spectrum of liquid water. We applied a method originally proposed in Remote Sensing of Environment by B.-C. Gao and A.F.H. Goetz (Estimating dry matter content of fresh leaves from the residuals between leaf and water reflectance, pp. 137–145, 1994) that used linear regression between the natural logarithm of leaf spectral reflectance and the specific absorption coefficient of liquid water over wavelengths from 1500 to 1800 nm to calculate an expected reflectance spectrum. The residual difference between the measured and expected leaf reflectance spectra enhanced the absorption feature of dry matter. The absorption feature was quantified using the normalized dry matter index (NDMI) based on the contrast between the residual leaf reflectance at 1649 and 1722 nm. NDMI was linearly related to leaf dry matter content (g cm−...

article i nfo Article history: Canopy foliar biomass, defined as the product of leaf dry matter content and leaf area index, is an important measurement for global biogeochemical cycles. This study explores the potential for retrieving foliar biomass in green canopies using a spectral index, the Normalized Dry Matter Index (NDMI). This narrow-band index is based on absorption at the C-H bond stretch overtone and is correlated with leaf dry matter content in fresh green leaves. PROSPECT and SAIL model simulations suggest that the NDMI at the canopy scale is able to minimize the effects of leaf thickness and leaf water content and to maximize sensitivity to variation in canopy foliar biomass. The simulation outputs were analyzed with an ANOVA, and 87% of the variation in the NDMI is explained by leaf dry matter content. The NDMI was linearly related to foliar biomass (g cm �2 ) from model simulations (R 2 =0.97). The NDMI calculated from spectral reflectances for one to four stacked leaves was also correlated with total leaf biomass (R 2 =0.59). These results suggest that it may be possible to determine foliar biomass from airborne and satellite-borne imaging spectrometers, such as NASA's HyspIRI mission.

Vegetation phenology describing the seasonal cycle of plants is currently one of the main concerns in the study of climate change and carbon balance estimation in ecosystems. Remote sensing techniques which can capture canopy reflectance allow vegetation photosynthetic capacity to be assessed. In this study, the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) measurements were used to identify onset date of greenness in deciduous broadleaf forest (DBF) over the continental United States from year 2000 to 2008. Onset dates determined by these three indices were compared with North American First Leaf Lilac Phenology Data over the same time period. NDVI has a best agreement with the field data among the three vegetation indices. Spatial analysis was performed on the onset dates predicted with NDVI. Four major conclusions were drawn from this study are: 1) onset dates are not only dependent on latitude but also associated with ecoregions and altitudes; 2) onset of greenness moves northward gradually and the average change of onset dates along latitude is about 3 days per degree. Interannual variability of onset dates is greater at higher latitudes (> 43 °N) than at lower latitudes (≤ 43 °N); 3) at the same latitude, DBF in mountain area tends to green up latter and coastal forest tends to green up earlier than other ecoregions; and 4) the impact of altitude is more obvious when the range of elevation achieves more than 1000 m. These conclusions provide insight for assessing vegetation indices in determining onset date of greenness at regional scale, and can be exploited to analyze the impacts of climate change on terrestrial ecosystem.

This study developed a rapid assessment algorithm for post-hurricane forest damage estimation using moderate resolution imaging spectroradiometer (MODIS) measurements. The performance of five commonly used vegetation indices as post-hurricane forest damage indicators was investigated through statistical analysis. The Normalized Difference Infrared Index (NDII) was identified as the optimal damage indicator among these vegetation indices. An approach for detecting forest damage at a regional scale, without relying on ground inventory or sampling, was designed and validated. The validation showed that the relative change of pre- and post-hurricane NDII was linearly related to the damage severity estimated by the ground inventory with the coefficient of determination 0.79. This approach was applied to evaluate forest damage severity and the impacted region caused by Hurricane Katrina. Published by Elsevier B.V.

Conversion between brightness temperature and band averaged radiance is frequently used in radiative transfer model simulations, algorithm development and remote sensing applications. While the formula looks quite simple, direct conversion is not very efficient. In this paper, a fast conversion algorithm for band averaged radiance calculations from brightness temperature is proposed based on the Chebyshev polynomial approximation, and is validated with the thermal infrared bands of the moderate resolution imaging spectroradiometer (MODIS). Due to the orthogonality of the Chebyshev polynomial, the look-up-tables (LUTs) can be constructed very easily, and only need the Chebyshev expansion coefficients and two constants. Error analysis and performance testing of approximations at different orders demonstrate that band averaged radiance can be calculated very accurately with the seventh to tenth Chebyshev expansion at a speed that is over 50 times faster than direct calculation using numerical integration. A toolkit is also implemented based on the proposed algorithm; this is freely available upon request.

The effects of soil moisture and leaf water content on canopy reflectance of MODIS shortwave infrared (SWIR) bands 5, 6, and 7 and water-related indices are studied quantitatively using the coupled soil-leaf-canopy reflectance model. Canopy spectra simulations under various input conditions show that soil moisture has a large effect on each SWIR reflectance at low leaf area index (LAI) values, among which band 5 is the most sensitive to soil moisture variations, while band 7 responds strongest to dry soil conditions. Band 5 is also better suited to measure leaf water content change, since it obtains a higher variation when leaf water content changes from dry to wet. In general, each SWIR band responds to soil moisture and leaf water content differently. By using the normalized calculation between the water absorption-sensitive band and insensitive band, the Normalized Difference Water Index shows the most capability to remove the soil background effect and enhance the sensitivity to leaf water content. These two moisture variables may be separated by combining multiple rather than one SWIR band with a near-infrared band considering that each SWIR band has a different response to soil moisture and leaf water content.

This paper presents the patterns and trends of haze over 31 provincial capitals in China between 1980 and 2005. The haze measurements were based on human visual range observations at 31 synoptic meteorological stations operated by the China Meteorological Administration (CMA). The high haze regions were found in largely populated cities such as Chongqing, Beijing, and Shenyang, while the low haze regions were located in the cities with small populations such as Lhasa, Kunming, and Guiyang in southwestern China and Haikou in southern China. The haziness of the 12 cities shows a significantly (95%) decreasing trend, while that of the 13 cities shows a significantly increasing trend over the 25-year study period. The increases are evident in the eastern and southwestern cities in China. It has also been found that there is a decreasing haze trend in winter but an increasing trend in summer for many cities. Nonetheless, the causality for the reduction of aerosol emissions has not been established. This report is the first survey and preliminary analysis done on haze patterns and trends from 1980 to 2005 over the capital cities of 31 provinces in Mainland China.

This study focuses on investigating the ability of selected satellite-derived indices, the normalized multi-band drought index (NMDI), normalized difference water index (NDWI), and the normalized burn ratio (NBR), for detecting forest fires burning in southern Georgia, USA and southern Greece in 2007. Index performance is evaluated using MODIS fire products. Satellite images generated from each index are compared with the active fire map provided by the MODIS rapid response team. Performance measures extracted from the statistical analyses using the confusion matrices are used to verify the capacity of the indices for active fire detection. For each test case, NMDI has strong signals corresponding to active fires and pinpoints the active fire spots accurately. Both, performance evaluations by image comparison and statistical analyses, indicate that active fire detection using NMDI is quite accurate. NMDI reveals the highest overall performance and discrimination power compared to NDWI and NBR. The successful application of NMDI for detecting fires in different areas proves that NMDI is not site-specific and is expected to be applicable to different areas for active fire detection. Such a capacity can help monitor large-scale fire hazards and is therefore useful to carry out regional and global studies.

Wildland fire is both one of the major natural hazards and a natural process for ecosystem persistence. Accurate assessment of fire danger potential and timely detection of active fires are critical for fire fighting and fuel management. Space-borne measurements have become the primary approaches for these efforts. Many research works have been conducted and some data products have been generated for practical applications. This paper presents a review of the major sensors and algorithms for active fire monitoring and fire danger potential detection from space. Major sensors and their characteristics, physical principles of the major algorithms are summarized. Limitations of these algorithms and future improvements are also discussed.

Live fuel moisture content (LFMC) is one of the most important fuel properties and a critical parameter for wildland fire danger rating estimation and fire behavior analysis. Direct ground measurement of live fuel moisture content has disadvantages of high cost and limited spatial distribution extent. This paper presents an algorithm to retrieve live fuel moisture content from multiple bands of MODIS measurements. We analyzed the physical relationship between surface reflectance and live fuel moisture content using simulated MODIS measurements of diverse leaf samples, derived approximate inversion models, and proposed a semi-physical approach for live fuel moisture retrieval employing multiple MODIS bands. Using simulated MODIS measurements, the correlation coefficients between the true LFMC and estimated LFMC with our inversion models are 0.7738, 0.8397, 0.9560 and 0.9576 respectively. For validation, we tested our inversion method with woody live fuel moisture measurements at fire weather stations in Georgia. The correlation coefficients between measured LFMC and estimated LFMC with our inversion models are 0.5727, 0.6522, 0.7551, and 0.7737 respectively. Both model simulation and station measurements demonstrated advantages of our approach in accuracy. Our study suggests the potential for near real-time applications of live fuel moisture.

Research has shown that remote sensing techniques can be used for assessing live fuel moisture content (LFMC) from space. The need for dynamic monitoring of the fire risk environment favors the use of fast, site-specific, empirical models for assessing local vegetation moisture status, albeit with some uncertainties. These uncertainties may affect the accuracy of decisions made by fire managers using remote sensing derived LFMC. Consequently, the analysis of these LFMC retrieval uncertainties and their impact on applications, such as fire spread prediction, is needed to ensure the informed use of remote sensing derived LFMC measurements by fire managers. The Okefenokee National Wildlife Refuge, one of the most fire-prone regions in the southeastern United States was chosen as our study area. Our study estimates the uncertainties associated with empirical site specific retrievals using NDWI (Normalized Difference Water Index; ( R 0.86 − R 1.24 ) / ( R 0.86 + R 1.24 )) and NDII (Normalized Difference Infrared Index; ( R 0.86 − R 1.64 ) / ( R 0.86 + R 1.64 )) that are simulated by coupled leaf and canopy radiative transfer models. In order to support the findings from those simulations, a second approach estimates uncertainties using actual MODIS derived indices over Georgia Forestry Commission stations that provide NFDRS model estimates of LFMC. Finally, we used the FARSITE surface fire behavior model to examine the sensitivity of fire spread rates to live fuel moisture content for the NFDRS high pocosin and southern rough fuel models found in Okefenokee. This allowed us to evaluate the effectiveness of satellite based LFMC estimations for use in fire behavior predictions. Sensitivity to LFMC (measured as percentage of moisture weight per unit dry weight of fuel) was analyzed in terms of no-wind no-slope spread rates as well as normalized spread rates. Normalized spread rates, defined as the ratio of spread rate at a particular LFMC to the spread rate at LFMC of 125 under similar conditions, were used in order to make the results adaptable to any wind-slope conditions. Our results show that NDWI has a stronger linear relationship to LFMC than NDII, and can consequently estimate LFMC with lesser uncertainty. Uncertainty analysis shows that 66% of NDWI based LFMC retrievals over non-sparsely vegetated regions are expected to have errors less than 32, while 90% of retrievals should be within an error margin of 56. In pocosin fuel models, under low LFMC conditions (