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【Objective】 Reclaimed soils are usually poor in nutrients and the aim of this paper is to investigate the efficacy of nitrogen-fixing bacteria combined with different nitrogen fertilizations in improving fertility of reclaimed soils. 【Method】 The experiments were conducted in pots filled with soil collected from a subsidized coal mining. The soil was incubated with nitrogen-fixing bacteria combined with different nitrogen fertilizers. In each treatment, we measured carbon and nitrogen in microbial biomass, enzymatic activity, total nitrogen, and ammonium and nitrate nitrogen in the soil. 【Result】 Combining nitrogen-fixing bacteria with ammonium or nitrate nitrogen fertilizer increases carbon and nitrogen in microbial biomass, total dissolved nitrogen, ammonium nitrogen in the soil. Compared with treatment with nitrate and ammonium nitrogen fertilization only, their combination with nitrogen-fixing bacteria increases the activity of catalase, sucrase, protease and urease by 4.96%, 17.85%, 12.53% and 6.12% respectively. Correlation analysis shows a close relationship between soil nutrients, enzymatic activity and carbon and nitrogen in microbial biomass; the activity of sucrase, protease and urease is positively correlated with total nitrogen, total dissolved nitrogen and ammonium nitrogen in the soil (P

We present a new version (v5.0) of the NOAA Center for Satellite Applications and Research (STAR) mid‐tropospheric temperature (TMT) time series. This data set uses a backward‐merging approach to intercalibrate 16 satellite‐based microwave sounding records. The instrument observations included those from the Microwave Sounding Unit (MSU) during 1979–2004, Advanced Microwave Sounding Unit‐A (AMSU‐A) during 1998–2017, and Advanced Technology Microwave Sounder (ATMS) from 2011 to present. A TMT time series during 2002–present based on satellite microwave observations in stable sun‐synchronous orbits was used as a reference in the backward merging process in which earlier satellites were adjusted and merged to the reference. Observations from earlier satellites were recalibrated to remove their calibration drifting errors relative to the reference using sequential overlapping observations. This included removal of spurious warming drifts in the MSU observations onboard NOAA‐11, NOAA‐12, and NOAA‐14 and a spurious cooling drift in the NOAA‐15 AMSU‐A observations. Temperature changes resulting from diurnal sampling drifts were corrected using an observation‐based semi‐physical model developed in this study. Other adjustments included channel frequency differences between MSU and AMSU‐A companion channels and instrument blackbody warm target effect on observed radiances. These adjustments resulted in inter‐consistent TMT records spanning MSU, AMSU‐A, and ATMS. The merged time series produced a global mean TMT trend of 0.092 ± 0.043 K/decade during 1979–2021 and a total tropospheric trend of 0.142 ± 0.045 K/decade after removal of a stratospheric cooling effect in TMT. Remarkably, the total tropospheric trends during the latest half period were nearly doubled the earlier half period over the global ocean. Plain Language Summary: Long‐term observations of global atmospheric temperatures from satellite microwave sounders play a vital role in climate change research. These observations involved multiple satellites spanning several decades. Careful intersatellite calibration and bias correction are needed to derive inter‐consistent records from multi‐satellite observations for reliable climate change detection. Here we develop a new version of the NOAA Center for Satellite Applications and Research (STAR) mid‐tropospheric temperature (TMT) time series for climate trend investigation. The time series includes instrument observations from three generations of satellite microwave sounders from 1979 to present. Intersatellite biases from several error sources were removed or minimized, including instrument degradation, changes in satellite local observation time, and spectral band differences between different generations of microwave sounders, etc. A unique feature in the time series was satellite merging started from the latest backward to the earlier ones. A TMT time series during 2002‐present was used as a reference in the backward merging, which was based on satellite microwave sounder observations with fixed local observation time. The reference TMT has a high accuracy in trend detection, allowing intercalibration and trend detection with better accuracy in time series of the entire period from 1979 to present. Key Points: A new version of the NOAA Center for Satellite Applications and Research (STAR) mid‐tropospheric layer temperature time series was developedInstrument recalibration has removed spurious warming drifts in observations from NOAA‐11 to NOAA‐14 and spurious cooling drifts in NOAA‐15The new record yields a trend of 0.14 K/decade during 1979–2021 with an even greater rate of warming after the year 2002 (0.22 K/decade) [ABSTRACT FROM AUTHOR]

We develop a post‐millennium mid‐tropospheric temperature time series from continuous observations by advanced microwave sounders onboard satellites in stable sun‐synchronous orbits. Such observations have high radiometric stability and do not experience diurnal sampling changes over time, allowing us to develop merged time series from multiple satellites with an accuracy better than 0.012 Kelvin/Decade. With such high accuracy, the resulting time series can be used as a reference measurement of climate variability and trends in atmospheric temperatures. The warming rate from this time series for the atmospheric layer between surface and 10 km is 0.230 ± 0.134 Kelvin/Decade during the period from 2002 to 2020, which is ∼14% higher than the existing satellite microwave sounder datasets. Our finding provides new insight on trend differences among microwave sounder temperature data sets developed by different research groups, and is also helpful in reconciling trend differences between satellite observations and climate model simulations. Plain Language Summary: Atmospheric temperature time series developed from satellite microwave sounder observations has been extensively used in climate change monitoring and verifying climate model simulations of climate change. However, uncertainties exist in the satellite merged products and their resulting atmospheric temperature trends, mainly caused by diurnal sampling changes over time and instrument calibration errors. Satellite products developed by different research groups produced different atmospheric temperature trends, undermining the capability of using satellite observations in global change monitoring. Here we develop a reference time series from 2002 to present using advanced satellite microwave sounder observations in stable sun‐synchronous orbits for the detection of global mid‐tropospheric temperature trends with accuracy better than 0.012 Kelvin/Decade. This high accuracy in trend detection was achieved because diurnal sampling drifting errors do not exit for satellites in stable orbits and that these measurements have high radiometric stability. This reference measurement is expected to help reconcile differences in climate trend comparisons among different satellite products and between climate model simulations and satellite observations during the post‐millennium periods. It may also be helpful in the development of atmospheric temperature time series with a better accuracy for satellites before the millennium when used as a reference. Key Points: A reference mid‐tropospheric temperature time series was developed using advanced satellite microwave sounder observations in stable orbitsThe reference time series can detect the mid‐tropospheric temperature trends with an accuracy better than 0.01 Kelvin/DecadeThe mid‐tropospheric temperature trends observed from the reference time series are 0.230 ± 0.134 Kelvin/Decade during 2002–2020 [ABSTRACT FROM AUTHOR]

Understanding the form-specific long-term effects of manure (liquid, solid, and composted) application on soil P is essential for nutrient management planning. This 8-yr study aimed to quantify changes in soil test P (STP, Olsen-P) with application of three forms of swine manure compared with inorganic fertilizer and to calculate the manure P source availability coefficient (PSAC) as the ratio of the specific manure form to inorganic P in providing crop P availability. The STP content increased linearly with both total and net P addition at the surface (0-15 cm) and subsurface (15-30 cm) soil depths, regardless of P source. The slope of STP vs. total or net P addition in the surface layer was steeper for liquid than for solid manure, whereas in the subsurface layer, composted manure showed a greater slope than either liquid or solid manure. The amount of applied P required to increase STP by one unit in the surface layer was 19.9, 15.7, 31.6, and 20.9 kg P ha-1 for inorganic fertilizer and liquid, solid, and composted manure, respectively. When increases in subsoil STP and grain P removal were considered, the net P additions of inorganic fertilizer and liquid, solid, and composted manure needed to increase STP by one unit were 12.2, 9.8, 16.1 and 10.7 kg ha-1, respectively. The PSAC values were 0.99, 1.08, and 0.97 for liquid, solid, and composted manure, respectively. The longterm availability of swine manure P was largely identical among forms and was similar to that of inorganic fertilizer. [ABSTRACT FROM AUTHOR]

Soil moisture, especially surface soil moisture (SSM), plays an important role in the development of various natural hazards that result from extreme weather events such as drought, flooding, and landslides. There have been many remote sensing methods for soil moisture retrieval based on microwave or optical thermal infrared (TIR) measurements. TIR remote sensing has been popular for SSM retrieval due to its fine spatial and temporal resolutions. However, because of limitations in the penetration of optical TIR radiation and cloud cover, TIR methods can only be used under clear sky conditions. Microwave SSM retrieval is based on solid physical principles, and has advantages in cases of cloud cover, but it has low spatial resolution. For applications at the local scale, SSM data at high spatial and temporal resolutions are important, especially for agricultural management and decision support systems. Current remote sensing measurements usually have either a high spatial resolution or a high temporal resolution, but not both. This study aims to retrieve SSM at both high spatial and temporal resolutions through the fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Land Remote Sensing Satellite (Landsat) data. Based on the universal triangle trapezoid, this study investigated the relationship between land surface temperature (LST) and the normalized difference vegetation index (NDVI) under different soil moisture conditions to construct an improved nonlinear model for SSM retrieval with LST and NDVI. A case study was conducted in Iowa, in the United States (USA) (Lat: 42.2°~42.7°, Lon: -93.6°~-93.2°), from 1 May 2016 to 31 August 2016. Daily SSM in an agricultural area during the crop-growing season was downscaled to 120-m spatial resolution by fusing Landsat 8 with MODIS, with an R² of 0.5766, and RMSE from 0.0302 to 0.1124 m³/m³. [ABSTRACT FROM AUTHOR]

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-Zacross 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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 R 2 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. [ABSTRACT FROM AUTHOR]

Abstract: 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 3days 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 1000m. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

Over the past 40 years, drought has affected more people in the world than any other natural hazard, affecting large segments of the population and destroying the natural resource base, livestock and livelihoods. Recent projections show that drought events are expected to increase in frequency and intensity due to climate change. According to studies conducted by the Food and Agriculture Organization of the United Nations (FAO), 83% of all damages and losses caused globally by drought between 2006 and 2016 have been absorbed by agriculture, putting a large part of the world's population at risk of food insecurity. This study shows the advantage of scaling-up FAO's agricultural drought monitoring and early warning system (ASIS) and building the bridge with the anticipatory action, drought financial mechanisms, social protection and other initiatives for preventing the deterioration of food security and strengthening resilience. The results of the methodology that is based on and supported by the digital innovation, machine learning, matured knowledge and experiences accumulated over the past 10 years are illustrated with practical examples from different countries, ecological environments and crops. A fused time series of Advanced Very-High-Resolution Radiometer (AVHRR) data from Meteorological Operational satellite (METOP) and National Oceanic and Atmospheric Administration (NOAA) was used to produce a consistent time series of a vegetation health index (VHI) at 1 km spatial resolution from 1984 to present. VHI is multiplied by the crop coefficient (kc) to provide more responsiveness to the VHI anomaly that occurs during sensitive phenological phases to water stress such as a flowering and grain filling. The weighted VHI (wVHI) is integrated from the start of the season (SOS) up to the end of season (EOS). Once the temporal analysis of wVHI is completed, the spatial average is calculated using the values of pixels within a specific crop mask and administrative unit. The system proposed different vegetation indices to assess the impact of drought in agriculture; including an agricultural drought forecast that provide more time to the decision makers for implementing anticipatory actions to mitigate the drought in agriculture. Next generation agricultural stress index system (ASIS) offers full capabilities to support: parametric crop insurance, social protection schemes, early action, national drought management plans and to guide public investments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Precipitation, especially convective precipitation, is highly associated with hydrological disasters (e.g., floods and drought) that have negative impacts on agricultural productivity, society, and the environment. To mitigate these negative impacts, it is crucial to monitor the precipitation status in real time. The new Advanced Baseline Imager (ABI) onboard the GOES-16 satellite provides such a precipitation product in higher spatiotemporal and spectral resolutions, especially during the daytime. This research proposes a deep neural network (DNN) method to classify rainy and non-rainy clouds based on the brightness temperature differences (BTDs) and reflectances (Ref) derived from ABI. Convective and stratiform rain clouds are also separated using similar spectral parameters expressing the characteristics of cloud properties. The precipitation events used for training and validation are obtained from the IMERG V05B data, covering the southeastern coast of the U.S. during the 2018 rainy season. The performance of the proposed method is compared with traditional machine learning methods, including support vector machines (SVMs) and random forest (RF). For rainy area detection, the DNN method outperformed the other methods, with a critical success index (CSI) of 0.71 and a probability of detection (POD) of 0.86. For convective precipitation delineation, the DNN models also show a better performance, with a CSI of 0.58 and POD of 0.72. This automatic cloud classification system could be deployed for extreme rainfall event detection, real-time forecasting, and decision-making support in rainfall-related disasters. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Traditional fire detection algorithms mainly rely on hot spot detection using thermal infrared (TIR) channels with fixed or contextual thresholds. Three solar reflectance channels (0. 65 μm, 0. 86 μm, and 2. 1 μm) were recently adopted into the MODIS version 4 contextual algorithm to improve the active fire detection. In the southeastern United States, where most fires are small and relatively cool, the MODIS version 4 contextual algorithm can be adjusted and improved for more accurate regional fire detection. Based on the MODIS version 4 contextual algorithm and a smoke detection algorithm, an improved algorithm using four TIR channels and seven solar reflectance channels is described. This approach is presented with fire events in the southeastern United States. The study reveals that the T 22 of most small, cool fires undetected by the MODIS version 4 contextual algorithm is lower than 310 K. The improved algorithm is more sensitive to small, cool fires in the southeast especially for fires detected at large scan angles. [Copyright &y& Elsevier]

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 (<100), retrieval errors could lead to normalized spread rate errors of 6. 5 which may be equivalent to an error of 47 m/h in no-wind no-slope conditions. For southern rough fuel models, when LFMC <175, LFMC retrieval errors could amount to normalized spread rate errors of 0. 6 or an equivalent error of 9. 3 m/h in no-wind no-slope conditions. These spread rate error estimates represent approximately the upper bound of errors resulting from uncertainties in empirical retrievals of LFMC over forested regions. [Copyright &y& Elsevier]

• A field experiment to monitor surface soil temperature and soil CO2 flux. • Estimate soil CO2 flux with satellite measurements in a forested area. • Generate CO2 flux from soil at both high spatial and temporal resolutions. Carbon dioxide (CO 2) flux plays a key role in the energy balance of ecosystems and effective monitor CO 2 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 CO 2 flux from soil with highly dense vegetation cover. In contrast to previous approaches, the developed model monitored daily CO 2 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 CO 2 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 CO 2 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 CO 2 flux had a coefficient of determination (R2) of 0.47, and a root mean square error (RMSE) of 1.25e + 05 μg CO 2 /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 CO 2 /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 CO 2 /m2/h. For Atlantic white cedar, the R2 was 0.62, and the RMSE was 1.15e + 05 μg CO 2 /m2/h. For pine pocosin, the R2 was 0.44, and the RMSE was 1.07e + 05 μg CO 2 /m2/h. The proposed method enables estimation of CO 2 flux within forested areas, and forest type affects the monitoring results. [ABSTRACT FROM AUTHOR]

• A novel method to retrieve crop water content at high spatial and temporal resolution. • Downscaling with improved data fusion method. • Daily crop water content for agricultural applications. 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. [ABSTRACT FROM AUTHOR]

Abstract 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 P o 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 P o , labile monoester P, and phytate-like P, but had no effect on the diester P and unknown P o contents. The interaction between soil type and freeze-thaw cycles significantly affected the NaHCO 3 -extractable P i , and phytate-like P fractions, but did not affect the bicarbonate-extractable P o , and labile monoester P fractions. The extent of increase in NaHCO 3 -extractable P i largely depended on the amount of organic matter in the soil. In most cases, bicarbonate-extractable P and hydrolysable P o 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 P o species, especially in the earlier stages. Highlights • NaHCO 3 -P and hydrolyzable P o in five soils were assayed during freeze-thaw cycles. • NaHCO 3 -P, labile monoester P, and phytate-like P were increased by FTC. • Soil type × FTC interaction merely affected the NaHCO 3 -P i and phytate-like P. • Labile P mostly peaked by two FTC and declined or remained constant thereafter. • The extent of increase in labile P was soil-specific, depending on SOM amount. [ABSTRACT FROM AUTHOR]

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, NH 4 -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 NH 4 -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. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]