Miracle-dz/UniFAR-FTUs huggingface dataset:
Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data

Mapping crop types and land cover in smallholder farming systems in sub-Saharan Africa remains a challenge due to data costs, high cloud cover, and poor temporal resolution of satellite data. With improvement in satellite technology and image processing techniques, there is a potential for integrating data from sensors with different spectral characteristics and temporal resolutions to effectively map crop types and land cover. In our Malawi study area, it is common that there are no cloud-free images available for the entire crop growth season. The goal of this experiment is to produce detailed crop type and land cover maps in agricultural landscapes using the Sentinel-1 (S-1) radar data, Sentinel-2 (S-2) optical data, S-2 and PlanetScope data fusion, and S-1 C2 matrix and S-1 H/α polarimetric decomposition. We evaluated the ability to combine these data to map crop types and land cover in two smallholder farming locations. The random forest algorithm, trained with crop and land cover type data collected in the field, complemented with samples digitized from Google Earth Pro and DigitalGlobe, was used for the classification experiments. The results show that the S-2 and PlanetScope fused image + S-1 covariance (C2) matrix + H/α polarimetric decomposition (an entropy-based decomposition method) fusion outperformed all other image combinations, producing higher overall accuracies (OAs) (>85%) and Kappa coefficients (>0.80). These OAs represent a 13.53% and 11.7% improvement on the Sentinel-2-only (OAs < 80%) experiment for Thimalala and Edundu, respectively. The experiment also provided accurate insights into the distribution of crop and land cover types in the area. The findings suggest that in cloud-dense and resource-poor locations, fusing high temporal resolution radar data with available optical data presents an opportunity for operational mapping of crop types and land cover to support food security and environmental management decision-making.
Chemical transformations of succinic acid recovered from fermentation broths by a novel direct vacuum distillation-crystallisation method

A novel alternative methodology (direct crystallisation) to the traditional calcium precipitation to obtain succinic acid (SA) from defined and wheat-based fermentation broths is reported. SA crystals were successfully recovered from fermentation broths (FB) using this method. A higher SA crystal purity (95%) and yield (70%) were obtained in the direct crystallisation method compared to a slightly modified traditional calcium precipitation method (90% and 24%, respectively). Chemical transformations (e.g. esterifications) to high-added value derivatives of both recovered SA crystals were then investigated using a range of solid acids including our acidic tunable mesoporous carbonaceous materials denoted as Starbon® acids. Results showed that SA crystals could be successfully converted into mono- and diesters in high yields and selectivities employing solid acids regardless of the reaction conditions. The order of reactivity was found to be: pure SA crystals > SA crystals from defined FB > FB SA crystals. Results demonstrate that SA can be effectively purified from actual fermentation broths, showing the importance of integrating the fermentation and downstream processing to optimise the fermentative production of SA and its chemical transformations to produce high-added value derivatives.
MODELING SELF-CONTACT FORCES IN THE ELASTIC THEORY OF DNA SUPERCOILING

A DNA polymer with thousands of base pairs is modeled as an elastic rod with the capability of treating each base pair independently. Elastic theory is used to develop a model of the double helix which incorporates intrinsic curvature as well as inhomogeneities in the bending, twisting, and stretching along the length of the polymer. Inhomogeneities in the elastic constants can also be dealt with; thus, sequence-dependent structure and deformability can be taken into account. Additionally, external forces have been included in the formalism, and since these forces can contain a repulsive force, DNA self-contact can be explicitly treated. Here the repulsive term takes the form of a modified Debye–Huckel force where screening can be varied to account for the effect of added salt. The supercoiling of a naturally straight, isotropic rod in 0.1M NaCl is investigated and compared with earlier treatments of supercoiled DNA modeled by a line of point charges subject to electrostatic interactions and an elastic po.
Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR

Monitoring of canopy density with related metrics such as leaf area index (LAI) makes a significant contribution to understanding and predicting processes in the soil–plant–atmosphere system and to indicating crop health and potential yield for farm management. Remote sensing methods using optical sensors that rely on spectral reflectance to calculate LAI have become more mainstream due to easy entry and availability. Methods with vegetation indices (VI) based on multispectral reflectance data essentially measure the green area index (GAI) or response to chlorophyll content of the canopy surface and not the entire aboveground biomass that may be present from non-green elements that are key to fully assessing the carbon budget. Methods with light detection and ranging (LiDAR) have started to emerge using gap fraction (GF) to estimate the plant area index (PAI) based on canopy density. These LiDAR methods have the main advantage of being sensitive to both green and non-green plant elements. They have primarily been applied to forest cover with manned airborne LiDAR systems (ALS) and have yet to be used extensively with crops such as winter wheat using LiDAR on unmanned aircraft systems (UAS). This study contributes to a better understanding of the potential of LiDAR as a tool to estimate canopy structure in precision farming. The LiDAR method proved to have a high to moderate correlation in spatial variation to the multispectral method. The LiDAR-derived PAI values closely resemble the SunScan Ceptometer GAI ground measurements taken early in the growing season before major stages of senescence. Later in the growing season, when the canopy density was at its highest, a possible overestimation may have occurred. This was most likely due to the chosen flight parameters not providing the best depictions of canopy density with consideration of the LiDAR’s perspective, as the ground-based destructive measurements provided lower values of PAI. Additionally, a distinction between total LiDAR-derived PAI, multispectral-derived GAI, and brown area index (BAI) is made to show how the active and passive optical sensor methods used in this study can complement each other throughout the growing season.
Quantifying Forest Fire and Post-Fire Vegetation Recovery in the Daxin'anling Area of Northeastern China Using Landsat Time-Series Data and Machine Learning

Many post-fire on-site factors, including fire severity, management strategies, topography, and local climate, are concerns for forest managers and recovery ecologists to formulate forest vegetation recovery plans in response to climate change. We used the Vegetation Change Tracker (VCT) algorithm to map forest disturbance in the Daxing’anling area, Northeastern China, from 1987 to 2016. A support vector machine (SVM) classifier and historical fire records were used to separate burned patches from disturbance patches obtained from VCT. Afterward, stepwise multiple linear regression (SMLR), SVM, and random forest (RF) were applied to assess the statistical relationships between vegetation recovery characteristics and various influential factors. The results indicated that the forest disturbance events obtained from VCT had high spatial accuracy, ranging from 70% to 86% for most years. The overall accuracy of the annual fire patches extracted from the proposed VCT-SVM algorithm was over 92%. The modeling accuracy of post-fire vegetation recovery was excellent, and the validation results confirmed that the RF algorithm provided better prediction accuracy than SVM and SMLR. In conclusion, topographic variables (e.g., elevation) and meteorological variables (e.g., the post-fire annual precipitation in the second year, the post-fire average relative humidity in the fifth year, and the post-fire extreme maximum temperature in the third year) jointly affect vegetation recovery in this cold temperate continental monsoon climate region.
Treatment of a Cutting Oil Emulsion by Microwave Irradiation

Abstract This work investigates the effect of a set of operating variables, including irradiation time, irradiation power, dosage of NaCl, settling time, pH, and the initial oil concentration, on the separation efficiency in the treatment of an oil in water (O/W) type cutting oil emulsion by microwave assisted demulsification. As a result of a series of batch demulsification tests a set of optimum operating conditions was found, consisting of 2 min of microwave irradiation at 280 W, the addition of 14 g/L of NaCl, 1 h settling time, and at a pH of 9.5. A separation efficiency of 93.8% was obtained with these conditions for 50 mL of cutting oil emulsion with an initial oil content of 10 g/L. In addition, data from these tests were treated by a stepwise-regression method which results in a multi-variable equation. This empirical equation was able to describe the separation efficiency fairly well, after excluding those with a separation efficiency less than 40% and temperatures higher than the boiling point. Our principal component analysis (PCA) extracted four principal components with 78% of the total variance explained.
Meteorological Drivers of Permian Basin Methane Anomalies Derived from TROPOMI

The launch of the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S-5P) satellite has revolutionized pollution observations from space. The purpose of this study was to link spatiotemporal variations in TROPOMI methane (CH4) columns to meteorological flow patterns over the Permian Basin, the largest oil and second-largest natural gas producing region in the United States. Over a two-year period (1 December 2018–1 December 2020), the largest average CH4 enhancements were observed near and to the north and west of the primary emission regions. Four case study periods—two with moderate westerly winds associated with passing weather disturbances (8–15 March 2019 and 1 April–10 May 2019) and two other periods dominated by high pressure and low wind speeds (16–23 March 2019 and 24 September–9 October 2020)—were analyzed to better understand meteorological drivers of the variability in CH4. Meteorological observations and analyses combined with TROPOMI observations suggest that weakened transport out of the Basin during low wind speed periods contributes to CH4 enhancements throughout the Basin, while valley and slope flows may explain the observed western expansion of the Permian Basin CH4 anomaly.
Groundwater Depletion Signals in the Beqaa Plain, Lebanon: Evidence from GRACE and Sentinel-1 Data

Regions with high productivity of agriculture, such as the Beqaa Plain, Lebanon, often rely on groundwater supplies for irrigation demand. Recent reports have indicated that groundwater consumption in this region has been unsustainable, and quantifying rates of groundwater depletion has remained a challenge. Here, we utilize 15 years of data (June 2002–April 2017) from the Gravity Recovery and Climate Experiment (GRACE) satellite mission to show Total Water Storage (TWS) changes in Lebanon’s Beqaa Plain. We then obtain complimentary information on various hydrologic cycle variables, such as soil moisture storage, snow water equivalent, and canopy water storage from the Global Land Data Assimilation System (GLDAS) model, and surface water data from the largest body of water in this region, the Qaraaoun Reservoir, to disentangle the TWS signal and calculate groundwater storage changes. After combining the information from the remaining hydrologic cycle variables, we determine that the majority of the losses in TWS are due to groundwater depletion in the Beqaa Plain. Results show that the rate of groundwater storage change in the West Beqaa is nearly +0.08 cm/year, in the Rashaya District is −0.01 cm/year, and in the Zahle District the level of depletion is roughly −1.10 cm/year. Results are confirmed using Sentinel-1 interferometric synthetic aperture radar (InSAR) data, which provide high-precision measurements of land subsidence changes caused by intense groundwater usage. Furthermore, data from local monitoring wells are utilized to further showcase the significant drop in groundwater level that is occurring through much of the region. For monitoring groundwater storage changes, our recommendation is to combine various data sources, and in areas where groundwater measurements are lacking, we especially recommend the use of data from remote sensing.
Use of Sentinel-1 Multi-Configuration and Multi-Temporal Series for Monitoring Parameters of Winter Wheat

The present study aims to investigate the potential of multi-configuration Sentinel-1 (S-1) synthetic aperture radar (SAR) images for characterizing four wheat parameters: total fresh mass (TFM), total dry mass (TDM), plant heights (He), and water content (WC). Because they are almost independent on the weather conditions, we have chosen to use only SAR. Samples of wheat parameters were collected over seven fields (three irrigated and four rainfed fields) in Southwestern France. We first analyzed the temporal behaviors of wheat parameters (He, TDM, TFM and WC) between February and June 2016. Then, the temporal profiles of the S-1 backscattering coefficients (VV, VH), the difference (VH − VV), the sum of the polarizations (VH + VV) and their cumulative values are analyzed for two orbits (30 and 132) during the wheat-growing season (from January to July 2016). After that, S-1 signals were statistically compared with all crop parameters considering the impact of pass orbit, irrigation and two vegetative periods in order to identify the best S-1 configuration for estimating crop parameters. Interesting S-1 backscattering behaviors were observed with the various wheat parameters after separating irrigation impacts and vegetative periods. For the orbit 30 (mean incidence angle of 33.6°); results show that the best S-1 configurations (with coefficient of determination (R2) > 0.7) were obtained using the VV and VH + VV as a function of the He, TDM and WC, over irrigated fields and during the second vegetative period. For the orbit 132 (mean incidence angle of 43.4°), the highest dynamic sensitivities (R2 > 0.8) were observed for the VV and VH + VV configurations with He, TDM and TFM over irrigated fields during the first vegetative period. Overall, the sensitivity of S-1 data to wheat variables depended on the radar configuration (orbits and polarizations), the vegetative periods and was often better over irrigated fields in comparison with rainfed ones. Significant improvements of the determination coefficients were obtained when the cumulative (VH + VV) index was considered for He (R² > 0.9), TDM (R² > 0.9) and TFM (R² > 0.75) for irrigated fields, all along the crop cycle. The estimate of WC was more limited (R² > 0.6) and remained limited to the second period of the vegetation cycle (from flowering onwards). Whatever parameters were considered, the relative errors never exceeded 23%. This study has shown the importance of considering the agricultural practices (irrigation) and vegetative periods to effectively monitor some wheat parameters with S-1 data.
Autonomous Repeat Image Feature Tracking (autoRIFT) and Its Application for Tracking Ice Displacement

In this paper, we build on past efforts with regard to the implementation of an efficient feature tracking algorithm for the mass processing of satellite images. This generic open-source feature tracking routine can be applied to any type of imagery to measure sub-pixel displacements between images. The routine consists of a feature tracking module (autoRIFT) that enhances computational efficiency and a geocoding module (Geogrid) that mitigates problems found in existing geocoding algorithms. When applied to satellite imagery, autoRIFT can run on a grid in the native image coordinates (such as radar or map) and, when used in conjunction with the Geogrid module, on a user-defined grid in geographic Cartesian coordinates such as Universal Transverse Mercator or Polar Stereographic. To validate the efficiency and accuracy of this approach, we demonstrate its use for tracking ice motion by using ESA’s Sentinel-1A/B radar data (seven pairs) and NASA’s Landsat-8 optical data (seven pairs) collected over Greenland’s Jakobshavn Isbræ glacier in 2017. Feature-tracked velocity errors are characterized over stable surfaces, where the best Sentinel-1A/B pair with a 6 day separation has errors in X/Y of 12 m/year or 39 m/year, compared to 22 m/year or 31 m/year for Landsat-8 with a 16-day separation. Different error sources for radar and optical image pairs are investigated, where the seasonal variation and the error dependence on the temporal baseline are analyzed. Estimated velocities were compared with reference velocities derived from DLR’s TanDEM-X SAR/InSAR data over the fast-moving glacier outlet, where Sentinel-1 results agree within 4% compared to 3–7% for Landsat-8. A comprehensive apples-to-apples comparison is made with regard to runtime and accuracy between multiple implementations of the proposed routine and the widely-used “dense ampcor" program from NASA/JPL’s ISCE software. autoRIFT is shown to provide two orders of magnitude of runtime improvement with a 20% improvement in accuracy.
Landslide-Induced Mass Transport of Radionuclides along Transboundary Mailuu-Suu River Networks in Central Asia

Seismically triggered landslides are a major hazard and have caused severe secondary losses. This problem is especially important in the seismic prone Mailuu-Suu catchment in Kyrgyzstan, as it hosts disproportionately sensitive active or legacy uranium sites with deposited radioactive extractive wastes. These sites show a quasi-continuous release of radioactive contamination into surface waters, and especially after natural hazards, a sudden and massive input of pollutants into the surface waters is expected. However, landslides of contaminated sediments into surface waters represent a substantial exposure pathway that has not been properly addressed in the existing river basin management to date. To fill this gap, satellite imagery was massively employed to extract topography and geometric information, and the seismic Scoops3D and the one-dimensional numerical model, Hydrologic Engineering Centre, River Analysis System (HEC-RAS), were chosen to simulate the landslide-induced mass transport of total suspended solids (TSS) and natural radionuclides (Pb-210 as a proxy for modeling purposes) within the Mailuu-Suu river networks under two earthquake and two hydrological scenarios. The results show that the seismically vulnerable areas dominated in the upstream areas, and the mass of landslides increased dramatically with the increase of earthquake levels. After the landslides, the concentrations of radionuclides increased suddenly and dramatically. The peak values decreased along the longitudinal gradient of river networks, with the concentration curves becoming flat and wide in the downstream sections, and the transport speed of radionuclides decreased along the river networks. The conclusions of this study are that landslides commonly release a significant amount of pollutants with a relatively fast transport along river networks. Improved quantitative understanding of waterborne pollution dispersion across national borders will contribute to better co-ordination between governments and regulatory authorities of riparian states and, consequently, to future prevention of transnational political conflicts that have flared up in the last two decades over alleged pollution of transboundary water bodies.
EFFECTS OF solUTION: SOIL RATIO AND VIGOUR OF SHAKING ON THE RATE OF PHOSPHATE ADSORPTION BY SOIL

Summary The effects were measured of varying the solution: soil ratio, and the vigour of shaking on the rate of adsorption of phosphate. One soil appeared to break down with vigorous shaking, prior to adding phosphate, especially at low solution: soil ratio. As a result, subsequent phosphate adsorption was faster. With gentle shaking, there was no effect of solution: soil ratio with the soil. Another soil was more stable and there were only small differences due to vigour of shaking and no effect of solution: soil ratio with any method of shaking. The change in phosphate concentration was not proportional to the reciprocal of time as reported by Ryden and Syers (1975) and Hope and Syers (1976). Hence the validity of extrapolating from short sections of reciprocal-time graphs in order to estimate equilibrium is questioned.
Sentinel-1 Big Data Processing with P-SBAS InSAR in the Geohazards Exploitation Platform: An Experiment on Coastal Land Subsidence and Landslides in Italy

The growing volume of synthetic aperture radar (SAR) imagery acquired by satellite constellations creates novel opportunities and opens new challenges for interferometric SAR (InSAR) applications to observe Earth’s surface processes and geohazards. In this paper, the Parallel Small BAseline Subset (P-SBAS) advanced InSAR processing chain running on the Geohazards Exploitation Platform (GEP) is trialed to process two unprecedentedly big stacks of Copernicus Sentinel-1 C-band SAR images acquired in 2014–2020 over a coastal study area in southern Italy, including 296 and 283 scenes in ascending and descending mode, respectively. Each stack was processed in the GEP in less than 3 days, from input SAR data retrieval via repositories, up to generation of the output P-SBAS datasets of coherent targets and their displacement histories. Use-cases of long-term monitoring of land subsidence at the Capo Colonna promontory (up −2.3 cm/year vertical and −1.0 cm/year east–west rate), slow-moving landslides and erosion landforms, and deformation at modern coastal protection infrastructure in the city of Crotone are used to: (i) showcase the type and precision of deformation products outputting from P-SBAS processing of big data, and the derivable key information to support value-adding and geological interpretation; and (ii) discuss potential and challenges of big data processing using cloud/grid infrastructure.
AVILNet: A New Pliable Network with a Novel Metric for Small-Object Segmentation and Detection in Infrared Images

Infrared small-object segmentation (ISOS) has a persistent trade-off problem—that is, which came first, recall or precision? Constructing a fine balance between of them is, au fond, of vital importance to obtain the best performance in real applications, such as surveillance, tracking, and many fields related to infrared searching and tracking. F1-score may be a good evaluation metric for this problem. However, since the F1-score only depends upon a specific threshold value, it cannot reflect the user’s requirements according to the various application environment. Therefore, several metrics are commonly used together. Now we introduce F-area, a novel metric for a panoptic evaluation of average precision and F1-score. It can simultaneously consider the performance in terms of real application and the potential capability of a model. Furthermore, we propose a new network, called the Amorphous Variable Inter-located Network (AVILNet), which is of pliable structure based on GridNet, and it is also an ensemble network consisting of the main and its sub-network. Compared with the state-of-the-art ISOS methods, our model achieved an AP of 51.69%, F1-score of 63.03%, and F-area of 32.58% on the International Conference on Computer Vision 2019 ISOS Single dataset by using one generator. In addition, an AP of 53.6%, an F1-score of 60.99%, and F-area of 32.69% by using dual generators, with beating the existing best record (AP, 51.42%; F1-score, 57.04%; and F-area, 29.33%).