Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha^?1, while remote sensing showed the RMSE of 397 kg ha^?1. Overall strength of relationship between estimated and actual grain yields were good with R² of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.     

Particle Filter-based assimilation algorithms for improved estimation of root-zone soil moisture under dynamic vegetation conditions

In this study, we implement Particle Filter (PF)-based assimilation algorithms to improve root-zone soil moisture (RZSM) estimates from a coupled SVAT-vegetation model during a growing season of sweet corn in North Central Florida. The results from four different PF algorithms were compared with those from the Ensemble Kalman Filter (EnKF) when near-surface soil moisture was assimilated every 3 days using both synthetic and field observations. In the synthetic case, the PF algorithm with the best performance used residual resampling of the states and obtained resampled parameters from a uniform distribution and provided reductions of 76% in root mean square error (RMSE) over the openloop estimates. The EnKF provided the RZSM and parameter estimates that were closer to the truth than the PF with an 84% reduction in RMSE. When field observations were assimilated, the PF algorithm that maintained maximum parameter diversity offered the largest reduction of 16% in root mean square difference (RMSD) over the openloop estimates. Minimal differences were observed in the overall performance of the EnKF and PF using field observations since errors in model physics affected both the filters in a similar manner, with maximum reductions in RMSD compared to the openloop during the mid and reproductive stages.     

Calibration of an integrated land surface process and radiobrightness (LSP/R) model during summertime

In this study, a soil vegetation and atmosphere transfer (SVAT) model was linked with a microwave emission model to simulate microwave signatures for different terrain during summertime, when the energy and moisture fluxes at the land surface are strong. The integrated model, land surface process/radiobrightness (LSP/R), was forced with weather and initial conditions observed during a field experiment. It simulated the fluxes and brightness temperatures for bare soil and brome grass in the Northern Great Plains. The model estimates of soil temperature and moisture profiles and terrain brightness temperatures were compared with the observed values. Overall, the LSP model provides realistic estimates of soil moisture and temperature profiles to be used with a microwave model. The maximum mean differences and standard deviations between the modeled and the observed temperatures (canopy and soil) were 2.6 K and 6.8 K, respectively; those for the volumetric soil moisture were 0.9% and 1.5%, respectively. Brightness temperatures at 19 GHz matched well with the observations for bare soil, when a rough surface model was incorporated indicating reduced dielectric sensitivity to soil moisture by surface roughness. The brightness temperatures of the brome grass matched well with the observations indicating that a simple emission model was sufficient to simulate accurate brightness temperatures for grass typical of that region and surface roughness was not a significant issue for grass-covered soil at 19 GHz. Such integrated SVAT-microwave models allow for direct assimilation of microwave observations and can also be used to understand sensitivity of microwave signatures to changes in weather forcings and soil conditions for different terrain types.     

The tin potential of Precambrian rare-metal bearing pegmatites of Bastar district,M.P., India

In Central India, Precambrian metasediments with intrusive metabasics suffered granite tectonism in the middle Proterozoic (1345±22 Ma) and which culminated in the emplacement of pegmatites. The rare-metal mineralisation is predominently associated with those pegmatites intruded into metabasics. The primary cassiterite occurs as discrete crystals, disseminations and occasionally as small veinlets, mainly in a quartz core; columbite-tantalite occurs in solid solution with cassiterite, as well as separately. The intensity of Sn-Nb-Ta ore mineralisation is directly dependent on the intensity and extension of imposed albitisation and greisenisation. However, the potential tin-ore deposits of the area are essentially confined to various Quaternary/sub-Recent eluvial, deluvial and alluvial placers. The typomorphic trace and ore elements of the pegmatites, together with their occurrence in the Precambrian metasediments/metabasics of the peninsular shield, places the tin-ore association of Bastar district, Madhya Pradesh of the Indian sub-contintent, as tin-bearing rare-metal type. They are of hypabyssal high-volatile type and can be assumed to be a part of world-wide occurrences of Precambrian tin mineralisation, predominantly associated with pegmatites.     

Using radiometric fingerprinting and phosphorus to elucidate sediment transport dynamics in an agricultural watershed

The major goals of this study were to determine stream bed sediment erosion/deposition rates, sediment age, percent ‘new’ sediment, and suspended sediment origin during two storm events of contrasting magnitudes (11.9 mm over 5 h and 58.9 mm over 39 h) using fallout radionuclides (excess lead 210 – ²¹⁰Pb_xs and beryllium 7 – ⁷Be) and link the nature and type of sediment source contributions to potential phosphorus (P) off‐site transport. The study was conducted in cropland‐dominated and mixed land use subwatersheds in the non‐glaciated Pleasant Valley watershed (50 km²) in South Central Wisconsin. Fine sediment deposition and erosion rates on stream beds varied from 0.76 to 119.29 mg cm^?2 day^?1 (at sites near the watershed outlet) and 1.72 to 7.72 mg cm^?2 day^?1 (at sites in the headwaters), respectively, during the two storm events. The suspended sediment age ranged from 123 ± 12 to 234 ± 33 days during the smaller storm event; however, older sediment was more prevalent (p = 0.037) in the streams during the larger event with suspended sediment age ranging from 226 ± 9 to 322 ± 114 days. During the small and large storm event, percent new sediment in suspended sediment ranged from 5.3 ± 2.1 to 21.0 ± 2.9% and 5.3 ± 2.7 to 6.7 ± 5.7%, respectively. In the cropland‐dominated subwatershed, upland soils were the major source of suspended sediment, whereas in the mixed land use subwatershed, both uplands and stream banks had relatively similar contributions to suspended sediment. In‐stream (suspended and bed) sediment P levels ranged from 703 ± 193 to 963 ± 84 mg kg^?1 during the two storm events. The P concentrations in suspended and bed sediment were reflective of the dominant sediment source (upland or stream bank or mixed). Overall, sediment transport dynamics showed significant variability between subwatersheds of different land use characteristics during two contrasting storm events. Copyright © 2014 John Wiley & Sons, Ltd.     

Probabilistic assessment of projected climatological drought characteristics over the Southeast USA

The study makes a probabilistic assessment of drought risks due to climate change over the southeast USA based on 15 Global Circulation Model (GCM) simulations and two emission scenarios. The effects of climate change on drought characteristics such as drought intensity, frequency, areal extent, and duration are investigated using the seasonal and continuous standard precipitation index (SPI) and the standard evapotranspiration index (SPEI). The GCM data are divided into four time periods namely Historical (1961–1990), Near (2010–2039), Mid (2040–2069), and Late (2070–2099), and significant differences between historical and future time periods are quantified using the mapping model agreement technique. Further, the kernel density estimation approach is used to derive a novel probability-based severity-area-frequency (PBS) curve for the study domain. Analysis suggests that future increases in temperature and evapotranspiration will outstrip increases in precipitation and significantly affect future droughts over the study domain. Seasonal drought analysis suggest that the summer season will be impacted the most based on SPI and SPEI. Projections based on SPI follow precipitation patterns and fewer GCMs agree on SPI and the direction of change compared to the SPEI. Long-term and extreme drought events are projected to be affected more than short-term and moderate ones. Based on an analysis of PBS curves, especially based on SPEI, droughts are projected to become more severe in the future. The development of PBS curves is a novel feature in this study and will provide policymakers with important tools for analyzing future drought risks, vulnerabilities and help build drought resilience. The PBS curves can be replicated for studies around the world for drought assessment under climate change.     

Effect of simultaneous state–parameter estimation and forcing uncertainties on root-zone soil moisture for dynamic vegetation using EnKF

In this study, an EnKF-based assimilation algorithm was implemented to estimate root-zone soil moisture (RZSM) using the coupled LSP–DSSAT model during a growing season of corn. Experiments using both synthetic and field observations were conducted to understand effects of simultaneous state–parameter estimation, spatial and temporal update frequency, and forcing uncertainties on RZSM estimates. Estimating the state–parameters simultaneously with every 3-day assimilation of volumetric soil moisture (VSM) observations at 5 depths lowered the average standard deviation (ASD) and the root mean square error (RMSE) for RZSM by approximately 1.77% VSM (78%) and 2.18% VSM (93%), respectively, compared to the open-loop ASD where as estimating only states lowered the ASD by approximately 1.26% VSM (56%) and the RMSE by 1.66% VSM (71%). The synthetic case obtained RZSM estimates closer to the observations than the MicroWEX-2 case, particularly after precipitation/irrigation events. The differences in EnKF performance between MicroWEX-2 and synthetic observations may indicate other sources of errors in addition to those in parameters and forcings, such as errors in model biophysics.     

Prediction of L-band signal attenuation in forests using 3D vegetation structure from airborne LiDAR

In this study, we propose a novel method to predict microwave attenuation in forested areas by using airborne Light Detection and Ranging (LiDAR). While propagating through a vegetative medium, microwave signals suffer from reflection, absorption, and scattering within vegetation, which cause signal attenuation and, consequently, deteriorate signal reception and information interpretation. A Fresnel zone enveloping the radio frequency line-of-sight is applied to segment vegetation structure occluding signal propagation. Return parameters and the spatial distribution of vegetation from the airborne LiDAR inside Fresnel zones are used to weight the laser points to estimate directional vegetation structure. A Directional Vegetation Density (DVD) model is developed through regression that links the vegetation structure to the signal attenuation at the L-band using GPS observations in a mixed forest in North Central Florida. The DVD model is compared with currently-used empirical models and obtained better R² values of 0.54 than the slab-based models. Finally, the model is evaluated by comparing with GPS observations of signal attenuation. An overall root mean square error of 3.51 dB and a maximum absolute error of 9.38 dB are found. Sophisticated classification algorithms and full-waveform LiDAR systems may significantly improve the estimation of signal attenuation.     

Pristinity and petrogenesis of eucrites

New petrography, mineral chemistry, and whole rock major, minor, and trace element abundance data are reported for 29 dominantly unbrecciated basaltic (noncumulate) eucrites and one cumulate eucrite. Among unbrecciated samples, several exhibit shock darkening and impact melt veins, with incomplete preservation of primary textures. There is extensive thermal metamorphism of some eucrites, consistent with prior work. A “pristinity filter” of textural information, siderophile element abundances, and Ni/Co ratios of bulk rocks is used to address whether eucrite samples preserve endogenous refractory geochemical signatures of their asteroid parent body (i.e., Vesta), or could have experienced exogenous impact contamination. Based on these criteria, Cumulus Hills 04049, Elephant Moraine 90020, Grosvenor Range 95533, Pecora Escarpment 91245, and possibly Queen Alexander Range 97053 and Northwest Africa 1923 are pristine eucrites. Eucrite major element compositions and refractory incompatible trace element abundances are minimally affected by metamorphism or impact contamination. Eucrite petrogenesis examined through the lens of these elements is consistent with partial melting of a silicate mantle that experienced prior metal–silicate equilibrium, rather than as melts associated with cumulate diogenites. In the absence of the requirement of a large-scale magma ocean to explain eucrite petrogenesis, the interior structure of Vesta could be more heterogeneous than for larger planetary bodies.