Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices

Accurate crop yield forecasting is essential in the food industry’s decision-making process, where vegetation condition index (VCI) and thermal condition index (TCI) coupled with machine learning (ML) algorithms play crucial roles. The drawback, however, is that a one-fits-all prediction model is often employed over an entire region without considering subregional VCI and TCI’s spatial variability resulting from environmental and climatic factors. Furthermore, when using nonlinear ML, redundant VCI/TCI data present additional challenges that adversely affect the models’ output. This study proposes a framework that (i) employs higher-order spatial independent component analysis (sICA), and (ii), exploits a combination of the principal component analysis (PCA) and ML (i.e., PCA-ML combination) to deal with the two challenges in order to enhance crop yield prediction accuracy. The proposed framework consolidates common VCI/TCI spatial variability into their respective subregions, using Vietnam as an example. Compared to the one-fits-all approach, subregional rice yield forecasting models over Vietnam improved by an average level of 20% up to 60%. PCA-ML combination outperformed ML-only by an average of 18.5% up to 45%. The framework generates rice yield predictions 1 to 2 months ahead of the harvest with an average of 5% error, displaying its reliability

A local lithospheric structure model for Vietnam derived from a high-resolution gravimetric geoid

International audienceHigh-resolution Moho and lithosphere-asthenosphere boundary depth models for Vietnam and its surrounding areas are determined based on a recently released geoid model constructed from surface and satellite gravity data (GEOID_LSC_C model) and on 3ʹʹ resolution topography data (mixed SRTM model). A linear density gradient for the crust and a temperature-dependent density for the lithospheric mantle were used to determine the lithospheric structure under the assumption of local isostasy. In a first step, the impact of correcting elevation data from sedimentary basins to estimate Moho depth has been evaluated using CRUST1.0 model. Results obtained from a test area where seismic data are available, which demonstrated that the sedimentary effect should be considered before the inversion process. The geoid height and elevation-corrected sedimentary layer were filtered to remove signals originating below the lithosphere. The resulting Moho and lithosphere-asthenosphere boundary depth models computed at 1ʹ resolution were evaluated against seismic data as well as global and local lithospheric models available in the study region. These comparisons indicate a consistency of our Moho depth estimation with the seismic data within 1.5 km in standard deviation for the whole Vietnam. This new Moho depth model for the study region represents a significant improvement over the global models CRUST1.0 and GEMMA, which have standard deviations of 3.2 and 3.3 km, respectively, when compared to the seismic data. Even if a detailed geological interpretation of the results is out of scope of this paper, a joint analysis of the obtained models with the high-resolution Bouguer gravity anomaly is finally discussed in terms of the main geological patterns of the study region. The high resolution of our Moho and lithosphere-asthenosphere boundary depth models contribute to better constrain the lithospheric structure as well as tectonic and geodynamic processes of this region. The differences in Moho depth visible in the northeast and southwest sides of the Red River Fault Zone confirmed that the Red River Fault Zone may be considered the boundary between two continental blocks: South China and Indochina blocks. However, no remarkable differences in lithosphere-asthenosphere boundary depth were obtained from our results. This suggests that the Red River Fault Zone developed within the crust and remained a crustal fault

Determination of the geopotential value on the permanent GNSS stations in Vietnam based on the Geodetic Boundary Value Problem approach

International audienceIn the realisation of the International Height Reference System, the determination of the geopotential value and its variations in time plays an important role. In this study, the geodetic boundary value problem approach is applied for direct determination of the gravity potential value using a GOCE global gravity field model enhanced with terrestrial gravity data. This determination is carried out on the Global Navigation Satellite System-Continuously Operating Reference Stations (GNSS-CORS) stations towards the realisation of the International Height Reference System in Vietnam. First, the effects of the GOCE global gravity field model omission error, the indirect bias term on the disturbing potential and the systematic cumulative errors in levelling data are estimated. These errors affect the estimated geopotential value. The results calculated on the GNSS/levelling points show that the effect of the GOCE DIR-R5 (up to degree/order 260) omission error on the offset potential value is quite significant. This effect was eliminated using high-resolution terrestrial gravity data using the remove-compute-restore technique. The indirect bias term on the disturbing potential can be safely neglected by using a GOCE global gravity field model for degrees higher than 60 for this study region. The systematic cumulative errors in levelling data can be modelled and removed using a third-order polynomial model. Then, the mean zero-height gravity potential of the Vietnam local vertical datum is estimated equal to ${\rm{W}}_0^{{\rm{LVD}}}$ = 62 636 846.69 m2 s-2 with standard deviation of 0.70 m2 s-2 based on the proposed approach. Finally, the geodetic boundary value problem approach was used to determine the geopotential on the surface of three GNSS-CORS stations in Vietnam. Based on time-series of the vertical component derived from the GNSS observations as well as InSAR data, temporal variations in geopotential are also estimated on these permanent GNSS stations. The purpose is to monitor deformation of the vertical datum. The results indicate that the geopotential value needs to be monitored and determined with the time-dependent component on the three Vietnamese permanent GNSS stations for a vertical datum. These stations may contribute to increase the density of reference points in the International Terrestrial Reference Frame, which is being researched and implemented by the International Association of Geodesy

Optimal choice of the number of ground control points for developing precise DSM using light-weight UAV in small and medium-sized open-pit mine

UAV technology is being applied for DSM generation in open-pit mines with a well-established fact that the precision of such DSM is improved by increasing the number of Ground Control Points (GCPs). However, DSMs are updated frequently in an open-pit mine where the surface is excavated continuously. This imposes a challenge to arrange and maintain the GCPs in the field. Therefore, an optimal number of GCPs should be determined to obtain sufficiently accurate DSMs while maintaining safety, time, and cost-effectiveness in the project. This study investigates the influence of the numbers of GCPs and their network configuration in the Long Son quarry, Vietnam. The analysis involved DSMs generated from eight cases with a total of 18 GCPs and each having five network configurations. The inter-case and intra-case accuracy of DSMs is assessed based on RMSEXY, RMSEZ, and RMSEXYZ. The results show that for a small- or medium-sized open-pit mine having an area of approximately 36 hectares, five GCPs are sufficient to achieve an overall accuracy of less than 10 cm. It is further shown that the optimal choice of the number of GCPs for DSM generation in such a mining site is seven due to a significant improvement in accuracy (<3.5 cm) and a decrease in configuration dependency compared to the five GCPs

Recent land deformation detected by Sentinel-1A InSAR data (2016–2020) over Hanoi, Vietnam, and the relationship with groundwater level change

Interferometric synthetic aperture radar (InSAR), one of the most commonly used remote sensing methods for observing and monitoring land subsidence, has been applied in Hanoi, Vietnam in several studies with results showing deformation up to 2014. However, freely accessible Sentinel-1 InSAR data have not been investigated thoroughly to date. Here, we investigate the most recent land surface deformation in Hanoi for the period 2016$$-$$2020 using Sentinel-1A SAR data. The analysis is conducted on 114 SAR scenes with both the Persistent Scatterer InSAR (PSInSAR) and Small BAseline Subset (SBAS) methods. The GPS-based deformation time series are used to verify InSAR results and borehole groundwater level measurements are employed to evaluate the relationship between groundwater depletion and surface subsidence. The results show that observed deformation from SBAS and PSInSAR is consistent in both spatial patterns and statistics, in which two high-rate subsiding bowls were detected in Dan Phuong/Hoai Duc and Ha Dong/Thanh Tri districts with the mean subsiding rates of ∼5 mm per year. GPS and InSAR deformation generally agree well except for the comparison at the JNAV station after 2017, which can be attributable to the local deformation detected by GPS and the average movement of a 100-m radius area captured by InSAR. An agreement in the drawdown trend between borehole groundwater and InSAR-derived deformation was found at four wells located within or in proximity to the two bowls. The declining rates of groundwater level at about 0.31 m per year were found at the two wells Q57a and Q58a located within the Dan Phuong/Hoai Duc bowl, corresponding to the surface subsidence rates found at 6–8 mm per year. The Q68a well was found to experience groundwater level declining at the highest rate of ∼0.9 m per year corresponding to the subsidence rate of ∼7 mm per year found by InSAR