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With the development of the hyperspectral remote sensing technique, extensive chemical weathering profiles have been identified on Mars. These weathering sequences, formed through precipitation-driven leaching processes, can reflect the paleoenvironments and paleoclimates during pedogenic processes. The specific composition and stratigraphic profiles mirror the mineralogical and chemical trends observed in weathered basalts on Hainan Island in south China. In this study, we investigated the laboratory reflectance spectra of a 53-m-long drilling core of a thick basaltic weathering profile collected from Hainan Island. We established a quantitative spectral model by combining the genetic algorithm and partial least squares regression (GA-PLSR) to predict the chemical properties (SiO2, Al2O3, Fe2O3) and index of laterization (IOL). The entire sample set was divided into a calibration set of 25 samples and a validation set of 12 samples. Specifically, the GA was used to select the spectral subsets for each composition, which were then input into the PLSR model to derive the chemical concentration. The coefficient of determination (R2) values on the validation set for SiO2, Al2O3, Fe2O3, and the IOL were greater than 0.9. In addition, the effects of various spectral preprocessing techniques on the model accuracy were evaluated. We found that the spectral derivative treatment boosted the prediction accuracy of the GA-PLSR model. The improvement achieved with the second derivative was more pronounced than when using the first derivative. The quantitative model developed in this work has the potential to estimate the contents of similar weathering basalt products, and thus infer the degree of alteration and provide insights into paleoclimatic conditions. Moreover, the informative bands selected by the GA can serve as a guideline for designing spectral channels for the next generation of spectrometers.

Nitrogen(N) is one of the most important elements for crop growth and yield formation. Insufficient or excessive application of N fertilizers can limit crop yield and quality, especially as excessive N fertilizers can damage the environment and proper fertilizer application is essential for agricultural production. Efficient monitoring of crop N content is the basis of precise fertilizer management, and therefore to increase crop yields and improve crop quality. Remote sensing has gradually replaced traditional destructive methods such as field surveys and laboratory testing for crop N diagnosis. With the rapid advancement of remote sensing, a review on crop N monitoring is badly in need of better summary and discussion. The purpose of this study was to identify current research trends and key issues related to N monitoring. It begins with a comprehensive statistical analysis of the literature on remote sensing monitoring of N in rice and wheat over the past 20 years. The study then elucidates the physiological mechanisms and spectral response characteristics of remote sensing monitoring of canopy N. The following section summarizes the techniques and methods applied in remote sensing monitoring of canopy N from three aspects: remote sensing platforms for N monitoring; correlation between remotely sensed data and N status; and the retrieval methods of N status. The influential factors of N retrieval were then discussed with detailed classification. However, there remain challenges and problems that need to be addressed in the future studies, including the fusion of multisource data from different platforms, and the uncertainty of canopy N inversion in the presence of background factors. The newly developed hybrid model integrates the flexibility of machine learning with the mechanism of physical models. It could be problem solving, which has the advantages of processing multi-source data and reducing the interference of confounding factors. It could be the future development direction of crop N inversion with both high precision and universality. [ABSTRACT FROM AUTHOR]

Nitrogen(N) is one of the most important elements for crop growth and yield formation. Insufficient or excessive application of N fertilizers can limit crop yield and quality, especially as excessive N fertilizers can damage the environment and proper fertilizer application is essential for agricultural production. Efficient monitoring of crop N content is the basis of precise fertilizer management, and therefore to increase crop yields and improve crop quality. Remote sensing has gradually replaced traditional destructive methods such as field surveys and laboratory testing for crop N diagnosis. With the rapid advancement of remote sensing, a review on crop N monitoring is badly in need of better summary and discussion. The purpose of this study was to identify current research trends and key issues related to N monitoring. It begins with a comprehensive statistical analysis of the literature on remote sensing monitoring of N in rice and wheat over the past 20 years. The study then elucidates the physiological mechanisms and spectral response characteristics of remote sensing monitoring of canopy N. The following section summarizes the techniques and methods applied in remote sensing monitoring of canopy N from three aspects: remote sensing platforms for N monitoring; correlation between remotely sensed data and N status; and the retrieval methods of N status. The influential factors of N retrieval were then discussed with detailed classification. However, there remain challenges and problems that need to be addressed in the future studies, including the fusion of multisource data from different platforms, and the uncertainty of canopy N inversion in the presence of background factors. The newly developed hybrid model integrates the flexibility of machine learning with the mechanism of physical models. It could be problem solving, which has the advantages of processing multi-source data and reducing the interference of confounding factors. It could be the future development direction of crop N inversion with both high precision and universality.

The recent development in remote sensing imagery and the use of remote sensing detection feature spectrum information together with the geochemical data is very useful for the surface element quantitative remote sensing inversion study. This aim of this article is to select appropriate methods that would make it possible to have rapid economic prospecting. The Qishitan gold polymetallic deposit in the Xinjiang Uygur Autonomous Region, Northwest China has been selected for this study. This paper establishes inversion maps based on the contents of metallic elements by integrating geochemical exploration data with ASTER and WorldView-2 remote sensing data. Inversion modelling maps for As, Cu, Hg, Mo, Pb, and Zn are consistent with the corresponding geochemical anomaly maps, which provide a reference for metallic ore prospecting in the study area. ASTER spectrum covers short-wave infrared and has better accuracy than WorldView-2 data for the inversion of some elements (e.g., Au, Hg, Pb, and As). However, the high spatial resolution of WorldView-2 drives the final content inversion map to be more precise and to better localize the anomaly centers of the inversion results. After scale conversion by re-sampling and kriging interpolation, the modeled and predicted accuracy of the models with square interpolation is much closer compare with the ground resolution of the used remote sensing data. This means our results are much satisfactory as compared to other interpolation methods. This study proves that quantitative remote sensing has great potential in ore prospecting and can be applied to replace traditional geochemical exploration to some extent.

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A grid-computing platform facilitates geocomputational workflow composition to process big geosciences data while fully using idle resources to accelerate processing speed. An experiment with aerosol optical depth retrieval from satellite data shows a 25 percent improvement in runtime over a single high-performance computer. [ABSTRACT FROM AUTHOR]

Locust hazard is one of the major disasters for farming and animal husbandry in Xinjiang, China. Currently locust disaster monitoring mainly relies on the limited observatory field sites and is not efficient due to Xinjiang's remote geographic location, vast area and inadequate technological support. Fortunately, remote sensing technique offers a valuable tool for locust hazard monitoring and prediction in a large area such as Xinjiang. This study presents a progressive modeling approach for locust hazard risk prediction of the rangeland in Xinjiang. The underlying thought is that the model is to be built based on the key 3 growth stages of locust, namely oviposition, incubation and development, and these processes are heavily affected or even determined by the locust habitats which can be resolved into some key ecological and environmental factors, such as land surface/air temperature, rainfall, soil moisture, soil type, vegetation type and coverage, geographic altitude. The suitability of locust habitat is assessed for these 3 stages using satellite remote sensing data, adopting locust oviposition suitability indicator (OSI), incubation suitability indicator (ISI) and development suitability indicator (DSI). The 3 types of suitability indicators are created mainly based on the derivatives from Terra/MODIS remote sensing data, digital elevation model (DEM) data and ground measured ancillary data. The OSI is created by the weighted combination of 3 sub-indices: soil type factor, soil moisture factor and vegetation factor for oviposition. The ISI is formed from the multiplication of land surface temperature factor and soil moisture factor. And geographic altitude factor, vegetation coverage factor in development stage and vegetation type factor are used to generate the DSI by a weighted combination. Each factor is normalized to the score from 1 to 10, indicating the degree of suitability of this factor. The number 1 represents least suitability and 10 most suitability. Afterwards, the 3 indicators OSI, ISI and DSI are incorporated into locust risk index (LRI) in a multiplicative manner, which is used as a quantitative index to assess the locust hazard risk spatially. The historical data of locust hazard and in-situ measurement data of locust density in 2014 are used to calibrate the model, and consequently the resultant LRI can be further classified into 4 risk levels: low, low-moderate, moderate-high, and high when LRI is less than 100, >100-200, >200-300 and greater than 300, respectively, which can be empirically used to represent the potential severity of locust disaster in the next few months. Considering the variations and interannual fluctuation, a progressive strategy is proposed and incorporated into the modeling process. Two types of modification are applied to the primary model prediction, i.e. oviposition modification and third-instar modification. This strategy allows to making use of the dynamic data acquired by satellite remote sensors, and periodically updates the habitat factors input of the model through quantitative inversion of remotely sensed data. Therefore, the modeled LRI can better reflect the incoming locust hazard possibility and provide more accurate prediction than conventional single input-output model run. The model is subsequently utilized to assess and predict the risk of the locust hazard in the rangeland of Xinjiang in 2010 and 2014. The result indicates that the proposed progressive strategy for the locust hazard risk can reflect the variability of the key habitat factors that affect the locust population development. It also shows that the progressive approach can avoid inaccuracy of one-time prediction, and the modeled results are well correlated to the actual locust hazard severity degree which is classified based on the in-situ measurements of locust density. On the basis of the validation between the modeled risk levels and in-situ measured locust disaster severity classes, the results indicate that approximately 74.4% of the sample sites are completely fallen into the same level, and 94.9% of the samples are different within one level. Thus, the model is useful for early warning of locust hazard and the disaster prevention and relief in Xinjiang area. The model can also be localized and applied in other arid areas of the world. [ABSTRACT FROM AUTHOR]

Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua and Terra satellites have unique advantages for monitoring coastal waters, owing to their high spatial resolution (250 m), short revisit period (1-2 days), and freedom from cost. An empirical retrieval model for concentration of total suspended matter (TSM) has been developed based on a statistical analysis of field surveys of TSM and remote sensing reflectance ( R) in the Bohai Sea of China. A robust linear relationship was established between the equivalent remote sensing reflectance (converting ASD-measured R by spectral response function) in the 620-670 nm band (band 1) of MODIS and the concentration of TSM ( R = 0.95; n = 27; RMS = 0.512) acquired in August and September 2008. The model was validated via in situ measurements in September 2009, resulting in a mean relative error of 12.9 %. Then, the corresponding MODIS products of monthly average concentration of TSM were produced from January to December 2009. The distribution characteristics of TSM in the Bohai Sea of China are closely related with the spatial pattern and seasonal variability. This study demonstrates that the moderately high resolution of MODIS 250 m data is available for monitoring the transport and fate of materials in relatively smaller bodies of water. [ABSTRACT FROM AUTHOR]

It is important to map dissolved inorganic nitrogen (DIN) concentrations in order to accurately predict the outbreak of red tides. This study aims to determine the feasibility of retrieving DIN in Haizhou Bay, East China from multi-temporal MODIS satellite data and to assess the impact of DIN concentration level on its inversion accuracy. DIN was modeled from the reflectance of MODIS bands and their combinations both linearly and nonlinearly. It was found that individual MODIS bands are loosely correlated with DIN concentrations. Of the various combinations of multiple bands, bands 3 and 4 bear a highly positive correlation with DIN concentrations. In particular, (R3 + R4)/(R3 - R4) is a more accurate predictor of DIN than R3 × R4/(R3 - R4). All the regression models involving this exploratory variable have an R2 value over 0.7 and a relative accuracy of around 70%. However, regression residuals are dependent of each other for DIN <70 μg/L. The removal of these observations from the regression analysis led to the establishment of the best model in the form of DIN<70 = 7.403 (R3 + R4)/(R3 - R4) + 37.14 (R2 = 0.87, n = 19). It improved the model accuracy to RMSE = 17.48 μg/L (15.8%) against the validation dataset. MODIS data allow accurate retrieval of DIN concentrations >70 μg/L in murky coastal waters. During the study period of 2004-2006, Haizhou Bay experienced a deteriorating trend in DIN in that low concentration areas decreased while areas of a high DIN concentration level expanded. [ABSTRACT FROM AUTHOR]

The recent development in remote sensing imagery and the use of remote sensing detection feature spectrum information together with the geochemical data is very useful for the surface element quantitative remote sensing inversion study. This aim of this article is to select appropriate methods that would make it possible to have rapid economic prospecting. The Qishitan gold polymetallic deposit in the Xinjiang Uygur Autonomous Region, Northwest China has been selected for this study. This paper establishes inversion maps based on the contents of metallic elements by integrating geochemical exploration data with ASTER and WorldView-2 remote sensing data. Inversion modelling maps for As, Cu, Hg, Mo, Pb, and Zn are consistent with the corresponding geochemical anomaly maps, which provide a reference for metallic ore prospecting in the study area. ASTER spectrum covers short-wave infrared and has better accuracy than WorldView-2 data for the inversion of some elements (e.g., Au, Hg, Pb, and As). However, the high spatial resolution of WorldView-2 drives the final content inversion map to be more precise and to better localize the anomaly centers of the inversion results. After scale conversion by re-sampling and kriging interpolation, the modeled and predicted accuracy of the models with square interpolation is much closer compare with the ground resolution of the used remote sensing data. This means our results are much satisfactory as compared to other interpolation methods. This study proves that quantitative remote sensing has great potential in ore prospecting and can be applied to replace traditional geochemical exploration to some extent. [ABSTRACT FROM AUTHOR]