Leveraging legacy data with targeted field sampling for low-cost mapping of soil organic carbon stocks on extensive rangeland properties

Geoderma, Год журнала: 2024, Номер 448, С. 116952 - 116952

Опубликована: Июль 5, 2024

Accurately quantifying high-resolution field-scale soil organic carbon (SOC) stocks is challenging yet crucial for improving site-specific land management and accounting. This challenge even greater when the study units are large heterogenous ranches. utilizes a digital mapping (DSM) approach U.S. legacy dataset, combined with soil, climate, biotic, topographic covariate datasets, to design targeted sampling plan acquiring local samples. The resulting samples were then used in combination data build optimal ranch-scale SOC stock models. We provide an example of this using ranch western as case study. In our we first applied clustering analysis generate spatial clusters. was followed by adopting conditioned Latin hypercube scheme within each cluster, sets strategically selected points. required improved estimates determined have sample size 15 40 cores, respective 13 36 km2 parcels. While modeling results concentrations at relatively homogeneous site eastern Montana showed significant two-fold improvement model fit individually calibration datasets point, opposed selecting dataset whole level, disparity between pixel- ranch-based models inconsequential other two sites Colorado that more spatially diverse terms vegetation cover. Compared concentration (R2 0.3 0.7), performance bulk density (BD) < 0.4) 0.2) poor. Strategies including utilizing subset covariates, incorporating broader-scale national depths did not further improve BD Future work should explore whether addition temporally dynamic environmental covariates can estimates, DSM-supported field strategy be successfully elsewhere.

Язык: Английский

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Fine-resolution baseline maps of soil nutrients in farmland of Jiangxi Province using digital soil mapping and interpretable machine learning

CATENA, Год журнала: 2024, Номер 249, С. 108635 - 108635

Опубликована: Дек. 9, 2024

Язык: Английский

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Driving Forces behind the Reduction in Cropland Area on Hainan Island, China: Implications for Sustainable Agricultural Development

Land, Год журнала: 2024, Номер 13(8), С. 1274 - 1274

Опубликована: Авг. 13, 2024

Food security is a major challenge for China at present and will be in the future. Revealing spatiotemporal changes cropland identifying their driving forces would helpful decision-making to maintain grain supply sustainable development. Hainan Island endowed with rich agricultural resources due its unique climatic conditions facing tremendous pressure protection huge variation natural human activities over past few decades. The purpose of this study assess on predict future under different scenarios. Key findings are as follows: (1) From 2000 2020, area decreased by 956.22 km2, causing center shift southwestward 8.20 km. This reduction mainly transformed into construction land woodland, particularly evident coastal areas. (2) Among anthropogenic factors, increase footprint primary reason decrease cropland. Land use driven population growth, especially economically active densely populated areas, key factors decrease. Natural such topography climate change also significantly impact changes. (3) Future scenarios show significant differences In development scenario, expected continue decreasing 597 while ecological conversion restricted 269.11 km2; however, trend reversed, increasing 448.75 km2. Our provide deep understanding behind and, through scenario analysis, demonstrate potential policy choices. These insights crucial formulating sound management policies protect resources, food security, promote balance.

Язык: Английский

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Multifaceted Spectral Feature Interaction Effects Enhance Remote Sensing Inversion of Chlorophyll in Cadmium-Stressed Rice

Опубликована: Янв. 1, 2023

AbstractEnhancing the remote sensing inversion of chlorophyll (Chl) in rice under cadmium (Cd) stress can help improve accuracy and efficiency large-scale monitoring soil Cd pollution. Spectral characteristics capture subtle changes Chl content stress; however, a more comprehensive exploration relationship between multifaceted spectral features has not been fully conducted. Moreover, most studies have overlooked impact interaction term effects on effectiveness prediction. In this study, sensitive to were selected, including first-order derivatives, envelope removal, inverse logarithmic transformations, wavelet parameters, characteristic using an interpretable neural network (GAMI-Net) quantify screen interactive terms. The application GAMI-Net model elucidated mechanisms by which these their respond stress. robustness enhanced grid-search algorithm based k-Fold cross-validation technique (GS-kFCV). Comparisons made traditional Vegetation Index (VI), Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN) models. Subsequently, Sentinel-2 satellite data used optimal invert modeling area prediction area, was validated with actual data. results indicated that improved model, compared original, showed increase 18.4% coefficient determination (R2) 90.9% ratio performance deviation (RPD), 76.5% reduction root mean square error (RMSE) test set. when other machine learning models, achieved R2 value 0.90 This surpassed values VI, RF, SVM, ANN, 0.71, 0.74, 0.34, respectively. addition, outperformed terms RMSE RPD metrics, 0.09 3.2, respectively, indicating higher robustness. Interpretative analysis significant variables revealed red-edge position accounted for 25.3% 17.7% variation stress, whereas 39.4% variation. predicted measurements 0.7988, 0.7233. Therefore, novel method proposed study exhibited high robustness, providing new insights into use estimation

Язык: Английский

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