Browsing by Author "Zheng, Baojuan"
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- Adaptive Crop Management under Climate Uncertainty: Changing the Game for Sustainable Water UseMyint, Soe W.; Aggarwal, Rimjhim; Zheng, Baojuan; Wentz, Elizabeth A.; Holway, Jim; Fan, Chao; Selover, Nancy J.; Wang, Chuyuan; Fischer, Heather A. (MDPI, 2021-08-23)Water supplies are projected to become increasingly scarce, driving farmers, energy producers, and urban dwellers towards an urgent and emerging need to improve the effectiveness and the efficiency of water use. Given that agricultural water use is the largest water consumer throughout the U.S. Southwest, this study sought to answer two specific research questions: (1) How does water consumption vary by crop type on a metropolitan spatial scale? (2) What is the impact of drought on agricultural water consumption? To answer the above research questions, 92 Landsat images were acquired to generate fine-resolution daily evapotranspiration (ET) maps at 30 m spatial resolution for both dry and wet years (a total of 1095 ET maps), and major crop types were identified for the Phoenix Active Management Area. The study area has a subtropical desert climate and relies almost completely on irrigation for farming. Results suggest that there are some factors that farmers and water managers can control. During dry years, crops of all types use more water. Practices that can offset this higher water use include double or multiple cropping practice, drought tolerant crop selection, and optimizing the total farmed area. Double and multiple cropping practices result in water savings because soil moisture is retained from one planting to another. Further water savings occur when drought tolerant crop types are selected, especially in dry years. Finally, disproportionately large area coverage of high water consuming crops can be balanced and/or reduced or replaced with more water efficient crops. This study provides strong evidence that water savings can be achieved through policies that create incentives for adopting smart cropping strategies, thus providing important guidelines for sustainable agriculture management and climate adaptation to improve future food security.
- Broad-scale Assessment of Crop Residue Management Using Multi-temporal Remote Sensing ImageryZheng, Baojuan (Virginia Tech, 2012-12-12)Tillage practices have changed dramatically during the past several decades as agricultural specialists have recognized the unfavorable environmental effects of mechanized tillage. Alternatively, conservation tillage management can mitigate adverse environmental impacts of tillage, such as soil and water degradation. Adoption of conservation tillage has continued to increase since its first introduction, which raises questions of when and where it is practiced. Spatial and temporal specifics of tillage practices form important dimensions for development of effective crop management practices and policies. Because Landsat has been and will continue to image the Earth globally, it provides opportunities for systematic mapping of crop residue cover (CRC) /tillage practices. Thus, the overall objective of this study is to develop methodologies to improve our ability to monitor crop management across different landscapes in a time-efficient and cost-effective manner using Landsat TM and ETM+ imagery, which is addressed in three separate studies. The first study found that previous efforts to estimate CRC along a continuum using Landsat-based tillage indices were unsuccessful because they neglected the key temporal changes in agricultural surfaces caused by tilling, planting, and crop emergence at the start of the growing season. The first study addressed this difficulty by extracting minimum values of multi-temporal NDTI (Normalized Difference Tillage Index) spectral profiles, designated here as the minNDTI method. The minNDTI improves crop residue estimation along a continuum (R2 = 0.87) as well as tillage classification accuracy (overall accuracy > 90%). A second study evaluated effectiveness of the minNDTI approach for assessing CRC at multiple locations over several years, and compared minNDTI to hyperspectral tillage index (CAI), and the ASTER tillage index (SINDRI). The minNDTI is effective across four different locations (R2 of 0.56 ~ 0.93). The third study, built upon the second study, addressed the Landsat ETM+ missing data issue, and devised methodologies for producing field-level tillage data at broad scales (multiple counties). In summary, this research demonstrates that the minNDTI technique is currently the best alternative for monitoring CRC and tillage practices from space, and provides a foundation for monitoring crop residue cover at broad spatial and temporal scales.
- Remote Sensing of Tillage StatusZheng, Baojuan; Campbell, James B. Jr.; Serbin, Guy; Daughtry, Craig S. T.; McNairn, Heather; Pacheco, Anna (2016)Remote Sensing of Tillage Status is Chapter 8 in the book Land Resources Monitoring, Modeling, and Mapping With Remote Sensing.