基于多源遥感数据融合的土地整治区产能动态监测:方法与案例

Dynamically monitoring productivity of cultivated land enrolled in land consolidation programs based on fusing multi-source remote sensing data: Methodology and a case study

土地整治作为实现耕地保护和促进粮食安全的重要措施,由其引起的耕地产能提升受到持续关注。为克服传统实地调研、农户问卷调查等方法在数据可靠性、监测连续性、区域覆盖性等方面的不足,以及现有遥感数据产品在项目区尺度时空分辨率上的限制,基于MODIS、TM/ETM+/OLI数据,结合CASA模型和ESTARFM方法,以典型土地整理项目区为试验区,探索基于多源遥感数据融合的土地整治区产能动态监测技术方法。研究表明:基于融合数据获取的高时空分辨率结果能较好地分离项目区整治前后的地类特征,细化产能季节波动,显化产能动态变化过程;从监测结果看,试验区产能呈现先减后增的总体趋势,多年变化范围为519.87~728.29 g C?m^(-2)?a^(-1),整治后产能均值提升,且产能稳定性提高。

Large-scale land consolidation programs have been carried out since the late 1990s in China. Besides cultivated land preservation and food security, these programs are also proposed to improve cultivated land productivity, which remains a perennial concern at home and abroad. The reliability of data sources plays a key role in evaluating the effectiveness and efficiency of these land consolidation programs. Traditional data collecting methods, such as field interviews and questionnaires, are often criticized for their obscurity in data credibility and/or the coverage and/or monitoring continuity. Moreover, contemporary remote sensing data products are generally of the weakness in spatial-temporal resolutions. Therefore, this study proposes a multi-source remote sensing data fusion method to overcome these issues while enhance the data credibility and its spatial-temporal resolution. Specifically, this method integrates red and near-infrared Terra MODIS images that are of great temporal resolution information and Landsat TM/ETM＋/OLI images that are of great spatial resolution information and further has been applied together with the CASA （the Carnegie-Ames-Stanford-Approach） model and the ESTARFM （Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model） algorithm to monitor the cultivated land productivity in a land consolidation project in Shizong town, Nantong city, Jiangsu province. The result shows that compared with traditional data collecting methods and contemporary remote sensing data products （MOD17A3）, our method is of better capability in differentiating land characteristics that have been influenced greatly by whether land consolidation programs are practiced, capturing fine seasonal changes in land productivity and showing dynamic process of cultivated land productivity changes in land consolidation projects. Furthermore, NPP based on the CASA model and MODIS data （MOD13Q1） could be used to monitor cultivated land productivity in land consolidation programs under the circumstance of corresponding land characteristics and regularity. In our case study area, cultivated land productivity is of the general trend of ＂decrease first, increase later＂ while the annual variation is between 519. 87 and 728. 29 g C ~ m 2, and land consolidation activity is not a determining factor to raise inter-annual fluctuation in cultivated land productivity. After land consolidation programs were put into practice, the average of cultivated land productivity increases and the stability of cultivated land productivity improves. In conclusion, the method which combines the CASA model and ESTARFM algorithm is feasible for dynamically monitoring the cultivated land productivity in land consolidation programs based on fusing multi-resources remote sensing data, and it could provide references for evaluating the effectiveness and efficiency of large-scale land consolidation programs.