Evaluating a thermal image sharpening model over a mixed agricultural landscape in India
Evaluating a thermal image sharpening model over a mixed agricultural landscape in India
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DOI：
10.1016/j.jag.2010.11.001
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发表时间：
2011-04-01
影响因子：
7.5
通讯作者：
Dadhwal, V. K.
中科院分区：
文献类型：
--
作者：
Jeganathan, C.;Hamm, N. A. S.;Dadhwal, V. K.
精细的空间分辨率（例如，300 m以下的地表温度资料是研究地表水分状况、水分胁迫和农业干旱与饥荒的重要资料。然而，当前的光学传感器不能以精细的空间分辨率提供频繁的热数据。TsHARP模型提供了一种可能性，即根据精细空间分辨率的归一化差异植被指数（NDVI）与粗略空间分辨率的地表温度之间的预期逆线性关系，从粗略空间分辨率（>= 1公里）数据生成精细空间分辨率的热数据。目前的研究利用TsHARP模型在印度的北方混合农业景观。分析了该模式的五个变体，包括原始模式，以确定其效率。这五个变体是全局模型（原始）：分辨率调整的全局模型;分段回归模型;分层模型;和局部模型。首先使用高级星载热发射反射辐射计（ASTER）的热数据（90米）对这些模型进行了评价，这些热数据按以下空间分辨率汇总：180米、270米、450米、630米、810米和990米。虽然对990米至90米的空间分辨率进行了锐化，但在ASTER数据中，平均而言，只有990米至270米的空间分辨率才能达到小于2 K的均方根误差。利用ASTER数据，在270 m处锐化图像的RMSE分别为1.91，1.89，1.96，1.91，1.70K。全球模型、经分辨率调整的全球模型和局部模型的精度较高，并用于将中分辨率成像分光仪热数据（1公里）锐化到目标空间分辨率。综合ASTER热数据被认为是在各自的目标空间分辨率的参考，以评估从中分辨率成像光谱仪数据的预测结果。在250 m处，使用全球、分辨率调整的全球和局部模型预测的来自MODIS的锐化图像的RMSE分别为3.08、2.92和1.98K。分别与其他变体相比，本地模型始终导致更准确的锐化预测。(c)2010 Elsevier B. V.保留所有权利。
Fine spatial resolution (e.g., < 300 m) thermal data are needed regularly to characterise the temporal pattern of surface moisture status, water stress, and to forecast agriculture drought and famine. However, current optical sensors do not provide frequent thermal data at a fine spatial resolution. The TsHARP model provides a possibility to generate fine spatial resolution thermal data from coarse spatial resolution (>= 1 km) data on the basis of an anticipated inverse linear relationship between the normalised difference vegetation index (NDVI) at fine spatial resolution and land surface temperature at coarse spatial resolution. The current study utilised the TsHARP model over a mixed agricultural landscape in the northern part of India. Five variants of the model were analysed, including the original model, for their efficiency. Those five variants were the global model (original): the resolution-adjusted global model; the piece-wise regression model; the stratified model; and the local model. The models were first evaluated using Advanced Space-borne Thermal Emission Reflection Radiometer (ASTER) thermal data (90 m) aggregated to the following spatial resolutions: 180 m, 270 m, 450 m, 630 m, 810 m and 990 m. Although sharpening was undertaken for spatial resolutions from 990 m to 90 m, root mean square error (RMSE) of < 2 K could, on average, be achieved only for 990-270 m in the ASTER data. The RMSE of the sharpened images at 270 m, using ASTER data, from the global, resolution-adjusted global, piecewise regression, stratification and local models were 1.91, 1.89, 1.96, 1.91, 1.70K, respectively. The global model, resolution-adjusted global model and local model yielded higher accuracy, and were applied to sharpen MODIS thermal data (1 km) to the target spatial resolutions. Aggregated ASTER thermal data were considered as a reference at the respective target spatial resolutions to assess the prediction results from MODIS data. The RMSE of the predicted sharpened image from MODIS using the global, resolution-adjusted global and local models at 250 m were 3.08, 2.92 and 1.98K. respectively. The local model consistently led to more accurate sharpened predictions by comparison to other variants. (c) 2010 Elsevier B.V. All rights reserved.