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首页> 外文期刊>International journal of remote sensing >Evaluation of remote-sensing-based models of gross primary productivity over Indian sal forest using flux tower and MODIS satellite data
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Evaluation of remote-sensing-based models of gross primary productivity over Indian sal forest using flux tower and MODIS satellite data

机译：基于通量塔和MODIS卫星数据的印度Sal森林基于遥感的总初级生产力模型的评估

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摘要

Forest plays a significant role in regulating the carbon budget and mitigating climate change in long term. However, lack of spatially explicit and accurate information on carbon exchange components from diverse forest ecosystem types in India limits carbon budgeting on a regional scale. Remote-sensing-driven ecosystem models are well-established tools for estimating gross primary productivity (GPP) over large areas but they are seldom found erroneous if implemented without proper calibration of biome-specific parameters. The present study evaluates the combined use of eddy covariance (EC) data and satellite-derived variables for estimating GPP over large areas. Four remote-sensing-driven models, (i) temperature-greenness (TG) model, (ii) greenness-radiation (GR) model, (iii) light use efficiency (LUE) model, and (iv) remote-sensing-based LUE (LUERS) model, were parameterized with EC measurements and compared with 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) GPP products for a moist Shorea robusta forest in northern part of India. EC observed 8-day average GPP varied from 5.38 to 12.42 g C m(-2) day(-1). Among the four tested models, TG model had the highest root mean square error (RMSE) of 1.28 g C m(-2) day(-1), while GR and LUERS models had moderate RMSE of 0.99 g C m(-2) day(-1) and 0.98 g C m(-2) day(-1), respectively. The closest GPP estimate was given by LUE model with RMSE of 0.93 g C m(-2) day(-1). The RMSE for all four models were four times lower than that of MODIS GPP. Lower maximum LUE (epsilon(max))and uncertainty in the environmental scalar used in MODIS GPP algorithm could have contributed to higher RMSE. More accurate modelling of GPP can help in better understanding of forest ecological functions with the changing climate.
机译：从长远来看，森林在调节碳预算和缓解气候变化方面发挥着重要作用。但是，印度缺乏关于来自不同森林生态系统类型的碳交换成分的明确的空间信息，这限制了区域规模的碳预算。遥感驱动的生态系统模型是用于在大面积上估算总初级生产力（GPP）的公认工具，但如果实施时未正确校准生物群特定参数，则很少发现它们是错误的。本研究评估了涡旋协方差（EC）数据和卫星衍生变量的组合使用，以估计大面积的GPP。四个遥感驱动模型，（i）温度-绿色（TG）模型，（ii）绿色-辐射（GR）模型，（iii）光利用效率（LUE）模型，以及（iv）基于遥感的模型LUE（LUERS）模型通过EC测量参数化，并与印度北部潮湿的浓香浓郁的肖拉罗布斯塔森林的8天中分辨率成像光谱仪（MODIS）GPP产品进行了比较。 EC观察到的8天平均GPP从5.38到12.42 g C m（-2）day（-1）不等。在这四个测试模型中，TG模型的均方根误差（RMSE）最高，为1.28 g C m（-2）day（-1），而GR和LUERS模型的均方根误差为0.99 g C m（-2）。 day（-1）和0.98 g C m（-2）day（-1）。 LUE模型给出了最接近的GPP估计值，RMSE为0.93 g C m（-2）day（-1）。四种型号的RMSE均比MODIS GPP低四倍。较低的最大LUE（ε（最大值））和MODIS GPP算法中使用的环境标量的不确定性可能导致较高的RMSE。 GPP的更精确建模可以帮助您更好地了解气候变化带来的森林生态功能。

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  • 来源
    《International journal of remote sensing》 |2017年第18期|5069-5090|共22页
  • 作者

    Watham T.; Patel N. R.; Kushwaha S. P. S.; Dadhwal V. K.; Kumar A. Senthil;

  • 作者单位

    Indian Inst Remote Sensing, ISRO, 4 Kalidas Rd, Dehra Dun 248001, Uttarakhand, India;

    Indian Inst Remote Sensing, ISRO, 4 Kalidas Rd, Dehra Dun 248001, Uttarakhand, India;

    Indian Inst Remote Sensing, ISRO, 4 Kalidas Rd, Dehra Dun 248001, Uttarakhand, India;

    Indian Inst Space Sci & Technol, Thiruvananthapuram, Kerala, India;

    Indian Inst Remote Sensing, ISRO, 4 Kalidas Rd, Dehra Dun 248001, Uttarakhand, India;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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