Prediction of High-Quality MODIS-NPP Product Data

Zhenhua Liu,Ya Wen,Ting Wang,Xiaofang Wu,Yonghua Qu,Huiming Liu

doi:10.3390/rs11121458

Abstract

Net primary productivity (NPP) is a key vegetation parameter and ecological indicator for tracking natural environmental change. High-quality Moderate Resolution Imaging Spectroradiometer Net primary productivity (MODIS-NPP) products are critical for assuring the scientific rigor of NPP analyses. However, obtaining high-quality MODIS-NPP products consistently is challenged by factors such as cloud contamination, heavy aerosol pollution, and atmospheric variability. This paper proposes a method combining the discrete wavelet transform (DWT) with an extended Kalman filter (EKF) for generating high-quality MODIS-NPP data. In this method, the DWT is used to remove noise in the original MODIS-NPP data, and the EKF is applied to the de-noised images. The de-noised images are modeled as a triply modulated cosine function that predicts the NPP data values when excessive cloudiness is present. This study was conducted in South China. By comparing measured NPP data to original MODIS-NPP and NPP estimates derived from combining the DWT and EKF, we found that the accuracy of the NPP estimates was significantly improved. The MODIS-NPP estimates had a mean relative error (RE) of 13.96% and relative root mean square error (rRMSE) of 15.67%, while the original MODIS-NPP had a mean RE of 23.58% and an rRMSE of 24.98%. The method combining DWT and EKF provides a feasible approach for generating new, high-quality NPP data in the absence of high-quality original MODIS-NPP data.

Highlights

Net primary productivity (NPP) is a key variable in the carbon exchange between the biosphere and the atmosphere [1]
We used the discrete wavelet transform (DWT) algorithm to reduce the noise in the MODIS-NPP images and imIpnrothvies tshtueddya,twa qeuuasleitdytohfeMDOWDTISalgimoraigthemry.toThreedwucaevethleet ndoeicsoeminptohseitiMonOoDfISa-nNiPmPagime abgyesDaWndT is imsphroowven tihneFdigautareq3u.ality of MODIS imagery
The relative root mean square error (rRMSE) was 15.67% for the predicted MODIS-NPP data, 19.5% for the de-noised MODIS-NPP data, and 24.98% for the original MODIS-NPP data, indicating that the method combining the DWT and the extended Kalman filter (EKF) notably increases the estimation accuracy of NPP in the study area

Summary

Introduction

Net primary productivity (NPP) is a key variable in the carbon exchange between the biosphere and the atmosphere [1]. NPP with 1 km spatial resolution and an 8 day interval has been produced operationally with the Moderate Resolution Imaging Spectroradiometer Net Primary Productivity product (MOD17) algorithm based on observations from the MODIS sensor (National Aeronautics and Space Administration, America) [3]. These MODIS-NPP data provide consistent spatial and temporal measures of crop yield, range forage, forest production, and other socio-economically significant products related to vegetation growth [4,5]. The MOD17 algorithm has undergone several improvements, MODIS-NPP

Methods

Results

Conclusion