[1]YANG Gang,GUO Peng,LI Xican,et al.Assessment on Applicability of Soil Moisture Products of Fengyun Satellite Microwave Remote Sensing[J].Research of Soil and Water Conservation,2020,27(01):104-111,118.
Copy
Assessment on Applicability of Soil Moisture Products of Fengyun Satellite Microwave Remote Sensing
Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume:
27
Number of periods:
2020 01
Page number:
104-111,118
Column:
Public date:
2020-02-20
- Title:
- Assessment on Applicability of Soil Moisture Products of Fengyun Satellite Microwave Remote Sensing
- Keywords:
- microwave remote sensing; soil moisture; FengYun; AMSR-2; applicability
- CLC:
- S152.7
- DOI:
- -
- Abstract:
- Soil moisture is a key variable in hydrological processes, bioecological processes, and biogeochemical processes. Long-term observations of soil moisture over large areas are critical to research on flooding and drought monitoring, water resource management, and crop yield forecasts. This paper is based on the measured data of 35 ground monitoring stations in the Agricultural Research Service(ARS)hydrological monitoring network of Southwest Oklahoma, USA, LWREW(Little Washita Experimental Watershed)and FCREW(Ft. Cobb Reservoir Experiment Watershed). The applicability of moisture products of FY-3B, FY-3C and AMSR-2 soil from May 1, 2016 to May 31, 2018 in this region was analyzed and compared. The results show that the soil moisture products of FY-3B and FY-3C are overestimated compared to the measured soil moisture in LWREW and FCREW areas, the average biases of the FY-3B soil moisture products in the two regions are 0.070 m3/m3 and 0.048 4 m3/m3, and the average biases of the FY-3C soil moisture products in the two regions are 0.060 2 m3/m3 and 0.053 2 m3/m3. In the seasons with more precipitation, the retrieval results of FY-3B and FY-3C products increased significantly, and JAXA soil moisture products underestimated the soil moisture in LWREW and FCREW areas, with an average biases of -0.100 5 m3/m3 and -0.100 3 m3/m3 in both regions. In the LWREW area, the FY-3B ascending orbital soil moisture product performed best, with the higher correlation coefficient(R=0.664 8), the lower root mean square(RMSE=0.075 5 m3/m3)and the average bias(0.061 6 m3/m3). In the FCREW area, JAXA had the best performance, with the highest correlation coefficient(R=0.726 1)and lowest unbiased root mean square(ubRMSE=0.035 7 m3/m3). In general, FY-3B, FY-3C, and JAXA soil moisture products are able to reflect the seasonal changes of soil moisture in the LWREW and FCREW areas.
- References:
-
[1] 赵天杰.被动微波反演土壤水分的L波段新发展及未来展望[J].地理科学进展,2018,37(2):198-213.
[2] 仝兆远,张万昌.土壤水分遥感监测的研究进展[J].水土保持通报,2007,7(4):107-113.
[3] Vereecken H, Huisman J A, Pachepsky Y, et al. On the spatio-temporal dynamics of soil moisture at the field scale[J]. Journal of Hydrology, 2014,516(4):76-96.
[4] 万红,高硕,郭鹏.青藏高原地区FY-3B微波遥感土壤水分产品适用性研究[J].干旱区资源与环境,2018,32(4):132-137.
[5] Kerr Y H, Waldteufel P, Richaume P, et al. The SMOS soil moisture retrieval algorithm[J]. IEEE Transactions on Geoscience & Remote Sensing, 2012,50(5):1384-1403.
[6] Koike T, Nakamura Y, Kaihotsu I, et al. Development of an advanced microwave scanning radiometer(AMSR-E)algorithm of soil moisture and vegetation water content[J]. Ann J. Hydra. Eng., JSCE, 2004,48:217-223.
[7] Cui H, Jiang L, Du J, et al. Evaluation and analysis of AMSR-2, SMOS, and SMAP soil moisture products in the Genhe area of China[J]. Journal of Geophysical Research:Atmospheres, 2017,122(16):8650-8666.
[8] Cui C, Xu J, Zeng J, et al. Soil moisture mapping from satellites:An intercomparison of SMAP, SMOS, FY3B, AMSR-2, and ESA CCI over two dense network regions at different spatial scales[J]. Remote Sens, 2018,10(1),33; https:∥doi. org/10.3390/rs10010033.
[9] 庄媛,师春香,沈润平,等.中国区域多种微波遥感土壤湿度产品质量评估[J].气象科学,2015,35(3):289-296.
[10] Elliott R L, Schiebe F R, Crawford K C, et al. A unique data capability for natural resources studies[C]∥International Winter Meeting of the American Society of Agricultural Engineers, Chicago, IL.1993:14-17.
[11] Starks P J, Fiebrich C A, Grimsley D L, et al. Upper Washita River experimental watersheds:Meteorologic and soil climate measurement networks[J]. Journal of Environmental Quality, 2014,43(4):1239-1249.
[12] Jackson T J, Cosh M H, Bindlish R, et al. Validation of advanced microwave scanning radiometer soil moisture products[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010,48(12):4256-4272.
[13] Shi J, Jiang L, Zhang L, et al. A parameterized multifrequency-polarization surface emission model[J]. Journal of Remote Sensing, 2006,43(12):2831-2841.
[14] Zhang N, Shi J, Sun G, et al. A simple algorithm for retrieval of the optical thickness at L-band from SMOS data[C]∥2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012:198-201.
[15] Kachi M, Hori M, Maeda T, et al. Status of validation of AMSR-2 on board the GCOM-W1 satellite[C]∥2014 IEEE Geoscience and Remote Sensing Symposium. IEEE,2014:110-113..
[16] 席家驹,文军,田辉,等. AMSR-E遥感土壤湿度产品在青藏高原地区的适用性[J].农业工程学报,2014,30(13):194-202.
[17] Brocca L, Hasenauer S, Lacava T, et al. Soil moisture estimation through ASCAT and AMSR-E sensors:An intercomparisonand validation study across Europe[J]. Remote Sensing of Environment, 2011,115(12):3390-3408.
[18] Zeng J, Chen K S, Bi H, et al. A preliminary evaluation of the SMAP radiometer soil moisture product over United States and Europe using ground-based measurements[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016,54(8):4929-4940.
- Similar References:
Memo
-
Last Update:
2020-02-25