Soil effective temperature and its application in passive remote sensing of soil moisture at L-band
143
0
0
Hele tekst
(2) 6 9 6ÿÿ391ÿ
(3)
(4) 438932ÿ3ÿ
(5) 113 6ÿ 6 296ÿ 1613ÿ27ÿ1234ÿ 2319 6ÿ9ÿ4ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ 1ÿ4ÿ. ÿ.
(6) ÿ. ÿ.
(7) 1234ÿ6776893 6ÿ96
(8) 6 9 6ÿÿ391ÿ
(9)
(10) 438932ÿ3ÿ
(11) 113 6ÿ 6 296ÿ 1613 ÿ 27 ÿ 123 4ÿ 23 19 6ÿ 9ÿ 4 ÿ ÿ ÿ. ÿ ÿ. ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ !"#ÿ $%ÿ&%'(%%ÿ )ÿ& * (ÿ! ÿ $%ÿ+#",%(-" .ÿ )ÿ/%# %0ÿ #ÿ $%ÿ!1 $ (" .ÿ )ÿ $%ÿ(%* (ÿ2!'#")"*1-0ÿ 3( )4&(4ÿ4454ÿ6!7- (!ÿ #ÿ!** 1# ÿ )ÿ $%ÿ&%*"-" #ÿ )ÿ $%ÿ * (! %ÿ8 !(&0ÿ ÿ%ÿ31 7"*7.ÿ&%)%#&%&ÿ #ÿ9%&#%-&!.ÿ:;ÿ3("7ÿ<;:=ÿ! ÿ:<4>?ÿ$(-ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ 8.ÿ ÿ $! #"#'ÿ@,ÿ ÿ ÿ (#ÿ #ÿAÿ5!.ÿ:=A?ÿ ÿ "#ÿ %"2 #' 70ÿB$"#!ÿ ÿ.
(12) 0123ÿ516323ÿ173ÿ8669ÿ7.
(13) 6ÿ8ÿ ÿÿÿ3 6 23
(14) ÿ ÿÿ 69ÿ3 6 23
(15) ÿ ÿ ÿ69 ÿ
(16) !3 6 23
(17) ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ. "0#ÿ2336 5752
(18) 9ÿ9$86 ÿ%&'ÿ "0#ÿ()ÿ*
(19) +ÿ,-'ÿ'.//ÿ01ÿ193166ÿ016ÿ26516 3793ÿ ÿ ÿ "*2ÿ4'5!4/!%6.!&'&4!4ÿ 7)"ÿ-/%44/8-4'54/%6.&'&44ÿ ÿ #
(20) 6 ÿ632 96ÿ8ÿ
(21) 8ÿ72$ÿ ( 2956ÿ8ÿ"0#ÿ( 29529 ÿ76 7 5$695ÿ #
(22) 2 15ÿ9ÿ,/++ÿ8ÿÿ ÿ. ÿ. ÿ ÿ. ÿ ÿ.
(23) 1234536789ÿ 8 766
(24)
(25) ÿ ÿ ÿ ÿ 28 42 72 ÿ ÿ
(26) !4"3 #ÿ ÿ $%&'('ÿ ÿ 28 42 ÿ) ÿ*5ÿ ÿ ÿ ÿ 28 42 ÿ1 ÿ2
(27) 9ÿÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ (7'$%&'(ÿ ÿ 82 ÿ9 ÿ)
(28) 95ÿ ÿ ÿ ÿ :;'ÿ ÿ 28 42 ÿ8 ÿ,39ÿ4
(29) 2ÿ23!ÿÿ ÿ 28 42 ÿ< ÿ=
(30) 2!
(31) ÿ ÿ ÿ 28 42 ÿ3 ÿ*7
(32) 2ÿ ÿ ÿ 28 42 ÿ9 ÿ=
(33) 22ÿÿ ÿ ÿ 82 ÿ* ÿ95
(34) 4ÿ ÿ ÿ ÿ 82 ÿ ÿ4
(35) ÿF8-93.ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ ÿ. ÿ. +97,
(36) 2-76.ÿ8 ÿ/0
(37) 96
(38) ÿ 34
(39) 9545ÿ+97,
(40) 2-76.ÿ8 ÿ69 82 36789ÿ /
(41) 498!85.ÿ +97,
(42) 2-76.ÿ8 ÿ/0
(43) 96
(44) ÿ +97,
(45) 2-76.ÿ8 ÿ/0
(46) 96
(47) ÿ +97,
(48) 2-76.ÿ8 ÿ/0
(49) 96
(50) ÿ >899ÿ+97,
(51) 2-76.ÿ 3?*>6@Aÿ*B@*ÿB7--789ÿ 6ÿ < * C1
(52) 6ÿ 28#5!-789ÿD3E823682.ÿ ?528#
(53) 39ÿ3
(54) 962
(55) ÿ 82ÿB
(56) 475 ÿF395
(57) ÿ 2
(58) 364
(59) 2ÿG82
(60) 3-6-ÿH?3B2GIÿ.
(61) ÿ.
(62) 23456789
(63) 95 ÿ. 0ÿ ÿ ÿÿ ÿ ÿÿ0ÿÿÿÿÿÿÿ! ÿ ÿ "ÿ ÿ #!ÿ$ÿ ÿ ÿ ÿ ÿ% ÿ ÿ 0ÿ ! ÿ&0ÿ'ÿ%ÿ$ÿ (ÿ"ÿ $ÿ& ÿ%ÿ0ÿ ÿ! ÿ! ÿ!ÿ ÿ ÿÿ ÿ! (!ÿ ÿ ÿ%"ÿ)!ÿ 'ÿ ÿ% ÿ ÿ0 ÿ ÿ!ÿ!%ÿ$ÿ (ÿÿÿ!ÿ ÿ00ÿ ÿÿ!ÿ ÿ *+ÿ ÿ ÿ' !ÿ0ÿ0ÿ,-./"ÿ0 ÿ$ÿ' 0(ÿ0ÿ!0ÿ 1%+ 0ÿ ÿ+!ÿ2 *0ÿ$0ÿ'ÿ3(ÿ42$35ÿ'ÿ ÿ+((ÿ0ÿ 6778"ÿ2$3ÿ0ÿ*ÿ ÿ0+0 (ÿ+!0+ÿ'ÿ ÿ ÿ! ÿ0 ÿÿ ÿ(0'ÿ+!0+ÿ ' "ÿ$ÿ ÿ '(ÿÿ ÿ% "ÿ9ÿ!0ÿ(ÿ+!0( ÿ!ÿÿ &ÿ ÿ ÿ ÿ ÿ! "ÿ ÿ $ÿ+ ÿ!ÿ ( ÿ+ ÿ ÿÿÿ'ÿ ÿ ÿ! ÿÿÿ'(( ÿ !ÿ:!0ÿ 00"ÿ;! &ÿ'ÿ (( 0ÿ ÿÿ (0<ÿ ÿ%0 ("ÿ9((ÿ!ÿ %%ÿ!ÿ ÿÿ!ÿ( ÿ!0ÿ ÿ ÿ!ÿ ÿ0'ÿ ÿ! ÿÿ 0ÿ "ÿ$ÿ+!ÿÿÿÿ ÿ0ÿ ÿ+!ÿ (ÿ0+ÿ (ÿ 0ÿ+0+ ÿÿ+!("ÿÿ ÿ #!ÿ+((ÿ 0ÿ ÿ+ 0ÿ% ÿ ÿÿ ÿ ÿ+!0+=ÿÿÿ '!ÿ 0!ÿÿÿÿÿ &"ÿ$ÿ0ÿÿ'0ÿ ÿ* ÿ ÿ'0 ÿÿ!ÿ ÿÿ +!ÿ%%0ÿ'ÿ ÿÿÿ!ÿ0&ÿ0+0ÿ40&ÿ0ÿ ÿ ÿ'ÿ !!(ÿ0 0ÿ0ÿ 0( ÿ:!0 5"ÿ>0!ÿ!ÿÿ''ÿ$ÿ+(ÿÿ ÿ! ÿ 0 ÿ ÿÿ &ÿ0ÿ;0ÿ !ÿ!ÿ 0ÿ0ÿ!0! ÿÿ ÿ ÿ0( ÿ !0+!ÿ0ÿ 0( ÿ0ÿ!ÿ%!ÿ "ÿ9(!!ÿ!ÿ''ÿ ÿÿÿ ÿ$ÿ ÿÿ 0ÿ ÿ ((=ÿ$ÿ ( ÿ! ÿ ÿÿ+ÿ0ÿ!ÿ3+ÿ( ÿÿ ÿ ÿ ÿÿ00 ÿ0ÿ)0*0"ÿ%00( ÿ$ÿ' 0(ÿ0ÿ!ÿ00 "ÿ9'ÿ (0<0ÿ!ÿ+ ÿ+(ÿÿ+! ÿ$ÿ ÿ ÿ+ ((ÿÿ(&ÿ'ÿ ÿ ÿ 00 "ÿ$ÿ ÿ(ÿ!ÿ0ÿ!ÿ+0ÿ ÿ0ÿ ÿ+ "ÿ;! ÿ ÿ !ÿ$ÿ ÿ?'"ÿ @ÿ# ÿ !ÿ ÿ ÿ% ÿ'ÿ!ÿ1ÿ'ÿ ÿ ÿ!ÿ:!0ÿ9+ ÿ 'ÿ+0+ ÿA <!"ÿ$ÿ ÿÿ! &ÿ ÿ:!0ÿ0ÿ% ÿ?'"ÿ@ÿ#ÿ ÿ(ÿ'ÿ0ÿ ÿ +&ÿÿ +&ÿ 0!ÿ!ÿ+ ÿÿ (ÿÿÿ0ÿ ÿ 0"ÿ#0!ÿÿ%% ÿ$ÿ (ÿÿ! ÿ! ÿ!ÿ+! +ÿÿÿ0ÿ !ÿ2!( ÿ ÿÿ+!ÿ ÿ+('(ÿ ("ÿ#0!ÿÿ%%ÿ ÿ 0ÿ.
(64) 234567892 23
(65) ÿ 2ÿ 20ÿ56ÿ35 ÿ 82ÿ223ÿ030 2ÿ56ÿ872ÿ88ÿ 9232756ÿ 5ÿ 2ÿ 0 82ÿÿ 82ÿ ÿ 2ÿ07 ÿ 0 2ÿÿ 2 ÿ75ÿ 5ÿ 6ÿ8ÿ67039ÿ8ÿ02ÿ48 8093ÿ03ÿ02 ÿ03ÿ !ÿ" ÿ 8 ÿ 0 2
(66) ÿ 2ÿ2ÿ0ÿ62ÿ 5ÿ8 0 62ÿ0432ÿ 02ÿÿ8ÿ85ÿ2 ÿ2 03ÿ03ÿ8ÿ 8ÿ 4563 ÿ 53ÿ24862ÿ5ÿ48 4 039ÿ8ÿ45ÿÿ56ÿ3227ÿ728 ÿ 8 ÿ532ÿ8ÿ 2ÿ 75257ÿ 5ÿÿ 55ÿ4872ÿ5ÿ56ÿ2ÿ ÿ 75 5 27ÿ5ÿ 2
(67) ÿ75ÿ 6ÿ8ÿ02ÿ8ÿ 2039ÿ75 ÿ8ÿ988#ÿ87ÿ87ÿ88ÿ83ÿ5039ÿ5 2ÿ 27056ÿ57ÿ253ÿ ÿ 0 89038 053ÿÿ$83ÿ 83ÿ 5ÿ 2ÿ% 0322ÿ 4 587 0 ÿ%56340
(68) ÿÿ95 ÿ83ÿ03 2702ÿ 5 57 630 ÿ57ÿ &ÿ 6ÿ03ÿ 2ÿ'2 2783ÿ3ÿ (
(69) ÿÿ95 ÿ8ÿ4 8342ÿ 5ÿ 87 ÿ. ÿ2453ÿ &ÿ487227ÿ7245. 232ÿÿ75ÿ 5ÿ 6ÿ&67039ÿ 2ÿ03 2702
(70) ÿ 75ÿ 6ÿ80
(71) ÿ)*
(72) ÿ0 +ÿ56,-ÿÿ2 ÿ0332
(73) ÿ 5 0 80
(74) ÿ83ÿ567ÿ 6 57ÿ83ÿ 72282ÿ ÿ 23053ÿ709 ÿ88ÿ 8 ÿ8ÿ8ÿ 0 57048ÿ03 2702ÿ24862ÿ% %ÿ83ÿ .'"/ÿ0.5303012ÿ'227832ÿ"82 02ÿ83ÿ/2 234 8 232ÿ 5 2ÿ 5798303039ÿ64 ÿ83ÿ03 2702ÿ75 ÿ 23ÿ53ÿ 0ÿ630462ÿ03 2702ÿ8ÿ85ÿ ÿ 07 ÿ 0 2ÿ 5ÿ95ÿ8758ÿ" 6039
(75) ÿÿ223ÿ7262ÿ 5ÿ 82ÿ 782ÿ 5 5ÿ24862ÿ ÿ83 2ÿ 5ÿ2032ÿ 0ÿ5 57 630 ÿ5ÿ 64
(76) ÿ35 ÿ16 ÿ 82ÿ5 2ÿ 04 672ÿ 8 ÿÿ483ÿ 7248ÿ 2ÿ8 048 053ÿ 7542672ÿ8 27ÿ 2ÿ03 2702ÿ8ÿ 2056ÿ83ÿ 57 67039
(77) ÿ 6 ÿ038
(78) ÿÿ 82ÿ0 ÿ0 ÿ 2 ÿ75 ÿ75ÿ 5ÿ 6ÿ83ÿ75ÿ563ÿ/23ÿÿ ÿ 2ÿ 6ÿ8 ÿ%ÿ8ÿ3227ÿ28ÿ24862ÿ2ÿ88ÿ4 82392ÿ 6 83+ÿ 35292ÿ753 027ÿ3ÿ 2ÿ29033039
(79) ÿ 2ÿ 5 08 053ÿ5ÿ 0ÿ 20ÿ8ÿ 5ÿ62ÿ 8 20 2ÿ72 5 2ÿ23039ÿ50ÿ 50 672ÿ 7564 ÿ03ÿ 2ÿ68 ÿ"08ÿ 6. 27ÿ$53553ÿ 57248 ÿ75227
(80) ÿ8 27ÿ95039ÿ57ÿ8ÿ 02
(81) ÿ 2ÿ722874 ÿ 5 2ÿ62ÿ 5ÿ 2ÿ 63427 803 02ÿ03ÿ 2ÿ8 20 2ÿ50ÿ 50 672ÿ 7564 ÿ 2 22ÿ" 27ÿ046039ÿ 0 ÿ75ÿ 6ÿ83ÿ&7ÿ8239
(82) ÿ2ÿ728032ÿ 8 ÿ 2ÿ 5 04ÿ 56ÿ95ÿ84ÿ 5ÿ 2ÿ 802ÿ 047582ÿ72 5 2ÿ23039ÿ062ÿ453027039ÿ567ÿ03358 053ÿ53ÿ 2ÿ50ÿ 224 02ÿ 2 278 672ÿ4 2 2ÿ 2ÿ539ÿ 874 ÿ 87 2ÿ0 ÿ567ÿ07 ÿ 8 27ÿ 8 ÿ 55ÿ9:ÿ 53 ÿ63 0ÿ0 ÿ 6048 053ÿÿ8ÿ76 78 2
(83) ÿ08 503 2ÿ83ÿ 5 2 0 2ÿ 57 672ÿ6 ÿÿ456ÿ88ÿ92 ÿ45302342
(84) ÿ 76
(85) ÿ83ÿ 2 ÿ75 ÿ75ÿ 6ÿ/0 56 ÿ 0ÿ234567892 23
(86) ÿ6992 053ÿ83ÿ 75 5 053
(87) ÿÿ48335 ÿ 83ÿ 272ÿ 83ÿ 82ÿ ÿ2232ÿ57ÿ672ÿ 2ÿ07 ÿ 8 27ÿ0ÿ16 ÿ8ÿ 57 ÿ27053ÿ5ÿ567ÿ 458578 053ÿ83ÿ 2ÿ8 2ÿ4232ÿ72 28 2ÿ 0 2ÿ83ÿ 0 2ÿ89803ÿ57ÿ 2ÿ8 ÿ0#ÿ 287ÿ;728 ÿ 83ÿ 5ÿ ÿ6 27057
(88) ÿ&7ÿ01083ÿ8239ÿ 5 ÿÿ483ÿ88ÿ2873ÿ8ÿ 5 ÿ75 ÿ35 ÿ53ÿ 0ÿ64426ÿ6 270053ÿ6 ÿ85ÿ 0ÿ03 2970 ÿ83ÿ4872632ÿ / 23227ÿÿ 82ÿ83ÿ028ÿ57ÿ5 2ÿ4783ÿ 569
(89) ÿ56ÿ872ÿ ÿ07 ÿ8602342ÿ 00ÿ.
(90) 23456547ÿ86549ÿ 34 ÿ
(91) 9 5 ÿ0ÿ
(92) 5ÿ40
(93) ÿ56549ÿ234ÿ 54065 ÿ ÿ56549ÿ 2045ÿ 3
(94)
(95) 5 ÿ0ÿ
(96) 5ÿ 42
(97) ÿ3
(98) 05 ÿ934ÿ4 ÿ 34ÿ ÿ 5
(99) 7ÿ2
(100) 54ÿ45
(101) 0ÿ
(102) 5ÿ5 ÿ30ÿ5225
(103) 065ÿ
(104) 5 54
(105) 45ÿ55ÿ 5ÿ23 ÿ
(106)
(107) ÿ
(108) 5ÿ 3
(109) 5
(110) 0ÿ 3
(111) 40
(112) 03ÿ0
(113) ÿ3
(114) ÿ5ÿ
(115) ÿÿ3
(116) 03ÿ0ÿ4 0
(117) 065ÿ234 4 ÿ3 507ÿ0ÿ. 0
(118) ÿ53455
(119) ÿ243ÿ 4327ÿ ÿ ÿ 47ÿ 5ÿÿ
(120) 45 ÿ9ÿ
(121) 3 0ÿ
(122) 3ÿ
(123) 5ÿ50ÿ 5
(124) ÿ ÿ 505 ÿ 4
(125) ÿ32ÿ
(126) 5ÿ5! 5405
(127) ÿ
(128) ÿ"#ÿ$5
(129) 347ÿ%59ÿ65ÿ5ÿ 53ÿ
(130) 055ÿ
(131) 05ÿÿ 5ÿÿ2 0ÿ
(132) 3ÿ 3ÿ
(133) 5ÿ4554ÿÿ
(134) 3
(135) ÿ ÿ. 34
(136) 057ÿ%59ÿ29ÿ 34
(137) 5 ÿ5ÿ
(138) 3ÿ0565ÿ
(139) 0ÿ 054
(140)
(141) 037ÿÿ ÿ &43ÿ
(142) 5ÿ 45ÿ32ÿ9ÿ 45
(143) ÿ
(144) 3ÿ
(145) 5ÿ20ÿ
(146) 3 0ÿ32ÿ
(147) 0ÿ
(148) 50ÿ35ÿ32ÿ0
(149) ÿ233 ÿ
(150) 5ÿ00
(151) 0ÿ ÿÿ 7ÿ 0049ÿ4554ÿ34ÿ565ÿ025ÿ0ÿ3
(152) ÿ05ÿ
(153) 5ÿ. 5
(154) 54'0
(155) 5ÿ2345
(156) 7ÿ(3ÿÿ 035ÿ3 5ÿ34ÿ565ÿ 45 0
(157) ÿ0
(158) ÿ
(159) 3ÿ35ÿ 5!
(160) 5
(161) 7ÿ)3 5654ÿ93ÿ 9ÿ65ÿ54434ÿ ÿ
(162) 55ÿ54434ÿ0455ÿ2
(163) ÿ 5ÿ
(164) 3ÿ 3054ÿ
(165) 5349ÿ ÿÿ3 5ÿ9ÿ2345
(166) ÿ#097ÿ*5ÿ3
(167) ÿ 45 0
(168) ÿ 3535+ÿ025ÿ
(169) ÿÿ 5ÿ3
(170) ÿ5ÿ,--.ÿ54
(171) 0ÿ32ÿ34ÿ 5
(172) 54'0
(173) 5ÿ 45 0
(174) 03ÿ
(175) 0ÿ0
(176) ÿ 57ÿÿ"3
(177) ÿ32ÿÿ3
(178) ÿ025ÿ ÿ 5
(179) 54ÿ 3ÿ3
(180) ÿ45 5
(181) 7ÿ$3/ 45 5
(182) 5ÿ0ÿÿ40
(183) 0ÿ25
(184) 45ÿ32ÿ5
(185) 534330ÿ
(186) 9ÿ55ÿ05ÿ 90ÿ34ÿ 50
(187) 49ÿ
(188)
(189) ÿ3 ÿ45 5
(190) ÿ 54ÿ35ÿ3 0
(191) 03ÿ45/ 34 ÿ 5
(192) 54'0
(193) 5ÿ 565
(194) ÿ45ÿ0#57ÿÿ 3 ÿ5654ÿ3 ÿ
(195) ÿ9ÿ025ÿ 3 ÿ5ÿ02ÿÿ 5
(196) ÿ
(197) 3ÿ
(198) 5ÿ 5ÿ06540
(199) 9ÿÿ9ÿ2
(200) 54ÿ 0 7ÿÿ 3 ÿ5654ÿ3 ÿ 545ÿÿ 3 ÿ5ÿ02ÿ 4327ÿ 0ÿ*5ÿ 0 ÿ3
(201) ÿ5
(202) ÿ57ÿÿ 3 ÿ5654ÿ3 ÿ
(203) ÿÿÿ0565ÿ02ÿÿ ÿ3
(204) ÿ 55ÿ
(205) ÿ%17ÿ*
(206) ÿÿ3 ÿ0ÿ
(207)
(208) ÿÿ255ÿ
(209) 0205 ÿ ÿ320 5
(210) ÿ2
(211) 54ÿ
(212) 5ÿ 7 7ÿ
(213) 9ÿ
(214) ÿ%17ÿ ÿ &34ÿ3
(215) 54ÿ
(216) 0ÿÿ ÿ
(217) 40ÿ 0
(218) ÿ
(219) 5ÿ5ÿ5
(220) 55ÿ50ÿ
(221) 5345
(222) 0ÿ ÿ 4
(223) 0ÿ 40ÿ9ÿ 35ÿ 7 7ÿÿ
(224) 5ÿ 34ÿ0ÿ
(225) 0ÿ 054
(226)
(227) 03ÿ
(228) 4
(229) ÿ 0
(230) ÿ ÿ
(231) 5345
(232) 0ÿ0
(233) 54ÿ ÿ
(234) 5
(235) 09ÿ 5425
(236) ÿ3
(237) 03ÿ234ÿ5225
(238) 065ÿ30ÿ
(239) 5 54
(240) 45ÿ
(241)
(242) ÿ0ÿ5 ÿ
(243) 5ÿ*050
(244) +ÿ557ÿ
(245) ÿ0ÿ3ÿ
(246) 5345
(247) 0ÿ
(248)
(249) ÿ0
(250) ÿ45ÿ
(251) 5ÿ5ÿ234ÿ
(252) 4
(253) 45ÿÿ
(254) 5ÿ
(255) 3 545ÿ4 0
(256) 065ÿ
(257) 4254ÿ 35ÿ ÿ0ÿ 400 5ÿ
(258) 59ÿ3 ÿ5ÿ
(259) 5ÿ57ÿ)3 5654ÿ*050
(260) +ÿ55ÿ0ÿ3
(261) ÿ 4
(262) 0ÿ 55ÿ0
(263) ÿ0ÿ4 ÿ
(264) 3ÿ5
(265) ÿ 50
(266) 5ÿ30ÿ30
(267) 45ÿ 43205ÿ565ÿ0ÿ34
(268) 34057ÿ %5452345ÿ
(269) 5ÿ
(270) 05ÿ32ÿ
(271) 5ÿ
(272) ÿ 5 5ÿ
(273) 5 ÿ
(274) 3ÿ5ÿ0
(275) ÿ0 57ÿ%3ÿ5ÿ0
(276) ÿ 0 5ÿ5ÿ
(277) 3ÿ5ÿ0
(278) ÿ 4
(279) 0ÿ05ÿ13 49+ÿ55ÿ
(280)
(281) ÿÿ39ÿ35ÿ 20!5 ÿ65ÿ234ÿÿ
(282) 5ÿ57ÿ ÿÿ0 020
(283) 03ÿ 35ÿ
(284) 5ÿ3
(285) ÿ
(286) 5ÿ3 5!0
(287) 9ÿ ÿ00025ÿ
(288) 5ÿ0
(289) ÿ33
(290) 54 05ÿ0
(291) ÿ 0ÿ5ÿÿ0/ 35 ÿ 4354ÿ
(292) ÿ0
(293) ÿ35ÿ 90ÿ ÿ
(294) 00
(295) 97ÿ*
(296) ÿ 0 ÿ 5ÿ35ÿ0ÿ
(297) 5ÿ0 020
(298) 035ÿÿ
(299) 545ÿÿ 000ÿ.
(300) 34560745ÿ89 ÿ
(301) 9 ÿ 4ÿ7949 5ÿ
(302) 5ÿ
(303) 5 ÿ9ÿ9 0 5ÿÿ8
(304) 9 ÿ56 5 ÿ0ÿ9ÿ 04030 9 0ÿ9 5 9745ÿÿ 459745ÿÿ
(305) 5ÿ8ÿ0ÿ
(306) 55 0 94ÿ559
(307) ÿ 8
(308) ÿ3ÿ740
(309) 0ÿÿ89 ÿ ÿ
(310) 9ÿ944ÿ
(311) 5ÿ9 ÿ510585ÿ9ÿ50 ÿ 3ÿ ÿ740 9 0ÿ
(312) 55ÿ9 ÿ 59 ÿ 0
(313) ÿ ÿ9 05 5ÿ9ÿ55ÿ
(314) 5ÿ 50ÿ9
(315) 59 0 94ÿ5019 0ÿ0ÿ
(316) 0ÿ ÿ84ÿ515ÿ ÿ0 ÿ9 0 94ÿ5ÿ 0ÿ
(317) 5ÿ3 5 ÿ ÿ
(318) 5ÿÿ89ÿ0ÿ
(319) 0ÿ
(320) ÿ
(321) 5ÿ9715ÿ5 0ÿ89ÿ9489 ÿ9ÿ7 9 45ÿ ÿ ÿ 559
(322) ÿ!5 05ÿ
(323) 5ÿ54 ÿ
(324) 4ÿ75ÿ535 ÿ9ÿ85ÿ0355ÿ3ÿ
(325) 5ÿ9
(326) ÿ 7 ÿ0 ÿ ÿ ÿ ÿ75ÿ 50745ÿ8
(327) 5ÿ ÿ 5 ÿ0 ÿ80
(328) ÿ7519 0 ÿ"ÿ
(329) 5ÿ
(330) 5ÿ
(331) 9ÿ85ÿ5 0ÿ
(332) 5ÿ#
(333) 0 ÿ500 94ÿ75ÿ9ÿ995 50$9 0ÿ7 ÿ
(334) 5 ÿÿ8ÿ ÿ
(335) 55ÿ95ÿ$5ÿ3ÿ59 0ÿ85ÿ51545ÿ0ÿ
(336) 0ÿ ÿ9ÿ
(337) 5ÿ510585ÿ 5 0ÿ94 ÿ5ÿ3ÿ
(338) 5ÿ8
(339) 5ÿ85ÿ855ÿ5940ÿ80
(340) ÿ 740 9 0 ÿ% ÿ3ÿ
(341) 5ÿ59 015ÿ 5 ÿ3 ÿÿ0 ÿ9 0 940 ÿÿ ÿ &4ÿÿ5' 5ÿ305
(342) 0ÿ9ÿ0 0ÿ80
(343) ÿ ÿ05'ÿ 9ÿ8
(344) ÿ40 55ÿ 9ÿ5 95ÿ5ÿ8
(345) 5ÿÿ89ÿ4 ÿÿ80
(346) ÿ ÿ
(347) 9ÿ ÿ(54ÿ)
(348) 5ÿ ÿ
(349) 90ÿ*
(350) 5ÿ ÿ+0ÿ,9ÿ ÿ-070ÿ 9ÿ ÿ.05ÿ19ÿ5ÿ/545ÿ 009ÿ 9ÿ,ÿ*
(351) 9ÿ ÿ109 ÿ+ ÿÿ4595ÿ9ÿ4 ÿ3ÿ ÿ 05 ÿÿ9ÿ 49ÿ ÿ
(352) 9ÿ10
(353) ÿ 9ÿ)
(354) 99ÿ ÿ(094ÿ+ÿ(ÿ+9ÿ20ÿ ÿ ÿ 500ÿ+9ÿ)
(355) 5409ÿ10ÿ9ÿ
(356) 5ÿ)
(357) 055ÿ305ÿ9 ÿ)ÿ3ÿ
(358) 50ÿ 3054 ÿ ÿÿ
(359) 9ÿ ÿ(0ÿ 9ÿ8
(360) ÿ0ÿ8534ÿ3054ÿ8ÿ9ÿ 9
(361) ÿ 5ÿ
(362) 8ÿ ÿ 59 5ÿ80
(363) ÿ
(364) 5 ÿÿ89 ÿ ÿ
(365) 9ÿ ÿ,0ÿ09ÿ8
(366) ÿ0ÿ 044ÿ 015ÿ9ÿ3010ÿ ÿ00ÿ05 ÿ%9 ÿ
(367) 9ÿ ÿ*409ÿ 9ÿ ÿ209ÿ20450ÿ)
(368) 5ÿ3ÿ
(369) 50ÿ30548ÿÿ
(370) 5ÿ31-&.&ÿ49ÿ905 55ÿ9ÿ %9 ÿ5 8ÿ5 9740
(371) 5 ÿ9ÿ90 59 5 ÿ%9 ÿ
(372) 9ÿ ÿ&5ÿ09ÿ 10 ÿ%9 ÿ3ÿ
(373) 50ÿ
(374) 54ÿ80
(375) ÿ ÿ ÿ9ÿ ÿ)ÿ 5 ÿ93390ÿ330 5ÿ3ÿ
(376) 540ÿ5ÿ80
(377) ÿ ÿ594ÿ93390 ÿÿ ÿ % ÿ
(378) 9ÿ ÿ944ÿ
(379) 5ÿ545ÿÿ5 ÿ0ÿ
(380) 5ÿ 59ÿ0ÿ3
(381) 55 ÿ
(382) 0ÿ
(383) 50ÿ89ÿ 5ÿ7 ÿ
(384) 5ÿ)!)ÿ945 ÿ6ÿ900ÿ)
(385) 09ÿ7ÿ85
(386) 549ÿ9 ÿ ÿ. 01ÿ. ÿ.
(387)
(388) Contents Acknowledgments .................................................................................................................................................... iii 1. Introduction ........................................................................................................................................................ 1 Scientific background ............................................................................................................................... 1 Problem statement .................................................................................................................................... 6 Objectives ................................................................................................................................................... 9 Passive microwave remote sensing of soil moisture .......................................................................... 10 1.4.1. Radiative transfer equations ......................................................................................................... 10. 1.4.2. Vegetation ........................................................................................................................................ 12. 1.4.3. Surface roughness........................................................................................................................... 13. 1.4.4. Dielectric constant model .............................................................................................................. 14. 1.4.5. Soil Effective Temperature ............................................................................................................ 14 Structure of the Thesis ............................................................................................................................ 15. 2 An Improved Two-layer Algorithm for Estimating Effective Soil Temperature in Microwave Radiometry using In Situ Temperature and Soil Moisture Measurements ...................................................... 17 Introduction ............................................................................................................................................. 18 Method and Materials ............................................................................................................................ 19 2.2.1. Derivation of the Two-Layer (Lv’s) Scheme ............................................................................... 19. 2.2.2. In-situ Dataset ................................................................................................................................. 22 Results ....................................................................................................................................................... 23. 2.3.1. Physical Implication of Parameter C ............................................................................................ 23. 2.3.2. Inter-comparison of Parameterization Schemes......................................................................... 26 Discussion ................................................................................................................................................ 30. 2.4.1. Determination of x ..................................................................................................................... 30. 2.4.2. Discrete formulation of the Lv Scheme ....................................................................................... 30 Conclusions .............................................................................................................................................. 32. 3 Determination of the Optimal Mounting Depth for Calculating Effective Soil Temperature at L-Band: Maqu Case ................................................................................................................................................................. 33 Introduction ............................................................................................................................................. 34 3.1.1. Motivation ....................................................................................................................................... 34. 3.1.2. Background ..................................................................................................................................... 34 Methodology and Data ........................................................................................................................... 36. 3.2.1. Characteristics of Parameter B .................................................................................................... 36 vi.
(389) 3.2.2. Optimal Mounting Depth.............................................................................................................. 38. 3.2.3. The Maqu Network ........................................................................................................................ 43 Results ....................................................................................................................................................... 43. 3.3.1. Evaluation of Installation Configuration..................................................................................... 43. 3.3.2. Test of the Optimal Mounting Depth .......................................................................................... 47 Discussion ................................................................................................................................................ 52 Conclusions .............................................................................................................................................. 53. 4. A Reappraisal of Global Soil Effective Temperature Schemes .................................................................. 55 Introduction ............................................................................................................................................. 56 Method and Data..................................................................................................................................... 57 4.2.1. MERRA-Land Data ........................................................................................................................ 57. 4.2.2. SMOS Brightness Temperature .................................................................................................... 57. 4.2.3. Effective Temperature Models...................................................................................................... 57. 4.2.4. Dielectric Models ............................................................................................................................ 60. 4.2.5. Statistical Metrics ............................................................................................................................ 60 Results and Discussions ......................................................................................................................... 61. 4.3.1. Soil Temperature Profile ................................................................................................................ 61. 4.3.2. Comparison of Dielectric Constant Models ................................................................................ 62. 4.3.3. Comparison of soil effective temperature schemes ................................................................... 63. 4.3.4. Emissivity affected by soil effective temperature schemes....................................................... 68 Conclusions .............................................................................................................................................. 70. 5 Estimation of Penetration Depth from Soil Effective Temperature at L-Band in Microwave Radiometry ................................................................................................................................................................ 71 Introduction ............................................................................................................................................. 72 Theoretical Background ......................................................................................................................... 73 5.2.1. Microwave Radiative Transfer Model ......................................................................................... 73. 5.2.2. Soil Effective Temperature ............................................................................................................ 75. 5.2.3. Penetration Depth........................................................................................................................... 76 Method and Data..................................................................................................................................... 77. 5.3.1. Predigest of Wilheit’s Teff Scheme .............................................................................................. 77. 5.3.2. Characteristic Expression of Teff .................................................................................................. 77. 5.3.3. In-Situ Data, MERRA-2, and SMAP ............................................................................................. 79 Results ....................................................................................................................................................... 82 vii.
(390) Discussion ................................................................................................................................................ 85 Conclusions .............................................................................................................................................. 87 6. A Closed-form Expression of Soil Temperature Sensing Depth at L-band ............................................. 90 Introduction ............................................................................................................................................. 91 Methodology and data ........................................................................................................................... 92 6.2.1. Soil Optical Depth and Soil Effective Temperature ................................................................... 92. 6.2.2. Formulation of Teff in soil optical depth and transmitting ...................................................... 93. 6.2.3. Normalization of the soil temperature profile............................................................................ 94. 6.2.4. Teff features in a ynst x1e coordinate: the nonlinear case ...................................................98. 6.2.5. In-situ Data ...................................................................................................................................... 99. . Results and Discussion ......................................................................................................................... 100 6.3.1. Estimation of Teff ......................................................................................................................... 100. 6.3.2. Estimation of Teff ......................................................................................................................... 101. 6.3.3. Application to SMAP ................................................................................................................... 103 Conclusions ............................................................................................................................................ 106. 7. Synthesis.......................................................................................................................................................... 107 Summary ................................................................................................................................................ 107 Discussion .............................................................................................................................................. 108 Future Perspectives ............................................................................................................................... 111 7.3.1. Forward simulations to determine the soil moisture sensing depth ..................................... 111. 7.3.2. Application in SMAP Cal/Val ..................................................................................................... 112. 7.3.3. Application in model assimilation ............................................................................................. 115. Samenvatting ........................................................................................................................................................... 116 Reference .................................................................................................................................................................. 118. viii.
(391) Abbreviations. AMSRe. the Advanced Microwave Scanning Radiometer-Earth Observing System. Cal/Val. Calibration/Validation. CCI. Climate Change Initiative. CMEM. Community Microwave Emission Modeling. COSMOS-UK. UK COsmic-ray Soil Moisture Observing System. ECMWF. European Centre for Medium-Range Weather Forecasts. ECV. Essential Climate Variables. ESA. the European Space Agency. ECOCLIMAP. The ECOCLIMAP programme is a dual database at 1 km resolution that includes an ecosystem classification and a coherent set of land surface parameters that are primarily mandatory in meteorological modeling (notably leaf area index and albedo). GCM. General Circulation Model. HTESSEL. Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land. ISMN. The International Soil Moisture Network. LAI. Leaf Area Index. L-MEB. the L-band Microwave Emission of the Biosphere. LPRM. Land Parameter Retrieval Model. MERRA-2. The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). NDVI. Normalized Difference Vegetation Index. RFI. Radio-frequency interference. NASA. National Aeronautics and Space Administration. SMAP. Soil Moisture Active Passive. SMOS. Soil Moisture and Ocean Salinity. SMMR. the Scanning Multichannel Microwave Radiometer. SNR. Signal-to-Noise Ratio. ix.
(392) Tb. Brightness temperature. Teff. soil effective temperature. . emissivity. x.
(393) Chapter 1 Introduction Scientific background The contribution of land surface conditions to the predictability of meteorological features is of interest to a wide community. The predictability at monthly to seasonal time scales is mainly attributed to anomalies of the sea surface temperature (SST), in particular, those related to El Nino events. However, Koster et al. [1] identified some key regions where soil moisture conditions may systematically affect precipitation variability in the boreal summer season, based on a model experiment involving multiple General Circulation Models (GCMs). The East Asia area is among sensitive regions regarding both soil moistureprecipitation and soil moisture-temperature interactions. In combination with a realistic initialization of soil moisture and a long enough memory in the soil water reservoir, increased predictability may be feasible in these regions [2]. Dirmeyer et al. [3] explored systematic soil moisture-precipitation interactions using a range of observations and (offline) land models across all seasons, roughly confirming the existence of areas where adequate soil moisture information could lead to improved forecasts at the monthly to the seasonal timescales. In general, these areas are found in transitional zones between dry and wet climates, where the coupling between soil moisture and evapotranspiration is expected to be strong and large enough to affect climate [4]. Several observational and modeling-based studies to a great extent agree on the location of these regions [5]. Douville [6] showed that soil moisture conditions in late spring played an important role in successfully modeling contrasting summers concerning precipitation and temperature in the Eurasian continent using a single GCM. While seeing great potential for improvement in climate forecast skills by using land surface soil moisture with either analysis or assimilation, soil moisture has also been utilized in any regular or operational forecast systems. Soil moisture in land surface models could be updated via assimilating brightness temperature observations. Different to brightness temperature as the Level 1 instrument data, soil moisture is not a direct measurement and thus cannot be used for assimilation. One of the reasons is that the impact of soil moisture on the atmosphere above is extremely complex compared to the impact of other land surface variables like albedo, snow cover, skin temperature, etc. Soil moisture may lead to both positive feedbacks and negative feedbacks at the same time [5, 7-9]. On the one hand, wet soil decreases the land surface albedo, while it increases the short radiation absorbed by the land surface. The sum of latent heat and sensible heat increases with rising soil moisture, but while latent heat does increase along with soil moisture, sensible heat does not. Latent heat, or evaporation regarding water dynamics, increases the water content in the atmosphere, especially in its lowest layer. At the same time, evaporation also reduces the humidity gradient between the atmosphere and the soil surface, restraining the evaporation.. 1.
(394) Water vapor either stabilizes the atmospheric layers by increasing atmospheric stability (via specific heat capacity) or provide the source for precipitation. If precipitation occurs, the soil will become wetter again. Thus a complete cycle is formed, with both negative and positive feedbacks in the soil moistureevaporation-precipitation-soil moisture chain (Figure 1.1 left panel). On the other hand, soil moisture can affect the air temperature as well, because when soil moisture decreases, evaporation will also decrease, which in turn leads to higher air temperature. The higher air temperature accelerates evaporation, making the soil even dryer. This way another complete cycle is formed by soil moisture-evaporation-air temperature-soil moisture (Figure 1.1 right panel). In summary, the mechanism of soil moisture-climate feedback is very complicated and current research is restricted to local effects and case studies [1, 10-19]. No general conclusion has been drawn, whether soil moisture has a clear impact that might help improve forecast skills.. Figure 1.1 The complexity of soil moisture-precipitation/temperature feedbacks cited from Seneviratne et al. (2010).. f This complexity hinders the application of soil moisture observation in weather/climate forecasts. Different to sea surface temperature (SST) which is considered as the most important factor in climate/weather forecast, soil moisture over land is not continuous either in spatial or temporal scales. Two soil moisture profiles tens of meters away from each other may be completely different due to topography, soil properties, vegetation, etc. Therefore, the determination of representativeness of in-situ soil moisture would always be difficult. Usually, to acquire soil moisture data comparable at model scale (e.g., tens of kilometers), the monitoring network would have to contain all terrain features and land surface cover types, dramatically increasing cost and labor intensity. In general, the current soil moisture networks may be categorized into three types, i.e., operational, auxiliary and special. 1) The operational soil moisture network is a long-term observation network, as financed institutionally. For example, the soil moisture network inherited from the Former Soviet Union's meteorology and agriculture operation system [20], as well as the stations set up by the China 2.
(395) Meteorological Administration [21]. These stations recorded soil moisture, as well soil temperature profiles, according to agricultural requirements, which means that the layers configured in the land surface (about 0-0.5 m) are coarse and may not represent the interaction between land and atmosphere. 2) The auxiliary network incorporates soil moisture networks built for other scientific research purposes, such as the Chinese Ecosystem Research Network [22] and the COSMOS-UK soil moisture network [23]. These networks were mainly set up by ecologists to monitor and estimate Gross Primary Productivity (GPP), drought, and flood, etc. These independent networks may thus adopt different standards and equipment in their set up. 3) The special network incorporates soil moisture networks used for satellite calibration/validation, such as the SMAP Cal/Val project [24], which partly overlapped and included the ISMN (the International Soil Moisture Network) [25]. Even before L-band satellites were in orbit, this kind of network existed for airborne radar/radiometer microwave remote sensing studies. Due to the large footprint of radar/radiometer measurements, these networks should consist of a cluster of in-situ soil moisture/temperature profile observations. These profiles are inter-comparable, and their entirety represents the soil moisture variation over a large area. However, although this last category would satisfy the scale requirements for climate models, it is still impossible to extend in-situ observations around the globe. To achieve medium (3-10 days) or seasonal forecasts, a land surface model or ocean model usually needs to be coupled with a General Circulation Model (GCM). The feedback from the land surface model can be on greater time steps when coupled with the GCM, because land surface variables, such as soil moisture, vary much less than the atmosphere. On the other hand, the soil thermal and hydraulic conductivities and the surface energy balance are very sensitive to soil moisture changes. Hence, it is necessary to establish an appropriate data assimilation system of soil moisture to improve the soil moisture initialization at fine temporal scales [26]. Nevertheless, although the impact of soil moisture is relatively large among the land surface variables like LAI, snow cover and so on, it is always one order of magnitude smaller than SST, cloud cover, etc. [5, 27, 28]. Therefore, soil moisture is more a prognostic than a forecasting factor. It means soil moisture is produced by the GCM-LSM coupled system, but not the dominant driving force for medium or longer scale weather/climate systems [29]. The soil moisture fields contained in the model outputs such as ERA-interim and MERRA-2 (The Modern-Era Retrospective analysis for Research and Applications, Version 2) strongly depend on the land surface models [30-32]. Besides in-situ monitoring and the reanalysis of soil moisture products, another option is to obtain global soil moisture distribution from satellite remote sensing. Early in the 1970s, Skylab already carried out microwave remote sensing of the earth’s surface, and the ensuing exploratory efforts led to the theory of remote sensing of soil moisture. In the 2000’s, AMSR-E (the Advanced Microwave Scanning RadiometerEarth Observing System), with its powerful passive-microwave radiometer, created the first operational global soil moisture map [33-35]. AMSR-E measures horizontally and vertically polarized brightness 3.
(396) temperatures at 6.9 GHz, 10.7 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz. The spatial resolution of the individual measurements varies from 54 km at 89 GHz to 56 km at 6.9 GHz. AMSR-E overpass times are around 1:30 a.m. (ascending) and 1:30 p.m. (descending) local time at the equator [36]. AMSR-E is not specifically designed for soil moisture detection and also provides measurements of precipitation rate, sea surface temperature, sea ice concentration, snow water equivalent, wind speed, atmospheric cloud water, and water vapor. The algorithms for AMSR-E soil moisture retrieval are different from precursors to those developed for L-band satellites such as SMOS and SMAP. AMSR-E uses a single-channel or dual-channel empirically to build a relationship between brightness temperature and soil moisture statistically [37]. Also, AMSR-E retrieves soil moisture from the emission model, i.e., LPRM (land parameter retrieval model) [38]. Soil moisture can only be detected to very shallow depth by AMSR-E, and the retrieved surface soil moisture is strongly affected by vegetation cover, as well as by evaporation and precipitation. To overcome these influences, L-band microwave remote sensing is imperative. SMOS is the first L-band satellite particularly designed for soil moisture detection using an interferometric approach [39, 40]. The satellite was launched in November 2009 and has provided continuous soil moisture data since then. SMOS monitors surface soil moisture with an accuracy of 0.04 m3/m3 (at 35–50 km spatial resolution) with repeat visits every three days at least for the middle and low latitude. More details on SMOS will be presented in the chapters containing the data. The next L-band satellite launched was Aquarius, although it was not specially designed for soil moisture detection [41]. Aquarius was a NASA instrument aboard the Argentine SAC-D spacecraft. Its mission was to measure global sea surface salinity to predict future climate conditions better. As is well known, the radiometer mounted on Aquarius, as well as SMOS/SMAP, does not distinguish between land surface and ocean surface. While soil moisture is the dominant factor controlling radiometry over land, sea surface salinity controls the signal over the ocean. So technically, the brightness temperature monitored by Aquarius could also be used to retrieve soil moisture over land. However, little research exists on soil moisture retrieval from Aquarius, mainly because its resolution is quite low (100 km) [42]. SMAP is another L-band microwave satellite [43, 44]. Different to SMOS, SMAP uses a real aperture antenna (incidence angle fixed at 40°) that combines passive and active microwave remote sensing techniques. The real aperture antenna guarantees that its spatial resolution reaches 36 km for passive and 3 km for active techniques. A real aperture antenna is also an advantage when dealing with RFI (Radio-frequency interference). SMAP can pick up spots and moments of strong RFI and then simply remove the unexpected data. In general, the passive soil moisture product is more precise, but the resolution is low compared to the active one. However, a 36 km resolution is good enough for most GCMs, and there is no evidence that 3 km or the active-passive merged 9 km soil moisture products are superior to the passive one [45]. In-situ measurement, reanalysis, and satellite remote sensing are three methodologies to obtain soil moisture, each with their advantages and disadvantages. In an attempt to take advantage of the different methodologies, all three could be merged to develop a soil moisture dataset, as in the ESA-CCI-Soil Moisture project (The European Space Agency-Climate Change Initiative) [46]. The latest version of ESA 4.
(397) CCI SM v04.2 comprises the three well-known active, passive and combined satellite soil moisture datasets. This latest release provides global soil moisture data until 31-12-2016. It merges all active and passive L2 products directly to generate a combined product (previously, this was created from the active and passive products). Also, soil moisture uncertainties are now available globally for all sensors except for SMMR (the Scanning Multichannel Microwave Radiometer) and spatial gaps in the triple collocation-based SNR (Signal-to-Noise Ratio) estimates are now filled using polynomial SNR-VOD regression. These techniques show that the merged data are not real soil moisture observations but more an objective analysis with interpolation, nudging, and even a simple model with water/energy restrictions [47]. This kind of soil moisture data set is useful, but in merging different data sources new errors are generated, either through the mathematical algorithms or from input data that may be incompatible. In principle, this kind of data should not be used in data assimilation to improve the weather forecasting skill as they are not real observations. In summary, passive remote sensing of soil moisture has good potential to improve weather forecasting via data assimilation in theory [48]. The endeavor to assimilate soil moisture or L-band satellite brightness temperature has been ongoing. Patricia de Rosnay et al. (2013) developed a new land surface analysis system based on a simplified point-wise Extended Kalman Filter (EKF), which was implemented at ECMWF in the global operational Integrated Forecasting System (IFS) in November 2010 [49, 50]. However, the assimilation of passive soil moisture products, especially SMOS and SMAP, has a mitigated impact[51]. First of all, the expected precision regarding soil moisture products designed by SMOS and SMAP is 0.04 m3/m3, but this accuracy cannot be attained at a local scale [52, 53]. For example, in each SMOS overpass (i.e., per day, per grid point) we compute the RMSE between SMOS TB (~15 incidence angles) versus the modeled one (~15 incidence angles) over the Tibetan Plateau via personal communications with Dr. Yan Kerr’s team. The RMSE over the Tibet Plateau is less than 20 K, but this region is filtered out by the current SMOS version due to suspected RFI. The region nearby has more severe RFI pollution, coinciding with the population distribution. The RMSE, which reflects the difference between brightness temperature and model forward simulation, reveals the missing part we have not been able to fully explain so far. While doing data assimilation, this bias and RMSE will lead to instability rather than improvement in the model. Because soil moisture deduced from. Tb , the difference in Tb will also propagate into soil moisture.. 5.
(398) Figure 1.2 An illustration of mismatched soil moisture definition in satellites remote sensing (MW observation, the rectangle between 0-2 cm), field measurement (or in-situ observation, the blue point) and models (NWP model, the columns). The blue profile gives an example of a soil moisture profile. The red line and red cross indicate the definition used in this thesis as the average moisture and form the sensing depth with a real soil moisture profile. The red circle shows where the satellites are measuring with the example profile, which gives a mismatch to the blue point.. Secondly, SMOS and SMAP directly measure. Tb , the emission, but not soil moisture. Instead of. assimilating soil moisture, it is more rational to assimilate. Tb [54-56]. With forward models and assimilation. tools like the Kalman Filter, it is possible to update soil moisture/temperature profiles while accounting for an observation error matrix [57]. Thirdly, if soil moisture is assimilated in models, a clear definition is necessary regarding where the satellites are measuring, as can be seen in Figure 1.2 [48, 58, 59]. Models, insitu observations, and satellite remote sensing each have their definitions of soil moisture depth, which is often not interchangeable. For example, in models, the soil moisture is usually defined at grid level, and the soil moisture within these nodes is considered uniform. In-situ observations measure at specific depths. Satellite remote sensing also has its definition but in principle, each dielectric profile has its unique emission behavior, and this profile constantly varies, even at one fixed point, as will be explained in Section 1.2.. Problem statement A detailed description of the microwave transfer process is critical to quantify the energy emitted from the soil, vegetation and the attenuation across different media like the soil-atmosphere surface, soil layers, etc. For shorter wavelengths, soil moisture monitored by a radiometer refers to only the top few centimeters of the soil. To the L-band, the signal can even be detected from meters deep in extreme cases (e.g., over desert or permafrost). In general, the penetration depth is defined as the depth where the residual of the radiation is reduced to a 1/e range, though this heavily depends on the dielectric profile. Based on a complicated description of the microwave transfer process in the soil column, the interface of different 6.
(399) media (e.g. soil-atmosphere), the vegetation and the atmosphere, application of L-band satellites such as SMOS and SMAP make it possible to acquire a higher accuracy in measuring soil moisture than with all other bands [60, 61]. As stated in Section 1.1, one problem of L-band microwave remote sensing is to know which layer the satellite is observing, i.e., what depth exactly corresponds to the remotely sensed soil moisture product. There are two concepts, which can be defined as follows, 1) Soil Temperature Sensing Depth: where the temperature weighting function for a given dielectric profile reduces to 1/e (about 0.36) ; 2) Soil Moisture Sensing Depth: At L-band, the temperature sensing depth could easily reach 20 to 40 cm. Does that mean L-band can detect soil moisture below 20-40 cm? Theoretically, any change in the dielectric profile will cause a change in the brightness temperature. Tb , and. everything depends on how precise the sensor is. In practice, however, if the change in. Tb is less. than a few Kelvin, it will be very difficult to separate this change from changes resulting from other factors, such as surface roughness and temperature profile variations. The sensor would not capture the soil moisture change at the few centimeters lower down because the surface layers dominate the emission signal. Therefore, Ulaby (1986) defined the moisture sensing depth as the most sensitive layer to correspond with. Tb [62].. Although the microwave emission/sensing depth is very important for improving the passive microwave retrieval accuracy of land surface parameters, forward simulation and soil moisture assimilation, quantified microwave emission/sensing depth models were few [63]. The emission/sensing depth has long been a neglected variable that was simplified to soil moisture from the first layers. While considering the error caused by roughness, vegetation, etc., which might be 10-20 K for. Tb , the soil moisture. gradient at the surface is ignored. However, the soil moisture gradient could be very steep especially after rainfall and is responsible for the different probability distributions between SMAP/SMOS and field observations [64]. Escorihuela et al. (2010) [65] used a typical explanation of moisture sensing depth as defined by “moisture sensing depth” above. The layer is defined as a fixed soil moisture layer if it has the highest correlation coefficient with. Tb . Since the surface layer contributes most to emission, the upper few. centimeters are expected to form the sensitive layer. This paper is a case study, but it tries to extend the sensitive layer to emissivity computation in forward modelling. A definition of sensitive layer means that the soil moisture at the sensitive layers should be used to compute emissivity in models. This conclusion could be questioned, as the dielectric profile changes all the time and a fixed layer can never be the most sensitive layer for all conditions. Besides, there is a difference between a sensitive layer (the rectangle in Figure 1.2) and an equivalent layer (the cross in Figure 1.2). Soil moisture used for calculating emissivity should represent the emission of the whole soil column because SMOS and SMAP have only one channel. 7.
GERELATEERDE DOCUMENTEN
In this study, a method based on soil texture is used to downscale the satellite products from coarse resolution to fine resolution (1 km); then CRNP and point measurement data
From the research it became apparent that the soil moisture in the Twente region in 2018 was heavily influenced by (actual) evapotranspiration. Although large differences exist
23 Figure 14: Time series showing the average retrieved coarse resolution SMAP surface soil moisture against the averaged in-situ soil moisture measurements for the study area and
There are three different types of soil moisture datasets, include in-situ data (Tibet-Obs), reanalysis data (ERA-Interim), and Satellites datasets (passive: AMSRE, SMOS, AMSR2,
regeling der Commissie te maken, ma~r alleen wordt op voorstel van Ouderling Hatting, ~esecondeerd door Diaken Engelbrecht, vastge.steld, dat de Commissie met haren
het probleem niet laten oplossen, we moeten zelf aan het werk - democratie verdedigen / - grenzen van onze - vuist tegen terrorisme Nederland veiliger maken rechtsstaat worden
For the survey, variables on four dimensions were deployed to describe purchasing situations (product characteristics, product cost and value, supply market and attributes of
Tydens die laaste konsert van die Musiekvereniging vir 1946 het Dolly Heiberg, sowel as vyf ander baie bekende orreliste van Bloemfontein, hul verskyning in die Tweetoringkerk
